Euanyu/merged_task_dataset_models · Datasets at Hugging Face

id stringlengths 11 95	author stringlengths 3 36	task_category stringclasses 16 values	tags sequencelengths 1 4.05k	created_time timestamp[s]date 2022-03-02 23:29:04 2025-03-18 02:34:30	last_modified timestamp[s]date 2021-05-13 19:09:22 2025-03-18 03:19:02	likes int64 0 4.86k	README stringlengths 246 1.01M	matched_task sequencelengths 1 8	matched_bigbio_names sequencelengths 1 8
twadada/nmc-300-w50k-b10k	twadada	null	[ "mteb", "model-index", "region:us" ]	2024-09-16T23:06:01	2024-09-16T23:06:15	0	--- tags: - mteb model-index: - name: nomic_classification_307_w50k_b10k results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.70149253731343 - type: ap value: 33.79646861902238 - type: f1 value: 65.39377031734182 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.577125 - type: ap value: 59.69737246109206 - type: f1 value: 64.3577747072318 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.748 - type: f1 value: 33.3254582178127 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.199 - type: map_at_10 value: 34.28 - type: map_at_100 value: 35.480000000000004 - type: map_at_1000 value: 35.504999999999995 - type: map_at_3 value: 29.682 - type: map_at_5 value: 32.385000000000005 - type: mrr_at_1 value: 20.91 - type: mrr_at_10 value: 34.536 - type: mrr_at_100 value: 35.743 - type: mrr_at_1000 value: 35.768 - type: mrr_at_3 value: 29.931 - type: mrr_at_5 value: 32.623000000000005 - type: ndcg_at_1 value: 20.199 - type: ndcg_at_10 value: 42.278 - type: ndcg_at_100 value: 47.924 - type: ndcg_at_1000 value: 48.537 - type: ndcg_at_3 value: 32.815 - type: ndcg_at_5 value: 37.681 - type: precision_at_1 value: 20.199 - type: precision_at_10 value: 6.792 - type: precision_at_100 value: 0.9390000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 13.963999999999999 - type: precision_at_5 value: 10.74 - type: recall_at_1 value: 20.199 - type: recall_at_10 value: 67.923 - type: recall_at_100 value: 93.88300000000001 - type: recall_at_1000 value: 98.578 - type: recall_at_3 value: 41.892 - type: recall_at_5 value: 53.698 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 31.715994496712955 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 22.014928355542406 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 52.73401198259723 - type: mrr value: 66.18574946137272 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.32819163750328 - type: cos_sim_spearman value: 76.32884763830812 - type: euclidean_pearson value: 77.6247892757331 - type: euclidean_spearman value: 76.32884763830812 - type: manhattan_pearson value: 77.4560490620549 - type: manhattan_spearman value: 76.11679461376502 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.16883116883118 - type: f1 value: 71.34298475263475 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 29.528784676707033 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 19.565519101446977 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.581 - type: map_at_10 value: 28.322999999999997 - type: map_at_100 value: 29.392000000000003 - type: map_at_1000 value: 29.547 - type: map_at_3 value: 26.214 - type: map_at_5 value: 27.339000000000002 - type: mrr_at_1 value: 27.182000000000002 - type: mrr_at_10 value: 34.075 - type: mrr_at_100 value: 34.92 - type: mrr_at_1000 value: 34.997 - type: mrr_at_3 value: 32.26 - type: mrr_at_5 value: 33.283 - type: ndcg_at_1 value: 27.182000000000002 - type: ndcg_at_10 value: 32.903999999999996 - type: ndcg_at_100 value: 37.852999999999994 - type: ndcg_at_1000 value: 41.177 - type: ndcg_at_3 value: 29.976999999999997 - type: ndcg_at_5 value: 31.039 - type: precision_at_1 value: 27.182000000000002 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 14.354 - type: precision_at_5 value: 10.157 - type: recall_at_1 value: 21.581 - type: recall_at_10 value: 40.487 - type: recall_at_100 value: 62.832 - type: recall_at_1000 value: 85.768 - type: recall_at_3 value: 30.842000000000002 - type: recall_at_5 value: 34.497 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.495 - type: map_at_10 value: 21.625 - type: map_at_100 value: 22.506 - type: map_at_1000 value: 22.633 - type: map_at_3 value: 19.819 - type: map_at_5 value: 20.817 - type: mrr_at_1 value: 20.892 - type: mrr_at_10 value: 25.768 - type: mrr_at_100 value: 26.533 - type: mrr_at_1000 value: 26.61 - type: mrr_at_3 value: 23.96 - type: mrr_at_5 value: 24.893 - type: ndcg_at_1 value: 20.892 - type: ndcg_at_10 value: 25.144 - type: ndcg_at_100 value: 29.425 - type: ndcg_at_1000 value: 32.436 - type: ndcg_at_3 value: 22.105 - type: ndcg_at_5 value: 23.416 - type: precision_at_1 value: 20.892 - type: precision_at_10 value: 4.6240000000000006 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.403 - type: precision_at_5 value: 7.4270000000000005 - type: recall_at_1 value: 16.495 - type: recall_at_10 value: 31.627 - type: recall_at_100 value: 50.653999999999996 - type: recall_at_1000 value: 71.38 - type: recall_at_3 value: 22.987 - type: recall_at_5 value: 26.518000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.19 - type: map_at_10 value: 33.159 - type: map_at_100 value: 34.223 - type: map_at_1000 value: 34.322 - type: map_at_3 value: 30.866 - type: map_at_5 value: 32.016 - type: mrr_at_1 value: 29.091 - type: mrr_at_10 value: 36.208 - type: mrr_at_100 value: 37.059999999999995 - type: mrr_at_1000 value: 37.124 - type: mrr_at_3 value: 34.001999999999995 - type: mrr_at_5 value: 35.089999999999996 - type: ndcg_at_1 value: 29.091 - type: ndcg_at_10 value: 37.696000000000005 - type: ndcg_at_100 value: 42.774 - type: ndcg_at_1000 value: 45.064 - type: ndcg_at_3 value: 33.298 - type: ndcg_at_5 value: 35.089 - type: precision_at_1 value: 29.091 - type: precision_at_10 value: 6.132 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.754000000000001 - type: precision_at_5 value: 10.082 - type: recall_at_1 value: 25.19 - type: recall_at_10 value: 48.542 - type: recall_at_100 value: 71.475 - type: recall_at_1000 value: 88.157 - type: recall_at_3 value: 36.512 - type: recall_at_5 value: 40.998000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.979 - type: map_at_10 value: 15.160000000000002 - type: map_at_100 value: 15.927 - type: map_at_1000 value: 16.039 - type: map_at_3 value: 13.905000000000001 - type: map_at_5 value: 14.603 - type: mrr_at_1 value: 12.09 - type: mrr_at_10 value: 16.317999999999998 - type: mrr_at_100 value: 17.094 - type: mrr_at_1000 value: 17.198 - type: mrr_at_3 value: 15.028 - type: mrr_at_5 value: 15.712000000000002 - type: ndcg_at_1 value: 12.09 - type: ndcg_at_10 value: 17.71 - type: ndcg_at_100 value: 21.923000000000002 - type: ndcg_at_1000 value: 25.407999999999998 - type: ndcg_at_3 value: 15.139 - type: ndcg_at_5 value: 16.372 - type: precision_at_1 value: 12.09 - type: precision_at_10 value: 2.768 - type: precision_at_100 value: 0.521 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.478000000000001 - type: precision_at_5 value: 4.542 - type: recall_at_1 value: 10.979 - type: recall_at_10 value: 24.548000000000002 - type: recall_at_100 value: 44.659 - type: recall_at_1000 value: 72.15899999999999 - type: recall_at_3 value: 17.552 - type: recall_at_5 value: 20.584 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.703 - type: map_at_10 value: 9.588000000000001 - type: map_at_100 value: 10.312000000000001 - type: map_at_1000 value: 10.428999999999998 - type: map_at_3 value: 8.473 - type: map_at_5 value: 9.118 - type: mrr_at_1 value: 8.706 - type: mrr_at_10 value: 11.818 - type: mrr_at_100 value: 12.568999999999999 - type: mrr_at_1000 value: 12.664 - type: mrr_at_3 value: 10.551 - type: mrr_at_5 value: 11.235000000000001 - type: ndcg_at_1 value: 8.706 - type: ndcg_at_10 value: 11.823 - type: ndcg_at_100 value: 15.674 - type: ndcg_at_1000 value: 19.256 - type: ndcg_at_3 value: 9.637 - type: ndcg_at_5 value: 10.661 - type: precision_at_1 value: 8.706 - type: precision_at_10 value: 2.251 - type: precision_at_100 value: 0.484 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 4.601999999999999 - type: precision_at_5 value: 3.458 - type: recall_at_1 value: 6.703 - type: recall_at_10 value: 16.579 - type: recall_at_100 value: 34.054 - type: recall_at_1000 value: 60.769 - type: recall_at_3 value: 10.530000000000001 - type: recall_at_5 value: 13.126 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.688000000000002 - type: map_at_10 value: 23.169 - type: map_at_100 value: 24.275 - type: map_at_1000 value: 24.409 - type: map_at_3 value: 21.284 - type: map_at_5 value: 22.171 - type: mrr_at_1 value: 22.233 - type: mrr_at_10 value: 27.857 - type: mrr_at_100 value: 28.76 - type: mrr_at_1000 value: 28.841 - type: mrr_at_3 value: 25.857999999999997 - type: mrr_at_5 value: 26.922 - type: ndcg_at_1 value: 22.233 - type: ndcg_at_10 value: 27.203 - type: ndcg_at_100 value: 32.543 - type: ndcg_at_1000 value: 35.654 - type: ndcg_at_3 value: 23.863 - type: ndcg_at_5 value: 25.117 - type: precision_at_1 value: 22.233 - type: precision_at_10 value: 4.957000000000001 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.036 - type: precision_at_5 value: 7.8149999999999995 - type: recall_at_1 value: 17.688000000000002 - type: recall_at_10 value: 34.969 - type: recall_at_100 value: 58.370999999999995 - type: recall_at_1000 value: 80.02 - type: recall_at_3 value: 25.332 - type: recall_at_5 value: 28.703 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 11.15 - type: map_at_10 value: 16.797 - type: map_at_100 value: 17.822 - type: map_at_1000 value: 17.956 - type: map_at_3 value: 14.985999999999999 - type: map_at_5 value: 16.044 - type: mrr_at_1 value: 14.155000000000001 - type: mrr_at_10 value: 20.01 - type: mrr_at_100 value: 20.966 - type: mrr_at_1000 value: 21.049 - type: mrr_at_3 value: 18.227 - type: mrr_at_5 value: 19.231 - type: ndcg_at_1 value: 14.155000000000001 - type: ndcg_at_10 value: 20.327 - type: ndcg_at_100 value: 25.490000000000002 - type: ndcg_at_1000 value: 28.854000000000003 - type: ndcg_at_3 value: 17.046 - type: ndcg_at_5 value: 18.647 - type: precision_at_1 value: 14.155000000000001 - type: precision_at_10 value: 3.893 - type: precision_at_100 value: 0.771 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.219 - type: precision_at_5 value: 6.164 - type: recall_at_1 value: 11.15 - type: recall_at_10 value: 27.750999999999998 - type: recall_at_100 value: 50.612 - type: recall_at_1000 value: 74.617 - type: recall_at_3 value: 19.101000000000003 - type: recall_at_5 value: 22.999 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 13.562333333333335 - type: map_at_10 value: 18.514583333333334 - type: map_at_100 value: 19.362916666666667 - type: map_at_1000 value: 19.48625 - type: map_at_3 value: 16.955583333333337 - type: map_at_5 value: 17.788 - type: mrr_at_1 value: 16.54575 - type: mrr_at_10 value: 21.549249999999997 - type: mrr_at_100 value: 22.318500000000004 - type: mrr_at_1000 value: 22.405583333333333 - type: mrr_at_3 value: 19.9585 - type: mrr_at_5 value: 20.82183333333333 - type: ndcg_at_1 value: 16.54575 - type: ndcg_at_10 value: 21.80341666666667 - type: ndcg_at_100 value: 26.133833333333328 - type: ndcg_at_1000 value: 29.348666666666666 - type: ndcg_at_3 value: 18.973499999999998 - type: ndcg_at_5 value: 20.200833333333332 - type: precision_at_1 value: 16.54575 - type: precision_at_10 value: 3.895333333333334 - type: precision_at_100 value: 0.7226666666666668 - type: precision_at_1000 value: 0.11775 - type: precision_at_3 value: 8.796666666666667 - type: precision_at_5 value: 6.278083333333333 - type: recall_at_1 value: 13.562333333333335 - type: recall_at_10 value: 28.738833333333336 - type: recall_at_100 value: 48.66516666666668 - type: recall_at_1000 value: 72.21291666666666 - type: recall_at_3 value: 20.722166666666663 - type: recall_at_5 value: 23.920416666666668 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.612 - type: map_at_10 value: 14.233 - type: map_at_100 value: 14.804999999999998 - type: map_at_1000 value: 14.887 - type: map_at_3 value: 13.050999999999998 - type: map_at_5 value: 13.642999999999999 - type: mrr_at_1 value: 12.577 - type: mrr_at_10 value: 16.256999999999998 - type: mrr_at_100 value: 16.830000000000002 - type: mrr_at_1000 value: 16.909 - type: mrr_at_3 value: 15.031 - type: mrr_at_5 value: 15.613 - type: ndcg_at_1 value: 12.577 - type: ndcg_at_10 value: 16.81 - type: ndcg_at_100 value: 20.085 - type: ndcg_at_1000 value: 22.684 - type: ndcg_at_3 value: 14.471 - type: ndcg_at_5 value: 15.384 - type: precision_at_1 value: 12.577 - type: precision_at_10 value: 2.8529999999999998 - type: precision_at_100 value: 0.49699999999999994 - type: precision_at_1000 value: 0.079 - type: precision_at_3 value: 6.544 - type: precision_at_5 value: 4.601 - type: recall_at_1 value: 10.612 - type: recall_at_10 value: 22.983999999999998 - type: recall_at_100 value: 38.745000000000005 - type: recall_at_1000 value: 58.886 - type: recall_at_3 value: 15.982 - type: recall_at_5 value: 18.433 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.04 - type: map_at_10 value: 10.277 - type: map_at_100 value: 10.873 - type: map_at_1000 value: 10.989 - type: map_at_3 value: 9.243 - type: map_at_5 value: 9.843 - type: mrr_at_1 value: 8.774999999999999 - type: mrr_at_10 value: 12.468 - type: mrr_at_100 value: 13.084999999999999 - type: mrr_at_1000 value: 13.184000000000001 - type: mrr_at_3 value: 11.293000000000001 - type: mrr_at_5 value: 12.034 - type: ndcg_at_1 value: 8.774999999999999 - type: ndcg_at_10 value: 12.527 - type: ndcg_at_100 value: 15.939 - type: ndcg_at_1000 value: 19.383 - type: ndcg_at_3 value: 10.565 - type: ndcg_at_5 value: 11.555 - type: precision_at_1 value: 8.774999999999999 - type: precision_at_10 value: 2.3640000000000003 - type: precision_at_100 value: 0.49 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 5.047 - type: precision_at_5 value: 3.8129999999999997 - type: recall_at_1 value: 7.04 - type: recall_at_10 value: 17.193 - type: recall_at_100 value: 33.33 - type: recall_at_1000 value: 59.134 - type: recall_at_3 value: 11.859 - type: recall_at_5 value: 14.243 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.381 - type: map_at_10 value: 15.676000000000002 - type: map_at_100 value: 16.448999999999998 - type: map_at_1000 value: 16.563 - type: map_at_3 value: 14.313 - type: map_at_5 value: 15.010000000000002 - type: mrr_at_1 value: 14.086000000000002 - type: mrr_at_10 value: 18.621 - type: mrr_at_100 value: 19.41 - type: mrr_at_1000 value: 19.506999999999998 - type: mrr_at_3 value: 17.149 - type: mrr_at_5 value: 17.918 - type: ndcg_at_1 value: 14.086000000000002 - type: ndcg_at_10 value: 18.647 - type: ndcg_at_100 value: 22.823 - type: ndcg_at_1000 value: 26.207 - type: ndcg_at_3 value: 15.986 - type: ndcg_at_5 value: 17.108 - type: precision_at_1 value: 14.086000000000002 - type: precision_at_10 value: 3.218 - type: precision_at_100 value: 0.585 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 7.369000000000001 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 11.381 - type: recall_at_10 value: 25.008999999999997 - type: recall_at_100 value: 44.368 - type: recall_at_1000 value: 69.587 - type: recall_at_3 value: 17.612 - type: recall_at_5 value: 20.506 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 12.641 - type: map_at_10 value: 19.067 - type: map_at_100 value: 20.046 - type: map_at_1000 value: 20.221 - type: map_at_3 value: 17.699 - type: map_at_5 value: 18.458 - type: mrr_at_1 value: 16.008 - type: mrr_at_10 value: 22.526 - type: mrr_at_100 value: 23.307 - type: mrr_at_1000 value: 23.391000000000002 - type: mrr_at_3 value: 21.047 - type: mrr_at_5 value: 21.956999999999997 - type: ndcg_at_1 value: 16.008 - type: ndcg_at_10 value: 23.029 - type: ndcg_at_100 value: 27.533 - type: ndcg_at_1000 value: 31.096 - type: ndcg_at_3 value: 20.806 - type: ndcg_at_5 value: 21.859 - type: precision_at_1 value: 16.008 - type: precision_at_10 value: 4.605 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.470000000000001 - type: recall_at_1 value: 12.641 - type: recall_at_10 value: 30.236 - type: recall_at_100 value: 51.543000000000006 - type: recall_at_1000 value: 76.265 - type: recall_at_3 value: 23.677999999999997 - type: recall_at_5 value: 26.456000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.288 - type: map_at_10 value: 15.101 - type: map_at_100 value: 15.725 - type: map_at_1000 value: 15.840000000000002 - type: map_at_3 value: 13.614 - type: map_at_5 value: 14.394000000000002 - type: mrr_at_1 value: 12.753999999999998 - type: mrr_at_10 value: 16.665 - type: mrr_at_100 value: 17.288 - type: mrr_at_1000 value: 17.393 - type: mrr_at_3 value: 15.096000000000002 - type: mrr_at_5 value: 15.974 - type: ndcg_at_1 value: 12.753999999999998 - type: ndcg_at_10 value: 17.821 - type: ndcg_at_100 value: 21.544 - type: ndcg_at_1000 value: 24.965 - type: ndcg_at_3 value: 14.789 - type: ndcg_at_5 value: 16.163 - type: precision_at_1 value: 12.753999999999998 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.518 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.621 - type: recall_at_1 value: 11.288 - type: recall_at_10 value: 24.941 - type: recall_at_100 value: 43.339 - type: recall_at_1000 value: 69.813 - type: recall_at_3 value: 16.679 - type: recall_at_5 value: 19.982 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.319 - type: map_at_10 value: 9.538 - type: map_at_100 value: 10.786 - type: map_at_1000 value: 10.979 - type: map_at_3 value: 7.693999999999999 - type: map_at_5 value: 8.623 - type: mrr_at_1 value: 11.922 - type: mrr_at_10 value: 19.683 - type: mrr_at_100 value: 20.881 - type: mrr_at_1000 value: 20.961 - type: mrr_at_3 value: 17.014000000000003 - type: mrr_at_5 value: 18.47 - type: ndcg_at_1 value: 11.922 - type: ndcg_at_10 value: 14.517 - type: ndcg_at_100 value: 20.541999999999998 - type: ndcg_at_1000 value: 24.648999999999997 - type: ndcg_at_3 value: 10.975 - type: ndcg_at_5 value: 12.276 - type: precision_at_1 value: 11.922 - type: precision_at_10 value: 4.893 - type: precision_at_100 value: 1.129 - type: precision_at_1000 value: 0.187 - type: precision_at_3 value: 8.382000000000001 - type: precision_at_5 value: 6.801 - type: recall_at_1 value: 5.319 - type: recall_at_10 value: 18.593 - type: recall_at_100 value: 39.957 - type: recall_at_1000 value: 63.748000000000005 - type: recall_at_3 value: 10.314 - type: recall_at_5 value: 13.564000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 3.819 - type: map_at_10 value: 7.771999999999999 - type: map_at_100 value: 10.424999999999999 - type: map_at_1000 value: 11.165 - type: map_at_3 value: 5.586 - type: map_at_5 value: 6.524000000000001 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 43.289 - type: mrr_at_100 value: 44.184 - type: mrr_at_1000 value: 44.239 - type: mrr_at_3 value: 40.75 - type: mrr_at_5 value: 42.175000000000004 - type: ndcg_at_1 value: 25.5 - type: ndcg_at_10 value: 19.994 - type: ndcg_at_100 value: 21.802 - type: ndcg_at_1000 value: 28.086 - type: ndcg_at_3 value: 22.279 - type: ndcg_at_5 value: 20.986 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 17.65 - type: precision_at_100 value: 5.317 - type: precision_at_1000 value: 1.146 - type: precision_at_3 value: 25.75 - type: precision_at_5 value: 22.400000000000002 - type: recall_at_1 value: 3.819 - type: recall_at_10 value: 11.533 - type: recall_at_100 value: 26.484999999999996 - type: recall_at_1000 value: 47.63 - type: recall_at_3 value: 6.268999999999999 - type: recall_at_5 value: 8.218 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.69500000000001 - type: f1 value: 39.81935458907266 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 10.544 - type: map_at_10 value: 16.105 - type: map_at_100 value: 16.91 - type: map_at_1000 value: 16.993 - type: map_at_3 value: 14.273 - type: map_at_5 value: 15.259 - type: mrr_at_1 value: 11.206000000000001 - type: mrr_at_10 value: 17.129 - type: mrr_at_100 value: 17.955 - type: mrr_at_1000 value: 18.032999999999998 - type: mrr_at_3 value: 15.214 - type: mrr_at_5 value: 16.249 - type: ndcg_at_1 value: 11.206000000000001 - type: ndcg_at_10 value: 19.546 - type: ndcg_at_100 value: 23.934 - type: ndcg_at_1000 value: 26.356 - type: ndcg_at_3 value: 15.706999999999999 - type: ndcg_at_5 value: 17.488999999999997 - type: precision_at_1 value: 11.206000000000001 - type: precision_at_10 value: 3.195 - type: precision_at_100 value: 0.557 - type: precision_at_1000 value: 0.078 - type: precision_at_3 value: 6.7860000000000005 - type: precision_at_5 value: 4.997999999999999 - type: recall_at_1 value: 10.544 - type: recall_at_10 value: 29.421999999999997 - type: recall_at_100 value: 50.54 - type: recall_at_1000 value: 69.53200000000001 - type: recall_at_3 value: 18.901 - type: recall_at_5 value: 23.183999999999997 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 5.688 - type: map_at_10 value: 9.454 - type: map_at_100 value: 10.459 - type: map_at_1000 value: 10.645 - type: map_at_3 value: 7.914000000000001 - type: map_at_5 value: 8.622 - type: mrr_at_1 value: 11.42 - type: mrr_at_10 value: 16.608 - type: mrr_at_100 value: 17.566000000000003 - type: mrr_at_1000 value: 17.675 - type: mrr_at_3 value: 14.712 - type: mrr_at_5 value: 15.638 - type: ndcg_at_1 value: 11.42 - type: ndcg_at_10 value: 13.293 - type: ndcg_at_100 value: 18.289 - type: ndcg_at_1000 value: 22.781000000000002 - type: ndcg_at_3 value: 10.835 - type: ndcg_at_5 value: 11.576 - type: precision_at_1 value: 11.42 - type: precision_at_10 value: 3.997 - type: precision_at_100 value: 0.897 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 7.356 - type: precision_at_5 value: 5.772 - type: recall_at_1 value: 5.688 - type: recall_at_10 value: 17.544 - type: recall_at_100 value: 37.358999999999995 - type: recall_at_1000 value: 65.735 - type: recall_at_3 value: 9.987 - type: recall_at_5 value: 12.337 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 13.248 - type: map_at_10 value: 18.584 - type: map_at_100 value: 19.348000000000003 - type: map_at_1000 value: 19.457 - type: map_at_3 value: 16.962 - type: map_at_5 value: 17.862000000000002 - type: mrr_at_1 value: 26.496 - type: mrr_at_10 value: 32.580999999999996 - type: mrr_at_100 value: 33.314 - type: mrr_at_1000 value: 33.387 - type: mrr_at_3 value: 30.808000000000003 - type: mrr_at_5 value: 31.805 - type: ndcg_at_1 value: 26.496 - type: ndcg_at_10 value: 24.198 - type: ndcg_at_100 value: 28.017999999999997 - type: ndcg_at_1000 value: 30.839 - type: ndcg_at_3 value: 21.002000000000002 - type: ndcg_at_5 value: 22.547 - type: precision_at_1 value: 26.496 - type: precision_at_10 value: 5.415 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 13.234000000000002 - type: precision_at_5 value: 9.164 - type: recall_at_1 value: 13.248 - type: recall_at_10 value: 27.076 - type: recall_at_100 value: 42.512 - type: recall_at_1000 value: 61.41799999999999 - type: recall_at_3 value: 19.851 - type: recall_at_5 value: 22.91 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.98560000000001 - type: ap value: 59.217561950701445 - type: f1 value: 63.818409911217046 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 5.179 - type: map_at_10 value: 9.055 - type: map_at_100 value: 9.814 - type: map_at_1000 value: 9.911 - type: map_at_3 value: 7.631 - type: map_at_5 value: 8.415000000000001 - type: mrr_at_1 value: 5.3580000000000005 - type: mrr_at_10 value: 9.302000000000001 - type: mrr_at_100 value: 10.075000000000001 - type: mrr_at_1000 value: 10.169 - type: mrr_at_3 value: 7.856000000000001 - type: mrr_at_5 value: 8.654 - type: ndcg_at_1 value: 5.33 - type: ndcg_at_10 value: 11.491 - type: ndcg_at_100 value: 15.735 - type: ndcg_at_1000 value: 18.721 - type: ndcg_at_3 value: 8.522 - type: ndcg_at_5 value: 9.943 - type: precision_at_1 value: 5.33 - type: precision_at_10 value: 1.983 - type: precision_at_100 value: 0.42 - type: precision_at_1000 value: 0.068 - type: precision_at_3 value: 3.763 - type: precision_at_5 value: 2.9770000000000003 - type: recall_at_1 value: 5.179 - type: recall_at_10 value: 19.069 - type: recall_at_100 value: 39.946 - type: recall_at_1000 value: 64.031 - type: recall_at_3 value: 10.91 - type: recall_at_5 value: 14.334 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.25444596443229 - type: f1 value: 88.34114464691379 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 59.3251253989968 - type: f1 value: 39.879870396124964 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.90316072629455 - type: f1 value: 59.6419867903448 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.5474108944183 - type: f1 value: 67.13260105586494 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.08360278577924 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.539985814012603 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 28.52217790319968 - type: mrr value: 29.375037759331086 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.202 - type: map_at_10 value: 6.216 - type: map_at_100 value: 7.902000000000001 - type: map_at_1000 value: 9.114 - type: map_at_3 value: 4.752 - type: map_at_5 value: 5.414 - type: mrr_at_1 value: 31.269000000000002 - type: mrr_at_10 value: 39.649 - type: mrr_at_100 value: 40.261 - type: mrr_at_1000 value: 40.338 - type: mrr_at_3 value: 37.049 - type: mrr_at_5 value: 38.643 - type: ndcg_at_1 value: 29.412 - type: ndcg_at_10 value: 21.224 - type: ndcg_at_100 value: 19.897000000000002 - type: ndcg_at_1000 value: 29.53 - type: ndcg_at_3 value: 24.635 - type: ndcg_at_5 value: 23.114 - type: precision_at_1 value: 31.269000000000002 - type: precision_at_10 value: 15.697 - type: precision_at_100 value: 5.842 - type: precision_at_1000 value: 1.8880000000000001 - type: precision_at_3 value: 23.013 - type: precision_at_5 value: 19.628 - type: recall_at_1 value: 3.202 - type: recall_at_10 value: 9.889000000000001 - type: recall_at_100 value: 21.366 - type: recall_at_1000 value: 56.267999999999994 - type: recall_at_3 value: 5.7459999999999996 - type: recall_at_5 value: 7.473000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 7.892 - type: map_at_10 value: 13.358999999999998 - type: map_at_100 value: 14.396 - type: map_at_1000 value: 14.499 - type: map_at_3 value: 11.335 - type: map_at_5 value: 12.375 - type: mrr_at_1 value: 8.98 - type: mrr_at_10 value: 14.762 - type: mrr_at_100 value: 15.787 - type: mrr_at_1000 value: 15.873000000000001 - type: mrr_at_3 value: 12.65 - type: mrr_at_5 value: 13.761000000000001 - type: ndcg_at_1 value: 8.98 - type: ndcg_at_10 value: 17.013 - type: ndcg_at_100 value: 22.582 - type: ndcg_at_1000 value: 25.546000000000003 - type: ndcg_at_3 value: 12.765 - type: ndcg_at_5 value: 14.662 - type: precision_at_1 value: 8.98 - type: precision_at_10 value: 3.152 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 5.997 - type: precision_at_5 value: 4.652 - type: recall_at_1 value: 7.892 - type: recall_at_10 value: 27.081 - type: recall_at_100 value: 53.36300000000001 - type: recall_at_1000 value: 76.419 - type: recall_at_3 value: 15.623999999999999 - type: recall_at_5 value: 20.104 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 61.224999999999994 - type: map_at_10 value: 73.768 - type: map_at_100 value: 74.54899999999999 - type: map_at_1000 value: 74.588 - type: map_at_3 value: 70.845 - type: map_at_5 value: 72.61 - type: mrr_at_1 value: 70.63000000000001 - type: mrr_at_10 value: 78.204 - type: mrr_at_100 value: 78.469 - type: mrr_at_1000 value: 78.477 - type: mrr_at_3 value: 76.67500000000001 - type: mrr_at_5 value: 77.644 - type: ndcg_at_1 value: 70.61 - type: ndcg_at_10 value: 78.586 - type: ndcg_at_100 value: 80.852 - type: ndcg_at_1000 value: 81.32000000000001 - type: ndcg_at_3 value: 74.902 - type: ndcg_at_5 value: 76.787 - type: precision_at_1 value: 70.61 - type: precision_at_10 value: 11.904 - type: precision_at_100 value: 1.438 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 32.503 - type: precision_at_5 value: 21.526 - type: recall_at_1 value: 61.224999999999994 - type: recall_at_10 value: 87.908 - type: recall_at_100 value: 96.63000000000001 - type: recall_at_1000 value: 99.367 - type: recall_at_3 value: 77.358 - type: recall_at_5 value: 82.584 - type: map_at_1 value: 2.585 - type: map_at_10 value: 5.988 - type: map_at_100 value: 7.21 - type: map_at_1000 value: 7.449999999999999 - type: map_at_3 value: 4.372 - type: map_at_5 value: 5.194 - type: mrr_at_1 value: 12.8 - type: mrr_at_10 value: 19.963 - type: mrr_at_100 value: 21.195 - type: mrr_at_1000 value: 21.29 - type: mrr_at_3 value: 17.533 - type: mrr_at_5 value: 18.853 - type: ndcg_at_1 value: 12.8 - type: ndcg_at_10 value: 10.874 - type: ndcg_at_100 value: 16.695 - type: ndcg_at_1000 value: 21.762999999999998 - type: ndcg_at_3 value: 10.209 - type: ndcg_at_5 value: 8.999 - type: precision_at_1 value: 12.8 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.264 - type: precision_at_3 value: 9.433 - type: precision_at_5 value: 7.88 - type: recall_at_1 value: 2.585 - type: recall_at_10 value: 11.455 - type: recall_at_100 value: 28.665000000000003 - type: recall_at_1000 value: 53.547999999999995 - type: recall_at_3 value: 5.748 - type: recall_at_5 value: 7.983 - type: map_at_1 value: 0.133 - type: map_at_10 value: 0.707 - type: map_at_100 value: 3.759 - type: map_at_1000 value: 9.02 - type: map_at_3 value: 0.27399999999999997 - type: map_at_5 value: 0.4 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 61.147 - type: mrr_at_100 value: 62.076 - type: mrr_at_1000 value: 62.076 - type: mrr_at_3 value: 57.99999999999999 - type: mrr_at_5 value: 59.3 - type: ndcg_at_1 value: 44.0 - type: ndcg_at_10 value: 36.039 - type: ndcg_at_100 value: 28.122999999999998 - type: ndcg_at_1000 value: 25.650000000000002 - type: ndcg_at_3 value: 38.173 - type: ndcg_at_5 value: 37.35 - type: precision_at_1 value: 52.0 - type: precision_at_10 value: 39.4 - type: precision_at_100 value: 29.82 - type: precision_at_1000 value: 12.690000000000001 - type: precision_at_3 value: 42.0 - type: precision_at_5 value: 40.400000000000006 - type: recall_at_1 value: 0.133 - type: recall_at_10 value: 0.897 - type: recall_at_100 value: 6.336 - type: recall_at_1000 value: 24.990000000000002 - type: recall_at_3 value: 0.301 - type: recall_at_5 value: 0.462 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 33.05951893381823 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 42.691497046210955 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 76.47089854443493 - type: cos_sim_spearman value: 67.65881641628117 - type: euclidean_pearson value: 72.75220596907191 - type: euclidean_spearman value: 67.65881507675402 - type: manhattan_pearson value: 71.2932268352905 - type: manhattan_spearman value: 66.28937203768146 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 73.59904123111602 - type: cos_sim_spearman value: 66.64191118455778 - type: euclidean_pearson value: 70.031991407929 - type: euclidean_spearman value: 66.64312867708462 - type: manhattan_pearson value: 70.87113974670322 - type: manhattan_spearman value: 67.87998624470126 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.1868454480634 - type: cos_sim_spearman value: 78.5663631376088 - type: euclidean_pearson value: 78.1441330499307 - type: euclidean_spearman value: 78.5663753212518 - type: manhattan_pearson value: 78.7258747377543 - type: manhattan_spearman value: 79.24251325682667 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.39709143417873 - type: cos_sim_spearman value: 74.33024682805708 - type: euclidean_pearson value: 76.65457389990631 - type: euclidean_spearman value: 74.33023713728515 - type: manhattan_pearson value: 76.73342787471654 - type: manhattan_spearman value: 74.74461118652161 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.9395037638594 - type: cos_sim_spearman value: 81.01819776486752 - type: euclidean_pearson value: 81.03043241994847 - type: euclidean_spearman value: 81.01819627953365 - type: manhattan_pearson value: 81.68968136619384 - type: manhattan_spearman value: 81.82363999592259 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.243504336461 - type: cos_sim_spearman value: 76.61917655422197 - type: euclidean_pearson value: 76.26910712210864 - type: euclidean_spearman value: 76.62000560376505 - type: manhattan_pearson value: 76.91613259757325 - type: manhattan_spearman value: 77.4215820608173 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.99178286054092 - type: cos_sim_spearman value: 84.2361483019332 - type: euclidean_pearson value: 84.30885968598922 - type: euclidean_spearman value: 84.23702233300253 - type: manhattan_pearson value: 84.64734537899606 - type: manhattan_spearman value: 84.71355882886535 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.18741235485141 - type: cos_sim_spearman value: 60.873579764468225 - type: euclidean_pearson value: 63.18427359110471 - type: euclidean_spearman value: 60.873579764468225 - type: manhattan_pearson value: 63.443408253414354 - type: manhattan_spearman value: 61.5997912341628 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 77.15144919426055 - type: cos_sim_spearman value: 75.76050778643061 - type: euclidean_pearson value: 77.30073366013343 - type: euclidean_spearman value: 75.76052625455534 - type: manhattan_pearson value: 77.41746598074477 - type: manhattan_spearman value: 75.98770131791319 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 70.66070662385174 - type: mrr value: 90.05894523051387 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 31.139 - type: map_at_10 value: 38.127 - type: map_at_100 value: 39.216 - type: map_at_1000 value: 39.290000000000006 - type: map_at_3 value: 35.667 - type: map_at_5 value: 37.317 - type: mrr_at_1 value: 33.333 - type: mrr_at_10 value: 39.972 - type: mrr_at_100 value: 40.892 - type: mrr_at_1000 value: 40.955000000000005 - type: mrr_at_3 value: 37.889 - type: mrr_at_5 value: 39.222 - type: ndcg_at_1 value: 33.333 - type: ndcg_at_10 value: 42.177 - type: ndcg_at_100 value: 47.772999999999996 - type: ndcg_at_1000 value: 49.738 - type: ndcg_at_3 value: 37.568 - type: ndcg_at_5 value: 40.294999999999995 - type: precision_at_1 value: 33.333 - type: precision_at_10 value: 5.867 - type: precision_at_100 value: 0.903 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 14.777999999999999 - type: precision_at_5 value: 10.4 - type: recall_at_1 value: 31.139 - type: recall_at_10 value: 53.056000000000004 - type: recall_at_100 value: 79.60000000000001 - type: recall_at_1000 value: 95.133 - type: recall_at_3 value: 40.75 - type: recall_at_5 value: 47.417 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.63663366336634 - type: cos_sim_ap value: 87.41569381811651 - type: cos_sim_f1 value: 80.8154730789336 - type: cos_sim_precision value: 84.66593647316539 - type: cos_sim_recall value: 77.3 - type: dot_accuracy value: 99.63663366336634 - type: dot_ap value: 87.41569381811651 - type: dot_f1 value: 80.8154730789336 - type: dot_precision value: 84.66593647316539 - type: dot_recall value: 77.3 - type: euclidean_accuracy value: 99.63663366336634 - type: euclidean_ap value: 87.41569381811651 - type: euclidean_f1 value: 80.8154730789336 - type: euclidean_precision value: 84.66593647316539 - type: euclidean_recall value: 77.3 - type: manhattan_accuracy value: 99.6930693069307 - type: manhattan_ap value: 90.67306262109962 - type: manhattan_f1 value: 84.03707518022657 - type: manhattan_precision value: 86.62420382165605 - type: manhattan_recall value: 81.6 - type: max_accuracy value: 99.6930693069307 - type: max_ap value: 90.67306262109962 - type: max_f1 value: 84.03707518022657 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 36.46819467809413 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.142679626551587 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.08118718504021 - type: mrr value: 43.547356442577026 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.26989671913281 - type: cos_sim_spearman value: 30.01993799277349 - type: dot_pearson value: 30.26989672303903 - type: dot_spearman value: 30.03106981258351 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.664 - type: map_at_10 value: 8.95 - type: map_at_100 value: 14.699000000000002 - type: map_at_1000 value: 16.275000000000002 - type: map_at_3 value: 4.963 - type: map_at_5 value: 6.707000000000001 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 48.016 - type: mrr_at_100 value: 48.826 - type: mrr_at_1000 value: 48.826 - type: mrr_at_3 value: 44.558 - type: mrr_at_5 value: 46.394999999999996 - type: ndcg_at_1 value: 33.672999999999995 - type: ndcg_at_10 value: 21.981 - type: ndcg_at_100 value: 35.227000000000004 - type: ndcg_at_1000 value: 46.428999999999995 - type: ndcg_at_3 value: 27.496 - type: ndcg_at_5 value: 24.886 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 19.184 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.486 - type: precision_at_3 value: 27.891 - type: precision_at_5 value: 24.898 - type: recall_at_1 value: 2.664 - type: recall_at_10 value: 13.309999999999999 - type: recall_at_100 value: 46.727000000000004 - type: recall_at_1000 value: 81.158 - type: recall_at_3 value: 5.872 - type: recall_at_5 value: 8.694 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.86019999999999 - type: ap value: 13.439585186117995 - type: f1 value: 53.53111224664294 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.539898132427844 - type: f1 value: 53.736121370681076 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.790329189415395 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.59063002920665 - type: cos_sim_ap value: 65.1646758019036 - type: cos_sim_f1 value: 61.95799041886746 - type: cos_sim_precision value: 57.96368650884855 - type: cos_sim_recall value: 66.54353562005278 - type: dot_accuracy value: 83.59063002920665 - type: dot_ap value: 65.1646758019036 - type: dot_f1 value: 61.95799041886746 - type: dot_precision value: 57.96368650884855 - type: dot_recall value: 66.54353562005278 - type: euclidean_accuracy value: 83.59063002920665 - type: euclidean_ap value: 65.1646758019036 - type: euclidean_f1 value: 61.95799041886746 - type: euclidean_precision value: 57.96368650884855 - type: euclidean_recall value: 66.54353562005278 - type: manhattan_accuracy value: 83.29856350956668 - type: manhattan_ap value: 63.803561536283404 - type: manhattan_f1 value: 60.45279383429673 - type: manhattan_precision value: 55.60478511298184 - type: manhattan_recall value: 66.2269129287599 - type: max_accuracy value: 83.59063002920665 - type: max_ap value: 65.1646758019036 - type: max_f1 value: 61.95799041886746 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.46264602010324 - type: cos_sim_ap value: 82.64331601180713 - type: cos_sim_f1 value: 74.66489420627008 - type: cos_sim_precision value: 71.73774214148868 - type: cos_sim_recall value: 77.8410840776101 - type: dot_accuracy value: 87.46264602010324 - type: dot_ap value: 82.64331811121104 - type: dot_f1 value: 74.66489420627008 - type: dot_precision value: 71.73774214148868 - type: dot_recall value: 77.8410840776101 - type: euclidean_accuracy value: 87.46264602010324 - type: euclidean_ap value: 82.64331792274162 - type: euclidean_f1 value: 74.66489420627008 - type: euclidean_precision value: 71.73774214148868 - type: euclidean_recall value: 77.8410840776101 - type: manhattan_accuracy value: 87.35203943027904 - type: manhattan_ap value: 82.69548093072707 - type: manhattan_f1 value: 74.90158915293776 - type: manhattan_precision value: 71.1171096345515 - type: manhattan_recall value: 79.11148752694795 - type: max_accuracy value: 87.46264602010324 - type: max_ap value: 82.69548093072707 - type: max_f1 value: 74.90158915293776 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/nmc-300-w75k-b10k	twadada	null	[ "mteb", "model-index", "region:us" ]	2024-09-16T23:06:27	2024-09-16T23:06:35	0	--- tags: - mteb model-index: - name: nomic_classification_307_w75k_b10k results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.71641791044776 - type: ap value: 33.83144952728368 - type: f1 value: 65.42257056593405 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.614075 - type: ap value: 59.729707124101274 - type: f1 value: 64.38848477168871 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.842 - type: f1 value: 33.39663457981403 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 19.986 - type: map_at_10 value: 34.064 - type: map_at_100 value: 35.275 - type: map_at_1000 value: 35.303000000000004 - type: map_at_3 value: 29.610999999999997 - type: map_at_5 value: 32.104 - type: mrr_at_1 value: 20.413 - type: mrr_at_10 value: 34.205000000000005 - type: mrr_at_100 value: 35.429 - type: mrr_at_1000 value: 35.457 - type: mrr_at_3 value: 29.813000000000002 - type: mrr_at_5 value: 32.245000000000005 - type: ndcg_at_1 value: 19.986 - type: ndcg_at_10 value: 42.03 - type: ndcg_at_100 value: 47.709 - type: ndcg_at_1000 value: 48.372 - type: ndcg_at_3 value: 32.815 - type: ndcg_at_5 value: 37.287 - type: precision_at_1 value: 19.986 - type: precision_at_10 value: 6.757000000000001 - type: precision_at_100 value: 0.936 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.035 - type: precision_at_5 value: 10.583 - type: recall_at_1 value: 19.986 - type: recall_at_10 value: 67.568 - type: recall_at_100 value: 93.599 - type: recall_at_1000 value: 98.649 - type: recall_at_3 value: 42.105 - type: recall_at_5 value: 52.916 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 32.3683689352825 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 22.612199003814784 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 52.783575323019186 - type: mrr value: 66.44813788858109 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.91542000311537 - type: cos_sim_spearman value: 76.96982609885886 - type: euclidean_pearson value: 78.2167413496553 - type: euclidean_spearman value: 76.96982609885886 - type: manhattan_pearson value: 78.37464609362752 - type: manhattan_spearman value: 77.44875267417638 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.1948051948052 - type: f1 value: 71.36111601799189 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.50999941209323 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 21.012511459928128 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.223 - type: map_at_10 value: 28.166000000000004 - type: map_at_100 value: 29.25 - type: map_at_1000 value: 29.408 - type: map_at_3 value: 26.027 - type: map_at_5 value: 27.211999999999996 - type: mrr_at_1 value: 26.753 - type: mrr_at_10 value: 33.852 - type: mrr_at_100 value: 34.726 - type: mrr_at_1000 value: 34.806 - type: mrr_at_3 value: 32.046 - type: mrr_at_5 value: 33.112 - type: ndcg_at_1 value: 26.753 - type: ndcg_at_10 value: 32.75 - type: ndcg_at_100 value: 37.772 - type: ndcg_at_1000 value: 41.116 - type: ndcg_at_3 value: 29.865000000000002 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 26.753 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.089 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 14.449000000000002 - type: precision_at_5 value: 10.272 - type: recall_at_1 value: 21.223 - type: recall_at_10 value: 40.269 - type: recall_at_100 value: 62.9 - type: recall_at_1000 value: 85.92699999999999 - type: recall_at_3 value: 30.89 - type: recall_at_5 value: 34.756 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.796 - type: map_at_10 value: 21.91 - type: map_at_100 value: 22.802 - type: map_at_1000 value: 22.925 - type: map_at_3 value: 20.143 - type: map_at_5 value: 21.066 - type: mrr_at_1 value: 21.274 - type: mrr_at_10 value: 26.076 - type: mrr_at_100 value: 26.857999999999997 - type: mrr_at_1000 value: 26.932000000000002 - type: mrr_at_3 value: 24.257 - type: mrr_at_5 value: 25.145 - type: ndcg_at_1 value: 21.274 - type: ndcg_at_10 value: 25.428 - type: ndcg_at_100 value: 29.804000000000002 - type: ndcg_at_1000 value: 32.766 - type: ndcg_at_3 value: 22.434 - type: ndcg_at_5 value: 23.639 - type: precision_at_1 value: 21.274 - type: precision_at_10 value: 4.662 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 10.552 - type: precision_at_5 value: 7.465 - type: recall_at_1 value: 16.796 - type: recall_at_10 value: 31.897 - type: recall_at_100 value: 51.413 - type: recall_at_1000 value: 71.885 - type: recall_at_3 value: 23.318 - type: recall_at_5 value: 26.636 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 24.834999999999997 - type: map_at_10 value: 32.919 - type: map_at_100 value: 34.035 - type: map_at_1000 value: 34.132 - type: map_at_3 value: 30.495 - type: map_at_5 value: 31.805 - type: mrr_at_1 value: 28.715000000000003 - type: mrr_at_10 value: 36.014 - type: mrr_at_100 value: 36.902 - type: mrr_at_1000 value: 36.964999999999996 - type: mrr_at_3 value: 33.71 - type: mrr_at_5 value: 34.935 - type: ndcg_at_1 value: 28.715000000000003 - type: ndcg_at_10 value: 37.524 - type: ndcg_at_100 value: 42.74 - type: ndcg_at_1000 value: 45.011 - type: ndcg_at_3 value: 32.927 - type: ndcg_at_5 value: 35.010000000000005 - type: precision_at_1 value: 28.715000000000003 - type: precision_at_10 value: 6.132 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.587 - type: precision_at_5 value: 10.169 - type: recall_at_1 value: 24.834999999999997 - type: recall_at_10 value: 48.5 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 88.402 - type: recall_at_3 value: 36.052 - type: recall_at_5 value: 41.22 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.527000000000001 - type: map_at_10 value: 14.774000000000001 - type: map_at_100 value: 15.579 - type: map_at_1000 value: 15.689 - type: map_at_3 value: 13.481000000000002 - type: map_at_5 value: 14.212 - type: mrr_at_1 value: 11.638 - type: mrr_at_10 value: 15.895999999999999 - type: mrr_at_100 value: 16.717000000000002 - type: mrr_at_1000 value: 16.819 - type: mrr_at_3 value: 14.576 - type: mrr_at_5 value: 15.305 - type: ndcg_at_1 value: 11.638 - type: ndcg_at_10 value: 17.331 - type: ndcg_at_100 value: 21.675 - type: ndcg_at_1000 value: 25.127 - type: ndcg_at_3 value: 14.688 - type: ndcg_at_5 value: 15.963 - type: precision_at_1 value: 11.638 - type: precision_at_10 value: 2.7119999999999997 - type: precision_at_100 value: 0.52 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.29 - type: precision_at_5 value: 4.452 - type: recall_at_1 value: 10.527000000000001 - type: recall_at_10 value: 24.269 - type: recall_at_100 value: 44.942 - type: recall_at_1000 value: 72.14 - type: recall_at_3 value: 17.043 - type: recall_at_5 value: 20.113 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.859 - type: map_at_10 value: 9.765 - type: map_at_100 value: 10.458 - type: map_at_1000 value: 10.58 - type: map_at_3 value: 8.417 - type: map_at_5 value: 9.248000000000001 - type: mrr_at_1 value: 8.831 - type: mrr_at_10 value: 11.966000000000001 - type: mrr_at_100 value: 12.681999999999999 - type: mrr_at_1000 value: 12.783 - type: mrr_at_3 value: 10.406 - type: mrr_at_5 value: 11.302 - type: ndcg_at_1 value: 8.831 - type: ndcg_at_10 value: 12.094000000000001 - type: ndcg_at_100 value: 15.875 - type: ndcg_at_1000 value: 19.563 - type: ndcg_at_3 value: 9.442 - type: ndcg_at_5 value: 10.8 - type: precision_at_1 value: 8.831 - type: precision_at_10 value: 2.313 - type: precision_at_100 value: 0.49 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 4.353 - type: precision_at_5 value: 3.483 - type: recall_at_1 value: 6.859 - type: recall_at_10 value: 17.201 - type: recall_at_100 value: 34.441 - type: recall_at_1000 value: 62.007 - type: recall_at_3 value: 10.012 - type: recall_at_5 value: 13.370000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.488 - type: map_at_10 value: 23.080000000000002 - type: map_at_100 value: 24.230999999999998 - type: map_at_1000 value: 24.371000000000002 - type: map_at_3 value: 21.288 - type: map_at_5 value: 22.192999999999998 - type: mrr_at_1 value: 21.848 - type: mrr_at_10 value: 27.642 - type: mrr_at_100 value: 28.609 - type: mrr_at_1000 value: 28.692 - type: mrr_at_3 value: 25.746000000000002 - type: mrr_at_5 value: 26.848 - type: ndcg_at_1 value: 21.848 - type: ndcg_at_10 value: 27.032 - type: ndcg_at_100 value: 32.489000000000004 - type: ndcg_at_1000 value: 35.692 - type: ndcg_at_3 value: 23.874000000000002 - type: ndcg_at_5 value: 25.192999999999998 - type: precision_at_1 value: 21.848 - type: precision_at_10 value: 4.936999999999999 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.1 - type: precision_at_5 value: 7.854 - type: recall_at_1 value: 17.488 - type: recall_at_10 value: 34.572 - type: recall_at_100 value: 58.347 - type: recall_at_1000 value: 80.541 - type: recall_at_3 value: 25.417 - type: recall_at_5 value: 28.998 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 11.131 - type: map_at_10 value: 16.721 - type: map_at_100 value: 17.743000000000002 - type: map_at_1000 value: 17.878 - type: map_at_3 value: 14.92 - type: map_at_5 value: 15.963 - type: mrr_at_1 value: 14.155000000000001 - type: mrr_at_10 value: 19.974 - type: mrr_at_100 value: 20.905 - type: mrr_at_1000 value: 20.988 - type: mrr_at_3 value: 18.132 - type: mrr_at_5 value: 19.176000000000002 - type: ndcg_at_1 value: 14.155000000000001 - type: ndcg_at_10 value: 20.302999999999997 - type: ndcg_at_100 value: 25.421 - type: ndcg_at_1000 value: 28.832 - type: ndcg_at_3 value: 16.973 - type: ndcg_at_5 value: 18.590999999999998 - type: precision_at_1 value: 14.155000000000001 - type: precision_at_10 value: 3.904 - type: precision_at_100 value: 0.768 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.219 - type: precision_at_5 value: 6.187 - type: recall_at_1 value: 11.131 - type: recall_at_10 value: 27.894000000000002 - type: recall_at_100 value: 50.52799999999999 - type: recall_at_1000 value: 74.969 - type: recall_at_3 value: 19.043 - type: recall_at_5 value: 22.994 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 13.556083333333332 - type: map_at_10 value: 18.54741666666667 - type: map_at_100 value: 19.411 - type: map_at_1000 value: 19.53608333333333 - type: map_at_3 value: 16.97208333333333 - type: map_at_5 value: 17.831583333333327 - type: mrr_at_1 value: 16.519666666666666 - type: mrr_at_10 value: 21.57283333333333 - type: mrr_at_100 value: 22.353166666666667 - type: mrr_at_1000 value: 22.441999999999997 - type: mrr_at_3 value: 19.954500000000003 - type: mrr_at_5 value: 20.84958333333333 - type: ndcg_at_1 value: 16.519666666666666 - type: ndcg_at_10 value: 21.846416666666666 - type: ndcg_at_100 value: 26.199750000000005 - type: ndcg_at_1000 value: 29.44333333333333 - type: ndcg_at_3 value: 18.98058333333334 - type: ndcg_at_5 value: 20.266666666666666 - type: precision_at_1 value: 16.519666666666666 - type: precision_at_10 value: 3.907999999999999 - type: precision_at_100 value: 0.7232500000000001 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.805166666666668 - type: precision_at_5 value: 6.317166666666665 - type: recall_at_1 value: 13.556083333333332 - type: recall_at_10 value: 28.798916666666663 - type: recall_at_100 value: 48.75083333333334 - type: recall_at_1000 value: 72.47908333333334 - type: recall_at_3 value: 20.702583333333333 - type: recall_at_5 value: 24.039250000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.536 - type: map_at_10 value: 14.155000000000001 - type: map_at_100 value: 14.77 - type: map_at_1000 value: 14.860999999999999 - type: map_at_3 value: 13.055 - type: map_at_5 value: 13.608999999999998 - type: mrr_at_1 value: 12.577 - type: mrr_at_10 value: 16.247 - type: mrr_at_100 value: 16.857 - type: mrr_at_1000 value: 16.942 - type: mrr_at_3 value: 15.056 - type: mrr_at_5 value: 15.647 - type: ndcg_at_1 value: 12.577 - type: ndcg_at_10 value: 16.636 - type: ndcg_at_100 value: 19.947 - type: ndcg_at_1000 value: 22.643 - type: ndcg_at_3 value: 14.472 - type: ndcg_at_5 value: 15.339 - type: precision_at_1 value: 12.577 - type: precision_at_10 value: 2.807 - type: precision_at_100 value: 0.48900000000000005 - type: precision_at_1000 value: 0.079 - type: precision_at_3 value: 6.544 - type: precision_at_5 value: 4.601 - type: recall_at_1 value: 10.536 - type: recall_at_10 value: 22.345000000000002 - type: recall_at_100 value: 37.877 - type: recall_at_1000 value: 58.648 - type: recall_at_3 value: 16.048000000000002 - type: recall_at_5 value: 18.267 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.292 - type: map_at_10 value: 10.58 - type: map_at_100 value: 11.173 - type: map_at_1000 value: 11.29 - type: map_at_3 value: 9.633 - type: map_at_5 value: 10.163 - type: mrr_at_1 value: 9.016 - type: mrr_at_10 value: 12.757 - type: mrr_at_100 value: 13.370000000000001 - type: mrr_at_1000 value: 13.472000000000001 - type: mrr_at_3 value: 11.688 - type: mrr_at_5 value: 12.328999999999999 - type: ndcg_at_1 value: 9.016 - type: ndcg_at_10 value: 12.806000000000001 - type: ndcg_at_100 value: 16.174 - type: ndcg_at_1000 value: 19.644000000000002 - type: ndcg_at_3 value: 11.021 - type: ndcg_at_5 value: 11.871 - type: precision_at_1 value: 9.016 - type: precision_at_10 value: 2.385 - type: precision_at_100 value: 0.49100000000000005 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 5.322 - type: precision_at_5 value: 3.8890000000000002 - type: recall_at_1 value: 7.292 - type: recall_at_10 value: 17.39 - type: recall_at_100 value: 33.239999999999995 - type: recall_at_1000 value: 59.223000000000006 - type: recall_at_3 value: 12.44 - type: recall_at_5 value: 14.544 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.661000000000001 - type: map_at_10 value: 15.949 - type: map_at_100 value: 16.689999999999998 - type: map_at_1000 value: 16.805999999999997 - type: map_at_3 value: 14.398 - type: map_at_5 value: 15.204 - type: mrr_at_1 value: 14.272000000000002 - type: mrr_at_10 value: 18.85 - type: mrr_at_100 value: 19.603 - type: mrr_at_1000 value: 19.698999999999998 - type: mrr_at_3 value: 17.18 - type: mrr_at_5 value: 18.005 - type: ndcg_at_1 value: 14.272000000000002 - type: ndcg_at_10 value: 19.082 - type: ndcg_at_100 value: 23.078000000000003 - type: ndcg_at_1000 value: 26.444000000000003 - type: ndcg_at_3 value: 16.006 - type: ndcg_at_5 value: 17.281 - type: precision_at_1 value: 14.272000000000002 - type: precision_at_10 value: 3.321 - type: precision_at_100 value: 0.585 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 7.3069999999999995 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 11.661000000000001 - type: recall_at_10 value: 26.07 - type: recall_at_100 value: 44.537 - type: recall_at_1000 value: 69.446 - type: recall_at_3 value: 17.471999999999998 - type: recall_at_5 value: 20.744 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.037 - type: map_at_10 value: 19.348000000000003 - type: map_at_100 value: 20.357 - type: map_at_1000 value: 20.53 - type: map_at_3 value: 17.979 - type: map_at_5 value: 18.773 - type: mrr_at_1 value: 16.403000000000002 - type: mrr_at_10 value: 22.846 - type: mrr_at_100 value: 23.622 - type: mrr_at_1000 value: 23.708000000000002 - type: mrr_at_3 value: 21.377 - type: mrr_at_5 value: 22.325 - type: ndcg_at_1 value: 16.403000000000002 - type: ndcg_at_10 value: 23.275000000000002 - type: ndcg_at_100 value: 27.79 - type: ndcg_at_1000 value: 31.371 - type: ndcg_at_3 value: 21.035 - type: ndcg_at_5 value: 22.209 - type: precision_at_1 value: 16.403000000000002 - type: precision_at_10 value: 4.644 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.5889999999999995 - type: recall_at_1 value: 13.037 - type: recall_at_10 value: 30.239 - type: recall_at_100 value: 51.649 - type: recall_at_1000 value: 76.57000000000001 - type: recall_at_3 value: 23.677999999999997 - type: recall_at_5 value: 26.755000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.288 - type: map_at_10 value: 15.201999999999998 - type: map_at_100 value: 15.844 - type: map_at_1000 value: 15.963 - type: map_at_3 value: 13.828999999999999 - type: map_at_5 value: 14.530999999999999 - type: mrr_at_1 value: 12.753999999999998 - type: mrr_at_10 value: 16.753999999999998 - type: mrr_at_100 value: 17.387 - type: mrr_at_1000 value: 17.498 - type: mrr_at_3 value: 15.28 - type: mrr_at_5 value: 16.066 - type: ndcg_at_1 value: 12.753999999999998 - type: ndcg_at_10 value: 17.896 - type: ndcg_at_100 value: 21.632 - type: ndcg_at_1000 value: 25.111 - type: ndcg_at_3 value: 15.03 - type: ndcg_at_5 value: 16.281000000000002 - type: precision_at_1 value: 12.753999999999998 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.516 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 6.531000000000001 - type: precision_at_5 value: 4.658 - type: recall_at_1 value: 11.288 - type: recall_at_10 value: 24.941 - type: recall_at_100 value: 43.246 - type: recall_at_1000 value: 69.991 - type: recall_at_3 value: 17.018 - type: recall_at_5 value: 20.074 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.522 - type: map_at_10 value: 9.718 - type: map_at_100 value: 10.987 - type: map_at_1000 value: 11.185 - type: map_at_3 value: 7.89 - type: map_at_5 value: 8.794 - type: mrr_at_1 value: 12.508 - type: mrr_at_10 value: 20.039 - type: mrr_at_100 value: 21.271 - type: mrr_at_1000 value: 21.353 - type: mrr_at_3 value: 17.394000000000002 - type: mrr_at_5 value: 18.889 - type: ndcg_at_1 value: 12.508 - type: ndcg_at_10 value: 14.698 - type: ndcg_at_100 value: 20.794 - type: ndcg_at_1000 value: 24.978 - type: ndcg_at_3 value: 11.229 - type: ndcg_at_5 value: 12.483 - type: precision_at_1 value: 12.508 - type: precision_at_10 value: 4.925 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 8.512 - type: precision_at_5 value: 6.893000000000001 - type: recall_at_1 value: 5.522 - type: recall_at_10 value: 18.645999999999997 - type: recall_at_100 value: 40.231 - type: recall_at_1000 value: 64.398 - type: recall_at_3 value: 10.520999999999999 - type: recall_at_5 value: 13.686000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 3.2840000000000003 - type: map_at_10 value: 7.598000000000001 - type: map_at_100 value: 10.347000000000001 - type: map_at_1000 value: 11.106 - type: map_at_3 value: 5.428999999999999 - type: map_at_5 value: 6.365 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 43.762 - type: mrr_at_100 value: 44.546 - type: mrr_at_1000 value: 44.592 - type: mrr_at_3 value: 41.125 - type: mrr_at_5 value: 42.699999999999996 - type: ndcg_at_1 value: 25.25 - type: ndcg_at_10 value: 20.081 - type: ndcg_at_100 value: 21.996 - type: ndcg_at_1000 value: 28.327999999999996 - type: ndcg_at_3 value: 22.421 - type: ndcg_at_5 value: 21.227999999999998 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 17.925 - type: precision_at_100 value: 5.437 - type: precision_at_1000 value: 1.157 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 23.0 - type: recall_at_1 value: 3.2840000000000003 - type: recall_at_10 value: 11.799999999999999 - type: recall_at_100 value: 27.08 - type: recall_at_1000 value: 48.348 - type: recall_at_3 value: 6.701 - type: recall_at_5 value: 8.515 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.57 - type: f1 value: 39.68887710167085 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 11.396 - type: map_at_10 value: 17.108999999999998 - type: map_at_100 value: 17.953 - type: map_at_1000 value: 18.035999999999998 - type: map_at_3 value: 15.177 - type: map_at_5 value: 16.232 - type: mrr_at_1 value: 12.166 - type: mrr_at_10 value: 18.236 - type: mrr_at_100 value: 19.1 - type: mrr_at_1000 value: 19.176000000000002 - type: mrr_at_3 value: 16.212 - type: mrr_at_5 value: 17.317 - type: ndcg_at_1 value: 12.166 - type: ndcg_at_10 value: 20.671999999999997 - type: ndcg_at_100 value: 25.227 - type: ndcg_at_1000 value: 27.613 - type: ndcg_at_3 value: 16.656000000000002 - type: ndcg_at_5 value: 18.549 - type: precision_at_1 value: 12.166 - type: precision_at_10 value: 3.3529999999999998 - type: precision_at_100 value: 0.582 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 7.141 - type: precision_at_5 value: 5.272 - type: recall_at_1 value: 11.396 - type: recall_at_10 value: 30.825999999999997 - type: recall_at_100 value: 52.641000000000005 - type: recall_at_1000 value: 71.34899999999999 - type: recall_at_3 value: 19.886 - type: recall_at_5 value: 24.417 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 5.534 - type: map_at_10 value: 9.422 - type: map_at_100 value: 10.471 - type: map_at_1000 value: 10.655000000000001 - type: map_at_3 value: 7.938000000000001 - type: map_at_5 value: 8.569 - type: mrr_at_1 value: 11.265 - type: mrr_at_10 value: 16.473 - type: mrr_at_100 value: 17.491 - type: mrr_at_1000 value: 17.592 - type: mrr_at_3 value: 14.738000000000001 - type: mrr_at_5 value: 15.470999999999998 - type: ndcg_at_1 value: 11.265 - type: ndcg_at_10 value: 13.194 - type: ndcg_at_100 value: 18.403 - type: ndcg_at_1000 value: 22.804 - type: ndcg_at_3 value: 10.92 - type: ndcg_at_5 value: 11.445 - type: precision_at_1 value: 11.265 - type: precision_at_10 value: 3.9510000000000005 - type: precision_at_100 value: 0.909 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 7.4590000000000005 - type: precision_at_5 value: 5.648000000000001 - type: recall_at_1 value: 5.534 - type: recall_at_10 value: 17.299999999999997 - type: recall_at_100 value: 37.901 - type: recall_at_1000 value: 65.745 - type: recall_at_3 value: 10.138 - type: recall_at_5 value: 12.078999999999999 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 13.633000000000001 - type: map_at_10 value: 19.076 - type: map_at_100 value: 19.851 - type: map_at_1000 value: 19.961000000000002 - type: map_at_3 value: 17.451 - type: map_at_5 value: 18.33 - type: mrr_at_1 value: 27.265 - type: mrr_at_10 value: 33.426 - type: mrr_at_100 value: 34.154 - type: mrr_at_1000 value: 34.226 - type: mrr_at_3 value: 31.648 - type: mrr_at_5 value: 32.613 - type: ndcg_at_1 value: 27.265 - type: ndcg_at_10 value: 24.81 - type: ndcg_at_100 value: 28.668 - type: ndcg_at_1000 value: 31.474000000000004 - type: ndcg_at_3 value: 21.586 - type: ndcg_at_5 value: 23.091 - type: precision_at_1 value: 27.265 - type: precision_at_10 value: 5.5440000000000005 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 13.594000000000001 - type: precision_at_5 value: 9.347999999999999 - type: recall_at_1 value: 13.633000000000001 - type: recall_at_10 value: 27.717999999999996 - type: recall_at_100 value: 43.322 - type: recall_at_1000 value: 62.107 - type: recall_at_3 value: 20.392 - type: recall_at_5 value: 23.369 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.9896 - type: ap value: 59.21073897376523 - type: f1 value: 63.83200686849996 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 5.203 - type: map_at_10 value: 9.144 - type: map_at_100 value: 9.93 - type: map_at_1000 value: 10.03 - type: map_at_3 value: 7.703 - type: map_at_5 value: 8.488 - type: mrr_at_1 value: 5.3580000000000005 - type: mrr_at_10 value: 9.392 - type: mrr_at_100 value: 10.188 - type: mrr_at_1000 value: 10.286 - type: mrr_at_3 value: 7.927 - type: mrr_at_5 value: 8.725 - type: ndcg_at_1 value: 5.344 - type: ndcg_at_10 value: 11.625 - type: ndcg_at_100 value: 15.953999999999999 - type: ndcg_at_1000 value: 19.003999999999998 - type: ndcg_at_3 value: 8.615 - type: ndcg_at_5 value: 10.038 - type: precision_at_1 value: 5.344 - type: precision_at_10 value: 2.013 - type: precision_at_100 value: 0.426 - type: precision_at_1000 value: 0.06899999999999999 - type: precision_at_3 value: 3.816 - type: precision_at_5 value: 3.0140000000000002 - type: recall_at_1 value: 5.203 - type: recall_at_10 value: 19.336000000000002 - type: recall_at_100 value: 40.534 - type: recall_at_1000 value: 65.063 - type: recall_at_3 value: 11.06 - type: recall_at_5 value: 14.488999999999999 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.34336525307798 - type: f1 value: 88.44434240756266 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 59.546283629730944 - type: f1 value: 40.1240023106233 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.956960322797585 - type: f1 value: 59.679846427125426 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.69535978480161 - type: f1 value: 67.29552687986417 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.401510844328 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 24.30315769309524 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 28.572900233549426 - type: mrr value: 29.419117722742016 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.627 - type: map_at_10 value: 6.662 - type: map_at_100 value: 8.429 - type: map_at_1000 value: 9.669 - type: map_at_3 value: 5.106 - type: map_at_5 value: 5.7410000000000005 - type: mrr_at_1 value: 31.889 - type: mrr_at_10 value: 40.531 - type: mrr_at_100 value: 41.237 - type: mrr_at_1000 value: 41.304 - type: mrr_at_3 value: 37.926 - type: mrr_at_5 value: 39.474 - type: ndcg_at_1 value: 29.256999999999998 - type: ndcg_at_10 value: 21.928 - type: ndcg_at_100 value: 20.74 - type: ndcg_at_1000 value: 30.375000000000004 - type: ndcg_at_3 value: 25.296000000000003 - type: ndcg_at_5 value: 23.602999999999998 - type: precision_at_1 value: 31.579 - type: precision_at_10 value: 16.067999999999998 - type: precision_at_100 value: 5.972 - type: precision_at_1000 value: 1.918 - type: precision_at_3 value: 23.735999999999997 - type: precision_at_5 value: 20.124 - type: recall_at_1 value: 3.627 - type: recall_at_10 value: 10.487 - type: recall_at_100 value: 22.634 - type: recall_at_1000 value: 57.052 - type: recall_at_3 value: 5.975 - type: recall_at_5 value: 7.495 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 7.954 - type: map_at_10 value: 13.513 - type: map_at_100 value: 14.582 - type: map_at_1000 value: 14.687 - type: map_at_3 value: 11.437999999999999 - type: map_at_5 value: 12.531999999999998 - type: mrr_at_1 value: 9.038 - type: mrr_at_10 value: 14.932 - type: mrr_at_100 value: 15.978 - type: mrr_at_1000 value: 16.066 - type: mrr_at_3 value: 12.775 - type: mrr_at_5 value: 13.937 - type: ndcg_at_1 value: 9.038 - type: ndcg_at_10 value: 17.224 - type: ndcg_at_100 value: 22.869 - type: ndcg_at_1000 value: 25.816 - type: ndcg_at_3 value: 12.878 - type: ndcg_at_5 value: 14.874 - type: precision_at_1 value: 9.038 - type: precision_at_10 value: 3.2009999999999996 - type: precision_at_100 value: 0.643 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 6.053999999999999 - type: precision_at_5 value: 4.728000000000001 - type: recall_at_1 value: 7.954 - type: recall_at_10 value: 27.448 - type: recall_at_100 value: 53.966 - type: recall_at_1000 value: 76.69 - type: recall_at_3 value: 15.740000000000002 - type: recall_at_5 value: 20.466 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 61.474 - type: map_at_10 value: 74.008 - type: map_at_100 value: 74.79599999999999 - type: map_at_1000 value: 74.833 - type: map_at_3 value: 71.127 - type: map_at_5 value: 72.858 - type: mrr_at_1 value: 70.86 - type: mrr_at_10 value: 78.428 - type: mrr_at_100 value: 78.693 - type: mrr_at_1000 value: 78.7 - type: mrr_at_3 value: 76.942 - type: mrr_at_5 value: 77.88199999999999 - type: ndcg_at_1 value: 70.89 - type: ndcg_at_10 value: 78.804 - type: ndcg_at_100 value: 81.082 - type: ndcg_at_1000 value: 81.527 - type: ndcg_at_3 value: 75.202 - type: ndcg_at_5 value: 77.031 - type: precision_at_1 value: 70.89 - type: precision_at_10 value: 11.923 - type: precision_at_100 value: 1.4409999999999998 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 32.65 - type: precision_at_5 value: 21.58 - type: recall_at_1 value: 61.474 - type: recall_at_10 value: 88.056 - type: recall_at_100 value: 96.814 - type: recall_at_1000 value: 99.395 - type: recall_at_3 value: 77.656 - type: recall_at_5 value: 82.796 - type: map_at_1 value: 2.675 - type: map_at_10 value: 6.066 - type: map_at_100 value: 7.263999999999999 - type: map_at_1000 value: 7.51 - type: map_at_3 value: 4.404 - type: map_at_5 value: 5.181 - type: mrr_at_1 value: 13.200000000000001 - type: mrr_at_10 value: 20.189 - type: mrr_at_100 value: 21.445 - type: mrr_at_1000 value: 21.536 - type: mrr_at_3 value: 17.666999999999998 - type: mrr_at_5 value: 19.002 - type: ndcg_at_1 value: 13.200000000000001 - type: ndcg_at_10 value: 11.025 - type: ndcg_at_100 value: 16.828000000000003 - type: ndcg_at_1000 value: 21.982 - type: ndcg_at_3 value: 10.231 - 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type: ndcg_at_100 value: 28.149 - type: ndcg_at_1000 value: 25.856 - type: ndcg_at_3 value: 39.184000000000005 - type: ndcg_at_5 value: 37.309 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 39.0 - type: precision_at_100 value: 29.7 - type: precision_at_1000 value: 12.864 - type: precision_at_3 value: 43.333 - type: precision_at_5 value: 40.400000000000006 - type: recall_at_1 value: 0.11100000000000002 - type: recall_at_10 value: 0.894 - type: recall_at_100 value: 6.444 - type: recall_at_1000 value: 25.243 - type: recall_at_3 value: 0.311 - type: recall_at_5 value: 0.471 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 34.90409674230351 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 43.15515899635581 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 76.73075802333739 - type: cos_sim_spearman value: 67.84289404493155 - type: euclidean_pearson value: 72.99765907409036 - type: euclidean_spearman value: 67.84297651880989 - type: manhattan_pearson value: 71.56824568898308 - type: manhattan_spearman value: 66.49154112140326 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.15762265059362 - type: cos_sim_spearman value: 67.06089989163219 - type: euclidean_pearson value: 70.47335445930443 - type: euclidean_spearman value: 67.06203229762373 - type: manhattan_pearson value: 71.4377325677922 - type: manhattan_spearman value: 68.40504987244773 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - 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type: euclidean_spearman value: 81.22730800357442 - type: manhattan_pearson value: 81.89815505669003 - type: manhattan_spearman value: 82.02113941562317 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.45738534759084 - type: cos_sim_spearman value: 76.91650991022456 - type: euclidean_pearson value: 76.63155234592621 - type: euclidean_spearman value: 76.9170601938253 - type: manhattan_pearson value: 77.33411034896274 - type: manhattan_spearman value: 77.75936963273696 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.88607666461446 - type: cos_sim_spearman value: 84.10579933135438 - type: euclidean_pearson value: 84.22914136437043 - type: euclidean_spearman value: 84.10667343750598 - type: manhattan_pearson value: 84.54468730779816 - 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type: precision_at_3 value: 15.333 - type: precision_at_5 value: 10.267 - type: recall_at_1 value: 32.639 - type: recall_at_10 value: 53.556000000000004 - type: recall_at_100 value: 80.51700000000001 - type: recall_at_1000 value: 96.133 - type: recall_at_3 value: 42.0 - type: recall_at_5 value: 46.583000000000006 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.63465346534653 - type: cos_sim_ap value: 87.35445585086691 - type: cos_sim_f1 value: 80.60956384655807 - type: cos_sim_precision value: 84.9390919158361 - type: cos_sim_recall value: 76.7 - type: dot_accuracy value: 99.63465346534653 - type: dot_ap value: 87.35445585086691 - type: dot_f1 value: 80.60956384655807 - type: dot_precision value: 84.9390919158361 - type: dot_recall value: 76.7 - type: euclidean_accuracy value: 99.63465346534653 - type: euclidean_ap value: 87.35445585086691 - type: euclidean_f1 value: 80.60956384655807 - type: euclidean_precision value: 84.9390919158361 - type: euclidean_recall value: 76.7 - type: manhattan_accuracy value: 99.6970297029703 - type: manhattan_ap value: 90.68997415814273 - type: manhattan_f1 value: 84.33981576253838 - type: manhattan_precision value: 86.37316561844864 - type: manhattan_recall value: 82.39999999999999 - type: max_accuracy value: 99.6970297029703 - type: max_ap value: 90.68997415814273 - type: max_f1 value: 84.33981576253838 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 37.79012296970403 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.17355447643775 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.12070306689919 - type: mrr value: 43.573166008827776 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.133865143991105 - type: cos_sim_spearman value: 29.440326561177667 - type: dot_pearson value: 30.133865125920966 - type: dot_spearman value: 29.435121388222697 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.664 - type: map_at_10 value: 8.955 - type: map_at_100 value: 14.658 - type: map_at_1000 value: 16.243 - type: map_at_3 value: 5.002 - type: map_at_5 value: 6.609 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 47.676 - type: mrr_at_100 value: 48.504999999999995 - type: mrr_at_1000 value: 48.504999999999995 - type: mrr_at_3 value: 43.197 - type: mrr_at_5 value: 46.054 - type: ndcg_at_1 value: 33.672999999999995 - type: ndcg_at_10 value: 21.986 - type: ndcg_at_100 value: 35.127 - type: ndcg_at_1000 value: 46.345 - type: ndcg_at_3 value: 27.017999999999997 - type: ndcg_at_5 value: 24.57 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 19.184 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.484 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.664 - type: recall_at_10 value: 13.305 - type: recall_at_100 value: 46.623 - type: recall_at_1000 value: 81.056 - type: recall_at_3 value: 5.844 - type: recall_at_5 value: 8.605 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.0554 - type: ap value: 13.578604741323947 - type: f1 value: 53.720604206564516 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.5851726089417 - type: f1 value: 53.78179835467554 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.95662139054496 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.62043273529235 - type: cos_sim_ap value: 65.34515253479756 - type: cos_sim_f1 value: 62.530532486565704 - type: cos_sim_precision value: 58.20827648931333 - type: cos_sim_recall value: 67.54617414248021 - type: dot_accuracy value: 83.62043273529235 - type: dot_ap value: 65.34515253479756 - type: dot_f1 value: 62.530532486565704 - type: dot_precision value: 58.20827648931333 - type: dot_recall value: 67.54617414248021 - type: euclidean_accuracy value: 83.62043273529235 - type: euclidean_ap value: 65.34515253479756 - type: euclidean_f1 value: 62.530532486565704 - type: euclidean_precision value: 58.20827648931333 - type: euclidean_recall value: 67.54617414248021 - type: manhattan_accuracy value: 83.40585325147524 - type: manhattan_ap value: 64.01718862141554 - type: manhattan_f1 value: 61.092923696801805 - type: manhattan_precision value: 58.429672447013495 - type: manhattan_recall value: 64.01055408970976 - type: max_accuracy value: 83.62043273529235 - type: max_ap value: 65.34515253479756 - type: max_f1 value: 62.530532486565704 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.48981255093724 - type: cos_sim_ap value: 82.67603725088865 - type: cos_sim_f1 value: 74.82442124038498 - type: cos_sim_precision value: 72.31200172376643 - type: cos_sim_recall value: 77.51770865414228 - type: dot_accuracy value: 87.48981255093724 - type: dot_ap value: 82.6760377923082 - type: dot_f1 value: 74.82442124038498 - type: dot_precision value: 72.31200172376643 - type: dot_recall value: 77.51770865414228 - type: euclidean_accuracy value: 87.48981255093724 - type: euclidean_ap value: 82.6760372072607 - type: euclidean_f1 value: 74.82442124038498 - type: euclidean_precision value: 72.31200172376643 - type: euclidean_recall value: 77.51770865414228 - type: manhattan_accuracy value: 87.40055109248263 - type: manhattan_ap value: 82.74988489269892 - type: manhattan_f1 value: 75.01184359170584 - type: manhattan_precision value: 71.21012938490279 - type: manhattan_recall value: 79.24237757930396 - type: max_accuracy value: 87.48981255093724 - type: max_ap value: 82.74988489269892 - type: max_f1 value: 75.01184359170584 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/nmc-nignore15	twadada	null	[ "mteb", "model-index", "region:us" ]	2024-09-17T00:36:10	2024-09-17T00:36:20	0	--- tags: - mteb model-index: - name: nomic_classification_nignore15 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.01492537313432 - type: ap value: 35.50250124630455 - type: f1 value: 66.89959317702703 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.84830000000001 - type: ap value: 59.73270245283254 - type: f1 value: 64.76353235413379 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 34.105999999999995 - type: f1 value: 33.54422658625557 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.622 - type: map_at_10 value: 35.826 - type: map_at_100 value: 37.053000000000004 - type: map_at_1000 value: 37.074 - type: map_at_3 value: 31.211 - type: map_at_5 value: 33.921 - type: mrr_at_1 value: 22.262 - type: mrr_at_10 value: 36.036 - type: mrr_at_100 value: 37.263000000000005 - type: mrr_at_1000 value: 37.284 - type: mrr_at_3 value: 31.484 - type: mrr_at_5 value: 34.144000000000005 - type: ndcg_at_1 value: 21.622 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.506 - type: ndcg_at_1000 value: 50.009 - type: ndcg_at_3 value: 34.372 - type: ndcg_at_5 value: 39.275 - type: precision_at_1 value: 21.622 - type: precision_at_10 value: 6.991 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.509 - type: precision_at_5 value: 11.094999999999999 - type: recall_at_1 value: 21.622 - type: recall_at_10 value: 69.915 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 43.528 - type: recall_at_5 value: 55.477 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 34.911008985215766 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.405265668502622 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.19334227044753 - type: mrr value: 69.16600712307083 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.93657061818256 - type: cos_sim_spearman value: 80.1584529707527 - type: euclidean_pearson value: 81.85076602737361 - type: euclidean_spearman value: 80.1584529707527 - type: manhattan_pearson value: 81.32446368945303 - type: manhattan_spearman value: 80.35183087097523 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.75974025974025 - type: f1 value: 72.01384314530861 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.205913552227386 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.518894282858902 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 23.200000000000003 - type: map_at_10 value: 30.767 - type: map_at_100 value: 31.909 - type: map_at_1000 value: 32.054 - type: map_at_3 value: 28.048000000000002 - type: map_at_5 value: 29.668 - type: mrr_at_1 value: 29.185 - type: mrr_at_10 value: 36.361 - type: mrr_at_100 value: 37.212 - type: mrr_at_1000 value: 37.275999999999996 - type: mrr_at_3 value: 34.168 - type: mrr_at_5 value: 35.463 - type: ndcg_at_1 value: 29.185 - type: ndcg_at_10 value: 35.735 - type: ndcg_at_100 value: 40.884 - type: ndcg_at_1000 value: 43.887 - type: ndcg_at_3 value: 31.839000000000002 - type: ndcg_at_5 value: 33.759 - type: precision_at_1 value: 29.185 - type: precision_at_10 value: 6.723999999999999 - type: precision_at_100 value: 1.163 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 15.165000000000001 - type: precision_at_5 value: 11.101999999999999 - type: recall_at_1 value: 23.200000000000003 - type: recall_at_10 value: 44.68 - type: recall_at_100 value: 67.47999999999999 - type: recall_at_1000 value: 88.152 - type: recall_at_3 value: 33.055 - type: recall_at_5 value: 38.481 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 17.410999999999998 - type: map_at_10 value: 23.605999999999998 - type: map_at_100 value: 24.532 - type: map_at_1000 value: 24.645 - type: map_at_3 value: 21.628 - type: map_at_5 value: 22.849 - type: mrr_at_1 value: 22.038 - type: mrr_at_10 value: 27.947 - type: mrr_at_100 value: 28.701 - type: mrr_at_1000 value: 28.772 - type: mrr_at_3 value: 26.008 - type: mrr_at_5 value: 27.187 - type: ndcg_at_1 value: 22.038 - type: ndcg_at_10 value: 27.632 - type: ndcg_at_100 value: 31.852000000000004 - type: ndcg_at_1000 value: 34.587 - type: ndcg_at_3 value: 24.274 - type: ndcg_at_5 value: 26.005 - type: precision_at_1 value: 22.038 - type: precision_at_10 value: 5.108 - type: precision_at_100 value: 0.911 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.591999999999999 - type: precision_at_5 value: 8.42 - type: recall_at_1 value: 17.410999999999998 - type: recall_at_10 value: 35.346 - type: recall_at_100 value: 53.849000000000004 - type: recall_at_1000 value: 72.56700000000001 - type: recall_at_3 value: 25.647 - type: recall_at_5 value: 30.288999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.1 - type: map_at_10 value: 35.973 - type: map_at_100 value: 37.1 - type: map_at_1000 value: 37.191 - type: map_at_3 value: 33.409 - type: map_at_5 value: 34.867 - type: mrr_at_1 value: 31.473000000000003 - type: mrr_at_10 value: 39.35 - type: mrr_at_100 value: 40.258 - type: mrr_at_1000 value: 40.314 - type: mrr_at_3 value: 37.106 - type: mrr_at_5 value: 38.316 - type: ndcg_at_1 value: 31.473000000000003 - type: ndcg_at_10 value: 40.831 - type: ndcg_at_100 value: 46.094 - type: ndcg_at_1000 value: 48.147 - type: ndcg_at_3 value: 36.187000000000005 - type: ndcg_at_5 value: 38.389 - type: precision_at_1 value: 31.473000000000003 - type: precision_at_10 value: 6.627 - type: precision_at_100 value: 1.013 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 16.092000000000002 - type: precision_at_5 value: 11.197 - type: recall_at_1 value: 27.1 - type: recall_at_10 value: 52.208 - type: recall_at_100 value: 75.913 - type: recall_at_1000 value: 90.623 - type: recall_at_3 value: 39.696999999999996 - type: recall_at_5 value: 45.068999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.164000000000001 - type: map_at_10 value: 17.209 - type: map_at_100 value: 18.078 - type: map_at_1000 value: 18.196 - type: map_at_3 value: 15.723999999999998 - type: map_at_5 value: 16.53 - type: mrr_at_1 value: 14.35 - type: mrr_at_10 value: 18.549 - type: mrr_at_100 value: 19.378999999999998 - type: mrr_at_1000 value: 19.485 - type: mrr_at_3 value: 17.024 - type: mrr_at_5 value: 17.832 - type: ndcg_at_1 value: 14.35 - type: ndcg_at_10 value: 19.949 - type: ndcg_at_100 value: 24.59 - type: ndcg_at_1000 value: 28.102 - type: ndcg_at_3 value: 16.894000000000002 - type: ndcg_at_5 value: 18.322 - type: precision_at_1 value: 14.35 - type: precision_at_10 value: 3.0620000000000003 - type: precision_at_100 value: 0.573 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.005999999999999 - type: precision_at_5 value: 5.017 - type: recall_at_1 value: 13.164000000000001 - type: recall_at_10 value: 27.282 - type: recall_at_100 value: 49.352000000000004 - type: recall_at_1000 value: 76.7 - type: recall_at_3 value: 18.93 - type: recall_at_5 value: 22.395 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.343 - type: map_at_10 value: 10.732999999999999 - type: map_at_100 value: 11.524 - type: map_at_1000 value: 11.658 - type: map_at_3 value: 9.501999999999999 - type: map_at_5 value: 10.118 - type: mrr_at_1 value: 9.577 - type: mrr_at_10 value: 13.293 - type: mrr_at_100 value: 14.126 - type: mrr_at_1000 value: 14.234 - type: mrr_at_3 value: 11.816 - type: mrr_at_5 value: 12.519 - type: ndcg_at_1 value: 9.577 - type: ndcg_at_10 value: 13.303999999999998 - type: ndcg_at_100 value: 17.596999999999998 - type: ndcg_at_1000 value: 21.406 - type: ndcg_at_3 value: 10.788 - type: ndcg_at_5 value: 11.815000000000001 - type: precision_at_1 value: 9.577 - type: precision_at_10 value: 2.512 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 5.1 - type: precision_at_5 value: 3.781 - type: recall_at_1 value: 7.343 - type: recall_at_10 value: 18.912000000000003 - type: recall_at_100 value: 38.389 - type: recall_at_1000 value: 66.424 - type: recall_at_3 value: 11.851 - type: recall_at_5 value: 14.424000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.055 - type: map_at_10 value: 24.271 - type: map_at_100 value: 25.418000000000003 - type: map_at_1000 value: 25.554 - type: map_at_3 value: 22.067 - type: map_at_5 value: 23.274 - type: mrr_at_1 value: 22.137 - type: mrr_at_10 value: 28.671000000000003 - type: mrr_at_100 value: 29.604000000000003 - type: mrr_at_1000 value: 29.68 - type: mrr_at_3 value: 26.372 - type: mrr_at_5 value: 27.766999999999996 - type: ndcg_at_1 value: 22.137 - type: ndcg_at_10 value: 28.687 - type: ndcg_at_100 value: 34.309 - type: ndcg_at_1000 value: 37.316 - type: ndcg_at_3 value: 24.761 - type: ndcg_at_5 value: 26.598 - type: precision_at_1 value: 22.137 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.9740000000000001 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.677999999999999 - type: precision_at_5 value: 8.527 - type: recall_at_1 value: 18.055 - type: recall_at_10 value: 37.701 - type: recall_at_100 value: 62.661 - type: recall_at_1000 value: 83.37299999999999 - type: recall_at_3 value: 26.491999999999997 - type: recall_at_5 value: 31.366 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.889999999999999 - type: map_at_10 value: 19.337 - type: map_at_100 value: 20.389 - type: map_at_1000 value: 20.53 - type: map_at_3 value: 17.404 - type: map_at_5 value: 18.356 - type: mrr_at_1 value: 17.122999999999998 - type: mrr_at_10 value: 22.951 - type: mrr_at_100 value: 23.919999999999998 - type: mrr_at_1000 value: 24.01 - type: mrr_at_3 value: 21.081 - type: mrr_at_5 value: 22.102 - type: ndcg_at_1 value: 17.122999999999998 - type: ndcg_at_10 value: 23.16 - type: ndcg_at_100 value: 28.337 - type: ndcg_at_1000 value: 31.808999999999997 - type: ndcg_at_3 value: 19.649 - type: ndcg_at_5 value: 21.047 - type: precision_at_1 value: 17.122999999999998 - type: precision_at_10 value: 4.406000000000001 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 9.361 - type: precision_at_5 value: 6.804 - type: recall_at_1 value: 13.889999999999999 - type: recall_at_10 value: 31.162 - type: recall_at_100 value: 53.862 - type: recall_at_1000 value: 78.668 - type: recall_at_3 value: 21.276999999999997 - type: recall_at_5 value: 24.945999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.876916666666668 - type: map_at_10 value: 20.335916666666666 - type: map_at_100 value: 21.264833333333335 - type: map_at_1000 value: 21.392083333333332 - type: map_at_3 value: 18.571416666666668 - type: map_at_5 value: 19.552416666666666 - type: mrr_at_1 value: 17.9935 - type: mrr_at_10 value: 23.497000000000003 - type: mrr_at_100 value: 24.318666666666665 - type: mrr_at_1000 value: 24.404583333333328 - type: mrr_at_3 value: 21.74641666666667 - type: mrr_at_5 value: 22.727416666666667 - type: ndcg_at_1 value: 17.9935 - type: ndcg_at_10 value: 23.92941666666667 - type: ndcg_at_100 value: 28.531999999999996 - type: ndcg_at_1000 value: 31.72616666666667 - type: ndcg_at_3 value: 20.738083333333332 - type: ndcg_at_5 value: 22.215416666666666 - type: precision_at_1 value: 17.9935 - type: precision_at_10 value: 4.256916666666667 - type: precision_at_100 value: 0.7820833333333335 - type: precision_at_1000 value: 0.12375000000000003 - type: precision_at_3 value: 9.594916666666666 - type: precision_at_5 value: 6.911333333333333 - type: recall_at_1 value: 14.876916666666668 - type: recall_at_10 value: 31.664250000000006 - type: recall_at_100 value: 52.60891666666667 - type: recall_at_1000 value: 75.82383333333334 - type: recall_at_3 value: 22.649833333333333 - type: recall_at_5 value: 26.4515 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.74 - type: map_at_10 value: 15.364 - type: map_at_100 value: 16.066 - type: map_at_1000 value: 16.147 - type: map_at_3 value: 13.871 - type: map_at_5 value: 14.724 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.657 - type: mrr_at_100 value: 18.299000000000003 - type: mrr_at_1000 value: 18.369 - type: mrr_at_3 value: 16.104 - type: mrr_at_5 value: 16.986 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.429000000000002 - type: ndcg_at_100 value: 22.144 - type: ndcg_at_1000 value: 24.647 - type: ndcg_at_3 value: 15.542 - type: ndcg_at_5 value: 16.929 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.19 - type: precision_at_100 value: 0.5579999999999999 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 7.156999999999999 - type: precision_at_5 value: 5.215 - type: recall_at_1 value: 10.74 - type: recall_at_10 value: 25.762 - type: recall_at_100 value: 43.132999999999996 - type: recall_at_1000 value: 62.26199999999999 - type: recall_at_3 value: 17.629 - type: recall_at_5 value: 21.125 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.707 - type: map_at_10 value: 11.26 - type: map_at_100 value: 11.918 - type: map_at_1000 value: 12.043 - type: map_at_3 value: 10.169 - type: map_at_5 value: 10.817 - type: mrr_at_1 value: 9.429 - type: mrr_at_10 value: 13.5 - type: mrr_at_100 value: 14.177999999999999 - type: mrr_at_1000 value: 14.280000000000001 - type: mrr_at_3 value: 12.273 - type: mrr_at_5 value: 13.048000000000002 - type: ndcg_at_1 value: 9.429 - type: ndcg_at_10 value: 13.697999999999999 - type: ndcg_at_100 value: 17.427 - type: ndcg_at_1000 value: 21.013 - type: ndcg_at_3 value: 11.639 - type: ndcg_at_5 value: 12.705 - type: precision_at_1 value: 9.429 - type: precision_at_10 value: 2.546 - type: precision_at_100 value: 0.534 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 5.666 - type: precision_at_5 value: 4.212 - type: recall_at_1 value: 7.707 - type: recall_at_10 value: 18.901 - type: recall_at_100 value: 36.38 - type: recall_at_1000 value: 63.017999999999994 - type: recall_at_3 value: 13.123999999999999 - type: recall_at_5 value: 15.834000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.034 - type: map_at_10 value: 17.943 - type: map_at_100 value: 18.787000000000003 - type: map_at_1000 value: 18.907 - type: map_at_3 value: 16.508 - type: map_at_5 value: 17.267 - type: mrr_at_1 value: 15.485 - type: mrr_at_10 value: 20.801 - type: mrr_at_100 value: 21.632 - type: mrr_at_1000 value: 21.731 - type: mrr_at_3 value: 19.279 - type: mrr_at_5 value: 20.09 - type: ndcg_at_1 value: 15.485 - type: ndcg_at_10 value: 21.301000000000002 - type: ndcg_at_100 value: 25.606 - type: ndcg_at_1000 value: 29.109 - type: ndcg_at_3 value: 18.451999999999998 - type: ndcg_at_5 value: 19.685 - type: precision_at_1 value: 15.485 - type: precision_at_10 value: 3.6470000000000002 - type: precision_at_100 value: 0.639 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.551 - type: precision_at_5 value: 5.989 - type: recall_at_1 value: 13.034 - type: recall_at_10 value: 28.909000000000002 - type: recall_at_100 value: 48.28 - type: recall_at_1000 value: 74.375 - type: recall_at_3 value: 20.871000000000002 - type: recall_at_5 value: 24.066000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.667 - type: map_at_10 value: 21.229 - type: map_at_100 value: 22.303 - type: map_at_1000 value: 22.512 - type: map_at_3 value: 19.527 - type: map_at_5 value: 20.415 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.829 - type: mrr_at_100 value: 25.623 - type: mrr_at_1000 value: 25.712000000000003 - type: mrr_at_3 value: 23.09 - type: mrr_at_5 value: 23.979 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 25.462 - type: ndcg_at_100 value: 30.255 - type: ndcg_at_1000 value: 34.019 - type: ndcg_at_3 value: 22.567999999999998 - type: ndcg_at_5 value: 23.79 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.9799999999999995 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 10.870000000000001 - type: precision_at_5 value: 7.866 - type: recall_at_1 value: 14.667 - type: recall_at_10 value: 33.550000000000004 - type: recall_at_100 value: 56.123999999999995 - type: recall_at_1000 value: 81.883 - type: recall_at_3 value: 24.944 - type: recall_at_5 value: 28.055000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.212 - type: map_at_10 value: 16.339000000000002 - type: map_at_100 value: 17.154 - type: map_at_1000 value: 17.268 - type: map_at_3 value: 15.0 - type: map_at_5 value: 15.744 - type: mrr_at_1 value: 13.863 - type: mrr_at_10 value: 18.055 - type: mrr_at_100 value: 18.892 - type: mrr_at_1000 value: 18.992 - type: mrr_at_3 value: 16.636 - type: mrr_at_5 value: 17.44 - type: ndcg_at_1 value: 13.863 - type: ndcg_at_10 value: 18.965 - type: ndcg_at_100 value: 23.289 - type: ndcg_at_1000 value: 26.672 - type: ndcg_at_3 value: 16.264 - type: ndcg_at_5 value: 17.541 - type: precision_at_1 value: 13.863 - type: precision_at_10 value: 2.939 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 6.901 - type: precision_at_5 value: 4.806 - type: recall_at_1 value: 12.212 - type: recall_at_10 value: 25.557999999999996 - type: recall_at_100 value: 45.884 - type: recall_at_1000 value: 71.841 - type: recall_at_3 value: 18.281 - type: recall_at_5 value: 21.368000000000002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.991 - type: map_at_10 value: 11.031 - type: map_at_100 value: 12.415 - type: map_at_1000 value: 12.601999999999999 - type: map_at_3 value: 8.752 - type: map_at_5 value: 9.873999999999999 - type: mrr_at_1 value: 13.355 - type: mrr_at_10 value: 22.002 - type: mrr_at_100 value: 23.146 - type: mrr_at_1000 value: 23.218 - type: mrr_at_3 value: 18.719 - type: mrr_at_5 value: 20.543 - type: ndcg_at_1 value: 13.355 - type: ndcg_at_10 value: 16.711000000000002 - type: ndcg_at_100 value: 23.073 - type: ndcg_at_1000 value: 27.108999999999998 - type: ndcg_at_3 value: 12.289 - type: ndcg_at_5 value: 13.943 - type: precision_at_1 value: 13.355 - type: precision_at_10 value: 5.648000000000001 - type: precision_at_100 value: 1.248 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 9.359 - type: precision_at_5 value: 7.739 - type: recall_at_1 value: 5.991 - type: recall_at_10 value: 21.898 - type: recall_at_100 value: 44.324000000000005 - type: recall_at_1000 value: 67.777 - type: recall_at_3 value: 11.527 - type: recall_at_5 value: 15.61 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.125 - type: map_at_10 value: 9.629999999999999 - type: map_at_100 value: 13.306000000000001 - type: map_at_1000 value: 14.26 - type: map_at_3 value: 6.894 - type: map_at_5 value: 8.19 - type: mrr_at_1 value: 39.25 - type: mrr_at_10 value: 49.495 - type: mrr_at_100 value: 50.139 - type: mrr_at_1000 value: 50.169 - type: mrr_at_3 value: 46.333 - type: mrr_at_5 value: 48.008 - type: ndcg_at_1 value: 28.499999999999996 - type: ndcg_at_10 value: 23.794 - type: ndcg_at_100 value: 26.632 - type: ndcg_at_1000 value: 33.382 - type: ndcg_at_3 value: 26.282 - type: ndcg_at_5 value: 25.113000000000003 - type: precision_at_1 value: 39.25 - type: precision_at_10 value: 21.075 - type: precision_at_100 value: 6.607 - type: precision_at_1000 value: 1.366 - type: precision_at_3 value: 31.667 - type: precision_at_5 value: 27.150000000000002 - type: recall_at_1 value: 4.125 - type: recall_at_10 value: 14.603 - type: recall_at_100 value: 32.888 - type: recall_at_1000 value: 55.901 - type: recall_at_3 value: 8.396 - type: recall_at_5 value: 10.902000000000001 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.39999999999999 - type: f1 value: 41.19798638036962 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 15.998999999999999 - type: map_at_10 value: 24.037 - type: map_at_100 value: 24.979000000000003 - type: map_at_1000 value: 25.052000000000003 - type: map_at_3 value: 21.537 - type: map_at_5 value: 22.926 - type: mrr_at_1 value: 17.072000000000003 - type: mrr_at_10 value: 25.526 - type: mrr_at_100 value: 26.464 - type: mrr_at_1000 value: 26.528000000000002 - type: mrr_at_3 value: 22.922 - type: mrr_at_5 value: 24.391 - type: ndcg_at_1 value: 17.072000000000003 - type: ndcg_at_10 value: 28.933999999999997 - type: ndcg_at_100 value: 33.812999999999995 - type: ndcg_at_1000 value: 35.874 - type: ndcg_at_3 value: 23.746000000000002 - type: ndcg_at_5 value: 26.264 - type: precision_at_1 value: 17.072000000000003 - type: precision_at_10 value: 4.6739999999999995 - type: precision_at_100 value: 0.732 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 10.326 - type: precision_at_5 value: 7.531000000000001 - type: recall_at_1 value: 15.998999999999999 - type: recall_at_10 value: 42.888999999999996 - type: recall_at_100 value: 65.864 - type: recall_at_1000 value: 81.872 - type: recall_at_3 value: 28.735 - type: recall_at_5 value: 34.817 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.0889999999999995 - type: map_at_10 value: 11.533 - type: map_at_100 value: 12.626999999999999 - type: map_at_1000 value: 12.831000000000001 - type: map_at_3 value: 9.524000000000001 - type: map_at_5 value: 10.484 - type: mrr_at_1 value: 13.889000000000001 - type: mrr_at_10 value: 20.035 - type: mrr_at_100 value: 21.041999999999998 - type: mrr_at_1000 value: 21.142 - type: mrr_at_3 value: 17.695 - type: mrr_at_5 value: 18.83 - type: ndcg_at_1 value: 13.889000000000001 - type: ndcg_at_10 value: 16.122 - type: ndcg_at_100 value: 21.485000000000003 - type: ndcg_at_1000 value: 26.101999999999997 - type: ndcg_at_3 value: 12.967999999999998 - type: ndcg_at_5 value: 13.975000000000001 - type: precision_at_1 value: 13.889000000000001 - type: precision_at_10 value: 4.769 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 8.693 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 7.0889999999999995 - type: recall_at_10 value: 21.163999999999998 - type: recall_at_100 value: 42.247 - type: recall_at_1000 value: 71.395 - type: recall_at_3 value: 11.694 - type: recall_at_5 value: 15.051 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 17.752000000000002 - type: map_at_10 value: 25.064999999999998 - type: map_at_100 value: 26.003999999999998 - type: map_at_1000 value: 26.116 - type: map_at_3 value: 23.064 - type: map_at_5 value: 24.149 - type: mrr_at_1 value: 35.503 - type: mrr_at_10 value: 42.649 - type: mrr_at_100 value: 43.389 - type: mrr_at_1000 value: 43.445 - type: mrr_at_3 value: 40.699999999999996 - type: mrr_at_5 value: 41.817 - type: ndcg_at_1 value: 35.503 - type: ndcg_at_10 value: 31.968000000000004 - type: ndcg_at_100 value: 36.257 - type: ndcg_at_1000 value: 38.928000000000004 - type: ndcg_at_3 value: 28.176000000000002 - type: ndcg_at_5 value: 29.994 - type: precision_at_1 value: 35.503 - type: precision_at_10 value: 7.0440000000000005 - type: precision_at_100 value: 1.046 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 17.763 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 17.752000000000002 - type: recall_at_10 value: 35.219 - type: recall_at_100 value: 52.309000000000005 - type: recall_at_1000 value: 70.162 - type: recall_at_3 value: 26.644000000000002 - type: recall_at_5 value: 30.25 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.9016 - type: ap value: 59.11956601013746 - type: f1 value: 63.68974251662037 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.561999999999999 - type: map_at_10 value: 11.378 - type: map_at_100 value: 12.258 - type: map_at_1000 value: 12.361 - type: map_at_3 value: 9.577 - type: map_at_5 value: 10.525 - type: mrr_at_1 value: 6.762 - type: mrr_at_10 value: 11.674 - type: mrr_at_100 value: 12.554000000000002 - type: mrr_at_1000 value: 12.654000000000002 - type: mrr_at_3 value: 9.833 - type: mrr_at_5 value: 10.806000000000001 - type: ndcg_at_1 value: 6.734 - type: ndcg_at_10 value: 14.459 - type: ndcg_at_100 value: 19.317999999999998 - type: ndcg_at_1000 value: 22.407 - type: ndcg_at_3 value: 10.666 - type: ndcg_at_5 value: 12.393 - type: precision_at_1 value: 6.734 - type: precision_at_10 value: 2.5069999999999997 - type: precision_at_100 value: 0.505 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.6850000000000005 - type: precision_at_5 value: 3.682 - type: recall_at_1 value: 6.561999999999999 - type: recall_at_10 value: 24.07 - type: recall_at_100 value: 47.856 - type: recall_at_1000 value: 72.654 - type: recall_at_3 value: 13.584 - type: recall_at_5 value: 17.76 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.86000911992703 - type: f1 value: 87.98975696911226 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 58.38349293205655 - type: f1 value: 40.01779036138312 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.852723604572965 - type: f1 value: 60.13917532573332 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.14727639542704 - type: f1 value: 66.7653952309667 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.504423289782554 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 25.331311574764182 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.79405962065405 - type: mrr value: 30.80981570313803 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.8309999999999995 - type: map_at_10 value: 8.15 - type: map_at_100 value: 10.295 - type: map_at_1000 value: 11.649 - type: map_at_3 value: 6.088 - type: map_at_5 value: 7.138 - type: mrr_at_1 value: 33.745999999999995 - type: mrr_at_10 value: 43.422 - type: mrr_at_100 value: 44.193 - type: mrr_at_1000 value: 44.261 - type: mrr_at_3 value: 40.506 - type: mrr_at_5 value: 42.812 - type: ndcg_at_1 value: 31.579 - type: ndcg_at_10 value: 25.357000000000003 - type: ndcg_at_100 value: 23.597 - type: ndcg_at_1000 value: 33.143 - type: ndcg_at_3 value: 28.778 - type: ndcg_at_5 value: 27.92 - type: precision_at_1 value: 33.745999999999995 - type: precision_at_10 value: 18.854000000000003 - type: precision_at_100 value: 6.464 - type: precision_at_1000 value: 1.9900000000000002 - type: precision_at_3 value: 27.450999999999997 - type: precision_at_5 value: 24.52 - type: recall_at_1 value: 3.8309999999999995 - type: recall_at_10 value: 12.18 - type: recall_at_100 value: 25.258999999999997 - type: recall_at_1000 value: 59.059 - type: recall_at_3 value: 7.353 - type: recall_at_5 value: 9.777 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.132 - type: map_at_10 value: 15.934999999999999 - type: map_at_100 value: 17.168 - type: map_at_1000 value: 17.266000000000002 - type: map_at_3 value: 13.296 - type: map_at_5 value: 14.713000000000001 - type: mrr_at_1 value: 10.342 - type: mrr_at_10 value: 17.535 - type: mrr_at_100 value: 18.689 - type: mrr_at_1000 value: 18.77 - type: mrr_at_3 value: 14.846 - type: mrr_at_5 value: 16.337 - type: ndcg_at_1 value: 10.342 - type: ndcg_at_10 value: 20.409 - type: ndcg_at_100 value: 26.672 - type: ndcg_at_1000 value: 29.321 - type: ndcg_at_3 value: 14.982000000000001 - type: ndcg_at_5 value: 17.522 - type: precision_at_1 value: 10.342 - type: precision_at_10 value: 3.8440000000000003 - type: precision_at_100 value: 0.741 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 7.0489999999999995 - type: precision_at_5 value: 5.632000000000001 - type: recall_at_1 value: 9.132 - type: recall_at_10 value: 32.800000000000004 - type: recall_at_100 value: 61.895999999999994 - type: recall_at_1000 value: 82.146 - type: recall_at_3 value: 18.342 - type: recall_at_5 value: 24.242 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.005 - type: map_at_10 value: 76.709 - type: map_at_100 value: 77.464 - type: map_at_1000 value: 77.498 - type: map_at_3 value: 73.75699999999999 - type: map_at_5 value: 75.553 - type: mrr_at_1 value: 73.81 - type: mrr_at_10 value: 81.006 - type: mrr_at_100 value: 81.23599999999999 - type: mrr_at_1000 value: 81.241 - type: mrr_at_3 value: 79.56800000000001 - type: mrr_at_5 value: 80.50399999999999 - type: ndcg_at_1 value: 73.85000000000001 - type: ndcg_at_10 value: 81.33399999999999 - type: ndcg_at_100 value: 83.378 - type: ndcg_at_1000 value: 83.726 - type: ndcg_at_3 value: 77.791 - type: ndcg_at_5 value: 79.636 - type: precision_at_1 value: 73.85000000000001 - type: precision_at_10 value: 12.262 - type: precision_at_100 value: 1.461 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.672999999999995 - type: precision_at_5 value: 22.253999999999998 - type: recall_at_1 value: 64.005 - type: recall_at_10 value: 90.137 - type: recall_at_100 value: 97.77799999999999 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 79.972 - type: recall_at_5 value: 85.10799999999999 - type: map_at_1 value: 3.008 - type: map_at_10 value: 7.086 - type: map_at_100 value: 8.498 - type: map_at_1000 value: 8.744 - type: map_at_3 value: 5.2330000000000005 - type: map_at_5 value: 6.188 - type: mrr_at_1 value: 14.799999999999999 - type: mrr_at_10 value: 22.731 - type: mrr_at_100 value: 23.963 - type: mrr_at_1000 value: 24.046 - type: mrr_at_3 value: 19.950000000000003 - type: mrr_at_5 value: 21.38 - type: ndcg_at_1 value: 14.799999999999999 - type: ndcg_at_10 value: 12.581999999999999 - type: ndcg_at_100 value: 19.024 - type: ndcg_at_1000 value: 24.075 - type: ndcg_at_3 value: 11.937000000000001 - type: ndcg_at_5 value: 10.427 - type: precision_at_1 value: 14.799999999999999 - type: precision_at_10 value: 6.5 - type: precision_at_100 value: 1.591 - type: precision_at_1000 value: 0.281 - type: precision_at_3 value: 11.1 - type: precision_at_5 value: 9.120000000000001 - type: recall_at_1 value: 3.008 - type: recall_at_10 value: 13.197000000000001 - type: recall_at_100 value: 32.323 - type: recall_at_1000 value: 57.172999999999995 - type: recall_at_3 value: 6.753000000000001 - type: recall_at_5 value: 9.248000000000001 - type: map_at_1 value: 0.129 - type: map_at_10 value: 0.717 - type: map_at_100 value: 3.6580000000000004 - type: map_at_1000 value: 9.374 - type: map_at_3 value: 0.302 - type: map_at_5 value: 0.422 - type: mrr_at_1 value: 56.00000000000001 - type: mrr_at_10 value: 67.475 - type: mrr_at_100 value: 68.018 - type: mrr_at_1000 value: 68.035 - type: mrr_at_3 value: 65.667 - type: mrr_at_5 value: 66.567 - type: ndcg_at_1 value: 50.0 - type: ndcg_at_10 value: 40.910999999999994 - type: ndcg_at_100 value: 30.386999999999997 - type: ndcg_at_1000 value: 27.009 - type: ndcg_at_3 value: 46.776 - type: ndcg_at_5 value: 42.504 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 43.6 - type: precision_at_100 value: 31.64 - type: precision_at_1000 value: 13.214 - type: precision_at_3 value: 50.0 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 0.129 - type: recall_at_10 value: 0.95 - type: recall_at_100 value: 6.526999999999999 - type: recall_at_1000 value: 25.894000000000002 - type: recall_at_3 value: 0.34299999999999997 - type: recall_at_5 value: 0.49899999999999994 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.423003368001304 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.05364643191673 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.82474380833001 - type: cos_sim_spearman value: 66.06084939474535 - type: euclidean_pearson value: 71.1524270169037 - type: euclidean_spearman value: 66.06095698159474 - type: manhattan_pearson value: 68.79401530108056 - type: manhattan_spearman value: 64.55149376982865 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.40557358335994 - type: cos_sim_spearman value: 67.61862451971042 - type: euclidean_pearson value: 70.41404072782692 - type: euclidean_spearman value: 67.6198686410611 - type: manhattan_pearson value: 68.97879579551457 - type: manhattan_spearman value: 67.2295683767691 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.0503994579303 - type: cos_sim_spearman value: 79.15391592614903 - type: euclidean_pearson value: 78.84085468610613 - type: euclidean_spearman value: 79.15395372943995 - type: manhattan_pearson value: 78.64185478146945 - type: manhattan_spearman value: 79.14714263528944 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 78.3615928485999 - type: cos_sim_spearman value: 74.97382110132605 - type: euclidean_pearson value: 77.23854527060215 - type: euclidean_spearman value: 74.97381140978526 - type: manhattan_pearson value: 76.07931709400336 - type: manhattan_spearman value: 74.24120638811475 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.20257664300357 - type: cos_sim_spearman value: 82.00047913551732 - type: euclidean_pearson value: 81.9647778954467 - type: euclidean_spearman value: 82.0004776230638 - type: manhattan_pearson value: 81.71577106207948 - type: manhattan_spearman value: 81.99682550355493 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 76.25724348036597 - type: cos_sim_spearman value: 77.44730180182509 - type: euclidean_pearson value: 77.07257885830403 - type: euclidean_spearman value: 77.44757329765216 - type: manhattan_pearson value: 77.56516524638705 - type: manhattan_spearman value: 77.96306993156203 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.12218819562516 - type: cos_sim_spearman value: 85.1670421446071 - type: euclidean_pearson value: 84.780712654921 - type: euclidean_spearman value: 85.16791563947264 - type: manhattan_pearson value: 84.55044493571614 - type: manhattan_spearman value: 85.27489322017652 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.36228072576271 - type: cos_sim_spearman value: 60.8804162279283 - type: euclidean_pearson value: 63.45076147869696 - type: euclidean_spearman value: 60.8804162279283 - type: manhattan_pearson value: 62.75925080903245 - type: manhattan_spearman value: 60.42286149492114 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.42867098481462 - type: cos_sim_spearman value: 77.27498110374914 - type: euclidean_pearson value: 78.31189930366395 - type: euclidean_spearman value: 77.27499957014132 - type: manhattan_pearson value: 77.5492342797482 - type: manhattan_spearman value: 76.70759132287284 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.88088283802969 - type: mrr value: 91.2407327603406 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 34.75 - type: map_at_10 value: 43.795 - type: map_at_100 value: 44.897999999999996 - type: map_at_1000 value: 44.964999999999996 - type: map_at_3 value: 41.235 - type: map_at_5 value: 42.785000000000004 - type: mrr_at_1 value: 37.0 - type: mrr_at_10 value: 45.608 - type: mrr_at_100 value: 46.556999999999995 - type: mrr_at_1000 value: 46.62 - type: mrr_at_3 value: 43.333 - type: mrr_at_5 value: 44.733000000000004 - type: ndcg_at_1 value: 37.0 - type: ndcg_at_10 value: 48.620000000000005 - type: ndcg_at_100 value: 53.772 - type: ndcg_at_1000 value: 55.403999999999996 - type: ndcg_at_3 value: 43.741 - type: ndcg_at_5 value: 46.358 - type: precision_at_1 value: 37.0 - type: precision_at_10 value: 6.800000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 17.666999999999998 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 34.75 - type: recall_at_10 value: 61.87200000000001 - type: recall_at_100 value: 85.317 - type: recall_at_1000 value: 97.8 - type: recall_at_3 value: 48.567 - type: recall_at_5 value: 55.233 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71485148514851 - type: cos_sim_ap value: 91.89197853928673 - type: cos_sim_f1 value: 85.48387096774194 - type: cos_sim_precision value: 86.1788617886179 - type: cos_sim_recall value: 84.8 - type: dot_accuracy value: 99.71485148514851 - type: dot_ap value: 91.89197853928673 - type: dot_f1 value: 85.48387096774194 - type: dot_precision value: 86.1788617886179 - type: dot_recall value: 84.8 - type: euclidean_accuracy value: 99.71485148514851 - type: euclidean_ap value: 91.89197853928673 - type: euclidean_f1 value: 85.48387096774194 - type: euclidean_precision value: 86.1788617886179 - type: euclidean_recall value: 84.8 - type: manhattan_accuracy value: 99.76633663366337 - type: manhattan_ap value: 93.70793412033116 - type: manhattan_f1 value: 87.77050830397583 - type: manhattan_precision value: 88.34853090172238 - type: manhattan_recall value: 87.2 - type: max_accuracy value: 99.76633663366337 - type: max_ap value: 93.70793412033116 - type: max_f1 value: 87.77050830397583 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 42.040101504017464 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.736735784987 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.30708664346592 - type: mrr value: 43.91143578643579 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.57246866942087 - type: cos_sim_spearman value: 30.69719029010722 - type: dot_pearson value: 30.572468627823802 - type: dot_spearman value: 30.675070232612644 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.8429999999999997 - type: map_at_10 value: 8.476 - type: map_at_100 value: 14.779 - type: map_at_1000 value: 16.381 - type: map_at_3 value: 4.361000000000001 - type: map_at_5 value: 6.064 - type: mrr_at_1 value: 28.571 - type: mrr_at_10 value: 44.039 - type: mrr_at_100 value: 45.024 - type: mrr_at_1000 value: 45.024 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 42.075 - type: ndcg_at_1 value: 27.551 - type: ndcg_at_10 value: 22.672 - type: ndcg_at_100 value: 35.961999999999996 - type: ndcg_at_1000 value: 47.365 - type: ndcg_at_3 value: 26.016000000000002 - type: ndcg_at_5 value: 23.794999999999998 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 20.816000000000003 - type: precision_at_100 value: 8.245 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 27.891 - type: precision_at_5 value: 24.082 - type: recall_at_1 value: 1.8429999999999997 - type: recall_at_10 value: 14.37 - type: recall_at_100 value: 49.120999999999995 - type: recall_at_1000 value: 83.98400000000001 - type: recall_at_3 value: 5.641 - type: recall_at_5 value: 8.321000000000002 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.1968 - type: ap value: 14.44161573930493 - type: f1 value: 54.83573235336061 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 50.94510469722694 - type: f1 value: 51.12605321866063 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 37.53693681679847 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.60851165285807 - type: cos_sim_ap value: 65.18156857786212 - type: cos_sim_f1 value: 62.61223512510475 - type: cos_sim_precision value: 57.30878807801885 - type: cos_sim_recall value: 68.99736147757255 - type: dot_accuracy value: 83.60851165285807 - type: dot_ap value: 65.18156857786212 - type: dot_f1 value: 62.61223512510475 - type: dot_precision value: 57.30878807801885 - type: dot_recall value: 68.99736147757255 - type: euclidean_accuracy value: 83.60851165285807 - type: euclidean_ap value: 65.18156857786212 - type: euclidean_f1 value: 62.61223512510475 - type: euclidean_precision value: 57.30878807801885 - type: euclidean_recall value: 68.99736147757255 - type: manhattan_accuracy value: 82.49389044525243 - type: manhattan_ap value: 62.03288965473206 - type: manhattan_f1 value: 59.25074695472304 - type: manhattan_precision value: 52.483713355048856 - type: manhattan_recall value: 68.02110817941951 - type: max_accuracy value: 83.60851165285807 - type: max_ap value: 65.18156857786212 - type: max_f1 value: 62.61223512510475 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.60235960724958 - type: cos_sim_ap value: 83.31122009321201 - type: cos_sim_f1 value: 75.5953714072415 - type: cos_sim_precision value: 73.36617881466454 - type: cos_sim_recall value: 77.96427471512165 - type: dot_accuracy value: 87.60235960724958 - type: dot_ap value: 83.31122039383348 - type: dot_f1 value: 75.5953714072415 - type: dot_precision value: 73.36617881466454 - type: dot_recall value: 77.96427471512165 - type: euclidean_accuracy value: 87.60235960724958 - type: euclidean_ap value: 83.31123735759617 - type: euclidean_f1 value: 75.5953714072415 - type: euclidean_precision value: 73.36617881466454 - type: euclidean_recall value: 77.96427471512165 - type: manhattan_accuracy value: 87.58295494236815 - type: manhattan_ap value: 83.15022211312501 - type: manhattan_f1 value: 75.28497215681878 - type: manhattan_precision value: 73.14982932674849 - type: manhattan_recall value: 77.54850631352016 - type: max_accuracy value: 87.60235960724958 - type: max_ap value: 83.31123735759617 - type: max_f1 value: 75.5953714072415 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/nmc-nignore30	twadada	null	[ "mteb", "model-index", "region:us" ]	2024-09-17T00:36:30	2024-09-17T00:36:40	0	--- tags: - mteb model-index: - name: nomic_classification_nignore30 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.02985074626865 - type: ap value: 36.54755879675939 - type: f1 value: 67.84911428462374 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 60.98745000000001 - type: ap value: 56.79972495487593 - type: f1 value: 60.79607311981127 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.606000000000005 - type: f1 value: 31.20575804283948 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.266 - type: map_at_10 value: 35.579 - type: map_at_100 value: 36.867 - type: map_at_1000 value: 36.887 - type: map_at_3 value: 31.105 - type: map_at_5 value: 33.512 - type: mrr_at_1 value: 21.764 - type: mrr_at_10 value: 35.768 - type: mrr_at_100 value: 37.049 - type: mrr_at_1000 value: 37.069 - type: mrr_at_3 value: 31.354 - type: mrr_at_5 value: 33.694 - type: ndcg_at_1 value: 21.266 - type: ndcg_at_10 value: 43.697 - type: ndcg_at_100 value: 49.444 - type: ndcg_at_1000 value: 49.918 - type: ndcg_at_3 value: 34.415 - type: ndcg_at_5 value: 38.751999999999995 - type: precision_at_1 value: 21.266 - type: precision_at_10 value: 6.97 - type: precision_at_100 value: 0.954 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.674999999999999 - type: precision_at_5 value: 10.91 - type: recall_at_1 value: 21.266 - type: recall_at_10 value: 69.70100000000001 - type: recall_at_100 value: 95.448 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 44.025999999999996 - type: recall_at_5 value: 54.552 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.45486521675564 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.270159650279354 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 53.62399843388994 - type: mrr value: 68.1675680429143 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.62266587849676 - type: cos_sim_spearman value: 80.48918339823612 - type: euclidean_pearson value: 82.46661732971302 - type: euclidean_spearman value: 80.48918339823612 - type: manhattan_pearson value: 81.55398066885756 - type: manhattan_spearman value: 80.27411825686711 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.07142857142857 - type: f1 value: 72.39723822054579 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.426645848653045 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.54829160604571 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 24.001 - type: map_at_10 value: 31.195 - type: map_at_100 value: 32.342999999999996 - type: map_at_1000 value: 32.489000000000004 - type: map_at_3 value: 28.814 - type: map_at_5 value: 30.014000000000003 - type: mrr_at_1 value: 30.186 - type: mrr_at_10 value: 37.034 - type: mrr_at_100 value: 37.881 - type: mrr_at_1000 value: 37.946000000000005 - type: mrr_at_3 value: 35.241 - type: mrr_at_5 value: 36.120999999999995 - type: ndcg_at_1 value: 30.186 - type: ndcg_at_10 value: 35.972 - type: ndcg_at_100 value: 41.25 - type: ndcg_at_1000 value: 44.171 - type: ndcg_at_3 value: 32.674 - type: ndcg_at_5 value: 33.833 - type: precision_at_1 value: 30.186 - type: precision_at_10 value: 6.723999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 10.815 - type: recall_at_1 value: 24.001 - type: recall_at_10 value: 44.057 - type: recall_at_100 value: 67.72500000000001 - type: recall_at_1000 value: 87.464 - type: recall_at_3 value: 33.817 - type: recall_at_5 value: 37.684 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.766000000000002 - type: map_at_10 value: 23.07 - type: map_at_100 value: 24.062 - type: map_at_1000 value: 24.178 - type: map_at_3 value: 21.364 - type: map_at_5 value: 22.3 - type: mrr_at_1 value: 21.146 - type: mrr_at_10 value: 27.24 - type: mrr_at_100 value: 28.092 - type: mrr_at_1000 value: 28.163 - type: mrr_at_3 value: 25.605 - type: mrr_at_5 value: 26.567 - type: ndcg_at_1 value: 21.146 - type: ndcg_at_10 value: 27.031 - type: ndcg_at_100 value: 31.430999999999997 - type: ndcg_at_1000 value: 34.086 - type: ndcg_at_3 value: 24.136 - type: ndcg_at_5 value: 25.462 - type: precision_at_1 value: 21.146 - type: precision_at_10 value: 5.006 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.762 - type: precision_at_5 value: 8.229000000000001 - type: recall_at_1 value: 16.766000000000002 - type: recall_at_10 value: 34.55 - type: recall_at_100 value: 53.542 - type: recall_at_1000 value: 71.66900000000001 - type: recall_at_3 value: 26.205000000000002 - type: recall_at_5 value: 29.854000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 26.579000000000004 - type: map_at_10 value: 35.482 - type: map_at_100 value: 36.564 - type: map_at_1000 value: 36.656 - type: map_at_3 value: 32.940999999999995 - type: map_at_5 value: 34.331 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 38.721 - type: mrr_at_100 value: 39.592 - type: mrr_at_1000 value: 39.653 - type: mrr_at_3 value: 36.468 - type: mrr_at_5 value: 37.688 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 40.351 - type: ndcg_at_100 value: 45.499 - type: ndcg_at_1000 value: 47.641 - type: ndcg_at_3 value: 35.605 - type: ndcg_at_5 value: 37.798 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 6.564 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.862000000000002 - type: precision_at_5 value: 11.008999999999999 - type: recall_at_1 value: 26.579000000000004 - type: recall_at_10 value: 51.978 - type: recall_at_100 value: 75.331 - type: recall_at_1000 value: 90.774 - type: recall_at_3 value: 39.149 - type: recall_at_5 value: 44.516 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.013 - type: map_at_10 value: 17.393 - type: map_at_100 value: 18.256 - type: map_at_1000 value: 18.364 - type: map_at_3 value: 15.812000000000001 - type: map_at_5 value: 16.601 - type: mrr_at_1 value: 14.237 - type: mrr_at_10 value: 18.706999999999997 - type: mrr_at_100 value: 19.553 - type: mrr_at_1000 value: 19.651 - type: mrr_at_3 value: 17.081 - type: mrr_at_5 value: 17.895 - type: ndcg_at_1 value: 14.237 - type: ndcg_at_10 value: 20.315 - type: ndcg_at_100 value: 24.914 - type: ndcg_at_1000 value: 28.244999999999997 - type: ndcg_at_3 value: 16.994 - type: ndcg_at_5 value: 18.396 - type: precision_at_1 value: 14.237 - type: precision_at_10 value: 3.198 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.0809999999999995 - type: precision_at_5 value: 4.994 - type: recall_at_1 value: 13.013 - type: recall_at_10 value: 28.297 - type: recall_at_100 value: 50.113 - type: recall_at_1000 value: 76.19500000000001 - type: recall_at_3 value: 19.062 - type: recall_at_5 value: 22.527 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.783 - type: map_at_10 value: 10.439 - type: map_at_100 value: 11.26 - type: map_at_1000 value: 11.394 - type: map_at_3 value: 9.314 - type: map_at_5 value: 9.832 - type: mrr_at_1 value: 8.831 - type: mrr_at_10 value: 12.902 - type: mrr_at_100 value: 13.799 - type: mrr_at_1000 value: 13.901 - type: mrr_at_3 value: 11.692 - type: mrr_at_5 value: 12.200999999999999 - type: ndcg_at_1 value: 8.831 - type: ndcg_at_10 value: 12.973 - type: ndcg_at_100 value: 17.465 - type: ndcg_at_1000 value: 21.203 - type: ndcg_at_3 value: 10.778 - type: ndcg_at_5 value: 11.601 - type: precision_at_1 value: 8.831 - type: precision_at_10 value: 2.475 - type: precision_at_100 value: 0.553 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.265000000000001 - type: precision_at_5 value: 3.781 - type: recall_at_1 value: 6.783 - type: recall_at_10 value: 18.386 - type: recall_at_100 value: 38.885999999999996 - type: recall_at_1000 value: 66.621 - type: recall_at_3 value: 12.235 - type: recall_at_5 value: 14.374999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.946 - type: map_at_10 value: 24.188000000000002 - type: map_at_100 value: 25.402 - type: map_at_1000 value: 25.544 - type: map_at_3 value: 22.157 - type: map_at_5 value: 23.315 - type: mrr_at_1 value: 22.233 - type: mrr_at_10 value: 28.703 - type: mrr_at_100 value: 29.669 - type: mrr_at_1000 value: 29.748 - type: mrr_at_3 value: 26.676 - type: mrr_at_5 value: 27.894000000000002 - type: ndcg_at_1 value: 22.233 - type: ndcg_at_10 value: 28.483999999999998 - type: ndcg_at_100 value: 34.239999999999995 - type: ndcg_at_1000 value: 37.351 - type: ndcg_at_3 value: 25.018 - type: ndcg_at_5 value: 26.679000000000002 - type: precision_at_1 value: 22.233 - type: precision_at_10 value: 5.236 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.806 - type: precision_at_5 value: 8.566 - type: recall_at_1 value: 17.946 - type: recall_at_10 value: 37.049 - type: recall_at_100 value: 62.473 - type: recall_at_1000 value: 83.829 - type: recall_at_3 value: 27.022000000000002 - type: recall_at_5 value: 31.435000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.966000000000001 - type: map_at_10 value: 19.797 - type: map_at_100 value: 20.764 - type: map_at_1000 value: 20.913 - type: map_at_3 value: 17.688000000000002 - type: map_at_5 value: 18.796 - type: mrr_at_1 value: 17.122999999999998 - type: mrr_at_10 value: 23.277 - type: mrr_at_100 value: 24.095 - type: mrr_at_1000 value: 24.197 - type: mrr_at_3 value: 21.176000000000002 - type: mrr_at_5 value: 22.323 - type: ndcg_at_1 value: 17.122999999999998 - type: ndcg_at_10 value: 23.860999999999997 - type: ndcg_at_100 value: 28.669 - type: ndcg_at_1000 value: 32.375 - type: ndcg_at_3 value: 19.983999999999998 - type: ndcg_at_5 value: 21.647 - type: precision_at_1 value: 17.122999999999998 - type: precision_at_10 value: 4.623 - type: precision_at_100 value: 0.839 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 9.551 - type: precision_at_5 value: 7.1 - type: recall_at_1 value: 13.966000000000001 - type: recall_at_10 value: 32.629999999999995 - type: recall_at_100 value: 53.842 - type: recall_at_1000 value: 80.583 - type: recall_at_3 value: 21.804000000000002 - type: recall_at_5 value: 26.101999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.974750000000004 - type: map_at_10 value: 20.34575 - type: map_at_100 value: 21.290416666666665 - type: map_at_1000 value: 21.41825 - type: map_at_3 value: 18.576500000000003 - type: map_at_5 value: 19.546166666666668 - type: mrr_at_1 value: 18.049249999999997 - type: mrr_at_10 value: 23.45216666666667 - type: mrr_at_100 value: 24.29241666666667 - type: mrr_at_1000 value: 24.37841666666667 - type: mrr_at_3 value: 21.728749999999998 - type: mrr_at_5 value: 22.680916666666665 - type: ndcg_at_1 value: 18.049249999999997 - type: ndcg_at_10 value: 23.90125 - type: ndcg_at_100 value: 28.57325 - type: ndcg_at_1000 value: 31.747583333333335 - type: ndcg_at_3 value: 20.71783333333333 - type: ndcg_at_5 value: 22.17008333333333 - type: precision_at_1 value: 18.049249999999997 - type: precision_at_10 value: 4.257666666666667 - type: precision_at_100 value: 0.7843333333333332 - type: precision_at_1000 value: 0.12375000000000003 - type: precision_at_3 value: 9.573750000000002 - type: precision_at_5 value: 6.871666666666666 - type: recall_at_1 value: 14.974750000000004 - type: recall_at_10 value: 31.535416666666666 - type: recall_at_100 value: 52.869583333333324 - type: recall_at_1000 value: 75.93208333333334 - type: recall_at_3 value: 22.561833333333333 - type: recall_at_5 value: 26.351583333333334 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.584 - type: map_at_10 value: 15.47 - type: map_at_100 value: 16.276 - type: map_at_1000 value: 16.361 - type: map_at_3 value: 14.022000000000002 - type: map_at_5 value: 14.884 - type: mrr_at_1 value: 13.65 - type: mrr_at_10 value: 17.566000000000003 - type: mrr_at_100 value: 18.335 - type: mrr_at_1000 value: 18.411 - type: mrr_at_3 value: 16.053 - type: mrr_at_5 value: 16.843 - type: ndcg_at_1 value: 13.65 - type: ndcg_at_10 value: 18.208 - type: ndcg_at_100 value: 22.352 - type: ndcg_at_1000 value: 24.969 - type: ndcg_at_3 value: 15.459 - type: ndcg_at_5 value: 16.817 - type: precision_at_1 value: 13.65 - type: precision_at_10 value: 3.083 - type: precision_at_100 value: 0.561 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.902 - type: precision_at_5 value: 4.968999999999999 - type: recall_at_1 value: 11.584 - type: recall_at_10 value: 24.629 - type: recall_at_100 value: 43.963 - type: recall_at_1000 value: 63.944 - type: recall_at_3 value: 17.155 - type: recall_at_5 value: 20.598 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.792000000000001 - type: map_at_10 value: 11.246 - type: map_at_100 value: 11.955 - type: map_at_1000 value: 12.076 - type: map_at_3 value: 10.176 - type: map_at_5 value: 10.802 - type: mrr_at_1 value: 9.67 - type: mrr_at_10 value: 13.591000000000001 - type: mrr_at_100 value: 14.285999999999998 - type: mrr_at_1000 value: 14.385 - type: mrr_at_3 value: 12.394 - type: mrr_at_5 value: 13.104 - type: ndcg_at_1 value: 9.67 - type: ndcg_at_10 value: 13.645 - type: ndcg_at_100 value: 17.562 - type: ndcg_at_1000 value: 21.101 - type: ndcg_at_3 value: 11.635 - type: ndcg_at_5 value: 12.638 - type: precision_at_1 value: 9.67 - type: precision_at_10 value: 2.54 - type: precision_at_100 value: 0.538 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.632000000000001 - type: precision_at_5 value: 4.136 - type: recall_at_1 value: 7.792000000000001 - type: recall_at_10 value: 18.63 - type: recall_at_100 value: 37.047999999999995 - type: recall_at_1000 value: 63.391 - type: recall_at_3 value: 12.956999999999999 - type: recall_at_5 value: 15.581 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.358999999999998 - type: map_at_10 value: 18.154999999999998 - type: map_at_100 value: 19.008 - type: map_at_1000 value: 19.125 - type: map_at_3 value: 16.645 - type: map_at_5 value: 17.544999999999998 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.973 - type: mrr_at_100 value: 21.782 - type: mrr_at_1000 value: 21.88 - type: mrr_at_3 value: 19.356 - type: mrr_at_5 value: 20.28 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 21.391 - type: ndcg_at_100 value: 25.71 - type: ndcg_at_1000 value: 29.016 - type: ndcg_at_3 value: 18.489 - type: ndcg_at_5 value: 19.916 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.573 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.488999999999999 - type: precision_at_5 value: 5.989 - type: recall_at_1 value: 13.358999999999998 - type: recall_at_10 value: 28.695999999999998 - type: recall_at_100 value: 48.165 - type: recall_at_1000 value: 72.64500000000001 - type: recall_at_3 value: 20.573 - type: recall_at_5 value: 24.284 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.881 - type: map_at_10 value: 21.754 - type: map_at_100 value: 22.88 - type: map_at_1000 value: 23.087 - type: map_at_3 value: 19.827 - type: map_at_5 value: 20.964 - type: mrr_at_1 value: 19.564999999999998 - type: mrr_at_10 value: 25.246000000000002 - type: mrr_at_100 value: 26.163999999999998 - type: mrr_at_1000 value: 26.240999999999996 - type: mrr_at_3 value: 23.352999999999998 - type: mrr_at_5 value: 24.587999999999997 - type: ndcg_at_1 value: 19.564999999999998 - type: ndcg_at_10 value: 25.740000000000002 - type: ndcg_at_100 value: 30.977 - type: ndcg_at_1000 value: 34.486 - type: ndcg_at_3 value: 22.625 - type: ndcg_at_5 value: 24.294 - type: precision_at_1 value: 19.564999999999998 - type: precision_at_10 value: 5.0200000000000005 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 10.738 - type: precision_at_5 value: 8.103 - type: recall_at_1 value: 15.881 - type: recall_at_10 value: 32.918 - type: recall_at_100 value: 58.184000000000005 - type: recall_at_1000 value: 81.76299999999999 - type: recall_at_3 value: 23.992 - type: recall_at_5 value: 28.265 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.027000000000001 - type: map_at_10 value: 15.959999999999999 - type: map_at_100 value: 16.715 - type: map_at_1000 value: 16.832 - type: map_at_3 value: 14.158000000000001 - type: map_at_5 value: 15.17 - type: mrr_at_1 value: 13.494 - type: mrr_at_10 value: 17.466 - type: mrr_at_100 value: 18.261 - type: mrr_at_1000 value: 18.365000000000002 - type: mrr_at_3 value: 15.65 - type: mrr_at_5 value: 16.667 - type: ndcg_at_1 value: 13.494 - type: ndcg_at_10 value: 18.844 - type: ndcg_at_100 value: 22.81 - type: ndcg_at_1000 value: 26.327 - type: ndcg_at_3 value: 15.217 - type: ndcg_at_5 value: 16.96 - type: precision_at_1 value: 13.494 - type: precision_at_10 value: 3.05 - type: precision_at_100 value: 0.532 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.769 - type: recall_at_1 value: 12.027000000000001 - type: recall_at_10 value: 26.605 - type: recall_at_100 value: 45.163 - type: recall_at_1000 value: 72.307 - type: recall_at_3 value: 16.771 - type: recall_at_5 value: 20.998 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.479 - type: map_at_10 value: 11.559 - type: map_at_100 value: 12.936 - type: map_at_1000 value: 13.120000000000001 - type: map_at_3 value: 9.377 - type: map_at_5 value: 10.494 - type: mrr_at_1 value: 14.396999999999998 - type: mrr_at_10 value: 23.039 - type: mrr_at_100 value: 24.141000000000002 - type: mrr_at_1000 value: 24.215999999999998 - type: mrr_at_3 value: 19.814999999999998 - type: mrr_at_5 value: 21.656 - type: ndcg_at_1 value: 14.396999999999998 - type: ndcg_at_10 value: 17.258000000000003 - type: ndcg_at_100 value: 23.615 - type: ndcg_at_1000 value: 27.605 - type: ndcg_at_3 value: 13.114999999999998 - type: ndcg_at_5 value: 14.698 - type: precision_at_1 value: 14.396999999999998 - type: precision_at_10 value: 5.713 - type: precision_at_100 value: 1.25 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 9.924 - type: precision_at_5 value: 8.104 - type: recall_at_1 value: 6.479 - type: recall_at_10 value: 22.088 - type: recall_at_100 value: 44.681 - type: recall_at_1000 value: 67.869 - type: recall_at_3 value: 12.203 - type: recall_at_5 value: 16.275000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.618 - type: map_at_10 value: 10.217 - type: map_at_100 value: 14.038999999999998 - type: map_at_1000 value: 15.03 - type: map_at_3 value: 7.523000000000001 - type: map_at_5 value: 8.688 - type: mrr_at_1 value: 41.75 - type: mrr_at_10 value: 51.991 - type: mrr_at_100 value: 52.711 - type: mrr_at_1000 value: 52.746 - type: mrr_at_3 value: 49.5 - type: mrr_at_5 value: 50.961999999999996 - type: ndcg_at_1 value: 30.875000000000004 - type: ndcg_at_10 value: 24.709999999999997 - type: ndcg_at_100 value: 27.584999999999997 - type: ndcg_at_1000 value: 34.508 - type: ndcg_at_3 value: 27.88 - type: ndcg_at_5 value: 26.168999999999997 - type: precision_at_1 value: 41.75 - type: precision_at_10 value: 21.45 - type: precision_at_100 value: 6.795 - type: precision_at_1000 value: 1.43 - type: precision_at_3 value: 33.083 - type: precision_at_5 value: 27.750000000000004 - type: recall_at_1 value: 4.618 - type: recall_at_10 value: 14.898 - type: recall_at_100 value: 33.027 - type: recall_at_1000 value: 57.036 - type: recall_at_3 value: 8.995000000000001 - type: recall_at_5 value: 11.23 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.19499999999999 - type: f1 value: 40.60048839070268 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.11 - type: map_at_10 value: 25.478 - type: map_at_100 value: 26.436 - type: map_at_1000 value: 26.51 - type: map_at_3 value: 22.996 - type: map_at_5 value: 24.329 - type: mrr_at_1 value: 18.317 - type: mrr_at_10 value: 27.090999999999998 - type: mrr_at_100 value: 28.037 - type: mrr_at_1000 value: 28.102 - type: mrr_at_3 value: 24.532 - type: mrr_at_5 value: 25.918999999999997 - type: ndcg_at_1 value: 18.317 - type: ndcg_at_10 value: 30.448999999999998 - type: ndcg_at_100 value: 35.302 - type: ndcg_at_1000 value: 37.325 - type: ndcg_at_3 value: 25.326999999999998 - type: ndcg_at_5 value: 27.716 - type: precision_at_1 value: 18.317 - type: precision_at_10 value: 4.8469999999999995 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 10.975999999999999 - type: precision_at_5 value: 7.846 - type: recall_at_1 value: 17.11 - type: recall_at_10 value: 44.466 - type: recall_at_100 value: 67.06299999999999 - type: recall_at_1000 value: 82.64200000000001 - type: recall_at_3 value: 30.509999999999998 - type: recall_at_5 value: 36.27 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.446999999999999 - type: map_at_10 value: 12.188 - type: map_at_100 value: 13.241 - type: map_at_1000 value: 13.450000000000001 - type: map_at_3 value: 10.184999999999999 - type: map_at_5 value: 11.266 - type: mrr_at_1 value: 15.123000000000001 - type: mrr_at_10 value: 21.397 - type: mrr_at_100 value: 22.303 - type: mrr_at_1000 value: 22.398 - type: mrr_at_3 value: 19.187 - type: mrr_at_5 value: 20.383000000000003 - type: ndcg_at_1 value: 15.123000000000001 - type: ndcg_at_10 value: 16.957 - type: ndcg_at_100 value: 22.147 - type: ndcg_at_1000 value: 26.759 - type: ndcg_at_3 value: 14.091000000000001 - type: ndcg_at_5 value: 15.135000000000002 - type: precision_at_1 value: 15.123000000000001 - type: precision_at_10 value: 4.938 - type: precision_at_100 value: 1.019 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 9.568 - type: precision_at_5 value: 7.438000000000001 - type: recall_at_1 value: 7.446999999999999 - type: recall_at_10 value: 22.094 - type: recall_at_100 value: 42.397 - type: recall_at_1000 value: 71.15700000000001 - type: recall_at_3 value: 12.879 - type: recall_at_5 value: 16.49 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.285 - type: map_at_10 value: 25.703 - type: map_at_100 value: 26.634 - type: map_at_1000 value: 26.741999999999997 - type: map_at_3 value: 23.642 - type: map_at_5 value: 24.826 - type: mrr_at_1 value: 36.57 - type: mrr_at_10 value: 43.772 - type: mrr_at_100 value: 44.51 - type: mrr_at_1000 value: 44.561 - type: mrr_at_3 value: 41.787 - type: mrr_at_5 value: 42.964 - type: ndcg_at_1 value: 36.57 - type: ndcg_at_10 value: 32.763999999999996 - type: ndcg_at_100 value: 37.077 - type: ndcg_at_1000 value: 39.666000000000004 - type: ndcg_at_3 value: 28.906 - type: ndcg_at_5 value: 30.86 - type: precision_at_1 value: 36.57 - type: precision_at_10 value: 7.202 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 18.231 - type: precision_at_5 value: 12.483 - type: recall_at_1 value: 18.285 - type: recall_at_10 value: 36.009 - type: recall_at_100 value: 53.27499999999999 - type: recall_at_1000 value: 70.635 - type: recall_at_3 value: 27.345999999999997 - type: recall_at_5 value: 31.208999999999996 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 61.361999999999995 - type: ap value: 57.09674595597791 - type: f1 value: 60.94720401382382 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.686 - type: map_at_10 value: 11.454 - type: map_at_100 value: 12.342 - type: map_at_1000 value: 12.447 - type: map_at_3 value: 9.722 - type: map_at_5 value: 10.632 - type: mrr_at_1 value: 6.891 - type: mrr_at_10 value: 11.768 - type: mrr_at_100 value: 12.651000000000002 - type: mrr_at_1000 value: 12.753 - type: mrr_at_3 value: 10.001999999999999 - type: mrr_at_5 value: 10.918999999999999 - type: ndcg_at_1 value: 6.848 - type: ndcg_at_10 value: 14.466000000000001 - type: ndcg_at_100 value: 19.301 - type: ndcg_at_1000 value: 22.458 - type: ndcg_at_3 value: 10.836 - type: ndcg_at_5 value: 12.475 - type: precision_at_1 value: 6.848 - type: precision_at_10 value: 2.48 - type: precision_at_100 value: 0.49899999999999994 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.766 - type: precision_at_5 value: 3.682 - type: recall_at_1 value: 6.686 - type: recall_at_10 value: 23.82 - type: recall_at_100 value: 47.349999999999994 - type: recall_at_1000 value: 72.66 - type: recall_at_3 value: 13.811000000000002 - type: recall_at_5 value: 17.76 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.67213862289103 - type: f1 value: 86.45841301738238 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.82170542635659 - type: f1 value: 39.12615117855274 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.18829858776058 - type: f1 value: 58.617914607265064 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.45595158036315 - type: f1 value: 64.9778374481982 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.531989286141012 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.070324322784792 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.516858081965257 - type: mrr value: 30.51047930520146 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.131 - type: map_at_10 value: 8.826 - type: map_at_100 value: 11.094999999999999 - type: map_at_1000 value: 12.484 - type: map_at_3 value: 6.723 - type: map_at_5 value: 7.683 - type: mrr_at_1 value: 34.985 - type: mrr_at_10 value: 44.921 - type: mrr_at_100 value: 45.62 - type: mrr_at_1000 value: 45.676 - type: mrr_at_3 value: 42.931000000000004 - type: mrr_at_5 value: 44.385999999999996 - type: ndcg_at_1 value: 32.507999999999996 - type: ndcg_at_10 value: 26.773000000000003 - type: ndcg_at_100 value: 24.751 - type: ndcg_at_1000 value: 34.19 - type: ndcg_at_3 value: 31.213 - type: ndcg_at_5 value: 29.249000000000002 - type: precision_at_1 value: 34.985 - type: precision_at_10 value: 20.247999999999998 - type: precision_at_100 value: 6.907000000000001 - type: precision_at_1000 value: 2.031 - type: precision_at_3 value: 30.341 - type: precision_at_5 value: 25.759 - type: recall_at_1 value: 4.131 - type: recall_at_10 value: 12.465 - type: recall_at_100 value: 25.776 - type: recall_at_1000 value: 59.876 - type: recall_at_3 value: 7.968 - type: recall_at_5 value: 9.968 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.277000000000001 - type: map_at_10 value: 15.709999999999999 - type: map_at_100 value: 16.980999999999998 - type: map_at_1000 value: 17.074 - type: map_at_3 value: 13.157 - type: map_at_5 value: 14.571000000000002 - type: mrr_at_1 value: 10.574 - type: mrr_at_10 value: 17.344 - type: mrr_at_100 value: 18.506 - type: mrr_at_1000 value: 18.584999999999997 - type: mrr_at_3 value: 14.677000000000001 - type: mrr_at_5 value: 16.213 - type: ndcg_at_1 value: 10.574 - type: ndcg_at_10 value: 20.044 - type: ndcg_at_100 value: 26.447 - type: ndcg_at_1000 value: 29.084 - type: ndcg_at_3 value: 14.787 - type: ndcg_at_5 value: 17.362 - type: precision_at_1 value: 10.574 - type: precision_at_10 value: 3.7600000000000002 - type: precision_at_100 value: 0.738 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 6.933 - type: precision_at_5 value: 5.608 - type: recall_at_1 value: 9.277000000000001 - type: recall_at_10 value: 31.948 - type: recall_at_100 value: 61.708 - type: recall_at_1000 value: 82.07799999999999 - type: recall_at_3 value: 18.045 - type: recall_at_5 value: 24.038999999999998 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.268 - type: map_at_10 value: 77.19500000000001 - type: map_at_100 value: 77.95299999999999 - type: map_at_1000 value: 77.986 - type: map_at_3 value: 74.30499999999999 - type: map_at_5 value: 76.054 - type: mrr_at_1 value: 74.09 - type: mrr_at_10 value: 81.384 - type: mrr_at_100 value: 81.592 - type: mrr_at_1000 value: 81.597 - type: mrr_at_3 value: 80.00500000000001 - type: mrr_at_5 value: 80.876 - type: ndcg_at_1 value: 74.16 - type: ndcg_at_10 value: 81.813 - type: ndcg_at_100 value: 83.787 - type: ndcg_at_1000 value: 84.11800000000001 - type: ndcg_at_3 value: 78.389 - type: ndcg_at_5 value: 80.123 - type: precision_at_1 value: 74.16 - type: precision_at_10 value: 12.35 - type: precision_at_100 value: 1.466 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.067 - type: precision_at_5 value: 22.442 - type: recall_at_1 value: 64.268 - type: recall_at_10 value: 90.67 - type: recall_at_100 value: 97.935 - type: recall_at_1000 value: 99.703 - type: recall_at_3 value: 80.752 - type: recall_at_5 value: 85.63300000000001 - type: map_at_1 value: 2.948 - type: map_at_10 value: 7.126 - type: map_at_100 value: 8.462 - type: map_at_1000 value: 8.713 - type: map_at_3 value: 5.143 - type: map_at_5 value: 6.117 - type: mrr_at_1 value: 14.499999999999998 - type: mrr_at_10 value: 22.455 - type: mrr_at_100 value: 23.666 - type: mrr_at_1000 value: 23.745 - type: mrr_at_3 value: 19.417 - type: mrr_at_5 value: 21.117 - type: ndcg_at_1 value: 14.499999999999998 - type: ndcg_at_10 value: 12.666 - type: ndcg_at_100 value: 18.993 - type: ndcg_at_1000 value: 24.09 - type: ndcg_at_3 value: 11.655999999999999 - type: ndcg_at_5 value: 10.342 - type: precision_at_1 value: 14.499999999999998 - type: precision_at_10 value: 6.65 - type: precision_at_100 value: 1.598 - type: precision_at_1000 value: 0.28300000000000003 - type: precision_at_3 value: 10.8 - type: precision_at_5 value: 9.1 - type: recall_at_1 value: 2.948 - type: recall_at_10 value: 13.492 - type: recall_at_100 value: 32.448 - type: recall_at_1000 value: 57.553 - type: recall_at_3 value: 6.578 - type: recall_at_5 value: 9.242 - type: map_at_1 value: 0.129 - type: map_at_10 value: 0.6890000000000001 - type: map_at_100 value: 3.511 - type: map_at_1000 value: 8.943 - type: map_at_3 value: 0.304 - type: map_at_5 value: 0.42700000000000005 - type: mrr_at_1 value: 56.00000000000001 - type: mrr_at_10 value: 65.908 - type: mrr_at_100 value: 66.60199999999999 - type: mrr_at_1000 value: 66.60199999999999 - type: mrr_at_3 value: 63.333 - type: mrr_at_5 value: 64.23299999999999 - type: ndcg_at_1 value: 51.0 - type: ndcg_at_10 value: 39.304 - type: ndcg_at_100 value: 29.392000000000003 - type: ndcg_at_1000 value: 26.044 - type: ndcg_at_3 value: 45.408 - type: ndcg_at_5 value: 41.997 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 40.8 - type: precision_at_100 value: 30.48 - type: precision_at_1000 value: 12.692 - type: precision_at_3 value: 48.0 - type: precision_at_5 value: 43.6 - type: recall_at_1 value: 0.129 - type: recall_at_10 value: 0.893 - type: recall_at_100 value: 6.324000000000001 - type: recall_at_1000 value: 24.964 - type: recall_at_3 value: 0.33999999999999997 - type: recall_at_5 value: 0.505 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.191626251430044 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.00784930616429 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.38146213095916 - type: cos_sim_spearman value: 65.36914729991646 - type: euclidean_pearson value: 70.34893420889419 - type: euclidean_spearman value: 65.36925972117625 - type: manhattan_pearson value: 68.16816720045782 - type: manhattan_spearman value: 64.0884396246228 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.45813185900207 - type: cos_sim_spearman value: 68.03206487736479 - type: euclidean_pearson value: 70.55331228911669 - type: euclidean_spearman value: 68.03330456319067 - type: manhattan_pearson value: 68.32513309931606 - type: manhattan_spearman value: 66.90519361570585 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.32203252223916 - type: cos_sim_spearman value: 78.44952447167366 - type: euclidean_pearson value: 78.18870184193474 - type: euclidean_spearman value: 78.44956228059971 - type: manhattan_pearson value: 77.82417744157945 - type: manhattan_spearman value: 78.17317129725184 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.98515479114604 - type: cos_sim_spearman value: 74.70914230860409 - type: euclidean_pearson value: 76.81874418213698 - type: euclidean_spearman value: 74.70913261737951 - type: manhattan_pearson value: 75.54410520012546 - type: manhattan_spearman value: 73.74596322038998 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.34912352314105 - type: cos_sim_spearman value: 82.13479378308254 - type: euclidean_pearson value: 82.07291865315551 - type: euclidean_spearman value: 82.13479226815167 - type: manhattan_pearson value: 81.51909627091456 - type: manhattan_spearman value: 81.70075499671213 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 76.1116492955691 - type: cos_sim_spearman value: 77.19800116078945 - type: euclidean_pearson value: 76.8231316467101 - type: euclidean_spearman value: 77.19883015620502 - type: manhattan_pearson value: 77.10588536013977 - type: manhattan_spearman value: 77.50215416532438 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.10770264372314 - type: cos_sim_spearman value: 84.97403648808209 - type: euclidean_pearson value: 84.41825024902698 - type: euclidean_spearman value: 84.97491009412074 - type: manhattan_pearson value: 84.16827578787243 - type: manhattan_spearman value: 84.92739867128569 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 62.91807215204278 - type: cos_sim_spearman value: 59.61282196137074 - type: euclidean_pearson value: 62.702286829442436 - type: euclidean_spearman value: 59.61282196137074 - type: manhattan_pearson value: 62.26491120673072 - type: manhattan_spearman value: 59.7161013914999 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.43114398442724 - type: cos_sim_spearman value: 77.35423527756463 - type: euclidean_pearson value: 78.2269102978861 - type: euclidean_spearman value: 77.35428366374488 - type: manhattan_pearson value: 77.26973789544932 - type: manhattan_spearman value: 76.58307796792111 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.61932743075317 - type: mrr value: 91.38920810489437 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 36.861 - type: map_at_10 value: 45.93 - type: map_at_100 value: 46.861000000000004 - type: map_at_1000 value: 46.924 - type: map_at_3 value: 43.283 - type: map_at_5 value: 44.675 - type: mrr_at_1 value: 39.333 - type: mrr_at_10 value: 47.906 - type: mrr_at_100 value: 48.665000000000006 - type: mrr_at_1000 value: 48.722 - type: mrr_at_3 value: 45.611000000000004 - type: mrr_at_5 value: 46.778 - type: ndcg_at_1 value: 39.333 - type: ndcg_at_10 value: 50.970000000000006 - type: ndcg_at_100 value: 55.491 - type: ndcg_at_1000 value: 57.099 - type: ndcg_at_3 value: 45.837 - type: ndcg_at_5 value: 48.081 - type: precision_at_1 value: 39.333 - type: precision_at_10 value: 7.199999999999999 - type: precision_at_100 value: 0.9730000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.4 - type: recall_at_1 value: 36.861 - type: recall_at_10 value: 64.839 - type: recall_at_100 value: 85.983 - type: recall_at_1000 value: 98.467 - type: recall_at_3 value: 50.678 - type: recall_at_5 value: 56.24400000000001 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.75742574257426 - type: cos_sim_ap value: 93.4938177052363 - type: cos_sim_f1 value: 87.45019920318725 - type: cos_sim_precision value: 87.10317460317461 - type: cos_sim_recall value: 87.8 - type: dot_accuracy value: 99.75742574257426 - type: dot_ap value: 93.4938177052363 - type: dot_f1 value: 87.45019920318725 - type: dot_precision value: 87.10317460317461 - type: dot_recall value: 87.8 - type: euclidean_accuracy value: 99.75742574257426 - type: euclidean_ap value: 93.4938177052363 - type: euclidean_f1 value: 87.45019920318725 - type: euclidean_precision value: 87.10317460317461 - type: euclidean_recall value: 87.8 - type: manhattan_accuracy value: 99.77425742574258 - type: manhattan_ap value: 94.11582049960462 - type: manhattan_f1 value: 88.3367139959432 - type: manhattan_precision value: 89.60905349794238 - type: manhattan_recall value: 87.1 - type: max_accuracy value: 99.77425742574258 - type: max_ap value: 94.11582049960462 - type: max_f1 value: 88.3367139959432 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 40.69098529569445 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.68544212745689 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.065922067847836 - type: mrr value: 43.64432136490961 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.750957237960492 - type: cos_sim_spearman value: 30.099771071145582 - type: dot_pearson value: 29.75095720371408 - type: dot_spearman value: 30.128683537072114 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.6179999999999999 - type: map_at_10 value: 8.232000000000001 - type: map_at_100 value: 14.643999999999998 - type: map_at_1000 value: 16.259 - type: map_at_3 value: 4.2090000000000005 - type: map_at_5 value: 5.401 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 43.963 - type: mrr_at_100 value: 45.022 - type: mrr_at_1000 value: 45.039 - type: mrr_at_3 value: 42.177 - type: mrr_at_5 value: 42.687000000000005 - type: ndcg_at_1 value: 23.469 - type: ndcg_at_10 value: 22.526 - type: ndcg_at_100 value: 36.411 - type: ndcg_at_1000 value: 47.461 - type: ndcg_at_3 value: 27.176000000000002 - type: ndcg_at_5 value: 23.787 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 20.0 - type: precision_at_100 value: 8.286 - type: precision_at_1000 value: 1.543 - type: precision_at_3 value: 29.252 - type: precision_at_5 value: 23.265 - type: recall_at_1 value: 1.6179999999999999 - type: recall_at_10 value: 14.443 - type: recall_at_100 value: 50.073 - type: recall_at_1000 value: 83.56700000000001 - type: recall_at_3 value: 5.831 - type: recall_at_5 value: 7.797 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.5932 - type: ap value: 13.748287764670659 - type: f1 value: 53.6121537777008 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 48.60498019241653 - type: f1 value: 48.8190614849162 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 37.40279692338929 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.11378673183525 - type: cos_sim_ap value: 63.412004464549696 - type: cos_sim_f1 value: 60.880921286952386 - type: cos_sim_precision value: 55.34211094323332 - type: cos_sim_recall value: 67.65171503957784 - type: dot_accuracy value: 83.11378673183525 - type: dot_ap value: 63.412004464549696 - type: dot_f1 value: 60.880921286952386 - type: dot_precision value: 55.34211094323332 - type: dot_recall value: 67.65171503957784 - type: euclidean_accuracy value: 83.11378673183525 - type: euclidean_ap value: 63.412004464549696 - type: euclidean_f1 value: 60.880921286952386 - type: euclidean_precision value: 55.34211094323332 - type: euclidean_recall value: 67.65171503957784 - type: manhattan_accuracy value: 82.13625797222389 - type: manhattan_ap value: 60.704142220415335 - type: manhattan_f1 value: 58.10686319668357 - type: manhattan_precision value: 51.55292194523907 - type: manhattan_recall value: 66.56992084432719 - type: max_accuracy value: 83.11378673183525 - type: max_ap value: 63.412004464549696 - type: max_f1 value: 60.880921286952386 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.48593161796096 - type: cos_sim_ap value: 83.09276630048417 - type: cos_sim_f1 value: 75.22376690154258 - type: cos_sim_precision value: 74.4328031958996 - type: cos_sim_recall value: 76.03172158915923 - type: dot_accuracy value: 87.48593161796096 - type: dot_ap value: 83.09276630048417 - type: dot_f1 value: 75.22376690154258 - type: dot_precision value: 74.4328031958996 - type: dot_recall value: 76.03172158915923 - type: euclidean_accuracy value: 87.48593161796096 - type: euclidean_ap value: 83.09276683624702 - type: euclidean_f1 value: 75.22376690154258 - type: euclidean_precision value: 74.4328031958996 - type: euclidean_recall value: 76.03172158915923 - type: manhattan_accuracy value: 87.49369348391353 - type: manhattan_ap value: 82.94869347657408 - type: manhattan_f1 value: 74.95875695376942 - type: manhattan_precision value: 74.70367821365757 - type: manhattan_recall value: 75.21558361564522 - type: max_accuracy value: 87.49369348391353 - type: max_ap value: 83.09276683624702 - type: max_f1 value: 75.22376690154258 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/nmc-nignore50	twadada	null	[ "mteb", "model-index", "region:us" ]	2024-09-17T00:36:52	2024-09-17T00:37:01	0	--- tags: - mteb model-index: - name: nomic_classification_nignore50 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.34328358208954 - type: ap value: 35.68453876944336 - type: f1 value: 67.06992889373645 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 59.672925 - type: ap value: 55.848120844301974 - type: f1 value: 59.45636290076794 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 30.610000000000003 - type: f1 value: 30.300316542261875 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.697 - type: map_at_10 value: 35.189 - type: map_at_100 value: 36.4 - type: map_at_1000 value: 36.425000000000004 - type: map_at_3 value: 30.714000000000002 - type: map_at_5 value: 32.918 - type: mrr_at_1 value: 21.337 - type: mrr_at_10 value: 35.424 - type: mrr_at_100 value: 36.634 - type: mrr_at_1000 value: 36.659000000000006 - type: mrr_at_3 value: 30.927 - type: mrr_at_5 value: 33.132 - type: ndcg_at_1 value: 20.697 - type: ndcg_at_10 value: 43.525999999999996 - type: ndcg_at_100 value: 48.978 - type: ndcg_at_1000 value: 49.527 - type: ndcg_at_3 value: 34.087 - type: ndcg_at_5 value: 38.080999999999996 - type: precision_at_1 value: 20.697 - type: precision_at_10 value: 7.034 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.628 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 20.697 - type: recall_at_10 value: 70.341 - type: recall_at_100 value: 94.879 - type: recall_at_1000 value: 99.004 - type: recall_at_3 value: 43.883 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.49094114007186 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.389506327986794 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.325008936884444 - type: mrr value: 69.04992745020445 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.44469574569193 - type: cos_sim_spearman value: 80.2397801250519 - type: euclidean_pearson value: 82.22256751323195 - type: euclidean_spearman value: 80.2397801250519 - type: manhattan_pearson value: 81.55829597082581 - type: manhattan_spearman value: 79.90603758074258 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 71.56818181818181 - type: f1 value: 70.79234518018967 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.120028817225204 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.27141125132152 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 22.482 - type: map_at_10 value: 29.986 - type: map_at_100 value: 31.19 - type: map_at_1000 value: 31.339 - type: map_at_3 value: 27.869 - type: map_at_5 value: 28.83 - type: mrr_at_1 value: 28.326 - type: mrr_at_10 value: 35.675000000000004 - type: mrr_at_100 value: 36.559000000000005 - type: mrr_at_1000 value: 36.632999999999996 - type: mrr_at_3 value: 34.049 - type: mrr_at_5 value: 34.8 - type: ndcg_at_1 value: 28.326 - type: ndcg_at_10 value: 34.788000000000004 - type: ndcg_at_100 value: 40.162 - type: ndcg_at_1000 value: 43.145 - type: ndcg_at_3 value: 31.903 - type: ndcg_at_5 value: 32.74 - type: precision_at_1 value: 28.326 - type: precision_at_10 value: 6.552 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 22.482 - type: recall_at_10 value: 42.995 - type: recall_at_100 value: 66.79899999999999 - type: recall_at_1000 value: 87.02600000000001 - type: recall_at_3 value: 33.495999999999995 - type: recall_at_5 value: 36.7 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 17.147000000000002 - type: map_at_10 value: 23.28 - type: map_at_100 value: 24.191 - type: map_at_1000 value: 24.309 - type: map_at_3 value: 21.542 - type: map_at_5 value: 22.486 - type: mrr_at_1 value: 21.529 - type: mrr_at_10 value: 27.366 - type: mrr_at_100 value: 28.138 - type: mrr_at_1000 value: 28.206999999999997 - type: mrr_at_3 value: 25.668999999999997 - type: mrr_at_5 value: 26.653 - type: ndcg_at_1 value: 21.529 - type: ndcg_at_10 value: 27.107999999999997 - type: ndcg_at_100 value: 31.381999999999998 - type: ndcg_at_1000 value: 34.050000000000004 - type: ndcg_at_3 value: 24.101 - type: ndcg_at_5 value: 25.465 - type: precision_at_1 value: 21.529 - type: precision_at_10 value: 4.9430000000000005 - type: precision_at_100 value: 0.8829999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.591999999999999 - type: precision_at_5 value: 8.153 - type: recall_at_1 value: 17.147000000000002 - type: recall_at_10 value: 34.510000000000005 - type: recall_at_100 value: 53.337999999999994 - type: recall_at_1000 value: 71.25200000000001 - type: recall_at_3 value: 25.711000000000002 - type: recall_at_5 value: 29.448999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.628 - type: map_at_10 value: 34.53 - type: map_at_100 value: 35.648 - type: map_at_1000 value: 35.746 - type: map_at_3 value: 32.001000000000005 - type: map_at_5 value: 33.516 - type: mrr_at_1 value: 29.842999999999996 - type: mrr_at_10 value: 37.679 - type: mrr_at_100 value: 38.607 - type: mrr_at_1000 value: 38.671 - type: mrr_at_3 value: 35.528 - type: mrr_at_5 value: 36.747 - type: ndcg_at_1 value: 29.842999999999996 - type: ndcg_at_10 value: 39.271 - type: ndcg_at_100 value: 44.507000000000005 - type: ndcg_at_1000 value: 46.79 - type: ndcg_at_3 value: 34.699999999999996 - type: ndcg_at_5 value: 37.032 - type: precision_at_1 value: 29.842999999999996 - type: precision_at_10 value: 6.3759999999999994 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.507000000000001 - type: precision_at_5 value: 10.871 - type: recall_at_1 value: 25.628 - type: recall_at_10 value: 50.552 - type: recall_at_100 value: 74.008 - type: recall_at_1000 value: 90.518 - type: recall_at_3 value: 38.397 - type: recall_at_5 value: 44.052 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.07 - type: map_at_10 value: 17.630000000000003 - type: map_at_100 value: 18.502 - type: map_at_1000 value: 18.614 - type: map_at_3 value: 16.213 - type: map_at_5 value: 17.029 - type: mrr_at_1 value: 14.124 - type: mrr_at_10 value: 18.706999999999997 - type: mrr_at_100 value: 19.604 - type: mrr_at_1000 value: 19.705000000000002 - type: mrr_at_3 value: 17.326 - type: mrr_at_5 value: 18.099999999999998 - type: ndcg_at_1 value: 14.124 - type: ndcg_at_10 value: 20.368 - type: ndcg_at_100 value: 25.020999999999997 - type: ndcg_at_1000 value: 28.360000000000003 - type: ndcg_at_3 value: 17.518 - type: ndcg_at_5 value: 18.914 - type: precision_at_1 value: 14.124 - type: precision_at_10 value: 3.164 - type: precision_at_100 value: 0.577 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.457999999999999 - type: precision_at_5 value: 5.266 - type: recall_at_1 value: 13.07 - type: recall_at_10 value: 27.811000000000003 - type: recall_at_100 value: 49.874 - type: recall_at_1000 value: 75.786 - type: recall_at_3 value: 20.06 - type: recall_at_5 value: 23.397000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.646000000000001 - type: map_at_10 value: 10.141 - type: map_at_100 value: 10.9 - type: map_at_1000 value: 11.036999999999999 - type: map_at_3 value: 8.781 - type: map_at_5 value: 9.581000000000001 - type: mrr_at_1 value: 8.333 - type: mrr_at_10 value: 12.504000000000001 - type: mrr_at_100 value: 13.331000000000001 - type: mrr_at_1000 value: 13.435 - type: mrr_at_3 value: 11.007 - type: mrr_at_5 value: 11.909 - type: ndcg_at_1 value: 8.333 - type: ndcg_at_10 value: 12.796 - type: ndcg_at_100 value: 17.115 - type: ndcg_at_1000 value: 20.84 - type: ndcg_at_3 value: 10.171 - type: ndcg_at_5 value: 11.496 - type: precision_at_1 value: 8.333 - type: precision_at_10 value: 2.5 - type: precision_at_100 value: 0.5519999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.017 - type: precision_at_5 value: 3.955 - type: recall_at_1 value: 6.646000000000001 - type: recall_at_10 value: 18.725 - type: recall_at_100 value: 38.590999999999994 - type: recall_at_1000 value: 65.955 - type: recall_at_3 value: 11.376999999999999 - type: recall_at_5 value: 14.673 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.865000000000002 - type: map_at_10 value: 23.877000000000002 - type: map_at_100 value: 25.058000000000003 - type: map_at_1000 value: 25.202999999999996 - type: map_at_3 value: 21.861 - type: map_at_5 value: 22.958000000000002 - type: mrr_at_1 value: 22.137 - type: mrr_at_10 value: 28.391 - type: mrr_at_100 value: 29.364 - type: mrr_at_1000 value: 29.454 - type: mrr_at_3 value: 26.372 - type: mrr_at_5 value: 27.551 - type: ndcg_at_1 value: 22.137 - type: ndcg_at_10 value: 28.183999999999997 - type: ndcg_at_100 value: 33.806000000000004 - type: ndcg_at_1000 value: 37.099 - type: ndcg_at_3 value: 24.621000000000002 - type: ndcg_at_5 value: 26.235000000000003 - type: precision_at_1 value: 22.137 - type: precision_at_10 value: 5.178 - type: precision_at_100 value: 0.9440000000000001 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 11.613999999999999 - type: precision_at_5 value: 8.373 - type: recall_at_1 value: 17.865000000000002 - type: recall_at_10 value: 36.885 - type: recall_at_100 value: 61.49400000000001 - type: recall_at_1000 value: 84.309 - type: recall_at_3 value: 26.51 - type: recall_at_5 value: 30.788 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.691999999999998 - type: map_at_10 value: 19.463 - type: map_at_100 value: 20.558 - type: map_at_1000 value: 20.703 - type: map_at_3 value: 17.277 - type: map_at_5 value: 18.607000000000003 - type: mrr_at_1 value: 16.895 - type: mrr_at_10 value: 22.939999999999998 - type: mrr_at_100 value: 23.902 - type: mrr_at_1000 value: 23.993000000000002 - type: mrr_at_3 value: 20.89 - type: mrr_at_5 value: 22.158 - type: ndcg_at_1 value: 16.895 - type: ndcg_at_10 value: 23.308 - type: ndcg_at_100 value: 28.675 - type: ndcg_at_1000 value: 32.156 - type: ndcg_at_3 value: 19.53 - type: ndcg_at_5 value: 21.519 - type: precision_at_1 value: 16.895 - type: precision_at_10 value: 4.417999999999999 - type: precision_at_100 value: 0.844 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.399000000000001 - type: precision_at_5 value: 7.146 - type: recall_at_1 value: 13.691999999999998 - type: recall_at_10 value: 31.424000000000003 - type: recall_at_100 value: 55.223 - type: recall_at_1000 value: 79.842 - type: recall_at_3 value: 20.949 - type: recall_at_5 value: 26.166 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.61475 - type: map_at_10 value: 19.97075 - type: map_at_100 value: 20.916083333333333 - type: map_at_1000 value: 21.046 - type: map_at_3 value: 18.229250000000004 - type: map_at_5 value: 19.20266666666667 - type: mrr_at_1 value: 17.608083333333333 - type: mrr_at_10 value: 23.027083333333334 - type: mrr_at_100 value: 23.87941666666667 - type: mrr_at_1000 value: 23.96783333333333 - type: mrr_at_3 value: 21.377083333333335 - type: mrr_at_5 value: 22.300833333333333 - type: ndcg_at_1 value: 17.608083333333333 - type: ndcg_at_10 value: 23.473083333333335 - type: ndcg_at_100 value: 28.176750000000002 - type: ndcg_at_1000 value: 31.36625 - type: ndcg_at_3 value: 20.38383333333334 - type: ndcg_at_5 value: 21.82283333333333 - type: precision_at_1 value: 17.608083333333333 - type: precision_at_10 value: 4.162666666666666 - type: precision_at_100 value: 0.7761666666666668 - type: precision_at_1000 value: 0.12325000000000001 - type: precision_at_3 value: 9.466000000000001 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 14.61475 - type: recall_at_10 value: 31.009500000000003 - type: recall_at_100 value: 52.470000000000006 - type: recall_at_1000 value: 75.52175 - type: recall_at_3 value: 22.22875 - type: recall_at_5 value: 26.018083333333337 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.016 - type: map_at_10 value: 15.293999999999999 - type: map_at_100 value: 16.08 - type: map_at_1000 value: 16.177 - type: map_at_3 value: 13.898 - type: map_at_5 value: 14.637 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.279 - type: mrr_at_100 value: 18.082 - type: mrr_at_1000 value: 18.169 - type: mrr_at_3 value: 15.9 - type: mrr_at_5 value: 16.667 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.073 - type: ndcg_at_100 value: 22.142 - type: ndcg_at_1000 value: 24.887999999999998 - type: ndcg_at_3 value: 15.405 - type: ndcg_at_5 value: 16.622999999999998 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.052 - type: precision_at_100 value: 0.5519999999999999 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 7.0040000000000004 - type: precision_at_5 value: 4.939 - type: recall_at_1 value: 11.016 - type: recall_at_10 value: 24.72 - type: recall_at_100 value: 43.553999999999995 - type: recall_at_1000 value: 64.189 - type: recall_at_3 value: 17.308 - type: recall_at_5 value: 20.455000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.148000000000001 - type: map_at_10 value: 10.878 - type: map_at_100 value: 11.547 - type: map_at_1000 value: 11.67 - type: map_at_3 value: 9.679 - type: map_at_5 value: 10.41 - type: mrr_at_1 value: 8.878 - type: mrr_at_10 value: 13.142999999999999 - type: mrr_at_100 value: 13.825999999999999 - type: mrr_at_1000 value: 13.927999999999999 - type: mrr_at_3 value: 11.786000000000001 - type: mrr_at_5 value: 12.623999999999999 - type: ndcg_at_1 value: 8.878 - type: ndcg_at_10 value: 13.414000000000001 - type: ndcg_at_100 value: 17.129 - type: ndcg_at_1000 value: 20.754 - type: ndcg_at_3 value: 11.135 - type: ndcg_at_5 value: 12.333 - type: precision_at_1 value: 8.878 - type: precision_at_10 value: 2.564 - type: precision_at_100 value: 0.525 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 5.448 - type: precision_at_5 value: 4.136 - type: recall_at_1 value: 7.148000000000001 - type: recall_at_10 value: 18.875 - type: recall_at_100 value: 36.321999999999996 - type: recall_at_1000 value: 63.273999999999994 - type: recall_at_3 value: 12.590000000000002 - type: recall_at_5 value: 15.6 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.178 - type: map_at_10 value: 17.538 - type: map_at_100 value: 18.343999999999998 - type: map_at_1000 value: 18.462999999999997 - type: map_at_3 value: 15.984000000000002 - type: map_at_5 value: 16.78 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.385 - type: mrr_at_100 value: 21.160999999999998 - type: mrr_at_1000 value: 21.252 - type: mrr_at_3 value: 18.766 - type: mrr_at_5 value: 19.554 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 20.785 - type: ndcg_at_100 value: 25.089 - type: ndcg_at_1000 value: 28.396 - type: ndcg_at_3 value: 17.701 - type: ndcg_at_5 value: 18.968 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.5069999999999997 - type: precision_at_100 value: 0.633 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.022 - type: precision_at_5 value: 5.634 - type: recall_at_1 value: 13.178 - type: recall_at_10 value: 28.227999999999998 - type: recall_at_100 value: 48.022999999999996 - type: recall_at_1000 value: 72.475 - type: recall_at_3 value: 19.425 - type: recall_at_5 value: 22.783 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.662999999999998 - type: map_at_10 value: 21.191 - type: map_at_100 value: 22.436 - type: map_at_1000 value: 22.634999999999998 - type: map_at_3 value: 19.493 - type: map_at_5 value: 20.543 - type: mrr_at_1 value: 19.368 - type: mrr_at_10 value: 24.866 - type: mrr_at_100 value: 25.875999999999998 - type: mrr_at_1000 value: 25.958 - type: mrr_at_3 value: 23.551 - type: mrr_at_5 value: 24.233 - type: ndcg_at_1 value: 19.368 - type: ndcg_at_10 value: 24.869 - type: ndcg_at_100 value: 30.54 - type: ndcg_at_1000 value: 33.994 - type: ndcg_at_3 value: 22.522000000000002 - type: ndcg_at_5 value: 23.674 - type: precision_at_1 value: 19.368 - type: precision_at_10 value: 4.704 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 10.671999999999999 - type: precision_at_5 value: 7.668 - type: recall_at_1 value: 15.662999999999998 - type: recall_at_10 value: 31.03 - type: recall_at_100 value: 57.861 - type: recall_at_1000 value: 81.179 - type: recall_at_3 value: 23.843 - type: recall_at_5 value: 27.223999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.842 - type: map_at_10 value: 15.841 - type: map_at_100 value: 16.539 - type: map_at_1000 value: 16.656000000000002 - type: map_at_3 value: 14.152999999999999 - type: map_at_5 value: 15.055 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.39 - type: mrr_at_100 value: 18.102999999999998 - type: mrr_at_1000 value: 18.209 - type: mrr_at_3 value: 15.681000000000001 - type: mrr_at_5 value: 16.614 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 18.712999999999997 - type: ndcg_at_100 value: 22.553 - type: ndcg_at_1000 value: 25.923000000000002 - type: ndcg_at_3 value: 15.299 - type: ndcg_at_5 value: 16.875 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.994 - type: precision_at_100 value: 0.529 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 6.4079999999999995 - type: precision_at_5 value: 4.695 - type: recall_at_1 value: 11.842 - type: recall_at_10 value: 26.358999999999998 - type: recall_at_100 value: 44.553 - type: recall_at_1000 value: 70.456 - type: recall_at_3 value: 17.079 - type: recall_at_5 value: 20.93 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.841 - type: map_at_10 value: 11.955 - type: map_at_100 value: 13.322000000000001 - type: map_at_1000 value: 13.517000000000001 - type: map_at_3 value: 9.818 - type: map_at_5 value: 10.875 - type: mrr_at_1 value: 15.504999999999999 - type: mrr_at_10 value: 24.087 - type: mrr_at_100 value: 25.149 - type: mrr_at_1000 value: 25.221 - type: mrr_at_3 value: 20.923 - type: mrr_at_5 value: 22.64 - type: ndcg_at_1 value: 15.504999999999999 - type: ndcg_at_10 value: 17.787 - type: ndcg_at_100 value: 24.032 - type: ndcg_at_1000 value: 28.058 - type: ndcg_at_3 value: 13.729 - type: ndcg_at_5 value: 15.165999999999999 - type: precision_at_1 value: 15.504999999999999 - type: precision_at_10 value: 5.811 - type: precision_at_100 value: 1.246 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 10.228 - type: precision_at_5 value: 8.155999999999999 - type: recall_at_1 value: 6.841 - type: recall_at_10 value: 22.451999999999998 - type: recall_at_100 value: 44.588 - type: recall_at_1000 value: 67.806 - type: recall_at_3 value: 12.824 - type: recall_at_5 value: 16.59 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.601999999999999 - type: map_at_10 value: 10.209 - type: map_at_100 value: 14.172 - type: map_at_1000 value: 15.121 - type: map_at_3 value: 7.59 - type: map_at_5 value: 8.853 - type: mrr_at_1 value: 41.75 - type: mrr_at_10 value: 51.949 - type: mrr_at_100 value: 52.678000000000004 - type: mrr_at_1000 value: 52.708 - type: mrr_at_3 value: 49.542 - type: mrr_at_5 value: 51.054 - type: ndcg_at_1 value: 31.125000000000004 - type: ndcg_at_10 value: 24.581 - type: ndcg_at_100 value: 27.894999999999996 - type: ndcg_at_1000 value: 34.438 - type: ndcg_at_3 value: 27.877999999999997 - type: ndcg_at_5 value: 26.273000000000003 - type: precision_at_1 value: 41.75 - type: precision_at_10 value: 21.15 - type: precision_at_100 value: 6.787999999999999 - type: precision_at_1000 value: 1.394 - type: precision_at_3 value: 32.917 - type: precision_at_5 value: 28.000000000000004 - type: recall_at_1 value: 4.601999999999999 - type: recall_at_10 value: 14.434 - type: recall_at_100 value: 33.838 - type: recall_at_1000 value: 56.438 - type: recall_at_3 value: 9.073 - type: recall_at_5 value: 11.395 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.364999999999995 - type: f1 value: 41.05024359810464 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.078 - type: map_at_10 value: 25.590000000000003 - type: map_at_100 value: 26.581 - type: map_at_1000 value: 26.651000000000003 - type: map_at_3 value: 23.113 - type: map_at_5 value: 24.457 - type: mrr_at_1 value: 18.257 - type: mrr_at_10 value: 27.171 - type: mrr_at_100 value: 28.147 - type: mrr_at_1000 value: 28.206999999999997 - type: mrr_at_3 value: 24.607 - type: mrr_at_5 value: 26.005 - type: ndcg_at_1 value: 18.257 - type: ndcg_at_10 value: 30.617 - type: ndcg_at_100 value: 35.625 - type: ndcg_at_1000 value: 37.566 - type: ndcg_at_3 value: 25.488 - type: ndcg_at_5 value: 27.897 - type: precision_at_1 value: 18.257 - type: precision_at_10 value: 4.877 - type: precision_at_100 value: 0.76 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 11.081000000000001 - type: precision_at_5 value: 7.933 - type: recall_at_1 value: 17.078 - type: recall_at_10 value: 44.866 - type: recall_at_100 value: 68.148 - type: recall_at_1000 value: 83.134 - type: recall_at_3 value: 30.830999999999996 - type: recall_at_5 value: 36.629 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.515 - type: map_at_10 value: 11.788 - type: map_at_100 value: 12.974 - type: map_at_1000 value: 13.177 - type: map_at_3 value: 9.844 - type: map_at_5 value: 10.8 - type: mrr_at_1 value: 14.352 - type: mrr_at_10 value: 20.134 - type: mrr_at_100 value: 21.288 - type: mrr_at_1000 value: 21.384 - type: mrr_at_3 value: 18.133 - type: mrr_at_5 value: 19.097 - type: ndcg_at_1 value: 14.352 - type: ndcg_at_10 value: 16.203 - type: ndcg_at_100 value: 21.891 - type: ndcg_at_1000 value: 26.653 - type: ndcg_at_3 value: 13.369 - type: ndcg_at_5 value: 14.327000000000002 - type: precision_at_1 value: 14.352 - type: precision_at_10 value: 4.645 - type: precision_at_100 value: 1.028 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 8.796 - type: precision_at_5 value: 6.728000000000001 - type: recall_at_1 value: 7.515 - type: recall_at_10 value: 21.018 - type: recall_at_100 value: 42.887 - type: recall_at_1000 value: 72.898 - type: recall_at_3 value: 12.215 - type: recall_at_5 value: 15.612 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.332 - type: map_at_10 value: 25.791999999999998 - type: map_at_100 value: 26.706999999999997 - type: map_at_1000 value: 26.810000000000002 - type: map_at_3 value: 23.742 - type: map_at_5 value: 24.903 - type: mrr_at_1 value: 36.664 - type: mrr_at_10 value: 44.067 - type: mrr_at_100 value: 44.756 - type: mrr_at_1000 value: 44.806000000000004 - type: mrr_at_3 value: 42.091 - type: mrr_at_5 value: 43.206 - type: ndcg_at_1 value: 36.664 - type: ndcg_at_10 value: 32.938 - type: ndcg_at_100 value: 37.184 - type: ndcg_at_1000 value: 39.723000000000006 - type: ndcg_at_3 value: 29.087000000000003 - type: ndcg_at_5 value: 30.986000000000004 - type: precision_at_1 value: 36.664 - type: precision_at_10 value: 7.245 - type: precision_at_100 value: 1.0670000000000002 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 18.37 - type: precision_at_5 value: 12.527 - type: recall_at_1 value: 18.332 - type: recall_at_10 value: 36.226 - type: recall_at_100 value: 53.342 - type: recall_at_1000 value: 70.42500000000001 - type: recall_at_3 value: 27.556000000000004 - type: recall_at_5 value: 31.317 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 58.64719999999999 - type: ap value: 55.19966477229678 - type: f1 value: 58.24961396114563 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.459 - type: map_at_10 value: 11.254999999999999 - type: map_at_100 value: 12.132 - type: map_at_1000 value: 12.237 - type: map_at_3 value: 9.522 - type: map_at_5 value: 10.456999999999999 - type: mrr_at_1 value: 6.633 - type: mrr_at_10 value: 11.518 - type: mrr_at_100 value: 12.395 - type: mrr_at_1000 value: 12.497 - type: mrr_at_3 value: 9.751999999999999 - type: mrr_at_5 value: 10.708 - type: ndcg_at_1 value: 6.59 - type: ndcg_at_10 value: 14.264 - type: ndcg_at_100 value: 19.070999999999998 - type: ndcg_at_1000 value: 22.235 - type: ndcg_at_3 value: 10.631 - type: ndcg_at_5 value: 12.339 - type: precision_at_1 value: 6.59 - type: precision_at_10 value: 2.461 - type: precision_at_100 value: 0.496 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.6850000000000005 - type: precision_at_5 value: 3.6790000000000003 - type: recall_at_1 value: 6.459 - type: recall_at_10 value: 23.649 - type: recall_at_100 value: 47.13 - type: recall_at_1000 value: 72.51100000000001 - type: recall_at_3 value: 13.641 - type: recall_at_5 value: 17.787 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.75695394436845 - type: f1 value: 84.39448911327825 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.072047423620596 - type: f1 value: 36.95943939246465 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.78614660390047 - type: f1 value: 57.44347017992026 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.28379287155346 - type: f1 value: 62.76816678345901 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.671378352049466 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 25.86783118767072 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.346696390536565 - type: mrr value: 30.303238754879626 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.9190000000000005 - type: map_at_10 value: 8.742999999999999 - type: map_at_100 value: 11.073 - type: map_at_1000 value: 12.447999999999999 - type: map_at_3 value: 6.2379999999999995 - type: map_at_5 value: 7.443 - type: mrr_at_1 value: 34.365 - type: mrr_at_10 value: 44.238 - type: mrr_at_100 value: 44.857 - type: mrr_at_1000 value: 44.91 - type: mrr_at_3 value: 41.744 - type: mrr_at_5 value: 43.478 - type: ndcg_at_1 value: 32.353 - type: ndcg_at_10 value: 26.695 - type: ndcg_at_100 value: 24.728 - type: ndcg_at_1000 value: 34.013 - type: ndcg_at_3 value: 30.268 - type: ndcg_at_5 value: 28.95 - type: precision_at_1 value: 34.365 - type: precision_at_10 value: 20.31 - type: precision_at_100 value: 6.984999999999999 - type: precision_at_1000 value: 2.019 - type: precision_at_3 value: 28.896 - type: precision_at_5 value: 25.697 - type: recall_at_1 value: 3.9190000000000005 - type: recall_at_10 value: 13.046 - type: recall_at_100 value: 26.08 - type: recall_at_1000 value: 60.207 - type: recall_at_3 value: 7.255000000000001 - type: recall_at_5 value: 9.853000000000002 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 8.408 - type: map_at_10 value: 15.064 - type: map_at_100 value: 16.282 - type: map_at_1000 value: 16.384999999999998 - type: map_at_3 value: 12.605 - type: map_at_5 value: 13.866 - type: mrr_at_1 value: 9.589 - type: mrr_at_10 value: 16.598 - type: mrr_at_100 value: 17.716 - type: mrr_at_1000 value: 17.8 - type: mrr_at_3 value: 14.021 - type: mrr_at_5 value: 15.39 - type: ndcg_at_1 value: 9.589 - type: ndcg_at_10 value: 19.408 - type: ndcg_at_100 value: 25.549 - type: ndcg_at_1000 value: 28.344 - type: ndcg_at_3 value: 14.258000000000001 - type: ndcg_at_5 value: 16.549 - type: precision_at_1 value: 9.589 - type: precision_at_10 value: 3.711 - type: precision_at_100 value: 0.717 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 6.769 - type: precision_at_5 value: 5.359 - type: recall_at_1 value: 8.408 - type: recall_at_10 value: 31.442999999999998 - type: recall_at_100 value: 59.931 - type: recall_at_1000 value: 81.40899999999999 - type: recall_at_3 value: 17.654 - 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task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.77623762376237 - type: cos_sim_ap value: 94.03474205524832 - type: cos_sim_f1 value: 88.17533129459734 - type: cos_sim_precision value: 89.91683991683992 - type: cos_sim_recall value: 86.5 - type: dot_accuracy value: 99.77623762376237 - type: dot_ap value: 94.03474205524832 - type: dot_f1 value: 88.17533129459734 - type: dot_precision value: 89.91683991683992 - type: dot_recall value: 86.5 - type: euclidean_accuracy value: 99.77623762376237 - type: euclidean_ap value: 94.03474205524832 - type: euclidean_f1 value: 88.17533129459734 - type: euclidean_precision value: 89.91683991683992 - type: euclidean_recall value: 86.5 - type: manhattan_accuracy value: 99.77722772277228 - type: manhattan_ap value: 94.18601558420747 - type: manhattan_f1 value: 88.37209302325581 - type: manhattan_precision value: 91.44385026737967 - 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type: euclidean_precision value: 72.30780253636169 - type: euclidean_recall value: 77.70249461040962 - type: manhattan_accuracy value: 87.37920596111304 - type: manhattan_ap value: 82.78475210950901 - type: manhattan_f1 value: 74.73338499575631 - type: manhattan_precision value: 71.7596201544894 - type: manhattan_recall value: 77.96427471512165 - type: max_accuracy value: 87.49563395040167 - type: max_ap value: 82.9054181491746 - type: max_f1 value: 74.9081462237892 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
Baiming123/Calcu_Disease_Similarity	Baiming123	sentence-similarity	[ "sentence-transformers", "pytorch", "bert", "sentence-similarity", "dataset:Baiming123/MeSHDS", "base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1", "base_model:finetune:sentence-transformers/multi-qa-MiniLM-L6-cos-v1", "doi:10.57967/hf/3108", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	2024-09-20T15:58:13	2024-12-14T10:10:29	3	--- base_model: - sentence-transformers/multi-qa-MiniLM-L6-cos-v1 datasets: - Baiming123/MeSHDS pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity --- # Model Description This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.The 'Calcu_Disease_Similarity' model is designed to encode two disease terms and compute their semantic similarity. The model has been fine-tuned on disease-related datasets 'MeSHDS' and achieves a high F1 score in distinguishing experimentally validated miRNA-target interactions (MTIs) and predicted MTIs by considering disease similarity. If you use this model in your research, please cite the following paper: ``` @article {Chen2024.05.17.594604, author = {Chen, Baiming}, title = {miRTarDS: High-Accuracy Refining Protein-level MicroRNA Target Interactions from Prediction Databases Using Sentence-BERT}, elocation-id = {2024.05.17.594604}, year = {2024}, doi = {10.1101/2024.05.17.594604}, publisher = {Cold Spring Harbor Laboratory}, abstract = {MicroRNAs (miRNAs) regulate gene expression by binding to mRNAs, inhibiting translation, or promoting mRNA degradation. miRNAs are of great importance in the development of various diseases. Currently, numerous sequence-based miRNA target prediction tools are available, however, only 1\% of their predictions have been experimentally validated. In this study, we propose a novel approach that leverages disease similarity between miRNAs and genes as a key feature to further refine and screen human sequence-based predicted miRNA target interactions (MTIs). To quantify the semantic similarity of diseases, we fine-tuned the Sentence-BERT model. Our method achieved an F1 score of 0.88 in accurately distinguishing human protein-level experimentally validated MTIs (functional MTIs, validated through western blot or reporter assay) and predicted MTIs. Moreover, this method exhibits exceptional generalizability across different databases. We applied the proposed method to analyze 1,220,904 human MTIs sourced from miRTarbase, miRDB, and miRWalk, encompassing 6,085 genes and 1,261 pre-miRNAs. Notably, we accurately identified 3,883 out of 3,962 MTIs with strong experimental evidence from miRTarbase. This study has the potential to provide valuable insights into the understanding of miRNA-gene regulatory networks and to promote advancements in disease diagnosis, treatment, and drug development.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/12/08/2024.05.17.594604}, eprint = {https://www.biorxiv.org/content/early/2024/12/08/2024.05.17.594604.full.pdf}, journal = {bioRxiv} } ``` ## Key Features: - Fine-tuned to compute semantic similarity between disease names. - Achieves an F1 score of 0.88 in distinguishing protein-level experimentally (western blot, reporter assay) validated MTIs and predicted MTIs. - Built for applications in understanding miRNA-gene regulatory networks, disease diagnosis, treatment, and drug discovery. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` # Usage (Sentence-Transformers) ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python # Load the pre-trained SBERT model from sentence_transformers import SentenceTransformer, util # Replace 'your/path/to/Calcu_Disease_Similarity' with the actual path to the model model = SentenceTransformer("Baiming123/Calcu_Disease_Similarity") # Example usage disease1 = "lung cancer" disease2 = "pulmonary fibrosis" def sts(sentence_a, sentence_b) -> float: query_emb = model.encode(sentence_a) doc_emb = model.encode(sentence_b) [score] = util.dot_score(query_emb, doc_emb)[0].tolist() return score similarity = sts(disease1, disease2) print(similarity) ``` # Additional Information ## License This model is licensed under CC-BY-NC 4.0 International license. If you use this model, please adhere to the license requirements. ## Questions or Issues If you encounter any issues or have any questions while using the model, feel free to reach out to the author for assistance. Thank you for your support and for using this model!	[ "SEMANTIC_SIMILARITY", "TRANSLATION" ]	[ "MIRNA" ]
tomaarsen/static-bert-uncased-gooaq	tomaarsen	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-09-26T11:47:56	2024-10-18T10:35:51	4	--- datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 6.448001991119035 energy_consumed: 0.0165885485310573 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.109 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 1024 dev type: gooaq-1024-dev metrics: - type: cosine_accuracy@1 value: 0.6309 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8409 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8986 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6309 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28029999999999994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17972000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6309 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8409 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8986 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7932643237589305 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7440336111111036 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7465739001132767 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 512 dev type: gooaq-512-dev metrics: - type: cosine_accuracy@1 value: 0.6271 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8366 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8946 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9431 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6271 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27886666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17892000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09431000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6271 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8366 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8946 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9431 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7904860196985286 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7408453174603101 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7434337897783787 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 256 dev type: gooaq-256-dev metrics: - type: cosine_accuracy@1 value: 0.6192 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8235 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8866 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9364 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6192 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27449999999999997 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17732000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09364000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6192 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8235 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9364 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7821476540310974 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7321259126984055 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7348893313013708 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 128 dev type: gooaq-128-dev metrics: - type: cosine_accuracy@1 value: 0.5942 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.804 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8721 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9249 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5942 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.268 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17442000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09249 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5942 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.804 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8721 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9249 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7627845665665897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7103426587301529 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7133975871277517 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 64 dev type: gooaq-64-dev metrics: - type: cosine_accuracy@1 value: 0.556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7553 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8267 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8945 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25176666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16534000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08945 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7553 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8267 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8945 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7246435400765202 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6701957142857087 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6743443703166442 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 32 dev type: gooaq-32-dev metrics: - type: cosine_accuracy@1 value: 0.4628 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6619 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7415 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8241 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4628 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2206333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1483 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08241 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4628 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6619 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7415 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8241 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6387155548290799 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5797731349206319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5857231820662888 name: Cosine Map@100 --- # Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using the [train_script.py](train_script.py) code. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 tokens - Similarity Function: Cosine Similarity - Training Dataset: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-bert-uncased-gooaq") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `gooaq-1024-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6309 \| \| cosine_accuracy@3 \| 0.8409 \| \| cosine_accuracy@5 \| 0.8986 \| \| cosine_accuracy@10 \| 0.9444 \| \| cosine_precision@1 \| 0.6309 \| \| cosine_precision@3 \| 0.2803 \| \| cosine_precision@5 \| 0.1797 \| \| cosine_precision@10 \| 0.0944 \| \| cosine_recall@1 \| 0.6309 \| \| cosine_recall@3 \| 0.8409 \| \| cosine_recall@5 \| 0.8986 \| \| cosine_recall@10 \| 0.9444 \| \| cosine_ndcg@10 \| 0.7933 \| \| cosine_mrr@10 \| 0.744 \| \| cosine_map@100 \| 0.7466 \| #### Information Retrieval * Dataset: `gooaq-512-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6271 \| \| cosine_accuracy@3 \| 0.8366 \| \| cosine_accuracy@5 \| 0.8946 \| \| cosine_accuracy@10 \| 0.9431 \| \| cosine_precision@1 \| 0.6271 \| \| cosine_precision@3 \| 0.2789 \| \| cosine_precision@5 \| 0.1789 \| \| cosine_precision@10 \| 0.0943 \| \| cosine_recall@1 \| 0.6271 \| \| cosine_recall@3 \| 0.8366 \| \| cosine_recall@5 \| 0.8946 \| \| cosine_recall@10 \| 0.9431 \| \| cosine_ndcg@10 \| 0.7905 \| \| cosine_mrr@10 \| 0.7408 \| \| cosine_map@100 \| 0.7434 \| #### Information Retrieval * Dataset: `gooaq-256-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6192 \| \| cosine_accuracy@3 \| 0.8235 \| \| cosine_accuracy@5 \| 0.8866 \| \| cosine_accuracy@10 \| 0.9364 \| \| cosine_precision@1 \| 0.6192 \| \| cosine_precision@3 \| 0.2745 \| \| cosine_precision@5 \| 0.1773 \| \| cosine_precision@10 \| 0.0936 \| \| cosine_recall@1 \| 0.6192 \| \| cosine_recall@3 \| 0.8235 \| \| cosine_recall@5 \| 0.8866 \| \| cosine_recall@10 \| 0.9364 \| \| cosine_ndcg@10 \| 0.7821 \| \| cosine_mrr@10 \| 0.7321 \| \| cosine_map@100 \| 0.7349 \| #### Information Retrieval * Dataset: `gooaq-128-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.5942 \| \| cosine_accuracy@3 \| 0.804 \| \| cosine_accuracy@5 \| 0.8721 \| \| cosine_accuracy@10 \| 0.9249 \| \| cosine_precision@1 \| 0.5942 \| \| cosine_precision@3 \| 0.268 \| \| cosine_precision@5 \| 0.1744 \| \| cosine_precision@10 \| 0.0925 \| \| cosine_recall@1 \| 0.5942 \| \| cosine_recall@3 \| 0.804 \| \| cosine_recall@5 \| 0.8721 \| \| cosine_recall@10 \| 0.9249 \| \| cosine_ndcg@10 \| 0.7628 \| \| cosine_mrr@10 \| 0.7103 \| \| cosine_map@100 \| 0.7134 \| #### Information Retrieval * Dataset: `gooaq-64-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.556 \| \| cosine_accuracy@3 \| 0.7553 \| \| cosine_accuracy@5 \| 0.8267 \| \| cosine_accuracy@10 \| 0.8945 \| \| cosine_precision@1 \| 0.556 \| \| cosine_precision@3 \| 0.2518 \| \| cosine_precision@5 \| 0.1653 \| \| cosine_precision@10 \| 0.0895 \| \| cosine_recall@1 \| 0.556 \| \| cosine_recall@3 \| 0.7553 \| \| cosine_recall@5 \| 0.8267 \| \| cosine_recall@10 \| 0.8945 \| \| cosine_ndcg@10 \| 0.7246 \| \| cosine_mrr@10 \| 0.6702 \| \| cosine_map@100 \| 0.6743 \| #### Information Retrieval * Dataset: `gooaq-32-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.4628 \| \| cosine_accuracy@3 \| 0.6619 \| \| cosine_accuracy@5 \| 0.7415 \| \| cosine_accuracy@10 \| 0.8241 \| \| cosine_precision@1 \| 0.4628 \| \| cosine_precision@3 \| 0.2206 \| \| cosine_precision@5 \| 0.1483 \| \| cosine_precision@10 \| 0.0824 \| \| cosine_recall@1 \| 0.4628 \| \| cosine_recall@3 \| 0.6619 \| \| cosine_recall@5 \| 0.7415 \| \| cosine_recall@10 \| 0.8241 \| \| cosine_ndcg@10 \| 0.6387 \| \| cosine_mrr@10 \| 0.5798 \| \| cosine_map@100 \| 0.5857 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| gooaq-1024-dev_cosine_map@100 \| gooaq-512-dev_cosine_map@100 \| gooaq-256-dev_cosine_map@100 \| gooaq-128-dev_cosine_map@100 \| gooaq-64-dev_cosine_map@100 \| gooaq-32-dev_cosine_map@100 \| \|:------:\|:----:\|:-------------:\|:---------------:\|:-----------------------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:---------------------------:\| \| 0 \| 0 \| - \| - \| 0.2095 \| 0.2010 \| 0.1735 \| 0.1381 \| 0.0750 \| 0.0331 \| \| 0.0007 \| 1 \| 34.953 \| - \| - \| - \| - \| - \| - \| - \| \| 0.0682 \| 100 \| 16.2504 \| - \| - \| - \| - \| - \| - \| - \| \| 0.1363 \| 200 \| 5.9502 \| - \| - \| - \| - \| - \| - \| - \| \| 0.1704 \| 250 \| - \| 1.6781 \| 0.6791 \| 0.6729 \| 0.6619 \| 0.6409 \| 0.5904 \| 0.4934 \| \| 0.2045 \| 300 \| 4.8411 \| - \| - \| - \| - \| - \| - \| - \| \| 0.2727 \| 400 \| 4.336 \| - \| - \| - \| - \| - \| - \| - \| \| 0.3408 \| 500 \| 4.0484 \| 1.3935 \| 0.7104 \| 0.7055 \| 0.6968 \| 0.6756 \| 0.6322 \| 0.5358 \| \| 0.4090 \| 600 \| 3.8378 \| - \| - \| - \| - \| - \| - \| - \| \| 0.4772 \| 700 \| 3.6765 \| - \| - \| - \| - \| - \| - \| - \| \| 0.5112 \| 750 \| - \| 1.2549 \| 0.7246 \| 0.7216 \| 0.7133 \| 0.6943 \| 0.6482 \| 0.5582 \| \| 0.5453 \| 800 \| 3.5439 \| - \| - \| - \| - \| - \| - \| - \| \| 0.6135 \| 900 \| 3.4284 \| - \| - \| - \| - \| - \| - \| - \| \| 0.6817 \| 1000 \| 3.3576 \| 1.1656 \| 0.7359 \| 0.7338 \| 0.7252 \| 0.7040 \| 0.6604 \| 0.5715 \| \| 0.7498 \| 1100 \| 3.2456 \| - \| - \| - \| - \| - \| - \| - \| \| 0.8180 \| 1200 \| 3.2014 \| - \| - \| - \| - \| - \| - \| - \| \| 0.8521 \| 1250 \| - \| 1.1133 \| 0.7438 \| 0.7398 \| 0.7310 \| 0.7099 \| 0.6704 \| 0.5796 \| \| 0.8862 \| 1300 \| 3.1536 \| - \| - \| - \| - \| - \| - \| - \| \| 0.9543 \| 1400 \| 3.0696 \| - \| - \| - \| - \| - \| - \| - \| \| 1.0 \| 1467 \| - \| - \| 0.7466 \| 0.7434 \| 0.7349 \| 0.7134 \| 0.6743 \| 0.5857 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.017 kWh - Carbon Emitted: 0.006 kg of CO2 - Hours Used: 0.109 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.2.0.dev0 - Transformers: 4.43.4 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
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type: main_score value: 86.05361584367344 - type: manhattan_accuracy value: 89.02472154305894 - type: manhattan_accuracy_threshold value: 1732.803726196289 - type: manhattan_ap value: 86.05361584367344 - type: manhattan_f1 value: 78.20484500404977 - type: manhattan_f1_threshold value: 1832.084083557129 - type: manhattan_precision value: 74.93297587131367 - type: manhattan_recall value: 81.7754850631352 - type: max_accuracy value: 89.02472154305894 - type: max_ap value: 86.05361584367344 - type: max_f1 value: 78.21080602302922 - type: max_precision value: 75.10632265381344 - type: max_recall value: 81.7754850631352 - type: similarity_accuracy value: 88.97038848139093 - type: similarity_accuracy_threshold value: 83.80404710769653 - type: similarity_ap value: 85.91804313814289 - type: similarity_f1 value: 78.21080602302922 - type: similarity_f1_threshold value: 82.21379518508911 - type: similarity_precision value: 75.10632265381344 - type: similarity_recall value: 81.5829996920234 --- Add stuff later!	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
zhw-e8/LAMAR	zhw-e8	null	[ "safetensors", "license:apache-2.0", "region:us" ]	2024-10-15T02:55:31	2024-10-15T05:43:35	0	--- {} --- # LAMAR A Foundation Language Model for RNA Regulation This repository contains pre-trained and fine-tuned weights for RNA foundation language model LAMAR. LAMAR outperformed benchmark models in various RNA regulation tasks, helping to decipher the regulation rules of RNA. LAMAR was developed by Rnasys Lab and Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences (CAS). ## Model weights SpliceSitePred: Weight of fine-tuned LAMAR predict splice site of pre-mRNA UTR5TEPred: Weight of fine-tuned LAMAR predict translation efficiency of mRNA based on 5' UTR UTR3DegPred: Weight of fine-tuned LAMAR predict degradation rate of mRNA based on 3' UTR IRESPred: Weight of fine-tuned LAMAR predicting internal ribosome entry site (IRES) mammalian80D_2048len1mer1sw_80M: Pretrained weights of LAMAR-2k mammalian80D_4096len1mer1sw_80M: Pretrained weights of LAMAR-4k ## Citation https://www.biorxiv.org/content/10.1101/2024.10.12.617732v2 Github link: https://github.com/zhw-e8/LAMAR	[ "TRANSLATION" ]	[ "CAS" ]
bobox/DeBERTa3-s-CustomPoolin-v3-step1	bobox	sentence-similarity	[ "sentence-transformers", "safetensors", "deberta-v2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:225247", "loss:CachedGISTEmbedLoss", "en", "dataset:tals/vitaminc", "arxiv:1908.10084", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-10-20T22:06:27	2024-10-24T21:36:59	0	--- base_model: microsoft/deberta-v3-small datasets: - tals/vitaminc language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:225247 - loss:CachedGISTEmbedLoss widget: - source_sentence: what is exfo toolbox sentences: - Eye dilation from eye drops used for examination of the eye usually lasts from 4 to 24 hours, depending upon the strength of the drop and upon the individual patient. - Garden Grove is a city in northern Orange County in the U.S. state of California, 34 miles (55 km) south of Los Angeles. The population was 170,883 at the 2010 United States Census. State Route 22, also known as the Garden Grove Freeway, passes through the city in an east-west direction. - EXFO ToolBox Office is a product that offers you a collection of viewers and analyzers. It enables you to manage and analyze results acquired from fiber optic test modules and instruments. - source_sentence: More than 273 people have died from the 2019-20 coronavirus outside mainland China . sentences: - 'More than 3,700 people have died : around 3,100 in mainland China and around 550 in all other countries combined .' - 'More than 3,200 people have died : almost 3,000 in mainland China and around 275 in other countries .' - more than 4,900 deaths have been attributed to COVID-19 . - source_sentence: Ultrasound, a diagnostic technology, uses high-frequency vibrations transmitted into any tissue in contact with the transducer. sentences: - What diagnostic technology uses high-frequency vibrations transmitted into any tissue in contact with the transducer? - The abnormal cells cannot carry oxygen properly and can get stuck where? - What type of organism is a bacteria? - source_sentence: When you add moles of gas to a baloon by blowing it up, the volume increases. sentences: - What shape is the lens of the eye? - What happens to the volume of a balloon when you add moles of gas to it by blowing up? - Most turtle bodies are covered by a special bony or cartilaginous shell developed from their what? - source_sentence: What was the name of eleven rulers of the 19th and 20th Egyptian dynasties? sentences: - 'Airlines Yugoslavia 1968 - 1968 Renamed ^ Comments : Aviogenex was formed on 21May1968 as Genex Airlines. Restarted under current name on 30Apr1969 & liquidated in Feb2015 ^ Genealogy : Genex Airlines >Aviogenex 1968 - 1986 Renamed ^ Comments : Adria Airways was formed on 14Mar1961 & operations started on 30Jun1961 as Adria Airways, renamed to Inex in 1968 and back to Adria again in 1986. National airline of Slovenia ^ Genealogy : Adria Airways >Inex Adria Airways >Adria Airways JAT (Jugoslovenski Aerotransport) 1947 - 2003 Renamed ^ Comments : Air Serbia was founded as Aeroput on 17Jun1927, renamed to JAT on 01Apr1947. Started ops on 15Apr1947, Renamed again on 08Aug2003 to JAT Airways & reformed as Air Serbia on 26Oct2013 ^ Genealogy : Aeroput >JAT (Jugoslovenski Aerotransport) >JAT Airways >Air Serbia Jugoslovenski Aerotransport' - List of Rulers of Ancient Egypt and Nubia \| Lists of Rulers \| Heilbrunn Timeline of Art History \| The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use. - 'What is the "Jack Sprat" nursery rhyme? \| Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose''s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king''s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.' model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7673854808079448 name: Pearson Cosine - type: spearman_cosine value: 0.7776198286738142 name: Spearman Cosine - type: pearson_manhattan value: 0.782368447545155 name: Pearson Manhattan - type: spearman_manhattan value: 0.7720687033298573 name: Spearman Manhattan - type: pearson_euclidean value: 0.7882638792170585 name: Pearson Euclidean - type: spearman_euclidean value: 0.7775073687564514 name: Spearman Euclidean - type: pearson_dot value: 0.7669147371310585 name: Pearson Dot - type: spearman_dot value: 0.7762894632049069 name: Spearman Dot - type: pearson_max value: 0.7882638792170585 name: Pearson Max - type: spearman_max value: 0.7776198286738142 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.708984375 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8714957237243652 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5913043478260869 name: Cosine F1 - type: cosine_f1_threshold value: 0.7768557071685791 name: Cosine F1 Threshold - type: cosine_precision value: 0.4738675958188153 name: Cosine Precision - type: cosine_recall value: 0.7861271676300579 name: Cosine Recall - type: cosine_ap value: 0.5644305887001508 name: Cosine Ap - type: dot_accuracy value: 0.7109375 name: Dot Accuracy - type: dot_accuracy_threshold value: 674.426025390625 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5913043478260869 name: Dot F1 - type: dot_f1_threshold value: 603.435302734375 name: Dot F1 Threshold - type: dot_precision value: 0.4738675958188153 name: Dot Precision - type: dot_recall value: 0.7861271676300579 name: Dot Recall - type: dot_ap value: 0.5664868031504724 name: Dot Ap - type: manhattan_accuracy value: 0.7109375 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 294.4728088378906 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5935483870967742 name: Manhattan F1 - type: manhattan_f1_threshold value: 401.1482849121094 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.4726027397260274 name: Manhattan Precision - type: manhattan_recall value: 0.7976878612716763 name: Manhattan Recall - type: manhattan_ap value: 0.5642688421649988 name: Manhattan Ap - type: euclidean_accuracy value: 0.7109375 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 14.565500259399414 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5913043478260869 name: Euclidean F1 - type: euclidean_f1_threshold value: 18.60409164428711 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4738675958188153 name: Euclidean Precision - type: euclidean_recall value: 0.7861271676300579 name: Euclidean Recall - type: euclidean_ap value: 0.5645557227019772 name: Euclidean Ap - type: max_accuracy value: 0.7109375 name: Max Accuracy - type: max_accuracy_threshold value: 674.426025390625 name: Max Accuracy Threshold - type: max_f1 value: 0.5935483870967742 name: Max F1 - type: max_f1_threshold value: 603.435302734375 name: Max F1 Threshold - type: max_precision value: 0.4738675958188153 name: Max Precision - type: max_recall value: 0.7976878612716763 name: Max Recall - type: max_ap value: 0.5664868031504724 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.6796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7726649045944214 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6925675675675677 name: Cosine F1 - type: cosine_f1_threshold value: 0.7317887544631958 name: Cosine F1 Threshold - type: cosine_precision value: 0.5758426966292135 name: Cosine Precision - type: cosine_recall value: 0.8686440677966102 name: Cosine Recall - type: cosine_ap value: 0.7302564198016936 name: Cosine Ap - type: dot_accuracy value: 0.67578125 name: Dot Accuracy - type: dot_accuracy_threshold value: 598.0419921875 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6912751677852348 name: Dot F1 - type: dot_f1_threshold value: 565.4718017578125 name: Dot F1 Threshold - type: dot_precision value: 0.5722222222222222 name: Dot Precision - type: dot_recall value: 0.8728813559322034 name: Dot Recall - type: dot_ap value: 0.7300462025003271 name: Dot Ap - type: manhattan_accuracy value: 0.6796875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 404.8309020996094 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6933333333333332 name: Manhattan F1 - type: manhattan_f1_threshold value: 444.99224853515625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5714285714285714 name: Manhattan Precision - type: manhattan_recall value: 0.8813559322033898 name: Manhattan Recall - type: manhattan_ap value: 0.7369214156436785 name: Manhattan Ap - type: euclidean_accuracy value: 0.6796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 18.790739059448242 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6934306569343065 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.35132598876953 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.6089743589743589 name: Euclidean Precision - type: euclidean_recall value: 0.8050847457627118 name: Euclidean Recall - type: euclidean_ap value: 0.7307381840067684 name: Euclidean Ap - type: max_accuracy value: 0.6796875 name: Max Accuracy - type: max_accuracy_threshold value: 598.0419921875 name: Max Accuracy Threshold - type: max_f1 value: 0.6934306569343065 name: Max F1 - type: max_f1_threshold value: 565.4718017578125 name: Max F1 Threshold - type: max_precision value: 0.6089743589743589 name: Max Precision - type: max_recall value: 0.8813559322033898 name: Max Recall - type: max_ap value: 0.7369214156436785 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( (linear_cls): Linear(in_features=768, out_features=768, bias=True) (linear_mean): Linear(in_features=768, out_features=768, bias=True) (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) ) (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_cls): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_mean): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-v3-step1") # Run inference sentences = [ 'What was the name of eleven rulers of the 19th and 20th Egyptian dynasties?', 'List of Rulers of Ancient Egypt and Nubia \| Lists of Rulers \| Heilbrunn Timeline of Art History \| The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use.', 'What is the "Jack Sprat" nursery rhyme? \| Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose\'s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king\'s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| pearson_cosine \| 0.7674 \| \| spearman_cosine \| 0.7776 \| \| pearson_manhattan \| 0.7824 \| \| spearman_manhattan \| 0.7721 \| \| pearson_euclidean \| 0.7883 \| \| spearman_euclidean \| 0.7775 \| \| pearson_dot \| 0.7669 \| \| spearman_dot \| 0.7763 \| \| pearson_max \| 0.7883 \| \| spearman_max \| 0.7776 \| #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) \| Metric \| Value \| \|:-----------------------------\|:-----------\| \| cosine_accuracy \| 0.709 \| \| cosine_accuracy_threshold \| 0.8715 \| \| cosine_f1 \| 0.5913 \| \| cosine_f1_threshold \| 0.7769 \| \| cosine_precision \| 0.4739 \| \| cosine_recall \| 0.7861 \| \| cosine_ap \| 0.5644 \| \| dot_accuracy \| 0.7109 \| \| dot_accuracy_threshold \| 674.426 \| \| dot_f1 \| 0.5913 \| \| dot_f1_threshold \| 603.4353 \| \| dot_precision \| 0.4739 \| \| dot_recall \| 0.7861 \| \| dot_ap \| 0.5665 \| \| manhattan_accuracy \| 0.7109 \| \| manhattan_accuracy_threshold \| 294.4728 \| \| manhattan_f1 \| 0.5935 \| \| manhattan_f1_threshold \| 401.1483 \| \| manhattan_precision \| 0.4726 \| \| manhattan_recall \| 0.7977 \| \| manhattan_ap \| 0.5643 \| \| euclidean_accuracy \| 0.7109 \| \| euclidean_accuracy_threshold \| 14.5655 \| \| euclidean_f1 \| 0.5913 \| \| euclidean_f1_threshold \| 18.6041 \| \| euclidean_precision \| 0.4739 \| \| euclidean_recall \| 0.7861 \| \| euclidean_ap \| 0.5646 \| \| max_accuracy \| 0.7109 \| \| max_accuracy_threshold \| 674.426 \| \| max_f1 \| 0.5935 \| \| max_f1_threshold \| 603.4353 \| \| max_precision \| 0.4739 \| \| max_recall \| 0.7977 \| \| max_ap \| 0.5665 \| #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) \| Metric \| Value \| \|:-----------------------------\|:-----------\| \| cosine_accuracy \| 0.6797 \| \| cosine_accuracy_threshold \| 0.7727 \| \| cosine_f1 \| 0.6926 \| \| cosine_f1_threshold \| 0.7318 \| \| cosine_precision \| 0.5758 \| \| cosine_recall \| 0.8686 \| \| cosine_ap \| 0.7303 \| \| dot_accuracy \| 0.6758 \| \| dot_accuracy_threshold \| 598.042 \| \| dot_f1 \| 0.6913 \| \| dot_f1_threshold \| 565.4718 \| \| dot_precision \| 0.5722 \| \| dot_recall \| 0.8729 \| \| dot_ap \| 0.73 \| \| manhattan_accuracy \| 0.6797 \| \| manhattan_accuracy_threshold \| 404.8309 \| \| manhattan_f1 \| 0.6933 \| \| manhattan_f1_threshold \| 444.9922 \| \| manhattan_precision \| 0.5714 \| \| manhattan_recall \| 0.8814 \| \| manhattan_ap \| 0.7369 \| \| euclidean_accuracy \| 0.6797 \| \| euclidean_accuracy_threshold \| 18.7907 \| \| euclidean_f1 \| 0.6934 \| \| euclidean_f1_threshold \| 19.3513 \| \| euclidean_precision \| 0.609 \| \| euclidean_recall \| 0.8051 \| \| euclidean_ap \| 0.7307 \| \| max_accuracy \| 0.6797 \| \| max_accuracy_threshold \| 598.042 \| \| max_f1 \| 0.6934 \| \| max_f1_threshold \| 565.4718 \| \| max_precision \| 0.609 \| \| max_recall \| 0.8814 \| \| max_ap \| 0.7369 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Evaluation Dataset #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 128 evaluation samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 128 samples: \| \| claim \| evidence \| \|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 9 tokens</li><li>mean: 21.42 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 35.55 tokens</li><li>max: 79 tokens</li></ul> \| * Samples: \| claim \| evidence \| \|:------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Dragon Con had over 5000 guests .</code> \| <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> \| \| <code>COVID-19 has reached more than 185 countries .</code> \| <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> \| \| <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> \| <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> \| * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.025} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 256 - `gradient_accumulation_steps`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05} - `warmup_ratio`: 0.33 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05} - `warmup_ratio`: 0.33 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| vitaminc-pairs loss \| negation-triplets loss \| scitail-pairs-pos loss \| scitail-pairs-qa loss \| xsum-pairs loss \| sciq pairs loss \| qasc pairs loss \| openbookqa pairs loss \| msmarco pairs loss \| nq pairs loss \| trivia pairs loss \| gooaq pairs loss \| paws-pos loss \| global dataset loss \| sts-test_spearman_cosine \| allNLI-dev_max_ap \| Qnli-dev_max_ap \| \|:------:\|:----:\|:-------------:\|:-------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------------:\|:------------------:\|:-------------:\|:-----------------:\|:----------------:\|:-------------:\|:-------------------:\|:------------------------:\|:-----------------:\|:---------------:\| \| 0.0168 \| 8 \| 10.2928 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0336 \| 16 \| 9.2166 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0504 \| 24 \| 9.4858 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0672 \| 32 \| 10.6143 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0840 \| 40 \| 8.7553 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1008 \| 48 \| 10.9939 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1176 \| 56 \| 7.6039 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1345 \| 64 \| 5.9498 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1513 \| 72 \| 7.3051 \| 3.2988 \| 3.9604 \| 1.9818 \| 2.1997 \| 6.0515 \| 0.6095 \| 6.3199 \| 4.8391 \| 6.4886 \| 6.6406 \| 6.4894 \| 6.1527 \| 2.0082 \| 4.9577 \| 0.3066 \| 0.3444 \| 0.5627 \| \| 0.1681 \| 80 \| 8.3034 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1849 \| 88 \| 7.6669 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2017 \| 96 \| 6.6415 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2185 \| 104 \| 5.7797 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2353 \| 112 \| 5.8361 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2521 \| 120 \| 5.3339 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2689 \| 128 \| 5.5908 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2857 \| 136 \| 5.3209 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3025 \| 144 \| 5.5359 \| 3.3310 \| 3.8580 \| 1.4769 \| 1.6994 \| 5.4819 \| 0.5385 \| 5.2021 \| 4.4410 \| 5.3419 \| 5.5506 \| 5.6972 \| 5.3376 \| 1.4170 \| 3.9169 \| 0.2954 \| 0.3795 \| 0.6317 \| \| 0.3193 \| 152 \| 5.4713 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3361 \| 160 \| 4.9368 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3529 \| 168 \| 4.6594 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3697 \| 176 \| 4.8392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3866 \| 184 \| 4.414 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4034 \| 192 \| 4.891 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4202 \| 200 \| 4.4553 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4370 \| 208 \| 3.9729 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4538 \| 216 \| 3.7705 \| 3.2468 \| 3.6435 \| 0.7890 \| 0.7356 \| 3.9327 \| 0.4082 \| 3.7175 \| 3.5404 \| 3.5351 \| 4.0506 \| 3.9953 \| 3.6074 \| 0.4195 \| 2.4726 \| 0.3791 \| 0.4133 \| 0.6779 \| \| 0.4706 \| 224 \| 3.8409 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4874 \| 232 \| 3.7894 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5042 \| 240 \| 3.3523 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5210 \| 248 \| 3.2407 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5378 \| 256 \| 3.3203 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5546 \| 264 \| 2.8457 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5714 \| 272 \| 2.4181 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5882 \| 280 \| 3.4589 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6050 \| 288 \| 2.8203 \| 3.1119 \| 3.1485 \| 0.4531 \| 0.2652 \| 2.6895 \| 0.2656 \| 2.5542 \| 2.7523 \| 2.6600 \| 3.1773 \| 3.2099 \| 2.7316 \| 0.2006 \| 1.6342 \| 0.5257 \| 0.4717 \| 0.7078 \| \| 0.6218 \| 296 \| 2.4697 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6387 \| 304 \| 2.4654 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6555 \| 312 \| 2.4236 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6723 \| 320 \| 2.2879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6891 \| 328 \| 2.2145 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7059 \| 336 \| 1.8464 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7227 \| 344 \| 2.0086 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7395 \| 352 \| 2.0635 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7563 \| 360 \| 1.8584 \| 3.3202 \| 2.5793 \| 0.3434 \| 0.1618 \| 1.6759 \| 0.1834 \| 1.6454 \| 2.1257 \| 2.1938 \| 2.5316 \| 2.4558 \| 2.0596 \| 0.0984 \| 1.2206 \| 0.6610 \| 0.5199 \| 0.7119 \| \| 0.7731 \| 368 \| 2.0286 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7899 \| 376 \| 1.9389 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8067 \| 384 \| 1.7453 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8235 \| 392 \| 1.6629 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8403 \| 400 \| 1.2724 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8571 \| 408 \| 1.7824 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8739 \| 416 \| 1.5826 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8908 \| 424 \| 1.1971 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9076 \| 432 \| 1.5228 \| 3.3624 \| 2.1952 \| 0.3006 \| 0.1223 \| 1.1091 \| 0.1582 \| 1.2383 \| 1.8664 \| 1.7434 \| 2.3959 \| 2.0697 \| 1.7563 \| 0.0766 \| 1.0193 \| 0.7292 \| 0.5194 \| 0.7126 \| \| 0.9244 \| 440 \| 1.3323 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9412 \| 448 \| 1.5124 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9580 \| 456 \| 1.5565 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9748 \| 464 \| 1.3672 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9916 \| 472 \| 1.0382 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0084 \| 480 \| 1.0626 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0252 \| 488 \| 1.3539 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0420 \| 496 \| 1.1723 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0588 \| 504 \| 1.4235 \| 3.4031 \| 1.9759 \| 0.2554 \| 0.0814 \| 0.9034 \| 0.1378 \| 1.1603 \| 1.7589 \| 1.5608 \| 2.1230 \| 1.7719 \| 1.6633 \| 0.0720 \| 0.9380 \| 0.7523 \| 0.5297 \| 0.7129 \| \| 1.0756 \| 512 \| 1.2283 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0924 \| 520 \| 1.2455 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1092 \| 528 \| 1.4265 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1261 \| 536 \| 1.296 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1429 \| 544 \| 0.8763 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1597 \| 552 \| 1.5678 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1765 \| 560 \| 1.2548 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.1933 \| 568 \| 1.3731 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.2101 \| 576 \| 1.3023 \| 3.3815 \| 1.8740 \| 0.2373 \| 0.0769 \| 0.7711 \| 0.1237 \| 0.9432 \| 1.6871 \| 1.5070 \| 1.9947 \| 1.6041 \| 1.5579 \| 0.0721 \| 0.8661 \| 0.7642 \| 0.5412 \| 0.7159 \| \| 1.2269 \| 584 \| 0.8135 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.2437 \| 592 \| 1.0259 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.2605 \| 600 \| 1.1896 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.2773 \| 608 \| 1.0532 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.2941 \| 616 \| 1.3221 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.3109 \| 624 \| 1.3136 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.3277 \| 632 \| 1.2238 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.3445 \| 640 \| 1.2407 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.3613 \| 648 \| 1.2245 \| 3.4717 \| 1.7962 \| 0.2242 \| 0.0488 \| 0.7472 \| 0.1108 \| 0.9272 \| 1.6692 \| 1.3845 \| 1.9117 \| 1.3410 \| 1.4387 \| 0.0701 \| 0.8505 \| 0.7680 \| 0.5471 \| 0.7227 \| \| 1.3782 \| 656 \| 1.0428 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.3950 \| 664 \| 1.1391 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4118 \| 672 \| 1.2632 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4286 \| 680 \| 0.9403 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4454 \| 688 \| 0.7571 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4622 \| 696 \| 0.9436 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4790 \| 704 \| 1.1239 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.4958 \| 712 \| 0.9499 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.5126 \| 720 \| 1.0945 \| 3.6495 \| 1.6693 \| 0.2157 \| 0.0492 \| 0.6830 \| 0.1049 \| 0.9140 \| 1.5967 \| 1.4397 \| 1.7394 \| 1.3303 \| 1.4334 \| 0.0603 \| 0.8185 \| 0.7815 \| 0.5606 \| 0.7098 \| \| 1.5294 \| 728 \| 1.1161 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.5462 \| 736 \| 1.0056 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.5630 \| 744 \| 1.1743 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.5798 \| 752 \| 0.9153 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.5966 \| 760 \| 1.1589 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.6134 \| 768 \| 0.9187 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.6303 \| 776 \| 0.6937 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.6471 \| 784 \| 0.9704 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.6639 \| 792 \| 0.7343 \| 3.5442 \| 1.6493 \| 0.2208 \| 0.0249 \| 0.6152 \| 0.0969 \| 0.7111 \| 1.5369 \| 1.4058 \| 1.7066 \| 1.2784 \| 1.3419 \| 0.0585 \| 0.7827 \| 0.7749 \| 0.5627 \| 0.7284 \| \| 1.6807 \| 800 \| 1.2878 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.6975 \| 808 \| 0.9898 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7143 \| 816 \| 0.7613 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7311 \| 824 \| 0.9612 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7479 \| 832 \| 1.1524 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7647 \| 840 \| 0.827 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7815 \| 848 \| 1.1898 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.7983 \| 856 \| 1.0117 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.8151 \| 864 \| 0.7019 \| 3.4544 \| 1.6149 \| 0.2035 \| 0.0181 \| 0.5525 \| 0.0999 \| 0.6641 \| 1.5456 \| 1.3911 \| 1.7188 \| 1.2547 \| 1.3517 \| 0.0562 \| 0.7473 \| 0.7684 \| 0.5697 \| 0.7329 \| \| 1.8319 \| 872 \| 0.8352 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.8487 \| 880 \| 0.7836 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.8655 \| 888 \| 1.0187 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.8824 \| 896 \| 0.74 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.8992 \| 904 \| 0.7263 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.9160 \| 912 \| 0.8073 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.9328 \| 920 \| 0.8185 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.9496 \| 928 \| 1.0992 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.9664 \| 936 \| 0.9973 \| 3.5110 \| 1.5776 \| 0.2035 \| 0.0250 \| 0.5881 \| 0.0934 \| 0.6719 \| 1.5059 \| 1.2970 \| 1.6186 \| 1.1815 \| 1.2714 \| 0.0564 \| 0.7213 \| 0.7799 \| 0.5544 \| 0.7341 \| \| 1.9832 \| 944 \| 0.6662 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0 \| 952 \| 0.533 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0168 \| 960 \| 0.7712 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0336 \| 968 \| 0.6879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0504 \| 976 \| 0.7975 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0672 \| 984 \| 0.873 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.0840 \| 992 \| 0.7995 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.1008 \| 1000 \| 1.0119 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.1176 \| 1008 \| 0.6317 \| 3.6778 \| 1.5845 \| 0.2102 \| 0.0228 \| 0.5851 \| 0.0977 \| 0.6411 \| 1.4752 \| 1.2992 \| 1.6314 \| 1.1260 \| 1.2683 \| 0.0556 \| 0.7329 \| 0.7693 \| 0.5614 \| 0.7274 \| \| 2.1345 \| 1016 \| 0.72 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.1513 \| 1024 \| 0.9418 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.1681 \| 1032 \| 0.7848 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.1849 \| 1040 \| 0.6965 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.2017 \| 1048 \| 1.0447 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.2185 \| 1056 \| 0.6361 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.2353 \| 1064 \| 0.6837 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.2521 \| 1072 \| 0.5713 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.2689 \| 1080 \| 0.8193 \| 3.6399 \| 1.5565 \| 0.2069 \| 0.0213 \| 0.5440 \| 0.0904 \| 0.6057 \| 1.4815 \| 1.2856 \| 1.6441 \| 1.1469 \| 1.2540 \| 0.0543 \| 0.7216 \| 0.7765 \| 0.5599 \| 0.7322 \| \| 2.2857 \| 1088 \| 0.9754 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3025 \| 1096 \| 0.8932 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3193 \| 1104 \| 0.8716 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3361 \| 1112 \| 0.8787 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3529 \| 1120 \| 0.9529 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3697 \| 1128 \| 0.775 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.3866 \| 1136 \| 0.6178 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.4034 \| 1144 \| 0.8384 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.4202 \| 1152 \| 0.9425 \| 3.5672 \| 1.5244 \| 0.2111 \| 0.0162 \| 0.5593 \| 0.0893 \| 0.5759 \| 1.4933 \| 1.2703 \| 1.5815 \| 1.1202 \| 1.2132 \| 0.0531 \| 0.7058 \| 0.7730 \| 0.5635 \| 0.7350 \| \| 2.4370 \| 1160 \| 0.4551 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.4538 \| 1168 \| 0.6392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.4706 \| 1176 \| 0.8341 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.4874 \| 1184 \| 0.7392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.5042 \| 1192 \| 0.7646 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.5210 \| 1200 \| 0.8613 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.5378 \| 1208 \| 0.7585 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.5546 \| 1216 \| 1.0611 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.5714 \| 1224 \| 0.6506 \| 3.6439 \| 1.5040 \| 0.2125 \| 0.0162 \| 0.5282 \| 0.0863 \| 0.5858 \| 1.5073 \| 1.2444 \| 1.5493 \| 1.1014 \| 1.2073 \| 0.0532 \| 0.7022 \| 0.7774 \| 0.5647 \| 0.7328 \| \| 2.5882 \| 1232 \| 0.8525 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6050 \| 1240 \| 0.6304 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6218 \| 1248 \| 0.6354 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6387 \| 1256 \| 0.6583 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6555 \| 1264 \| 0.5964 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6723 \| 1272 \| 0.818 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.6891 \| 1280 \| 0.8635 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.7059 \| 1288 \| 0.6389 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.7227 \| 1296 \| 0.6819 \| 3.6131 \| 1.5104 \| 0.2084 \| 0.0148 \| 0.5229 \| 0.0854 \| 0.5588 \| 1.4963 \| 1.2766 \| 1.5679 \| 1.0982 \| 1.2203 \| 0.0529 \| 0.7059 \| 0.7762 \| 0.5659 \| 0.7355 \| \| 2.7395 \| 1304 \| 0.7878 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.7563 \| 1312 \| 0.7638 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.7731 \| 1320 \| 0.8885 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.7899 \| 1328 \| 0.8184 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.8067 \| 1336 \| 0.7472 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.8235 \| 1344 \| 0.7012 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.8403 \| 1352 \| 0.4622 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.8571 \| 1360 \| 0.846 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.8739 \| 1368 \| 0.8308 \| 3.6224 \| 1.5088 \| 0.2084 \| 0.0148 \| 0.5118 \| 0.0858 \| 0.5523 \| 1.4941 \| 1.2756 \| 1.5808 \| 1.0925 \| 1.2114 \| 0.0521 \| 0.7022 \| 0.7765 \| 0.5662 \| 0.7366 \| \| 2.8908 \| 1376 \| 0.5334 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9076 \| 1384 \| 0.7893 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9244 \| 1392 \| 0.6897 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9412 \| 1400 \| 0.7803 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9580 \| 1408 \| 0.841 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9748 \| 1416 \| 0.787 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 2.9916 \| 1424 \| 0.5861 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 3.0 \| 1428 \| - \| 3.6139 \| 1.5071 \| 0.2084 \| 0.0150 \| 0.5124 \| 0.0862 \| 0.5532 \| 1.4924 \| 1.2700 \| 1.5806 \| 1.0905 \| 1.2081 \| 0.0519 \| 0.6997 \| 0.7776 \| 0.5665 \| 0.7369 \| </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY" ]	[ "SCIQ", "SCITAIL" ]
tomaarsen/static-bert-uncased-gooaq-beir	tomaarsen	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-10-23T11:12:32	2024-10-23T11:12:40	0	--- datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 6.483463467240631 energy_consumed: 0.01667977902671103 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.112 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.148 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10566666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22233333333333336 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.30566666666666664 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.40399999999999997 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3021857757296797 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.35745238095238085 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23166090256020686 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5133333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.48 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.43800000000000006 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04048260039152364 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.10679067052991392 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16517406885695451 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.29331552217012935 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5008496215473859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6488571428571429 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3752676117852694 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.68 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08599999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3966666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6466666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7766666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5890710274148659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.546047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5325906780111076 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.106 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1734126984126984 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.32126984126984126 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3737936507936508 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.47868253968253976 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.384612736899094 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.44405555555555554 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.32183898737919203 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.58 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.74 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.78 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.58 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3133333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.21600000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.126 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.29 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.63 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5630232180814766 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.675079365079365 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.48992202928149226 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.52 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06000000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.52 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.41343867686046815 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3524603174603175 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3712333972436779 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.36 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3440000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.264 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.019665573227317924 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07420738619382097 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.09536630802985016 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.11619353053313819 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3204704228749859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.48633333333333334 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.12237170785886863 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.36 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.34 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.45 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.62 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3932776776815765 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.33038888888888884 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33440957968177043 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.9 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.98 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.98 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.38666666666666655 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23599999999999993 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8106666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.922 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9259999999999999 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.99 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9424143419536263 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9395238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9189180735930736 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16799999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.13166666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17266666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.26666666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.24548416934230666 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3969603174603174 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1751060490177909 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06400000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.64 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3703136948358056 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2856587301587301 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2982488157827007 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.46 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.076 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.425 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.58 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.685 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5455895863246394 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5181349206349206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5100938735556383 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.6326530612244898 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9591836734693877 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6326530612244898 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6326530612244897 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5877551020408164 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5163265306122449 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04221140303473122 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12126049151597706 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18889590300402684 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3304256352667907 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.563482462405376 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7857142857142857 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42991471736271825 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4040502354788069 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5922448979591837 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6692307692307693 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7692307692307693 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4040502354788069 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2794348508634223 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2233657770800628 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1671020408163265 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22103376474868755 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3635534658597092 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.41878691774496013 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5254577354604563 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.47186257015009897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5205128205128206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.39319818639334664 name: Cosine Map@100 --- # Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 tokens - Similarity Function: Cosine Similarity - Training Dataset: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-bert-uncased-gooaq-beir-4") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| cosine_accuracy@1 \| 0.2 \| 0.52 \| 0.42 \| 0.36 \| 0.58 \| 0.2 \| 0.38 \| 0.2 \| 0.9 \| 0.28 \| 0.12 \| 0.46 \| 0.6327 \| \| cosine_accuracy@3 \| 0.42 \| 0.7 \| 0.68 \| 0.48 \| 0.74 \| 0.5 \| 0.54 \| 0.36 \| 0.98 \| 0.44 \| 0.4 \| 0.5 \| 0.9592 \| \| cosine_accuracy@5 \| 0.58 \| 0.82 \| 0.68 \| 0.54 \| 0.78 \| 0.52 \| 0.68 \| 0.48 \| 0.98 \| 0.56 \| 0.48 \| 0.6 \| 1.0 \| \| cosine_accuracy@10 \| 0.76 \| 0.9 \| 0.82 \| 0.64 \| 0.86 \| 0.6 \| 0.68 \| 0.64 \| 1.0 \| 0.76 \| 0.64 \| 0.7 \| 1.0 \| \| cosine_precision@1 \| 0.2 \| 0.52 \| 0.42 \| 0.36 \| 0.58 \| 0.2 \| 0.38 \| 0.2 \| 0.9 \| 0.28 \| 0.12 \| 0.46 \| 0.6327 \| \| cosine_precision@3 \| 0.1667 \| 0.5133 \| 0.2333 \| 0.22 \| 0.3133 \| 0.1667 \| 0.36 \| 0.12 \| 0.3867 \| 0.2133 \| 0.1333 \| 0.1733 \| 0.6327 \| \| cosine_precision@5 \| 0.148 \| 0.48 \| 0.14 \| 0.16 \| 0.216 \| 0.104 \| 0.344 \| 0.096 \| 0.236 \| 0.168 \| 0.096 \| 0.128 \| 0.5878 \| \| cosine_precision@10 \| 0.104 \| 0.438 \| 0.086 \| 0.106 \| 0.126 \| 0.06 \| 0.264 \| 0.068 \| 0.134 \| 0.13 \| 0.064 \| 0.076 \| 0.5163 \| \| cosine_recall@1 \| 0.1057 \| 0.0405 \| 0.3967 \| 0.1734 \| 0.29 \| 0.2 \| 0.0197 \| 0.19 \| 0.8107 \| 0.0597 \| 0.12 \| 0.425 \| 0.0422 \| \| cosine_recall@3 \| 0.2223 \| 0.1068 \| 0.6467 \| 0.3213 \| 0.47 \| 0.5 \| 0.0742 \| 0.34 \| 0.922 \| 0.1317 \| 0.4 \| 0.47 \| 0.1213 \| \| cosine_recall@5 \| 0.3057 \| 0.1652 \| 0.6467 \| 0.3738 \| 0.54 \| 0.52 \| 0.0954 \| 0.45 \| 0.926 \| 0.1727 \| 0.48 \| 0.58 \| 0.1889 \| \| cosine_recall@10 \| 0.404 \| 0.2933 \| 0.7767 \| 0.4787 \| 0.63 \| 0.6 \| 0.1162 \| 0.62 \| 0.99 \| 0.2667 \| 0.64 \| 0.685 \| 0.3304 \| \| cosine_ndcg@10 \| 0.3022 \| 0.5008 \| 0.5891 \| 0.3846 \| 0.563 \| 0.4134 \| 0.3205 \| 0.3933 \| 0.9424 \| 0.2455 \| 0.3703 \| 0.5456 \| 0.5635 \| \| cosine_mrr@10 \| 0.3575 \| 0.6489 \| 0.546 \| 0.4441 \| 0.6751 \| 0.3525 \| 0.4863 \| 0.3304 \| 0.9395 \| 0.397 \| 0.2857 \| 0.5181 \| 0.7857 \| \| cosine_map@100 \| 0.2317 \| 0.3753 \| 0.5326 \| 0.3218 \| 0.4899 \| 0.3712 \| 0.1224 \| 0.3344 \| 0.9189 \| 0.1751 \| 0.2982 \| 0.5101 \| 0.4299 \| #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.4041 \| \| cosine_accuracy@3 \| 0.5922 \| \| cosine_accuracy@5 \| 0.6692 \| \| cosine_accuracy@10 \| 0.7692 \| \| cosine_precision@1 \| 0.4041 \| \| cosine_precision@3 \| 0.2794 \| \| cosine_precision@5 \| 0.2234 \| \| cosine_precision@10 \| 0.1671 \| \| cosine_recall@1 \| 0.221 \| \| cosine_recall@3 \| 0.3636 \| \| cosine_recall@5 \| 0.4188 \| \| cosine_recall@10 \| 0.5255 \| \| cosine_ndcg@10 \| 0.4719 \| \| cosine_mrr@10 \| 0.5205 \| \| cosine_map@100 \| 0.3932 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \| \|:------:\|:----:\|:-------------:\|:---------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\| \| 0 \| 0 \| - \| - \| 0.0726 \| 0.3715 \| 0.2100 \| 0.1058 \| 0.3196 \| 0.3109 \| 0.2221 \| 0.1401 \| 0.6737 \| 0.1618 \| 0.1183 \| 0.4337 \| 0.1331 \| 0.2518 \| \| 0.0007 \| 1 \| 35.3437 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0682 \| 100 \| 16.3878 \| 2.4139 \| 0.2927 \| 0.4729 \| 0.5725 \| 0.3235 \| 0.5905 \| 0.3674 \| 0.2994 \| 0.3324 \| 0.9123 \| 0.2326 \| 0.3407 \| 0.5618 \| 0.5352 \| 0.4488 \| \| 0.1363 \| 200 \| 5.94 \| 1.8298 \| 0.2897 \| 0.4880 \| 0.5624 \| 0.3447 \| 0.5683 \| 0.4311 \| 0.3066 \| 0.3502 \| 0.9129 \| 0.2533 \| 0.3335 \| 0.5696 \| 0.5365 \| 0.4575 \| \| 0.2045 \| 300 \| 4.8307 \| 1.5955 \| 0.2780 \| 0.4896 \| 0.5746 \| 0.3513 \| 0.5815 \| 0.4040 \| 0.3125 \| 0.3897 \| 0.9190 \| 0.2578 \| 0.3556 \| 0.5461 \| 0.5401 \| 0.4615 \| \| 0.2727 \| 400 \| 4.33 \| 1.4696 \| 0.3113 \| 0.4909 \| 0.5920 \| 0.3795 \| 0.5836 \| 0.3919 \| 0.3201 \| 0.4023 \| 0.9355 \| 0.2535 \| 0.3419 \| 0.5236 \| 0.5524 \| 0.4676 \| \| 0.3408 \| 500 \| 4.0423 \| 1.3887 \| 0.3085 \| 0.4966 \| 0.5986 \| 0.3794 \| 0.5914 \| 0.3914 \| 0.3174 \| 0.3590 \| 0.9309 \| 0.2441 \| 0.3537 \| 0.5311 \| 0.5534 \| 0.4658 \| \| 0.4090 \| 600 \| 3.8422 \| 1.3120 \| 0.3034 \| 0.5052 \| 0.6075 \| 0.3680 \| 0.5834 \| 0.4136 \| 0.3122 \| 0.3725 \| 0.9257 \| 0.2477 \| 0.3583 \| 0.5309 \| 0.5646 \| 0.4687 \| \| 0.4772 \| 700 \| 3.6795 \| 1.2693 \| 0.2975 \| 0.4988 \| 0.5954 \| 0.3785 \| 0.5811 \| 0.4160 \| 0.3142 \| 0.3908 \| 0.9362 \| 0.2471 \| 0.3479 \| 0.5520 \| 0.5601 \| 0.4704 \| \| 0.5453 \| 800 \| 3.5367 \| 1.2285 \| 0.3011 \| 0.4947 \| 0.5829 \| 0.3463 \| 0.5689 \| 0.4369 \| 0.3224 \| 0.3791 \| 0.9310 \| 0.2430 \| 0.3663 \| 0.5577 \| 0.5585 \| 0.4684 \| \| 0.6135 \| 900 \| 3.4279 \| 1.1963 \| 0.3059 \| 0.5027 \| 0.5894 \| 0.3674 \| 0.5758 \| 0.4126 \| 0.3186 \| 0.4066 \| 0.9349 \| 0.2456 \| 0.3672 \| 0.5560 \| 0.5624 \| 0.4727 \| \| 0.6817 \| 1000 \| 3.3637 \| 1.1652 \| 0.3056 \| 0.5022 \| 0.5849 \| 0.3702 \| 0.5714 \| 0.4238 \| 0.3161 \| 0.4007 \| 0.9373 \| 0.2430 \| 0.3699 \| 0.5618 \| 0.5657 \| 0.4733 \| \| 0.7498 \| 1100 \| 3.2336 \| 1.1312 \| 0.3006 \| 0.5038 \| 0.5920 \| 0.3884 \| 0.5733 \| 0.4241 \| 0.3247 \| 0.3974 \| 0.9369 \| 0.2431 \| 0.3670 \| 0.5644 \| 0.5608 \| 0.4751 \| \| 0.8180 \| 1200 \| 3.1952 \| 1.1132 \| 0.3044 \| 0.4987 \| 0.5770 \| 0.3630 \| 0.5735 \| 0.4259 \| 0.3279 \| 0.3955 \| 0.9428 \| 0.2416 \| 0.3798 \| 0.5659 \| 0.5641 \| 0.4739 \| \| 0.8862 \| 1300 \| 3.1535 \| 1.0926 \| 0.2983 \| 0.4968 \| 0.5753 \| 0.3812 \| 0.5684 \| 0.4108 \| 0.3203 \| 0.3965 \| 0.9421 \| 0.2428 \| 0.3685 \| 0.5608 \| 0.5628 \| 0.4711 \| \| 0.9543 \| 1400 \| 3.0691 \| 1.0862 \| 0.3109 \| 0.5008 \| 0.5870 \| 0.3761 \| 0.5612 \| 0.4121 \| 0.3204 \| 0.3947 \| 0.9426 \| 0.2414 \| 0.3708 \| 0.5456 \| 0.5588 \| 0.4709 \| \| 1.0 \| 1467 \| - \| - \| 0.3022 \| 0.5008 \| 0.5891 \| 0.3846 \| 0.5630 \| 0.4134 \| 0.3205 \| 0.3933 \| 0.9424 \| 0.2455 \| 0.3703 \| 0.5456 \| 0.5635 \| 0.4719 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.017 kWh - Carbon Emitted: 0.006 kg of CO2 - Hours Used: 0.112 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.45.2 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.1-dev.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
sentence-transformers/static-retrieval-mrl-en-v1	sentence-transformers	sentence-similarity	[ "sentence-transformers", "onnx", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:80543469", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/squad", "dataset:sentence-transformers/s2orc", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/paq", "dataset:sentence-transformers/trivia-qa", "dataset:bclavie/msmarco-10m-triplets", "dataset:nthakur/swim-ir-monolingual", "dataset:sentence-transformers/pubmedqa", "dataset:sentence-transformers/miracl", "dataset:sentence-transformers/mldr", "dataset:sentence-transformers/mr-tydi", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-10-24T07:17:18	2025-01-17T10:54:17	32	--- datasets: - sentence-transformers/gooaq - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/squad - sentence-transformers/s2orc - sentence-transformers/all-nli - sentence-transformers/paq - sentence-transformers/trivia-qa - bclavie/msmarco-10m-triplets - nthakur/swim-ir-monolingual - sentence-transformers/pubmedqa - sentence-transformers/miracl - sentence-transformers/mldr - sentence-transformers/mr-tydi language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:80543469 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: What was the Office of Foods in charge of? sentences: - This area, stretching northward from the centrally located Great Hall of State, is believed to have been the site of the Office of Foods. This office stocked foods other than the rice that was paid as tax, and was in charge of providing meals for state banquets and rituals held in the palace. - In 2002, Barclay Records, then as part of Universal Music France, released a digitally remastered version of the original vinyl in CD and in 10" (25 cm) vinyl record (LP), under the same name, as part of a compilation containing re-releases of all of Dalida's studio albums recorded under the Barclay label. The album was again re-released in 2005. - Kevin Jon Davies is a British television and video director primarily associated with documentaries and spin-off videos associated with "Doctor Who", "The Hitchhiker's Guide to the Galaxy" and "Blake's 7". He also worked on the BAFTA award-winning animation sequences of the 1981 "Hitchhiker's Guide" television adaptation. - source_sentence: jak wysłać sf do krs? sentences: - '[''Kliknij na pole „Przygotowanie i składanie zgłoszeń”'', ''Następnie kliknij niebieski przycisk Dodaj zgłoszenie i uzupełnij o numer KRS Twojej jednostki na stronie „Rejestracja nowego zgłoszenia – Krok 1”'']' - When you experience a trigger, the insides of your airways swell even more. This narrows the space for air to move in and out of the lungs. The muscles that wrap around your airways also can tighten, making breathing even harder. When that happens, it's called an asthma flare-up, asthma episode or asthma "attack." - Lie (L) – The Lie scale is intended to identify individuals who are deliberately trying to avoid answering the MMPI honestly and in a frank manner. The scale measures attitudes and practices that are culturally laudable, but rarely found in most people. - source_sentence: 'Air Pollution in Eastern Asia: An Integrated Perspective: Chapter 14: Observation of Air Pollution over China Using the IASI Thermal Infrared Space Sensor' sentences: - In this chapter we describe what is achievable in terms of pollutant tracking from space using observations provided by thermal infrared remote sensors. After a general introduction on infrared remote sensing, we exploit the data provided by the Infrared Atmospheric Sounding Interferometer (IASI) missions onboard the Metop series of satellite to illustrate pollution detection at various spatial and temporal scales. Then, we focus on air pollution over China and discuss three case studies involving different pollutants. The first example discusses the geophysical conditions for detection of ammonia (NH3) and sulfur dioxide (SO2), both precursors of particulate matter (PM). The second case illustrates the seasonal variation of ozone (O3), in particular during the monsoon period. The third case shows the local accumulation of enhanced levels of carbon monoxide (CO) when pollution episodes occur. - This article explores the political aspects of Islamic parties in Jombang, East Java. The issue came about during the controversy arising out of the defection of the leader of the Tarekat Qadiriyah Wa Naqsyabandiyah, an Islamic order, prior to the 1977 general election. It raised questions as to the political orientation of Islamic groups in Indonesia during the 1970s and 1980s. - Abstract Unexpected findings on bone scintigraphy such as asymmetrical uptake in extremities may cause confusion for the diagnosis. The authors describe three cases of accidental intraarterial injection of Tc- 99m methylene diphosphonate ( 99m Tc-MDP) on the antecubital region and discuss the findings and differential diagnosis. - source_sentence: What type of stimuli do nociceptors response to? sentences: - 'After independence, Dutch was dropped as an official language and replaced by Malay. Yet the Indonesian language inherited many words from Dutch: words for everyday life as well as scientific and technological terms. One scholar argues that 20% of Indonesian words can be traced back to Dutch words, many of which are transliterated to reflect phonetic pronunciation e.g. kantoor (Dutch for "office") in Indonesian is kantor, while bus ("bus") becomes bis. In addition, many Indonesian words are calques on Dutch, for example, rumah sakit (Indonesian for "hospital") is calqued on the Dutch ziekenhuis (literally "house of the sick"), kebun binatang ("zoo") on dierentuin (literally "animal garden"), undang-undang dasar ("constitution") from grondwet (literally "ground law"). These account for some of the differences in vocabulary between Indonesian and Malay.' - Wilhelm Erb's (1874) "intensive" theory, that a pain signal can be generated by intense enough stimulation of any sensory receptor, has been soundly disproved. Some sensory fibers do not differentiate between noxious and non-noxious stimuli, while others, nociceptors, respond only to noxious, high intensity stimuli. At the peripheral end of the nociceptor, noxious stimuli generate currents that, above a given threshold, begin to send signals along the nerve fiber to the spinal cord. The "specificity" (whether it responds to thermal, chemical or mechanical features of its environment) of a nociceptor is determined by which ion channels it expresses at its peripheral end. Dozens of different types of nociceptor ion channels have so far been identified, and their exact functions are still being determined. - The first attempt to establish a proper governing body and adopted the current set of Rugby rules was the Foot Ball Association of Canada, organized on March 24, 1873 followed by the Canadian Rugby Football Union (CRFU) founded June 12, 1880, which included teams from Ontario and Quebec. Later both the Ontario and Quebec Rugby Football Union (ORFU and QRFU) were formed (January 1883), and then the Interprovincial (1907) and Western Interprovincial Football Union (1936) (IRFU and WIFU). The CRFU reorganized into an umbrella organization forming the Canadian Rugby Union (CRU) in 1891. The original forerunners to the current Canadian Football League, was established in 1956 when the IRFU and WIFU formed an umbrella organization, The Canadian Football Council (CFC). And then in 1958 the CFC left The CRFU to become The CFL. - source_sentence: 'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?' sentences: - Prior studies have demonstrated improved clinical outcomes for surgeons with a high-volume experience with certain open vascular operations. A high-volume experience with carotid artery stenting (CAS) improves clinical outcomes. Moreover, it is not known whether experience with other endovascular procedures, including percutaneous coronary interventions (PCIs), is an adequate substitute for experience with CAS. The goal of this study was to quantify the effect of increasing clinician volume of CAS, endovascular aneurysm repair (EVAR), and thoracic endovascular aortic aneurysm repair (TEVAR), and PCI on the outcomes for CAS. - While sensitive to internal carotid artery (ICA) occlusion, carotid ultrasound can produce false-positive results. CT angiography (CTA) has a high specificity for ICA occlusion and is safer and cheaper than catheter angiography, although less accurate. We determined the cost-effectiveness of CTA versus catheter angiography for confirming an ICA occlusion first suggested by carotid ultrasound. - Brachial artery FMD and GMD and carotid intima media thickness (cIMT) were studied using ultrasound in 20 patients diagnosed with early RA in whom symptoms had been present for less than 12 months, and in 20 control subjects matched for age, sex and established cardiovascular risk factors. FMD and GMD were re-assessed after 12 months in RA patients and the change in each parameter was calculated. Data were analysed by univariate regression. - Compared with preoperative clinical and conventional MR data, (1)H MRS improved the accuracy of MR imaging from 60.9% to 83%. We found (1)H MRS reliably distinguished between abscess and high-grade tumour, and between high-grade glioma and low-grade glioma, but was not able to reliably distinguish between recurrent glioma and radiation necrosis. In 12/23 cases (52%) the (1)H MRS findings positively altered our clinical management. Two representative cases are presented. - Different in-plane resolutions have been used for carotid 3T MRI. We compared the reproducibility, as well as the within- and between reader variability of high and routinely used spatial resolution in scans of patients with atherosclerotic carotid artery disease. Since no consensus exists about the optimal segmentation method, we analysed all imaging data using two different segmentation methods. - In the population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (1016 subjects all aged 70), the prevalence of overt plaques and echogenectity (grey scale median, GSM) of carotid artery plaques were recorded by ultrasound in both of the carotid arteries. The thickness (IMT) and echogenicity (IM-GSM) of the intima-media complex were also measured. Bisphenol A (BPA) and 10 phthalate metabolites were analyzed in serum by a API 4000 liquid chromatograph/tandem mass spectrometer. - In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices. - Development of an improved MR sequence for examining the lung. - Thirty-seven carotid artery disease patients participated in this study, of whom 24 underwent magnetic resonance imaging before and after CEA. Seventeen control subjects spanning 5 decades underwent magnetic resonance imaging to assess age-related changes. Hemodynamic metrics (that is, relative time to peak and amplitude) were calculated with a γ-variate model. Linear regression was used to relate carotid artery disease burden to downstream hemodynamics in the circle of Willis. - Carotid artery stenting (CAS) is associated with a higher risk of both hemodynamic depression and new ischemic brain lesions on diffusion-weighted imaging than carotid endarterectomy (CEA). We assessed whether the occurrence of hemodynamic depression is associated with these lesions in patients with symptomatic carotid stenosis treated by CAS or CEA in the randomized International Carotid Stenting Study (ICSS)-MRI substudy. - Magnetic resonance imaging (MRI) guidance may improve the accuracy of Gleason score (GS) determination by directing the biopsy to regions of interest (ROI) that are likely to harbor high-grade prostate cancer (CaP). The aim of this study was to determine the frequency and predictors of GS upgrading when a subsequent MRI-guided biopsy is performed on patients with a diagnosis of GS 6 disease on the basis of conventional, transrectal ultrasound-guided biopsy. - Measures of carotid-femoral pulse wave velocity (cf-PWV) and carotid augmentation index (cAI) may be affected by the presence of an abdominal aortic aneurysm (AAA). We, therefore, investigated series of various measures of arterial stiffness and wave reflections in patients with AAA, before and 4 weeks after endovascular aneurysm repair (EVAR). - High spatial resolution of dynamic contrast-enhanced (DCE) MR imaging allows characterization of heterogenous tumor microenvironment. Our purpose was to determine which is the best advanced MR imaging protocol, focused on additional MR perfusion method, for predicting recurrent metastatic brain tumor following gamma-knife radiosurgery (GKRS). - To determine whether acromegalic patients have increased thyroidal vascularity and blood flow on colour flow Doppler sonography (CFDS). - To investigate whether an existing method for correction of phase offset errors in phase-contrast velocity quantification is applicable for assessment of main pulmonary artery flow with an MR scanner equipped with a high-power gradient system. - Consecutive patients (n = 292) undergoing carotid endarterectomy for symptomatic and asymptomatic carotid stenosis were included in the study. Mortality and cardiovascular ischemic events were recorded during a median follow-up of 5.2 years. Baseline plasma concentrations of adiponectin were measured. Cox regression models stratified for gender were used for estimation of risk of events. - To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms. - Phase-contrast Cardiovascular Magnetic Resonance Imaging (CMR) generally requires the analysis of stationary tissue adjacent to a blood vessel to serve as a baseline reference for zero velocity. However, for the heart and great vessels, there is often no stationary tissue immediately adjacent to the vessel. Consequently, uncorrected velocity offsets may introduce substantial errors in flow quantification. The purpose of this study was to assess the magnitude of these flow errors and to validate a clinically applicable method for their correction. - This study was a post hoc analysis of a prospective cohort comprising 485 consecutive patients undergoing carotid endarterectomy for high-grade ICAS. Patients were classified by their clinical presentation, ie, asymptomatic (n = 213) or symptomatic (within 6 months of surgery; n = 272, comprising both transient ischemic attack [TIA; n = 163] and stroke [n = 109]). We investigated the association of cl-ICAS with the primary outcome in adjusted regression models. - Several studies reported on the moderate diagnostic yield of elective invasive coronary angiography (ICA) regarding the presence of coronary artery disease (CAD), but limited data are available on how prior testing for ischaemia may contribute to improve the diagnostic yield in an every-day clinical setting. This study aimed to assess the value and use of cardiac myocardial perfusion single photon emission computed tomography (MPS) in patient selection prior to elective ICA. - The feasibility of carotid stenting (CS) is no longer questionable, although its indications remain debatable. Until the results of randomized trials are available, personal series and registries should help in the comparison of long-term results of CS with those of endarterectomy. We report here the long-term results of a large series of CS in our department with a long follow-up. This retrospective study reviews a single surgeon's 11-year experience with CS. Our results are compared with those of conventional surgery emanating from our own series and the North American Symptomatic Carotid Endarterectomy Trial (NASCET), European Carotid Surgery Trial (ECST), and Asymptomatic Carotid Atherosclerosis Study (ACAS). co2_eq_emissions: emissions: 1014.8766030829654 energy_consumed: 2.610937435575236 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 17.883 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on various datasets results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14666666666666664 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.239 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.27899999999999997 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4196666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33085031011968163 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.44530158730158725 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.259819611075427 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.84 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5866666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.544 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.45199999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0804732343549837 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.16047472236902457 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.21798474210348842 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.31433571884014205 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5681388031303078 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7853888888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4334843491187922 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.46 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4366666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8033333333333332 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9033333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6921500788245725 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.639690476190476 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.62054338159709 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15188888888888888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29826984126984124 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3792936507936508 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4837936507936508 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3651145030243953 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3914603174603174 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3023673541934707 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.96 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.26 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14799999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.56 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.65 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6547177705459605 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7485238095238096 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5797919554359183 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.42 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.66 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4040678769319761 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3244682539682539 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33886403445504565 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.32 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.244 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04278202363094378 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09842444348194118 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11962677523904507 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1389182072247147 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3241949561078219 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5040793650793652 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1448579573714899 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.124 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.43 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.58 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.69 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4533881733265689 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3764047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3890107375543526 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.8 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.38666666666666655 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.24799999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7106666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9253333333333333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9626666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9793333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.895097527564125 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.88 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8594406482406483 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.188 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14166666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19466666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2886666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.26425784158945775 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39979365079365076 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20502449880105952 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11200000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.46 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.56 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4077879341218404 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3033888888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.31510434322531095 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.485 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.57 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.595 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.75 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6111476167014296 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5836904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5683309026222819 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8979591836734694 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9795918367346939 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6054421768707482 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.6204081632653061 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5306122448979592 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03980518443040866 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12364050983083796 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.20953289383493803 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.33697859476017505 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5702638593808323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.744047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4469881140237455 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4239560439560439 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6336891679748822 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7030455259026686 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8107692307692307 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4239560439560439 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2963160648874934 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.24064678178963897 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17327786499215073 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2302781536901455 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3979597064321778 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4654695945105992 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5726943208937448 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.503167480874536 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5481721611721612 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.420279068285741 name: Cosine Map@100 - task: type: Retrieval dataset: name: MTEB AILACasedocs (default) type: mteb/AILA_casedocs config: default split: test revision: 4106e6bcc72e0698d714ea8b101355e3e238431a metrics: - type: main_score value: 25.342 - type: map_at_1 value: 7.2059999999999995 - type: map_at_10 value: 17.343 - type: map_at_100 value: 21.356 - type: map_at_1000 value: 21.719 - type: map_at_20 value: 18.765 - type: map_at_3 value: 12.395 - type: map_at_5 value: 14.796000000000001 - type: mrr_at_1 value: 20.0 - type: mrr_at_10 value: 29.019047619047615 - type: mrr_at_100 value: 30.079478514482233 - type: mrr_at_1000 value: 30.11575302428615 - type: mrr_at_20 value: 29.311976911976913 - type: mrr_at_3 value: 25.0 - type: mrr_at_5 value: 27.399999999999995 - type: ndcg_at_1 value: 20.0 - type: ndcg_at_10 value: 25.342 - type: ndcg_at_100 value: 39.728 - type: ndcg_at_1000 value: 42.605 - type: ndcg_at_20 value: 28.157 - type: ndcg_at_3 value: 21.041999999999998 - type: ndcg_at_5 value: 22.147 - type: precision_at_1 value: 20.0 - type: precision_at_10 value: 12.2 - type: precision_at_100 value: 3.38 - type: precision_at_1000 value: 0.38999999999999996 - type: precision_at_20 value: 8.3 - type: precision_at_3 value: 18.0 - type: precision_at_5 value: 16.0 - type: recall_at_1 value: 7.2059999999999995 - type: recall_at_10 value: 32.214 - type: recall_at_100 value: 85.658 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 39.265 - type: recall_at_3 value: 16.335 - type: recall_at_5 value: 21.897 - task: type: Retrieval dataset: name: MTEB AILAStatutes (default) type: mteb/AILA_statutes config: default split: test revision: ebfcd844eadd3d667efa3c57fc5c8c87f5c2867e metrics: - type: main_score value: 22.184 - type: map_at_1 value: 5.167 - type: map_at_10 value: 12.325 - type: map_at_100 value: 19.326999999999998 - type: map_at_1000 value: 19.326999999999998 - type: map_at_20 value: 14.405999999999999 - type: map_at_3 value: 9.4 - type: map_at_5 value: 10.05 - type: mrr_at_1 value: 22.0 - type: mrr_at_10 value: 36.64682539682539 - type: mrr_at_100 value: 37.85209121304697 - type: mrr_at_1000 value: 37.85209121304697 - type: mrr_at_20 value: 37.4718241682638 - type: mrr_at_3 value: 32.666666666666664 - type: mrr_at_5 value: 33.46666666666667 - type: ndcg_at_1 value: 22.0 - type: ndcg_at_10 value: 22.184 - type: ndcg_at_100 value: 45.896 - type: ndcg_at_1000 value: 45.896 - type: ndcg_at_20 value: 27.881 - type: ndcg_at_3 value: 18.976000000000003 - type: ndcg_at_5 value: 16.728 - type: precision_at_1 value: 22.0 - type: precision_at_10 value: 10.8 - type: precision_at_100 value: 4.34 - type: precision_at_1000 value: 0.434 - type: precision_at_20 value: 8.6 - type: precision_at_3 value: 17.333000000000002 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 5.167 - type: recall_at_10 value: 26.133 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 40.5 - type: recall_at_3 value: 13.633000000000001 - type: recall_at_5 value: 15.533 - task: type: Retrieval dataset: name: MTEB ARCChallenge (default) type: RAR-b/ARC-Challenge config: default split: test revision: c481e0da3dcbbad8bce7721dea9085b74320a0a3 metrics: - type: main_score value: 6.042 - type: map_at_1 value: 1.451 - type: map_at_10 value: 4.253 - type: map_at_100 value: 4.8340000000000005 - type: map_at_1000 value: 4.9430000000000005 - type: map_at_20 value: 4.475 - type: map_at_3 value: 3.2140000000000004 - type: map_at_5 value: 3.7600000000000002 - type: mrr_at_1 value: 1.4505119453924915 - type: mrr_at_10 value: 4.250196381169083 - type: mrr_at_100 value: 4.831355631222712 - type: mrr_at_1000 value: 4.940667606986945 - type: mrr_at_20 value: 4.472323277417513 - type: mrr_at_3 value: 3.213879408418658 - type: mrr_at_5 value: 3.742889647326509 - type: ndcg_at_1 value: 1.451 - type: ndcg_at_10 value: 6.042 - type: ndcg_at_100 value: 9.765 - type: ndcg_at_1000 value: 13.655000000000001 - type: ndcg_at_20 value: 6.914 - type: ndcg_at_3 value: 3.852 - type: ndcg_at_5 value: 4.836 - type: precision_at_1 value: 1.451 - type: precision_at_10 value: 1.1860000000000002 - type: precision_at_100 value: 0.313 - type: precision_at_1000 value: 0.064 - type: precision_at_20 value: 0.772 - type: precision_at_3 value: 1.9060000000000001 - type: precision_at_5 value: 1.6209999999999998 - type: recall_at_1 value: 1.451 - type: recall_at_10 value: 11.86 - type: recall_at_100 value: 31.313999999999997 - type: recall_at_1000 value: 64.078 - type: recall_at_20 value: 15.443999999999999 - type: recall_at_3 value: 5.717 - type: recall_at_5 value: 8.106 - task: type: Retrieval dataset: name: MTEB AlphaNLI (default) type: RAR-b/alphanli config: default split: test revision: 303f40ef3d50918d3dc43577d33f2f7344ad72c1 metrics: - type: main_score value: 20.586 - type: map_at_1 value: 13.184999999999999 - type: map_at_10 value: 17.898 - type: map_at_100 value: 18.593 - type: map_at_1000 value: 18.679000000000002 - type: map_at_20 value: 18.238 - type: map_at_3 value: 16.362 - type: map_at_5 value: 17.217 - type: mrr_at_1 value: 13.185378590078328 - type: mrr_at_10 value: 17.89819822620082 - type: mrr_at_100 value: 18.5933695842888 - type: mrr_at_1000 value: 18.679218327500653 - type: mrr_at_20 value: 18.237717139340596 - type: mrr_at_3 value: 16.362053959965195 - type: mrr_at_5 value: 17.21714534377719 - type: ndcg_at_1 value: 13.184999999999999 - type: ndcg_at_10 value: 20.586 - type: ndcg_at_100 value: 24.571 - type: ndcg_at_1000 value: 27.161 - type: ndcg_at_20 value: 21.834 - type: ndcg_at_3 value: 17.375 - type: ndcg_at_5 value: 18.926000000000002 - type: precision_at_1 value: 13.184999999999999 - type: precision_at_10 value: 2.924 - type: precision_at_100 value: 0.49300000000000005 - type: precision_at_1000 value: 0.06999999999999999 - type: precision_at_20 value: 1.71 - type: precision_at_3 value: 6.7669999999999995 - type: precision_at_5 value: 4.817 - type: recall_at_1 value: 13.184999999999999 - type: recall_at_10 value: 29.243000000000002 - type: recall_at_100 value: 49.282 - type: recall_at_1000 value: 70.366 - type: recall_at_20 value: 34.204 - type: recall_at_3 value: 20.3 - type: recall_at_5 value: 24.086 - task: type: Retrieval dataset: name: MTEB AppsRetrieval (default) type: CoIR-Retrieval/apps config: default split: test revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5 metrics: - type: main_score value: 4.557 - type: map_at_1 value: 2.895 - type: map_at_10 value: 3.91 - type: map_at_100 value: 4.294 - type: map_at_1000 value: 4.391 - type: map_at_20 value: 4.089 - type: map_at_3 value: 3.4750000000000005 - type: map_at_5 value: 3.7130000000000005 - type: mrr_at_1 value: 2.895086321381142 - type: mrr_at_10 value: 3.909515377642868 - type: mrr_at_100 value: 4.293672586421636 - type: mrr_at_1000 value: 4.390523922890202 - type: mrr_at_20 value: 4.08917821169434 - type: mrr_at_3 value: 3.474988933156263 - type: mrr_at_5 value: 3.712704736609119 - type: ndcg_at_1 value: 2.895 - type: ndcg_at_10 value: 4.557 - type: ndcg_at_100 value: 6.868 - type: ndcg_at_1000 value: 10.407 - type: ndcg_at_20 value: 5.219 - type: ndcg_at_3 value: 3.6609999999999996 - type: ndcg_at_5 value: 4.088 - type: precision_at_1 value: 2.895 - type: precision_at_10 value: 0.6669999999999999 - type: precision_at_100 value: 0.185 - type: precision_at_1000 value: 0.049 - type: precision_at_20 value: 0.46499999999999997 - type: precision_at_3 value: 1.399 - type: precision_at_5 value: 1.046 - type: recall_at_1 value: 2.895 - type: recall_at_10 value: 6.666999999999999 - type: recall_at_100 value: 18.539 - type: recall_at_1000 value: 48.579 - type: recall_at_20 value: 9.296 - type: recall_at_3 value: 4.197 - type: recall_at_5 value: 5.232 - task: type: Retrieval dataset: name: MTEB ArguAna (default) type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: main_score value: 44.41 - type: map_at_1 value: 21.479 - type: map_at_10 value: 35.995 - type: map_at_100 value: 37.258 - type: map_at_1000 value: 37.273 - type: map_at_20 value: 36.908 - type: map_at_3 value: 31.247000000000003 - type: map_at_5 value: 33.751 - type: mrr_at_1 value: 21.763869132290186 - type: mrr_at_10 value: 36.12420691368057 - type: mrr_at_100 value: 37.378777397870884 - type: mrr_at_1000 value: 37.39392969162474 - type: mrr_at_20 value: 37.03325723357897 - type: mrr_at_3 value: 31.33001422475101 - type: mrr_at_5 value: 33.91180654338541 - type: ndcg_at_1 value: 21.479 - type: ndcg_at_10 value: 44.41 - type: ndcg_at_100 value: 50.032 - type: ndcg_at_1000 value: 50.388 - type: ndcg_at_20 value: 47.642 - type: ndcg_at_3 value: 34.505 - type: ndcg_at_5 value: 39.031 - type: precision_at_1 value: 21.479 - type: precision_at_10 value: 7.148000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 4.202999999999999 - type: precision_at_3 value: 14.651 - type: precision_at_5 value: 10.996 - type: recall_at_1 value: 21.479 - type: recall_at_10 value: 71.479 - type: recall_at_100 value: 96.65700000000001 - type: recall_at_1000 value: 99.36 - type: recall_at_20 value: 84.068 - type: recall_at_3 value: 43.954 - type: recall_at_5 value: 54.979 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval (default) type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: main_score value: 31.177 - type: map_at_1 value: 19.758 - type: map_at_10 value: 26.619999999999997 - type: map_at_100 value: 27.784 - type: map_at_1000 value: 27.937 - type: map_at_20 value: 27.206999999999997 - type: map_at_3 value: 24.245 - type: map_at_5 value: 25.713 - type: mrr_at_1 value: 24.892703862660944 - type: mrr_at_10 value: 31.704702863501144 - type: mrr_at_100 value: 32.608301500063966 - type: mrr_at_1000 value: 32.68725795268983 - type: mrr_at_20 value: 32.177359559981575 - type: mrr_at_3 value: 29.685264663805444 - type: mrr_at_5 value: 30.958512160228906 - type: ndcg_at_1 value: 24.893 - type: ndcg_at_10 value: 31.177 - type: ndcg_at_100 value: 36.546 - type: ndcg_at_1000 value: 39.706 - type: ndcg_at_20 value: 32.926 - type: ndcg_at_3 value: 27.58 - type: ndcg_at_5 value: 29.465000000000003 - type: precision_at_1 value: 24.893 - type: precision_at_10 value: 5.966 - type: precision_at_100 value: 1.079 - type: precision_at_1000 value: 0.166 - type: precision_at_20 value: 3.5909999999999997 - type: precision_at_3 value: 13.209000000000001 - type: precision_at_5 value: 9.728 - type: recall_at_1 value: 19.758 - type: recall_at_10 value: 39.397 - type: recall_at_100 value: 63.446999999999996 - type: recall_at_1000 value: 85.083 - type: recall_at_20 value: 45.846 - type: recall_at_3 value: 28.855999999999998 - type: recall_at_5 value: 34.165 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval (default) type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: main_score value: 25.901999999999997 - type: map_at_1 value: 16.730999999999998 - type: map_at_10 value: 22.24 - type: map_at_100 value: 23.168 - type: map_at_1000 value: 23.289 - type: map_at_20 value: 22.720000000000002 - type: map_at_3 value: 20.335 - type: map_at_5 value: 21.371000000000002 - type: mrr_at_1 value: 20.31847133757962 - type: mrr_at_10 value: 25.743908603781197 - type: mrr_at_100 value: 26.511937121538402 - type: mrr_at_1000 value: 26.58619222690668 - type: mrr_at_20 value: 26.15910380102564 - type: mrr_at_3 value: 23.77919320594478 - type: mrr_at_5 value: 24.846072186836484 - type: ndcg_at_1 value: 20.318 - type: ndcg_at_10 value: 25.901999999999997 - type: ndcg_at_100 value: 30.259999999999998 - type: ndcg_at_1000 value: 32.984 - type: ndcg_at_20 value: 27.47 - type: ndcg_at_3 value: 22.432 - type: ndcg_at_5 value: 23.999000000000002 - type: precision_at_1 value: 20.318 - type: precision_at_10 value: 4.707 - type: precision_at_100 value: 0.8580000000000001 - type: precision_at_1000 value: 0.134 - type: precision_at_20 value: 2.869 - type: precision_at_3 value: 10.552 - type: precision_at_5 value: 7.567 - type: recall_at_1 value: 16.730999999999998 - type: recall_at_10 value: 33.48 - type: recall_at_100 value: 52.245 - type: recall_at_1000 value: 70.634 - type: recall_at_20 value: 39.189 - type: recall_at_3 value: 23.805 - type: recall_at_5 value: 27.898 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval (default) type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: main_score value: 36.312 - type: map_at_1 value: 23.072 - type: map_at_10 value: 31.64 - type: map_at_100 value: 32.761 - type: map_at_1000 value: 32.862 - type: map_at_20 value: 32.24 - type: map_at_3 value: 28.921999999999997 - type: map_at_5 value: 30.603 - type: mrr_at_1 value: 26.70846394984326 - type: mrr_at_10 value: 34.47342886998059 - type: mrr_at_100 value: 35.406961114894706 - type: mrr_at_1000 value: 35.47747613970401 - type: mrr_at_20 value: 34.984787094473404 - type: mrr_at_3 value: 32.15256008359454 - type: mrr_at_5 value: 33.544409613375095 - type: ndcg_at_1 value: 26.708 - type: ndcg_at_10 value: 36.312 - type: ndcg_at_100 value: 41.748000000000005 - type: ndcg_at_1000 value: 44.206 - type: ndcg_at_20 value: 38.257000000000005 - type: ndcg_at_3 value: 31.439 - type: ndcg_at_5 value: 34.036 - type: precision_at_1 value: 26.708 - type: precision_at_10 value: 6.0440000000000005 - type: precision_at_100 value: 0.966 - type: precision_at_1000 value: 0.125 - type: precision_at_20 value: 3.549 - type: precision_at_3 value: 14.086000000000002 - type: precision_at_5 value: 10.169 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 47.687000000000005 - type: recall_at_100 value: 72.469 - type: recall_at_1000 value: 90.568 - type: recall_at_20 value: 54.861000000000004 - type: recall_at_3 value: 34.758 - type: recall_at_5 value: 41.052 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval (default) type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: main_score value: 19.756999999999998 - type: map_at_1 value: 12.546 - type: map_at_10 value: 17.009 - type: map_at_100 value: 17.758 - type: map_at_1000 value: 17.866 - type: map_at_20 value: 17.399 - type: map_at_3 value: 15.532000000000002 - type: map_at_5 value: 16.305 - type: mrr_at_1 value: 13.559322033898304 - type: mrr_at_10 value: 18.144067796610162 - type: mrr_at_100 value: 18.89867843656649 - type: mrr_at_1000 value: 18.995819754371045 - type: mrr_at_20 value: 18.54987150762981 - type: mrr_at_3 value: 16.666666666666664 - type: mrr_at_5 value: 17.43502824858757 - type: ndcg_at_1 value: 13.559 - type: ndcg_at_10 value: 19.756999999999998 - type: ndcg_at_100 value: 23.931 - type: ndcg_at_1000 value: 27.203 - type: ndcg_at_20 value: 21.173000000000002 - type: ndcg_at_3 value: 16.778000000000002 - type: ndcg_at_5 value: 18.104 - type: precision_at_1 value: 13.559 - type: precision_at_10 value: 3.141 - type: precision_at_100 value: 0.5539999999999999 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.8929999999999998 - type: precision_at_3 value: 7.156 - type: precision_at_5 value: 5.04 - type: recall_at_1 value: 12.546 - type: recall_at_10 value: 27.093 - type: recall_at_100 value: 47.325 - type: recall_at_1000 value: 72.965 - type: recall_at_20 value: 32.491 - type: recall_at_3 value: 19.122 - type: recall_at_5 value: 22.264999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval (default) type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: main_score value: 12.806000000000001 - type: map_at_1 value: 5.881 - type: map_at_10 value: 9.803 - type: map_at_100 value: 10.717 - type: map_at_1000 value: 10.816 - type: map_at_20 value: 10.244 - type: map_at_3 value: 8.163 - type: map_at_5 value: 8.956 - type: mrr_at_1 value: 7.462686567164178 - type: mrr_at_10 value: 12.077805417357661 - type: mrr_at_100 value: 12.966483256051529 - type: mrr_at_1000 value: 13.047246067329347 - type: mrr_at_20 value: 12.525499289049966 - type: mrr_at_3 value: 10.261194029850747 - type: mrr_at_5 value: 11.113184079601991 - type: ndcg_at_1 value: 7.463 - type: ndcg_at_10 value: 12.806000000000001 - type: ndcg_at_100 value: 17.807000000000002 - type: ndcg_at_1000 value: 20.979999999999997 - type: ndcg_at_20 value: 14.350999999999999 - type: ndcg_at_3 value: 9.468 - type: ndcg_at_5 value: 10.776 - type: precision_at_1 value: 7.463 - type: precision_at_10 value: 2.637 - type: precision_at_100 value: 0.613 - type: precision_at_1000 value: 0.101 - type: precision_at_20 value: 1.7229999999999999 - type: precision_at_3 value: 4.643 - type: precision_at_5 value: 3.6319999999999997 - type: recall_at_1 value: 5.881 - type: recall_at_10 value: 20.013 - type: recall_at_100 value: 42.92 - type: recall_at_1000 value: 66.943 - type: recall_at_20 value: 25.621 - type: recall_at_3 value: 10.768 - type: recall_at_5 value: 14.007 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval (default) type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: main_score value: 28.765 - type: map_at_1 value: 17.519000000000002 - type: map_at_10 value: 24.136 - type: map_at_100 value: 25.352999999999998 - type: map_at_1000 value: 25.499 - type: map_at_20 value: 24.776999999999997 - type: map_at_3 value: 21.59 - type: map_at_5 value: 23.017000000000003 - type: mrr_at_1 value: 21.36669874879692 - type: mrr_at_10 value: 28.47930702598651 - type: mrr_at_100 value: 29.504734721457147 - type: mrr_at_1000 value: 29.58610606296599 - type: mrr_at_20 value: 29.06239382160277 - type: mrr_at_3 value: 25.85819698427977 - type: mrr_at_5 value: 27.523259544433753 - type: ndcg_at_1 value: 21.367 - type: ndcg_at_10 value: 28.765 - type: ndcg_at_100 value: 34.772999999999996 - type: ndcg_at_1000 value: 37.924 - type: ndcg_at_20 value: 30.891999999999996 - type: ndcg_at_3 value: 24.248 - type: ndcg_at_5 value: 26.479999999999997 - type: precision_at_1 value: 21.367 - type: precision_at_10 value: 5.351 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.147 - type: precision_at_20 value: 3.325 - type: precision_at_3 value: 11.325000000000001 - type: precision_at_5 value: 8.469999999999999 - type: recall_at_1 value: 17.519000000000002 - type: recall_at_10 value: 38.602 - type: recall_at_100 value: 65.377 - type: recall_at_1000 value: 86.812 - type: recall_at_20 value: 46.161 - type: recall_at_3 value: 25.898 - type: recall_at_5 value: 31.654 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval (default) type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: main_score value: 22.706 - type: map_at_1 value: 12.979 - type: map_at_10 value: 18.898 - type: map_at_100 value: 20.087 - type: map_at_1000 value: 20.223 - type: map_at_20 value: 19.516 - type: map_at_3 value: 16.955000000000002 - type: map_at_5 value: 18.043 - type: mrr_at_1 value: 15.639269406392694 - type: mrr_at_10 value: 22.00872472276581 - type: mrr_at_100 value: 23.052476310365996 - type: mrr_at_1000 value: 23.1408565851826 - type: mrr_at_20 value: 22.589418169993035 - type: mrr_at_3 value: 19.99619482496195 - type: mrr_at_5 value: 21.2062404870624 - type: ndcg_at_1 value: 15.639 - type: ndcg_at_10 value: 22.706 - type: ndcg_at_100 value: 28.477999999999998 - type: ndcg_at_1000 value: 31.756 - type: ndcg_at_20 value: 24.836 - type: ndcg_at_3 value: 19.049 - type: ndcg_at_5 value: 20.807000000000002 - type: precision_at_1 value: 15.639 - type: precision_at_10 value: 4.258 - type: precision_at_100 value: 0.865 - type: precision_at_1000 value: 0.133 - type: precision_at_20 value: 2.7969999999999997 - type: precision_at_3 value: 9.056000000000001 - type: precision_at_5 value: 6.758 - type: recall_at_1 value: 12.979 - type: recall_at_10 value: 31.16 - type: recall_at_100 value: 56.245 - type: recall_at_1000 value: 79.526 - type: recall_at_20 value: 38.696000000000005 - type: recall_at_3 value: 21.302 - type: recall_at_5 value: 25.615 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval (default) type: CQADupstackRetrieval_is_a_combined_dataset config: default split: test revision: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 21.956083333333336 - type: ndcg_at_10 value: 21.956083333333336 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval (default) type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: main_score value: 17.395 - type: map_at_1 value: 10.088 - type: map_at_10 value: 14.539 - type: map_at_100 value: 15.362 - type: map_at_1000 value: 15.464 - type: map_at_20 value: 14.924000000000001 - type: map_at_3 value: 13.136999999999999 - type: map_at_5 value: 13.937 - type: mrr_at_1 value: 11.96319018404908 - type: mrr_at_10 value: 16.467767065926576 - type: mrr_at_100 value: 17.283614344094357 - type: mrr_at_1000 value: 17.375873381573232 - type: mrr_at_20 value: 16.854301079891787 - type: mrr_at_3 value: 15.08179959100204 - type: mrr_at_5 value: 15.848670756646216 - type: ndcg_at_1 value: 11.963 - type: ndcg_at_10 value: 17.395 - type: ndcg_at_100 value: 21.911 - type: ndcg_at_1000 value: 24.796000000000003 - type: ndcg_at_20 value: 18.773999999999997 - type: ndcg_at_3 value: 14.719 - type: ndcg_at_5 value: 16.032 - type: precision_at_1 value: 11.963 - type: precision_at_10 value: 2.96 - type: precision_at_100 value: 0.569 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.817 - type: precision_at_3 value: 6.748 - type: precision_at_5 value: 4.877 - type: recall_at_1 value: 10.088 - type: recall_at_10 value: 24.356 - type: recall_at_100 value: 45.73 - type: recall_at_1000 value: 67.577 - type: recall_at_20 value: 29.534 - type: recall_at_3 value: 16.944 - type: recall_at_5 value: 20.392 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval (default) type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: main_score value: 12.592999999999998 - type: map_at_1 value: 7.1290000000000004 - type: map_at_10 value: 10.302 - type: map_at_100 value: 10.994 - type: map_at_1000 value: 11.118 - type: map_at_20 value: 10.653 - type: map_at_3 value: 9.361 - type: map_at_5 value: 9.803 - type: mrr_at_1 value: 8.843771507226428 - type: mrr_at_10 value: 12.421700040419921 - type: mrr_at_100 value: 13.14928923530448 - type: mrr_at_1000 value: 13.252448388769883 - type: mrr_at_20 value: 12.803691756524197 - type: mrr_at_3 value: 11.344345033264513 - type: mrr_at_5 value: 11.824386327139244 - type: ndcg_at_1 value: 8.844000000000001 - type: ndcg_at_10 value: 12.592999999999998 - type: ndcg_at_100 value: 16.409000000000002 - type: ndcg_at_1000 value: 19.906 - type: ndcg_at_20 value: 13.831 - type: ndcg_at_3 value: 10.7 - type: ndcg_at_5 value: 11.359 - type: precision_at_1 value: 8.844000000000001 - type: precision_at_10 value: 2.33 - type: precision_at_100 value: 0.506 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 1.512 - type: precision_at_3 value: 5.116 - type: precision_at_5 value: 3.599 - type: recall_at_1 value: 7.1290000000000004 - type: recall_at_10 value: 17.549999999999997 - type: recall_at_100 value: 35.393 - type: recall_at_1000 value: 61.23800000000001 - type: recall_at_20 value: 22.124 - type: recall_at_3 value: 12.109 - type: recall_at_5 value: 13.832 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval (default) type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: main_score value: 19.111 - type: map_at_1 value: 12.577 - type: map_at_10 value: 16.275000000000002 - type: map_at_100 value: 17.083000000000002 - type: map_at_1000 value: 17.206 - type: map_at_20 value: 16.68 - type: map_at_3 value: 14.783 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 14.645522388059701 - type: mrr_at_10 value: 18.617404051172702 - type: mrr_at_100 value: 19.434952661619388 - type: mrr_at_1000 value: 19.536374825069274 - type: mrr_at_20 value: 19.039596975787966 - type: mrr_at_3 value: 16.977611940298516 - type: mrr_at_5 value: 17.938432835820894 - type: ndcg_at_1 value: 14.646 - type: ndcg_at_10 value: 19.111 - type: ndcg_at_100 value: 23.541999999999998 - type: ndcg_at_1000 value: 26.901999999999997 - type: ndcg_at_20 value: 20.593 - type: ndcg_at_3 value: 16.104 - type: ndcg_at_5 value: 17.577 - type: precision_at_1 value: 14.646 - type: precision_at_10 value: 3.237 - type: precision_at_100 value: 0.607 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 1.9869999999999999 - type: precision_at_3 value: 7.027 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 12.577 - type: recall_at_10 value: 25.642 - type: recall_at_100 value: 46.296 - type: recall_at_1000 value: 70.901 - type: recall_at_20 value: 31.202 - type: recall_at_3 value: 17.396 - type: recall_at_5 value: 21.046 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval (default) type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: main_score value: 22.359 - type: map_at_1 value: 14.202 - 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type: recall_at_1 value: 14.202 - type: recall_at_10 value: 29.507 - type: recall_at_100 value: 52.574 - type: recall_at_1000 value: 77.41799999999999 - type: recall_at_20 value: 35.733 - type: recall_at_3 value: 19.345000000000002 - type: recall_at_5 value: 22.99 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval (default) type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: main_score value: 14.59 - type: map_at_1 value: 9.341000000000001 - type: map_at_10 value: 12.495000000000001 - type: map_at_100 value: 13.328000000000001 - type: map_at_1000 value: 13.443 - type: map_at_20 value: 12.919 - type: map_at_3 value: 11.448 - type: map_at_5 value: 12.016 - type: mrr_at_1 value: 10.536044362292053 - type: mrr_at_10 value: 13.933045799958926 - type: mrr_at_100 value: 14.753327128738034 - type: mrr_at_1000 value: 14.84798752653836 - type: mrr_at_20 value: 14.348175993628182 - type: mrr_at_3 value: 12.6309303758472 - 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type: precision_at_20 value: 4.531000000000001 - type: precision_at_3 value: 11.965 - type: precision_at_5 value: 9.707 - type: recall_at_1 value: 7.469 - type: recall_at_10 value: 26.285999999999998 - type: recall_at_100 value: 52.376999999999995 - type: recall_at_1000 value: 75.261 - type: recall_at_20 value: 34.035 - type: recall_at_3 value: 14.526 - type: recall_at_5 value: 19.306 - task: type: Retrieval dataset: name: MTEB ClimateFEVERHardNegatives (default) type: mteb/ClimateFEVER_test_top_250_only_w_correct-v2 config: default split: test revision: 3a309e201f3c2c4b13bd4a367a8f37eee2ec1d21 metrics: - type: main_score value: 22.605 - type: map_at_1 value: 8.6 - type: map_at_10 value: 15.281 - type: map_at_100 value: 17.282 - type: map_at_1000 value: 17.522 - type: map_at_20 value: 16.339000000000002 - type: map_at_3 value: 12.429 - type: map_at_5 value: 13.922 - type: mrr_at_1 value: 18.5 - type: mrr_at_10 value: 29.625952380952388 - type: mrr_at_100 value: 30.77892068089231 - type: mrr_at_1000 value: 30.83853566495065 - 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type: main_score value: 46.045 - type: map_at_1 value: 38.412 - type: map_at_10 value: 43.41 - type: map_at_100 value: 43.976 - type: map_at_1000 value: 44.037 - type: map_at_20 value: 43.736000000000004 - type: map_at_3 value: 42.055 - type: map_at_5 value: 42.829 - type: mrr_at_1 value: 38.41229193341869 - type: mrr_at_10 value: 43.40960498582686 - type: mrr_at_100 value: 43.97561445897139 - type: mrr_at_1000 value: 44.03658359938551 - type: mrr_at_20 value: 43.73609592536922 - type: mrr_at_3 value: 42.05518314880356 - type: mrr_at_5 value: 42.828701262835196 - type: ndcg_at_1 value: 38.412 - type: ndcg_at_10 value: 46.045 - type: ndcg_at_100 value: 49.061 - type: ndcg_at_1000 value: 50.941 - type: ndcg_at_20 value: 47.245 - type: ndcg_at_3 value: 43.245 - type: ndcg_at_5 value: 44.639 - type: precision_at_1 value: 38.412 - type: precision_at_10 value: 5.442 - type: precision_at_100 value: 0.6910000000000001 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_20 value: 2.959 - type: precision_at_3 value: 15.562999999999999 - type: precision_at_5 value: 10.014000000000001 - type: recall_at_1 value: 38.412 - type: recall_at_10 value: 54.417 - type: recall_at_100 value: 69.15 - type: recall_at_1000 value: 84.515 - type: recall_at_20 value: 59.185 - type: recall_at_3 value: 46.69 - type: recall_at_5 value: 50.072 - task: type: Retrieval dataset: name: MTEB CodeFeedbackST (default) type: CoIR-Retrieval/codefeedback-st config: default split: test revision: d213819e87aab9010628da8b73ab4eb337c89340 metrics: - type: main_score value: 45.592 - type: map_at_1 value: 34.54 - type: map_at_10 value: 41.855 - type: map_at_100 value: 42.528 - type: map_at_1000 value: 42.587 - type: map_at_20 value: 42.239 - type: map_at_3 value: 39.985 - type: map_at_5 value: 41.075 - type: mrr_at_1 value: 34.42151664217722 - type: mrr_at_10 value: 41.78311069737728 - type: mrr_at_100 value: 42.45614518998771 - type: mrr_at_1000 value: 42.51517890438335 - type: mrr_at_20 value: 42.16798542795404 - 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type: precision_at_5 value: 2.04 - type: recall_at_1 value: 3.2 - type: recall_at_10 value: 13.8 - type: recall_at_100 value: 39.2 - type: recall_at_1000 value: 78.0 - type: recall_at_20 value: 19.0 - type: recall_at_3 value: 7.6 - type: recall_at_5 value: 10.2 - task: type: Retrieval dataset: name: MTEB DBPedia (default) type: mteb/dbpedia config: default split: dev revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 29.817 - type: map_at_1 value: 6.151 - type: map_at_10 value: 12.292 - type: map_at_100 value: 18.139 - type: map_at_1000 value: 19.84 - type: map_at_20 value: 14.495 - type: map_at_3 value: 8.426 - type: map_at_5 value: 10.192 - type: mrr_at_1 value: 46.26865671641791 - type: mrr_at_10 value: 57.92466240227434 - type: mrr_at_100 value: 58.67349319471301 - type: mrr_at_1000 value: 58.68212283999546 - type: mrr_at_20 value: 58.47241542478595 - type: mrr_at_3 value: 54.726368159203986 - type: mrr_at_5 value: 57.33830845771145 - type: ndcg_at_1 value: 38.06 - type: ndcg_at_10 value: 29.817 - type: ndcg_at_100 value: 36.472 - type: ndcg_at_1000 value: 45.576 - type: ndcg_at_20 value: 30.009000000000004 - type: ndcg_at_3 value: 32.839 - type: ndcg_at_5 value: 32.301 - type: precision_at_1 value: 46.269 - type: precision_at_10 value: 25.820999999999998 - type: precision_at_100 value: 8.552 - type: precision_at_1000 value: 1.576 - type: precision_at_20 value: 20.075000000000003 - type: precision_at_3 value: 35.821 - type: precision_at_5 value: 34.327999999999996 - type: recall_at_1 value: 6.151 - type: recall_at_10 value: 16.838 - type: recall_at_100 value: 48.427 - type: recall_at_1000 value: 77.018 - type: recall_at_20 value: 26.147 - type: recall_at_3 value: 9.221 - type: recall_at_5 value: 12.453 - task: type: Retrieval dataset: name: MTEB DBPedia (default) type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 27.377000000000002 - type: map_at_1 value: 5.527 - type: map_at_10 value: 12.384 - type: map_at_100 value: 17.660999999999998 - type: map_at_1000 value: 18.98 - type: map_at_20 value: 14.424999999999999 - type: map_at_3 value: 8.484 - type: map_at_5 value: 10.174 - type: mrr_at_1 value: 44.25 - type: mrr_at_10 value: 55.620238095238086 - type: mrr_at_100 value: 56.311713506324445 - type: mrr_at_1000 value: 56.33739917164095 - type: mrr_at_20 value: 56.11873017655717 - type: mrr_at_3 value: 52.95833333333334 - type: mrr_at_5 value: 54.595833333333324 - type: ndcg_at_1 value: 31.75 - type: ndcg_at_10 value: 27.377000000000002 - type: ndcg_at_100 value: 32.164 - type: ndcg_at_1000 value: 40.050000000000004 - type: ndcg_at_20 value: 27.424 - type: ndcg_at_3 value: 28.683999999999997 - type: ndcg_at_5 value: 28.283 - type: precision_at_1 value: 44.25 - type: precision_at_10 value: 24.45 - type: precision_at_100 value: 7.704999999999999 - type: precision_at_1000 value: 1.5970000000000002 - type: precision_at_20 value: 18.462 - type: precision_at_3 value: 35.167 - type: precision_at_5 value: 30.95 - type: recall_at_1 value: 5.527 - type: recall_at_10 value: 18.016 - type: recall_at_100 value: 41.656 - type: recall_at_1000 value: 67.38300000000001 - type: recall_at_20 value: 24.21 - type: recall_at_3 value: 9.936 - type: recall_at_5 value: 13.187999999999999 - task: type: Retrieval dataset: name: MTEB DBPediaHardNegatives (default) type: mteb/DBPedia_test_top_250_only_w_correct-v2 config: default split: test revision: 943ec7fdfef3728b2ad1966c5b6479ff9ffd26c9 metrics: - type: main_score value: 30.444 - type: map_at_1 value: 5.722 - type: map_at_10 value: 13.688 - type: map_at_100 value: 22.032 - type: map_at_1000 value: 25.386999999999997 - type: map_at_20 value: 16.307 - type: map_at_3 value: 9.008 - type: map_at_5 value: 11.056000000000001 - type: mrr_at_1 value: 47.0 - type: mrr_at_10 value: 59.097420634920624 - type: mrr_at_100 value: 59.821938296328106 - type: mrr_at_1000 value: 59.83760663887243 - type: mrr_at_20 value: 59.59489258161859 - type: mrr_at_3 value: 56.458333333333336 - type: mrr_at_5 value: 58.18333333333333 - type: ndcg_at_1 value: 33.5 - type: ndcg_at_10 value: 30.444 - type: ndcg_at_100 value: 40.474 - type: ndcg_at_1000 value: 51.964 - type: ndcg_at_20 value: 31.356 - type: ndcg_at_3 value: 30.772 - type: ndcg_at_5 value: 30.576999999999998 - type: precision_at_1 value: 47.0 - type: precision_at_10 value: 27.975 - type: precision_at_100 value: 12.055 - type: precision_at_1000 value: 2.9579999999999997 - type: precision_at_20 value: 22.275 - type: precision_at_3 value: 38.083 - type: precision_at_5 value: 33.75 - type: recall_at_1 value: 5.722 - type: recall_at_10 value: 20.571 - type: recall_at_100 value: 55.967999999999996 - type: recall_at_1000 value: 91.362 - type: recall_at_20 value: 28.526 - type: recall_at_3 value: 10.761999999999999 - type: recall_at_5 value: 14.854999999999999 - task: type: Retrieval dataset: name: MTEB FEVER (default) type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: main_score value: 42.997 - type: map_at_1 value: 22.017 - type: map_at_10 value: 35.199000000000005 - type: map_at_100 value: 36.254999999999995 - type: map_at_1000 value: 36.298 - type: map_at_20 value: 35.855 - type: map_at_3 value: 31.072 - type: map_at_5 value: 33.461 - type: mrr_at_1 value: 23.582358235823584 - type: mrr_at_10 value: 37.35784888012616 - type: mrr_at_100 value: 38.36344206815839 - type: mrr_at_1000 value: 38.39238175644681 - type: mrr_at_20 value: 38.01212529885376 - type: mrr_at_3 value: 33.098309830982956 - type: mrr_at_5 value: 35.579557955795615 - type: ndcg_at_1 value: 23.582 - type: ndcg_at_10 value: 42.997 - type: ndcg_at_100 value: 47.979 - type: ndcg_at_1000 value: 48.994 - type: ndcg_at_20 value: 45.35 - type: ndcg_at_3 value: 34.579 - type: ndcg_at_5 value: 38.851 - type: precision_at_1 value: 23.582 - type: precision_at_10 value: 7.1290000000000004 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_20 value: 4.0840000000000005 - type: precision_at_3 value: 15.342 - type: precision_at_5 value: 11.479000000000001 - type: recall_at_1 value: 22.017 - type: recall_at_10 value: 65.354 - type: recall_at_100 value: 87.75800000000001 - type: recall_at_1000 value: 95.212 - type: recall_at_20 value: 74.38 - type: recall_at_3 value: 42.581 - type: recall_at_5 value: 52.844 - task: type: Retrieval dataset: name: MTEB FEVERHardNegatives (default) type: mteb/FEVER_test_top_250_only_w_correct-v2 config: default split: test revision: 080c9ed6267b65029207906e815d44a9240bafca metrics: - type: main_score value: 46.277 - type: map_at_1 value: 23.945 - type: map_at_10 value: 37.564 - type: map_at_100 value: 38.562000000000005 - type: map_at_1000 value: 38.602 - type: map_at_20 value: 38.173 - type: map_at_3 value: 32.208999999999996 - type: map_at_5 value: 35.538 - type: mrr_at_1 value: 25.5 - type: mrr_at_10 value: 39.60134920634916 - type: mrr_at_100 value: 40.523753358000675 - type: mrr_at_1000 value: 40.53762691263701 - type: mrr_at_20 value: 40.19828779622504 - 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type: precision_at_3 value: 5.762 - type: precision_at_5 value: 5.436 - type: recall_at_1 value: 5.779 - type: recall_at_10 value: 40.989 - type: recall_at_100 value: 86.337 - type: recall_at_1000 value: 98.335 - type: recall_at_20 value: 55.387 - type: recall_at_3 value: 17.287 - type: recall_at_5 value: 27.179 - task: type: Retrieval dataset: name: MTEB FeedbackQARetrieval (default) type: lt2c/fqa config: default split: test revision: 1ee1cd0 metrics: - type: main_score value: 27.159 - type: map_at_1 value: 27.159 - type: map_at_10 value: 36.533 - type: map_at_100 value: 37.653999999999996 - type: map_at_1000 value: 37.719 - type: map_at_20 value: 37.19 - type: map_at_3 value: 33.650999999999996 - type: map_at_5 value: 35.338 - type: mrr_at_1 value: 27.158634538152608 - type: mrr_at_10 value: 36.53293730477466 - type: mrr_at_100 value: 37.65359357224721 - type: mrr_at_1000 value: 37.71854110065475 - type: mrr_at_20 value: 37.18989930977979 - type: mrr_at_3 value: 33.65127175368139 - type: mrr_at_5 value: 35.33801874163323 - type: ndcg_at_1 value: 27.159 - type: ndcg_at_10 value: 41.756 - type: ndcg_at_100 value: 47.424 - type: ndcg_at_1000 value: 49.128 - type: ndcg_at_20 value: 44.111 - type: ndcg_at_3 value: 35.798 - type: ndcg_at_5 value: 38.827 - type: precision_at_1 value: 27.159 - type: precision_at_10 value: 5.848 - type: precision_at_100 value: 0.855 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 3.386 - type: precision_at_3 value: 14.005999999999998 - type: precision_at_5 value: 9.869 - type: recall_at_1 value: 27.159 - type: recall_at_10 value: 58.484 - type: recall_at_100 value: 85.492 - type: recall_at_1000 value: 98.845 - type: recall_at_20 value: 67.721 - type: recall_at_3 value: 42.018 - type: recall_at_5 value: 49.347 - task: type: Retrieval dataset: name: MTEB FiQA2018 (default) type: mteb/fiqa config: default split: dev revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 19.159000000000002 - 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type: precision_at_5 value: 7.76 - type: recall_at_1 value: 9.577 - type: recall_at_10 value: 24.362000000000002 - type: recall_at_100 value: 48.222 - type: recall_at_1000 value: 74.358 - type: recall_at_20 value: 30.465999999999998 - type: recall_at_3 value: 16.057 - type: recall_at_5 value: 19.516 - task: type: Retrieval dataset: name: MTEB FiQA2018 (default) type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 19.986 - type: map_at_1 value: 9.157 - type: map_at_10 value: 14.877 - type: map_at_100 value: 16.185 - type: map_at_1000 value: 16.366 - type: map_at_20 value: 15.551 - type: map_at_3 value: 12.574 - type: map_at_5 value: 13.694999999999999 - type: mrr_at_1 value: 16.51234567901235 - type: mrr_at_10 value: 23.66169410150891 - type: mrr_at_100 value: 24.6092001023614 - type: mrr_at_1000 value: 24.695544929151346 - type: mrr_at_20 value: 24.17998019538123 - type: mrr_at_3 value: 20.98765432098765 - type: mrr_at_5 value: 22.32253086419753 - 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type: map_at_10 value: 13.209000000000001 - type: map_at_100 value: 14.477 - type: map_at_1000 value: 14.671000000000001 - type: map_at_20 value: 13.864 - type: map_at_3 value: 11.218 - type: map_at_5 value: 12.239 - type: mrr_at_1 value: 15.363636363636363 - type: mrr_at_10 value: 21.62342712842711 - type: mrr_at_100 value: 22.63936235019517 - type: mrr_at_1000 value: 22.735838806851703 - type: mrr_at_20 value: 22.175914005467874 - type: mrr_at_3 value: 19.448484848484895 - type: mrr_at_5 value: 20.589393939394007 - type: ndcg_at_1 value: 15.364 - type: ndcg_at_10 value: 17.858 - type: ndcg_at_100 value: 23.794999999999998 - type: ndcg_at_1000 value: 28.17 - type: ndcg_at_20 value: 19.901 - type: ndcg_at_3 value: 14.888000000000002 - type: ndcg_at_5 value: 15.926000000000002 - type: precision_at_1 value: 15.364 - type: precision_at_10 value: 5.024 - type: precision_at_100 value: 1.087 - type: precision_at_1000 value: 0.184 - type: precision_at_20 value: 3.293 - type: precision_at_3 value: 9.751999999999999 - type: precision_at_5 value: 7.465 - type: recall_at_1 value: 8.012 - type: recall_at_10 value: 23.233999999999998 - type: recall_at_100 value: 46.623999999999995 - type: recall_at_1000 value: 74.092 - type: recall_at_20 value: 29.854000000000003 - type: recall_at_3 value: 14.216000000000001 - type: recall_at_5 value: 17.713 - task: type: Retrieval dataset: name: MTEB HellaSwag (default) type: RAR-b/hellaswag config: default split: test revision: a5c990205e017d10761197ccab3000936689c3ae metrics: - type: main_score value: 16.377 - type: map_at_1 value: 8.474 - type: map_at_10 value: 13.479 - type: map_at_100 value: 14.296000000000001 - type: map_at_1000 value: 14.393 - type: map_at_20 value: 13.905000000000001 - type: map_at_3 value: 11.878 - type: map_at_5 value: 12.733 - type: mrr_at_1 value: 8.474407488548099 - type: mrr_at_10 value: 13.47926012335491 - type: mrr_at_100 value: 14.296018190032331 - type: mrr_at_1000 value: 14.39320635735857 - type: mrr_at_20 value: 13.905283977590932 - type: mrr_at_3 value: 11.878443869083188 - type: mrr_at_5 value: 12.733353249684685 - type: ndcg_at_1 value: 8.474 - type: ndcg_at_10 value: 16.377 - type: ndcg_at_100 value: 20.878 - type: ndcg_at_1000 value: 23.878 - type: ndcg_at_20 value: 17.93 - type: ndcg_at_3 value: 13.014999999999999 - type: ndcg_at_5 value: 14.557999999999998 - type: precision_at_1 value: 8.474 - type: precision_at_10 value: 2.571 - type: precision_at_100 value: 0.48 - type: precision_at_1000 value: 0.073 - type: precision_at_20 value: 1.593 - type: precision_at_3 value: 5.437 - type: precision_at_5 value: 4.013 - type: recall_at_1 value: 8.474 - type: recall_at_10 value: 25.712000000000003 - type: recall_at_100 value: 48.008 - type: recall_at_1000 value: 72.52499999999999 - type: recall_at_20 value: 31.856 - type: recall_at_3 value: 16.311 - type: recall_at_5 value: 20.066 - task: type: Retrieval dataset: name: MTEB HotpotQA (default) type: mteb/hotpotqa config: default split: dev revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: main_score value: 49.524 - type: map_at_1 value: 27.583999999999996 - type: map_at_10 value: 40.455000000000005 - type: map_at_100 value: 41.567 - type: map_at_1000 value: 41.665 - type: map_at_20 value: 41.099000000000004 - type: map_at_3 value: 37.438 - type: map_at_5 value: 39.202999999999996 - type: mrr_at_1 value: 55.16798237561961 - type: mrr_at_10 value: 63.83496376336482 - type: mrr_at_100 value: 64.32844309604842 - type: mrr_at_1000 value: 64.35048997347738 - type: mrr_at_20 value: 64.14047945884145 - type: mrr_at_3 value: 61.93929380086919 - type: mrr_at_5 value: 63.0802888440121 - type: ndcg_at_1 value: 55.16799999999999 - type: ndcg_at_10 value: 49.524 - type: ndcg_at_100 value: 53.879 - type: ndcg_at_1000 value: 55.911 - type: ndcg_at_20 value: 51.31 - type: ndcg_at_3 value: 44.527 - type: ndcg_at_5 value: 47.102 - type: precision_at_1 value: 55.16799999999999 - type: precision_at_10 value: 10.718 - type: precision_at_100 value: 1.4160000000000001 - type: precision_at_1000 value: 0.169 - type: precision_at_20 value: 5.935 - type: precision_at_3 value: 28.26 - type: precision_at_5 value: 18.990000000000002 - type: recall_at_1 value: 27.583999999999996 - type: recall_at_10 value: 53.589 - type: recall_at_100 value: 70.782 - type: recall_at_1000 value: 84.276 - type: recall_at_20 value: 59.354 - type: recall_at_3 value: 42.39 - type: recall_at_5 value: 47.476 - task: type: Retrieval dataset: name: MTEB HotpotQAHardNegatives (default) type: mteb/HotpotQA_test_top_250_only_w_correct-v2 config: default split: test revision: 617612fa63afcb60e3b134bed8b7216a99707c37 metrics: - type: main_score value: 50.415 - type: map_at_1 value: 26.8 - type: map_at_10 value: 40.503 - type: map_at_100 value: 42.092 - type: map_at_1000 value: 42.198 - type: map_at_20 value: 41.394999999999996 - type: map_at_3 value: 36.75 - type: map_at_5 value: 38.945 - type: mrr_at_1 value: 53.6 - type: mrr_at_10 value: 63.90658730158732 - type: mrr_at_100 value: 64.46665914282829 - type: mrr_at_1000 value: 64.4775151418674 - type: mrr_at_20 value: 64.20681999545006 - type: mrr_at_3 value: 61.40000000000001 - type: mrr_at_5 value: 62.98000000000005 - type: ndcg_at_1 value: 53.6 - type: ndcg_at_10 value: 50.415 - type: ndcg_at_100 value: 56.48800000000001 - type: ndcg_at_1000 value: 58.388 - type: ndcg_at_20 value: 52.68000000000001 - type: ndcg_at_3 value: 44.165 - type: ndcg_at_5 value: 47.429 - type: precision_at_1 value: 53.6 - type: precision_at_10 value: 11.31 - type: precision_at_100 value: 1.614 - type: precision_at_1000 value: 0.186 - type: precision_at_20 value: 6.375 - type: precision_at_3 value: 28.433000000000003 - type: precision_at_5 value: 19.62 - type: recall_at_1 value: 26.8 - type: recall_at_10 value: 56.55 - type: recall_at_100 value: 80.7 - type: recall_at_1000 value: 93.05 - type: recall_at_20 value: 63.74999999999999 - type: recall_at_3 value: 42.65 - type: recall_at_5 value: 49.05 - task: type: Retrieval dataset: name: MTEB LEMBNarrativeQARetrieval (default) type: dwzhu/LongEmbed config: default split: test revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 47.11 - type: map_at_1 value: 38.176 - type: map_at_10 value: 44.11 - type: map_at_100 value: 44.885999999999996 - type: map_at_1000 value: 45.005 - type: map_at_20 value: 44.486 - type: map_at_3 value: 42.669000000000004 - type: map_at_5 value: 43.441 - type: mrr_at_1 value: 38.17590200019141 - type: mrr_at_10 value: 44.109989260003694 - type: mrr_at_100 value: 44.886475970293596 - type: mrr_at_1000 value: 45.00541901614199 - type: mrr_at_20 value: 44.48565175776022 - type: mrr_at_3 value: 42.66915494305687 - type: mrr_at_5 value: 43.44052062398311 - type: ndcg_at_1 value: 38.176 - type: ndcg_at_10 value: 47.11 - type: ndcg_at_100 value: 51.644999999999996 - type: ndcg_at_1000 value: 54.366 - type: ndcg_at_20 value: 48.475 - type: ndcg_at_3 value: 44.101 - type: ndcg_at_5 value: 45.494 - type: precision_at_1 value: 38.176 - type: precision_at_10 value: 5.6610000000000005 - type: precision_at_100 value: 0.796 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 3.1 - type: precision_at_3 value: 16.078 - type: precision_at_5 value: 10.324 - type: recall_at_1 value: 38.176 - type: recall_at_10 value: 56.608000000000004 - type: recall_at_100 value: 79.644 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 61.995999999999995 - type: recall_at_3 value: 48.234 - type: recall_at_5 value: 51.622 - type: main_score value: 29.847 - type: map_at_1 value: 18.271 - type: map_at_10 value: 25.415 - type: map_at_100 value: 27.086 - type: map_at_1000 value: 27.134000000000004 - type: map_at_20 value: 26.135 - type: map_at_3 value: 22.659000000000002 - type: map_at_5 value: 24.198 - type: mrr_at_1 value: 18.271119842829076 - type: mrr_at_10 value: 25.414678641594147 - type: mrr_at_100 value: 27.086094547163714 - type: mrr_at_1000 value: 27.13383971528746 - type: mrr_at_20 value: 26.13474777243653 - type: mrr_at_3 value: 22.658808120497696 - type: mrr_at_5 value: 24.197773411918757 - type: ndcg_at_1 value: 18.271 - type: ndcg_at_10 value: 29.847 - type: ndcg_at_100 value: 39.669 - type: ndcg_at_1000 value: 40.528999999999996 - type: ndcg_at_20 value: 32.509 - type: ndcg_at_3 value: 24.151 - type: ndcg_at_5 value: 26.927 - type: precision_at_1 value: 18.271 - type: precision_at_10 value: 4.42 - type: precision_at_100 value: 0.9400000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 2.741 - type: precision_at_3 value: 9.496 - type: precision_at_5 value: 7.045999999999999 - type: recall_at_1 value: 18.271 - type: recall_at_10 value: 44.204 - type: recall_at_100 value: 93.975 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 54.813 - type: recall_at_3 value: 28.487000000000002 - type: recall_at_5 value: 35.232 - type: main_score value: 77.203 - type: map_at_1 value: 69.333 - type: map_at_10 value: 74.787 - type: map_at_100 value: 75.348 - type: map_at_1000 value: 75.369 - type: map_at_20 value: 75.16000000000001 - type: map_at_3 value: 73.611 - type: map_at_5 value: 74.42800000000001 - type: mrr_at_1 value: 69.33333333333334 - 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task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_1024 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 94.667 - type: map_at_100 value: 94.667 - type: map_at_1000 value: 94.667 - type: map_at_20 value: 94.667 - type: map_at_3 value: 94.667 - type: map_at_5 value: 94.667 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 94.66666666666666 - type: mrr_at_100 value: 94.66666666666666 - type: mrr_at_1000 value: 94.66666666666666 - type: mrr_at_20 value: 94.66666666666666 - type: mrr_at_3 value: 94.66666666666666 - type: mrr_at_5 value: 94.66666666666666 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 96.04700000000001 - type: ndcg_at_100 value: 96.04700000000001 - type: ndcg_at_1000 value: 96.04700000000001 - type: ndcg_at_20 value: 96.04700000000001 - type: ndcg_at_3 value: 96.04700000000001 - type: ndcg_at_5 value: 96.04700000000001 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_16384 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 94.167 - type: map_at_100 value: 94.167 - type: map_at_1000 value: 94.167 - type: map_at_20 value: 94.167 - type: map_at_3 value: 93.667 - type: map_at_5 value: 94.167 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 94.16666666666666 - type: mrr_at_100 value: 94.16666666666666 - type: mrr_at_1000 value: 94.16666666666666 - type: mrr_at_20 value: 94.16666666666666 - type: mrr_at_3 value: 93.66666666666666 - type: mrr_at_5 value: 94.16666666666666 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 95.647 - type: ndcg_at_100 value: 95.647 - type: ndcg_at_1000 value: 95.647 - type: ndcg_at_20 value: 95.647 - type: ndcg_at_3 value: 94.786 - type: ndcg_at_5 value: 95.647 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 32.667 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 98.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_2048 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 92.0 - type: map_at_1 value: 92.0 - type: map_at_10 value: 95.667 - type: map_at_100 value: 95.667 - type: map_at_1000 value: 95.667 - type: map_at_20 value: 95.667 - type: map_at_3 value: 95.667 - type: map_at_5 value: 95.667 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 95.66666666666666 - type: mrr_at_100 value: 95.66666666666666 - type: mrr_at_1000 value: 95.66666666666666 - type: mrr_at_20 value: 95.66666666666666 - type: mrr_at_3 value: 95.66666666666666 - type: mrr_at_5 value: 95.66666666666666 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 96.786 - type: ndcg_at_100 value: 96.786 - type: ndcg_at_1000 value: 96.786 - type: ndcg_at_20 value: 96.786 - type: ndcg_at_3 value: 96.786 - type: ndcg_at_5 value: 96.786 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 92.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_256 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 95.0 - type: map_at_100 value: 95.0 - type: map_at_1000 value: 95.0 - type: map_at_20 value: 95.0 - type: map_at_3 value: 95.0 - type: map_at_5 value: 95.0 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 95.0 - type: mrr_at_100 value: 95.0 - type: mrr_at_1000 value: 95.0 - type: mrr_at_20 value: 95.0 - type: mrr_at_3 value: 95.0 - type: mrr_at_5 value: 95.0 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 96.309 - type: ndcg_at_100 value: 96.309 - type: ndcg_at_1000 value: 96.309 - type: ndcg_at_20 value: 96.309 - type: ndcg_at_3 value: 96.309 - type: ndcg_at_5 value: 96.309 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_32768 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 84.0 - type: map_at_1 value: 84.0 - type: map_at_10 value: 91.167 - type: map_at_100 value: 91.167 - type: map_at_1000 value: 91.167 - type: map_at_20 value: 91.167 - type: map_at_3 value: 90.667 - type: map_at_5 value: 91.167 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 91.16666666666666 - type: mrr_at_100 value: 91.16666666666666 - type: mrr_at_1000 value: 91.16666666666666 - type: mrr_at_20 value: 91.16666666666666 - type: mrr_at_3 value: 90.66666666666666 - type: mrr_at_5 value: 91.16666666666666 - type: ndcg_at_1 value: 84.0 - type: ndcg_at_10 value: 93.43299999999999 - type: ndcg_at_100 value: 93.43299999999999 - type: ndcg_at_1000 value: 93.43299999999999 - type: ndcg_at_20 value: 93.43299999999999 - type: ndcg_at_3 value: 92.571 - type: ndcg_at_5 value: 93.43299999999999 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 32.667 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 84.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 98.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_4096 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 92.0 - type: map_at_1 value: 92.0 - type: map_at_10 value: 96.0 - type: map_at_100 value: 96.0 - type: map_at_1000 value: 96.0 - type: map_at_20 value: 96.0 - type: map_at_3 value: 96.0 - type: map_at_5 value: 96.0 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 96.0 - type: mrr_at_100 value: 96.0 - type: mrr_at_1000 value: 96.0 - type: mrr_at_20 value: 96.0 - type: mrr_at_3 value: 96.0 - type: mrr_at_5 value: 96.0 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 97.04700000000001 - type: ndcg_at_100 value: 97.04700000000001 - type: ndcg_at_1000 value: 97.04700000000001 - type: ndcg_at_20 value: 97.04700000000001 - type: ndcg_at_3 value: 97.04700000000001 - type: ndcg_at_5 value: 97.04700000000001 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 92.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_512 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 94.0 - type: map_at_1 value: 94.0 - type: map_at_10 value: 97.0 - type: map_at_100 value: 97.0 - type: map_at_1000 value: 97.0 - type: map_at_20 value: 97.0 - type: map_at_3 value: 97.0 - type: map_at_5 value: 97.0 - type: mrr_at_1 value: 94.0 - type: mrr_at_10 value: 97.0 - type: mrr_at_100 value: 97.0 - type: mrr_at_1000 value: 97.0 - type: mrr_at_20 value: 97.0 - type: mrr_at_3 value: 97.0 - type: mrr_at_5 value: 97.0 - type: ndcg_at_1 value: 94.0 - type: ndcg_at_10 value: 97.786 - type: ndcg_at_100 value: 97.786 - type: ndcg_at_1000 value: 97.786 - type: ndcg_at_20 value: 97.786 - type: ndcg_at_3 value: 97.786 - type: ndcg_at_5 value: 97.786 - type: precision_at_1 value: 94.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 94.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_8192 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 88.0 - type: map_at_1 value: 88.0 - type: map_at_10 value: 93.167 - type: map_at_100 value: 93.167 - type: map_at_1000 value: 93.167 - type: map_at_20 value: 93.167 - type: map_at_3 value: 92.667 - type: map_at_5 value: 93.167 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.16666666666666 - type: mrr_at_100 value: 93.16666666666666 - type: mrr_at_1000 value: 93.16666666666666 - type: mrr_at_20 value: 93.16666666666666 - type: mrr_at_3 value: 92.66666666666666 - type: mrr_at_5 value: 93.16666666666666 - type: ndcg_at_1 value: 88.0 - type: ndcg_at_10 value: 94.90899999999999 - type: ndcg_at_100 value: 94.90899999999999 - type: ndcg_at_1000 value: 94.90899999999999 - type: ndcg_at_20 value: 94.90899999999999 - type: ndcg_at_3 value: 94.047 - type: ndcg_at_5 value: 94.90899999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 32.667 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 88.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 98.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBSummScreenFDRetrieval (default) type: dwzhu/LongEmbed config: default split: validation revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.19 - type: map_at_1 value: 81.548 - type: map_at_10 value: 87.57300000000001 - type: map_at_100 value: 87.703 - type: map_at_1000 value: 87.703 - type: map_at_20 value: 87.703 - type: map_at_3 value: 86.657 - type: map_at_5 value: 87.178 - type: mrr_at_1 value: 81.54761904761905 - type: mrr_at_10 value: 87.57345993953139 - 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type: main_score value: 61.623000000000005 - type: map_at_1 value: 40.909 - type: map_at_10 value: 54.376999999999995 - type: map_at_100 value: 55.150999999999996 - type: map_at_1000 value: 55.150999999999996 - type: map_at_20 value: 54.881 - type: map_at_3 value: 49.916 - type: map_at_5 value: 52.883 - type: mrr_at_1 value: 41.16161616161616 - type: mrr_at_10 value: 54.502765752765725 - type: mrr_at_100 value: 55.27732053682153 - type: mrr_at_1000 value: 55.27732053682153 - type: mrr_at_20 value: 55.00715286102044 - type: mrr_at_3 value: 50.04208754208756 - type: mrr_at_5 value: 53.00925925925923 - type: ndcg_at_1 value: 40.909 - type: ndcg_at_10 value: 61.623000000000005 - type: ndcg_at_100 value: 65.08500000000001 - type: ndcg_at_1000 value: 65.08500000000001 - type: ndcg_at_20 value: 63.427 - type: ndcg_at_3 value: 52.735 - type: ndcg_at_5 value: 58.114 - type: precision_at_1 value: 40.909 - type: precision_at_10 value: 8.459999999999999 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.583 - type: precision_at_3 value: 20.286 - type: precision_at_5 value: 14.798 - type: recall_at_1 value: 40.909 - type: recall_at_10 value: 84.596 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 91.667 - type: recall_at_3 value: 60.858999999999995 - type: recall_at_5 value: 73.99 - task: type: Retrieval dataset: name: MTEB LegalBenchCorporateLobbying (default) type: mteb/legalbench_corporate_lobbying config: default split: test revision: f69691c650464e62546d7f2a4536f8f87c891e38 metrics: - type: main_score value: 89.377 - type: map_at_1 value: 79.706 - type: map_at_10 value: 86.652 - type: map_at_100 value: 86.756 - type: map_at_1000 value: 86.76 - type: map_at_20 value: 86.749 - type: map_at_3 value: 85.784 - type: map_at_5 value: 86.387 - type: mrr_at_1 value: 79.70588235294119 - type: mrr_at_10 value: 86.65184407096169 - type: mrr_at_100 value: 86.75604621101796 - type: mrr_at_1000 value: 86.76035993650815 - 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type: main_score value: 54.98800000000001 - type: map_at_1 value: 37.468 - type: map_at_10 value: 48.509 - type: map_at_100 value: 49.681 - type: map_at_1000 value: 49.757 - type: map_at_20 value: 49.021 - type: map_at_3 value: 44.59 - type: map_at_5 value: 46.867999999999995 - type: mrr_at_1 value: 42.6056338028169 - type: mrr_at_10 value: 53.24223116476639 - type: mrr_at_100 value: 53.82518326740263 - type: mrr_at_1000 value: 53.86171229208665 - type: mrr_at_20 value: 53.51133505321795 - type: mrr_at_3 value: 49.76525821596244 - type: mrr_at_5 value: 51.87793427230047 - type: ndcg_at_1 value: 42.606 - type: ndcg_at_10 value: 54.98800000000001 - type: ndcg_at_100 value: 60.111000000000004 - type: ndcg_at_1000 value: 61.382000000000005 - type: ndcg_at_20 value: 56.428999999999995 - type: ndcg_at_3 value: 48.367 - type: ndcg_at_5 value: 51.72 - type: precision_at_1 value: 42.606 - type: precision_at_10 value: 9.331 - type: precision_at_100 value: 1.398 - type: precision_at_1000 value: 0.155 - type: precision_at_20 value: 5.176 - type: precision_at_3 value: 21.009 - type: precision_at_5 value: 15.211 - type: recall_at_1 value: 37.468 - type: recall_at_10 value: 69.607 - type: recall_at_100 value: 91.57 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 74.553 - type: recall_at_3 value: 51.856 - type: recall_at_5 value: 60.309999999999995 - task: type: Retrieval dataset: name: MTEB LitSearchRetrieval (default) type: princeton-nlp/LitSearch config: default split: test revision: 9573fb284a1026c998df47024b888a163f0f0e25 metrics: - type: main_score value: 25.226 - type: map_at_1 value: 15.326999999999998 - type: map_at_10 value: 21.394 - type: map_at_100 value: 22.343 - type: map_at_1000 value: 22.429 - type: map_at_20 value: 21.941 - type: map_at_3 value: 19.123 - type: map_at_5 value: 20.14 - type: mrr_at_1 value: 15.745393634840871 - type: mrr_at_10 value: 21.745699396453173 - type: mrr_at_100 value: 22.713350239272813 - type: mrr_at_1000 value: 22.79392349547639 - 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type: precision_at_3 value: 57.364000000000004 - type: precision_at_5 value: 56.279 - type: recall_at_1 value: 1.055 - type: recall_at_10 value: 8.778 - type: recall_at_100 value: 36.775999999999996 - type: recall_at_1000 value: 72.783 - type: recall_at_20 value: 14.529 - type: recall_at_3 value: 3.019 - type: recall_at_5 value: 4.987 - task: type: Retrieval dataset: name: MTEB MSMARCOHardNegatives (default) type: mteb/MSMARCO_test_top_250_only_w_correct-v2 config: default split: test revision: 67c0b4f7f15946e0b15cf6cf3b8993d04cb3efc6 metrics: - type: main_score value: 49.17 - type: map_at_1 value: 1.871 - type: map_at_10 value: 11.155 - type: map_at_100 value: 38.995000000000005 - type: map_at_1000 value: 58.543 - type: map_at_20 value: 16.564 - type: map_at_3 value: 5.378 - type: map_at_5 value: 7.403 - type: mrr_at_1 value: 69.76744186046511 - type: mrr_at_10 value: 78.7624584717608 - type: mrr_at_100 value: 78.97387496224707 - type: mrr_at_1000 value: 78.97387496224707 - type: mrr_at_20 value: 78.97387496224707 - 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type: precision_at_5 value: 12.461 - type: recall_at_1 value: 23.926 - type: recall_at_10 value: 73.48599999999999 - type: recall_at_100 value: 88.86699999999999 - type: recall_at_1000 value: 97.89999999999999 - type: recall_at_20 value: 79.541 - type: recall_at_3 value: 50.586 - type: recall_at_5 value: 62.305 - task: type: Retrieval dataset: name: MTEB NFCorpus (default) type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: main_score value: 30.044999999999998 - type: map_at_1 value: 5.015 - type: map_at_10 value: 10.93 - type: map_at_100 value: 13.592 - type: map_at_1000 value: 14.890999999999998 - type: map_at_20 value: 12.005 - type: map_at_3 value: 8.518 - type: map_at_5 value: 9.646 - type: mrr_at_1 value: 41.17647058823529 - type: mrr_at_10 value: 49.96486313823774 - type: mrr_at_100 value: 50.73871199227761 - type: mrr_at_1000 value: 50.788364180879874 - type: mrr_at_20 value: 50.53695651632227 - type: mrr_at_3 value: 47.936016511867905 - 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type: map_at_1 value: 2.026 - type: map_at_10 value: 15.136 - type: map_at_100 value: 31.539 - type: map_at_1000 value: 34.672 - type: map_at_20 value: 21.477 - type: map_at_3 value: 5.931 - type: map_at_5 value: 9.476999999999999 - type: mrr_at_1 value: 63.26530612244898 - type: mrr_at_10 value: 77.57045675413023 - type: mrr_at_100 value: 77.75598551108757 - type: mrr_at_1000 value: 77.75598551108757 - type: mrr_at_20 value: 77.75598551108757 - type: mrr_at_3 value: 75.85034013605441 - type: mrr_at_5 value: 77.27891156462586 - type: ndcg_at_1 value: 54.081999999999994 - type: ndcg_at_10 value: 52.575 - type: ndcg_at_100 value: 55.051 - type: ndcg_at_1000 value: 67.027 - type: ndcg_at_20 value: 46.561 - type: ndcg_at_3 value: 58.48799999999999 - type: ndcg_at_5 value: 57.115 - type: precision_at_1 value: 63.26500000000001 - type: precision_at_10 value: 56.531 - type: precision_at_100 value: 18.898 - type: precision_at_1000 value: 3.084 - type: precision_at_20 value: 44.082 - type: precision_at_3 value: 68.027 - type: precision_at_5 value: 65.714 - type: recall_at_1 value: 2.026 - type: recall_at_10 value: 19.494 - type: recall_at_100 value: 59.349 - type: recall_at_1000 value: 89.84 - type: recall_at_20 value: 29.953000000000003 - type: recall_at_3 value: 6.819999999999999 - type: recall_at_5 value: 11.386000000000001 - task: type: Retrieval dataset: name: MTEB WinoGrande (default) type: RAR-b/winogrande config: default split: test revision: f74c094f321077cf909ddfb8bccc1b5912a4ac28 metrics: - type: main_score value: 61.800999999999995 - type: map_at_1 value: 29.044999999999998 - type: map_at_10 value: 51.514 - type: map_at_100 value: 51.896 - type: map_at_1000 value: 51.897000000000006 - type: map_at_20 value: 51.842 - type: map_at_3 value: 46.803 - type: map_at_5 value: 49.957 - type: mrr_at_1 value: 29.36069455406472 - type: mrr_at_10 value: 51.617619423460056 - type: mrr_at_100 value: 52.01415572431666 - type: mrr_at_1000 value: 52.014274589675495 - type: mrr_at_20 value: 51.95996098874236 - type: mrr_at_3 value: 46.86924493554325 - type: mrr_at_5 value: 50.04209418574064 - type: ndcg_at_1 value: 29.044999999999998 - type: ndcg_at_10 value: 61.800999999999995 - type: ndcg_at_100 value: 63.315 - type: ndcg_at_1000 value: 63.324000000000005 - type: ndcg_at_20 value: 62.986 - type: ndcg_at_3 value: 52.525 - type: ndcg_at_5 value: 58.160999999999994 - type: precision_at_1 value: 29.044999999999998 - type: precision_at_10 value: 9.361 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.913 - type: precision_at_3 value: 23.02 - type: precision_at_5 value: 16.527 - type: recall_at_1 value: 29.044999999999998 - type: recall_at_10 value: 93.607 - type: recall_at_100 value: 99.921 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 98.264 - type: recall_at_3 value: 69.06099999999999 - type: recall_at_5 value: 82.636 --- # Static Embeddings with BERT uncased tokenizer finetuned on various datasets This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [squad](https://huggingface.co/datasets/sentence-transformers/squad), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli), [paq](https://huggingface.co/datasets/sentence-transformers/paq), [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets), [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual), [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa), [miracl](https://huggingface.co/datasets/sentence-transformers/miracl), [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) and [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and is designed to be used for semantic search. Read our [Static Embeddings blogpost](https://huggingface.co/blog/static-embeddings) to learn more about this model and how it was trained. * 0 Active Parameters: This model does not use any active parameters, instead consisting exclusively of averaging pre-computed token embeddings. * 100x to 400x faster: On CPU, this model is 100x to 400x faster than common options like [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). On GPU, it's 10x to 25x faster. * Matryoshka: This model was trained with a [Matryoshka loss](https://huggingface.co/blog/matryoshka), allowing you to truncate the embeddings for faster retrieval at minimal performance costs. * Evaluations: See [Evaluations](#evaluation) for details on performance on NanoBEIR, embedding speed, and Matryoshka dimensionality truncation. In short, this model is 87.4% as performant as the commonly used [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). * Training Script: See [train.py](train.py) for the training script used to train this model from scratch. See [`static-similarity-mrl-multilingual-v1`](https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1) for a general-purpose multilingual static embedding model. It's been trained for semantic textual similarity, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 tokens - Similarity Function: Cosine Similarity - Training Datasets: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) - [squad](https://huggingface.co/datasets/sentence-transformers/squad) - [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) - [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) - [paq](https://huggingface.co/datasets/sentence-transformers/paq) - [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) - [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) - [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) - [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) - [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) - [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-retrieval-mrl-en-v1") # Run inference sentences = [ 'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?', 'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.', 'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` This model was trained with Matryoshka loss, allowing this model to be used with lower dimensionalities with minimal performance loss (See [Matryoshka Evaluations](#matryoshka-evaluations) for evaluations). Notably, a lower dimensionality allows for much faster and cheaper information retrieval. You can specify a lower dimensionality with the `truncate_dim` argument when initializing the Sentence Transformer model: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/static-retrieval-mrl-en-v1", truncate_dim=256) embeddings = model.encode([ "what is the difference between chronological order and spatial order?", "can lavender grow indoors?" ]) print(embeddings.shape) # => (2, 256) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| cosine_accuracy@1 \| 0.32 \| 0.7 \| 0.46 \| 0.28 \| 0.64 \| 0.18 \| 0.42 \| 0.24 \| 0.8 \| 0.28 \| 0.1 \| 0.52 \| 0.5714 \| \| cosine_accuracy@3 \| 0.52 \| 0.84 \| 0.8 \| 0.44 \| 0.82 \| 0.42 \| 0.56 \| 0.44 \| 0.96 \| 0.48 \| 0.46 \| 0.6 \| 0.898 \| \| cosine_accuracy@5 \| 0.6 \| 0.9 \| 0.84 \| 0.54 \| 0.86 \| 0.5 \| 0.62 \| 0.58 \| 1.0 \| 0.54 \| 0.56 \| 0.62 \| 0.9796 \| \| cosine_accuracy@10 \| 0.78 \| 0.94 \| 0.94 \| 0.64 \| 0.96 \| 0.66 \| 0.72 \| 0.7 \| 1.0 \| 0.7 \| 0.74 \| 0.76 \| 1.0 \| \| cosine_precision@1 \| 0.32 \| 0.7 \| 0.46 \| 0.28 \| 0.64 \| 0.18 \| 0.42 \| 0.24 \| 0.8 \| 0.28 \| 0.1 \| 0.52 \| 0.5714 \| \| cosine_precision@3 \| 0.1933 \| 0.5867 \| 0.2667 \| 0.1933 \| 0.3733 \| 0.14 \| 0.3733 \| 0.1467 \| 0.3867 \| 0.2267 \| 0.1533 \| 0.2067 \| 0.6054 \| \| cosine_precision@5 \| 0.14 \| 0.544 \| 0.18 \| 0.16 \| 0.26 \| 0.1 \| 0.32 \| 0.124 \| 0.248 \| 0.188 \| 0.112 \| 0.132 \| 0.6204 \| \| cosine_precision@10 \| 0.104 \| 0.452 \| 0.1 \| 0.104 \| 0.148 \| 0.066 \| 0.244 \| 0.076 \| 0.13 \| 0.14 \| 0.074 \| 0.084 \| 0.5306 \| \| cosine_recall@1 \| 0.1467 \| 0.0805 \| 0.4367 \| 0.1519 \| 0.32 \| 0.18 \| 0.0428 \| 0.24 \| 0.7107 \| 0.0597 \| 0.1 \| 0.485 \| 0.0398 \| \| cosine_recall@3 \| 0.239 \| 0.1605 \| 0.7467 \| 0.2983 \| 0.56 \| 0.42 \| 0.0984 \| 0.43 \| 0.9253 \| 0.1417 \| 0.46 \| 0.57 \| 0.1236 \| \| cosine_recall@5 \| 0.279 \| 0.218 \| 0.8033 \| 0.3793 \| 0.65 \| 0.5 \| 0.1196 \| 0.58 \| 0.9627 \| 0.1947 \| 0.56 \| 0.595 \| 0.2095 \| \| cosine_recall@10 \| 0.4197 \| 0.3143 \| 0.9033 \| 0.4838 \| 0.74 \| 0.66 \| 0.1389 \| 0.69 \| 0.9793 \| 0.2887 \| 0.74 \| 0.75 \| 0.337 \| \| cosine_ndcg@10 \| 0.3309 \| 0.5681 \| 0.6922 \| 0.3651 \| 0.6547 \| 0.4041 \| 0.3242 \| 0.4534 \| 0.8951 \| 0.2643 \| 0.4078 \| 0.6111 \| 0.5703 \| \| cosine_mrr@10 \| 0.4453 \| 0.7854 \| 0.6397 \| 0.3915 \| 0.7485 \| 0.3245 \| 0.5041 \| 0.3764 \| 0.88 \| 0.3998 \| 0.3034 \| 0.5837 \| 0.744 \| \| cosine_map@100 \| 0.2598 \| 0.4335 \| 0.6205 \| 0.3024 \| 0.5798 \| 0.3389 \| 0.1449 \| 0.389 \| 0.8594 \| 0.205 \| 0.3151 \| 0.5683 \| 0.447 \| #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.424 \| \| cosine_accuracy@3 \| 0.6337 \| \| cosine_accuracy@5 \| 0.703 \| \| cosine_accuracy@10 \| 0.8108 \| \| cosine_precision@1 \| 0.424 \| \| cosine_precision@3 \| 0.2963 \| \| cosine_precision@5 \| 0.2406 \| \| cosine_precision@10 \| 0.1733 \| \| cosine_recall@1 \| 0.2303 \| \| cosine_recall@3 \| 0.398 \| \| cosine_recall@5 \| 0.4655 \| \| cosine_recall@10 \| 0.5727 \| \| cosine_ndcg@10 \| 0.5032 \| \| cosine_mrr@10 \| 0.5482 \| \| cosine_map@100 \| 0.4203 \| We've evaluated [sentence-transformers/static-retrieval-mrl-en-v1](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) on NanoBEIR and plotted it against the inference speed computed on a RTX 3090 and i7-13700K. For the inference speed tests, we calculated the number of computed query embeddings of the [GooAQ dataset](https://huggingface.co/datasets/sentence-transformers/gooaq) per second, either on CPU or GPU. We evaluate against 3 types of models: 1. Attention-based dense embedding models, e.g. traditional Sentence Transformer models like [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [`bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5), and [`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 2. Static Embedding-based models, e.g. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1), [`potion-base-8M`](https://huggingface.co/minishlab/potion-base-8M), [`M2V_base_output`](https://huggingface.co/minishlab/M2V_base_output), and [`glove.6B.300d`](https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d). 3. Sparse bag-of-words model, BM25, often a strong baseline. <details><summary>Click to expand BM25 implementation details</summary> I relied on the highly efficient [bm25s](https://github.com/xhluca/bm25s) implementation, using `model.get_scores()` on tokens after tokenization and stemming with the English `PyStemmer`. </details> > NOTE: Many of the attention-based dense embedding models are finetuned on the training splits of the (Nano)BEIR evaluation datasets. This gives the models an unfair advantage in this benchmark and can result in lower downstream performance on real retrieval tasks. > > [static-retrieval-mrl-en-v1](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is purposefully not trained on any of these datasets. ##### GPU ![NanoBEIR performance vs inference speed](img/nano_beir_vs_speed_gpu.png) ##### CPU ![NanoBEIR performance vs inference speed](img/nano_beir_vs_speed_cpu.png) We can draw some notable conclusions from these figures: 1. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) outperforms all other Static Embedding models. 2. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is the only Static Embedding model to outperform BM25. 3. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is * 87.4% as performant as the commonly used [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), * 24x faster on GPU, * 397x faster on CPU. 4. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is quicker on CPU than on GPU: This model can run extraordinarily quickly everywhere, including consumer-grade PCs, tiny servers, phones, or in-browser. #### Matryoshka Evaluations We experimented with the results on NanoBEIR performance when we performed Matryoshka-style dimensionality reduction by truncating the output embeddings to a lower dimensionality. \| Dimensionality \| NanoBEIR_mean \| NanoArguAna \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoSciFact \| NanoTouche2020 \| \|----------------\|---------------\|-------------\|------------------\|-------------\|-----------\|--------------\|--------------\|-------------\|--------------\|--------\|--------------------\|-------------\|-------------\|----------------\| \| 1024 \| 0.5031 \| 0.4077 \| 0.3308 \| 0.5681 \| 0.6921 \| 0.3651 \| 0.6547 \| 0.4040 \| 0.3241 \| 0.4533 \| 0.8950 \| 0.2642 \| 0.6111 \| 0.5702 \| \| 512 \| 0.4957 \| 0.3878 \| 0.3360 \| 0.5626 \| 0.6945 \| 0.3517 \| 0.6280 \| 0.3892 \| 0.3206 \| 0.4505 \| 0.8986 \| 0.2657 \| 0.5953 \| 0.5635 \| \| 256 \| 0.4819 \| 0.3855 \| 0.3203 \| 0.5407 \| 0.6734 \| 0.3518 \| 0.6027 \| 0.4144 \| 0.2860 \| 0.4254 \| 0.8948 \| 0.2466 \| 0.5620 \| 0.5605 \| \| 128 \| 0.4622 \| 0.4001 \| 0.2982 \| 0.5266 \| 0.6273 \| 0.3188 \| 0.5606 \| 0.4025 \| 0.2693 \| 0.4021 \| 0.8930 \| 0.2283 \| 0.5447 \| 0.5368 \| \| 64 \| 0.4176 \| 0.3424 \| 0.2809 \| 0.5022 \| 0.5480 \| 0.2831 \| 0.4680 \| 0.3739 \| 0.2153 \| 0.3845 \| 0.8525 \| 0.1680 \| 0.5045 \| 0.5050 \| \| 32 \| 0.3532 \| 0.2866 \| 0.1870 \| 0.4292 \| 0.4193 \| 0.2292 \| 0.3602 \| 0.3587 \| 0.1444 \| 0.3525 \| 0.8325 \| 0.1525 \| 0.3983 \| 0.4408 \| ![NanoBEIR performance vs Matryoshka dimensionality reduction](img/nano_beir_matryoshka.png) These findings show that reducing the dimensionality by e.g. 2x only has a 1.47% reduction in performance (0.5031 NDCG@10 vs 0.4957 NDCG@10), while realistically resulting in a 2x speedup in retrieval speed. <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Datasets <details><summary>gooaq</summary> * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco</summary> * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 11 characters</li><li>mean: 33.26 characters</li><li>max: 197 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 356.24 characters</li><li>max: 1006 characters</li></ul> \| <ul><li>min: 68 characters</li><li>mean: 327.52 characters</li><li>max: 995 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:---------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>when was the sullivan acts</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \| \| <code>can lavender grow indoors</code> \| <code>Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.</code> \| <code>Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.</code> \| \| <code>what kind of barley do you malt</code> \| <code>Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.</code> \| <code>Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>squad</summary> * Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) * Size: 87,599 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 14 characters</li><li>mean: 59.66 characters</li><li>max: 150 characters</li></ul> \| <ul><li>min: 156 characters</li><li>mean: 769.53 characters</li><li>max: 3706 characters</li></ul> \| * Samples: \| question \| answer \| \|:-------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What did Business Insider call San Diego in 2013?</code> \| <code>San Diego was ranked as the 20th-safest city in America in 2013 by Business Insider. According to Forbes magazine, San Diego was the ninth-safest city in the top 10 list of safest cities in the U.S. in 2010. Like most major cities, San Diego had a declining crime rate from 1990 to 2000. Crime in San Diego increased in the early 2000s. In 2004, San Diego had the sixth lowest crime rate of any U.S. city with over half a million residents. From 2002 to 2006, the crime rate overall dropped 0.8%, though not evenly by category. While violent crime decreased 12.4% during this period, property crime increased 1.1%. Total property crimes per 100,000 people were lower than the national average in 2008.</code> \| \| <code>What did the Spanish call this region?</code> \| <code>The name Montana comes from the Spanish word Montaña, meaning "mountain", or more broadly, "mountainous country". Montaña del Norte was the name given by early Spanish explorers to the entire mountainous region of the west. The name Montana was added to a bill by the United States House Committee on Territories, which was chaired at the time by Rep. James Ashley of Ohio, for the territory that would become Idaho Territory. The name was successfully changed by Representatives Henry Wilson (Massachusetts) and Benjamin F. Harding (Oregon), who complained that Montana had "no meaning". When Ashley presented a bill to establish a temporary government in 1864 for a new territory to be carved out of Idaho, he again chose Montana Territory. This time Rep. Samuel Cox, also of Ohio, objected to the name. Cox complained that the name was a misnomer given that most of the territory was not mountainous and that a Native American name would be more appropriate than a Spanish one. Other names such as...</code> \| \| <code>Small missiles were designed that could be mounted on what?</code> \| <code>As this process continued, the missile found itself being used for more and more of the roles formerly filled by guns. First to go were the large weapons, replaced by equally large missile systems of much higher performance. Smaller missiles soon followed, eventually becoming small enough to be mounted on armored cars and tank chassis. These started replacing, or at least supplanting, similar gun-based SPAAG systems in the 1960s, and by the 1990s had replaced almost all such systems in modern armies. Man-portable missiles, MANPADs as they are known today, were introduced in the 1960s and have supplanted or even replaced even the smallest guns in most advanced armies.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>s2orc</summary> * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 90,000 training samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: \| \| title \| abstract \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 31 characters</li><li>mean: 80.02 characters</li><li>max: 185 characters</li></ul> \| <ul><li>min: 84 characters</li><li>mean: 635.31 characters</li><li>max: 1023 characters</li></ul> \| * Samples: \| title \| abstract \| \|:----------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions</code> \| <code>The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.</code> \| \| <code>Enlightening on medical administration by clinical governance in British</code> \| <code>Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.</code> \| \| <code>APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION</code> \| <code>Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>allnli</summary> * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 557,850 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 34.88 characters</li><li>max: 193 characters</li></ul> \| <ul><li>min: 15 characters</li><li>mean: 46.49 characters</li><li>max: 181 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 50.47 characters</li><li>max: 204 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\| \| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \| \| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \| \| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>paq</summary> * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 25 characters</li><li>mean: 50.56 characters</li><li>max: 104 characters</li></ul> \| <ul><li>min: 509 characters</li><li>mean: 620.96 characters</li><li>max: 773 characters</li></ul> \| * Samples: \| query \| answer \| \|:----------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>in veetla visheshanga ganesh is the husband of</code> \| <code>Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him</code> \| \| <code>when did simon property group became a publicly traded company</code> \| <code>of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly</code> \| \| <code>what was the nationality of antoine faivre</code> \| <code>Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>trivia</summary>_qa * Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 73,346 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 21 characters</li><li>mean: 76.91 characters</li><li>max: 455 characters</li></ul> \| <ul><li>min: 136 characters</li><li>mean: 3273.89 characters</li><li>max: 4096 characters</li></ul> \| * Samples: \| query \| answer \| \|:--------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What type of rock is formed by the solidification of molten magma?</code> \| <code>igneous rock - Dictionary Definition : Vocabulary.com igneous rock n rock formed by the solidification of molten magma Types: a rare type of peridotite that sometimes contains diamonds; found in South Africa and Siberia Type of: material consisting of the aggregate of minerals like those making up the Earth's crust Word Family Usage Examples Sign up, it's free! Whether you're a student, an educator, or a life-long learner, Vocabulary.com can put you on the path to systematic vocabulary improvement.</code> \| \| <code>Which river flows through the town of Shrewsbury?</code> \| <code>River Severn \| river, Wales and England, United Kingdom \| Britannica.com river, Wales and England, United Kingdom Written By: Wales River Severn, Welsh Hafren, Britain’s longest river from source to tidal waters—about 180 miles (290 km) long, with the Severn estuary adding some 40 miles (64 km) to its total length. The Severn rises near the River Wye on the northeastern slopes of Plynlimon (Welsh: Pumlumon), Wales , and follows a semicircular course basically southward to the Bristol Channel and the Atlantic Ocean . It drains an area of 4,350 square miles (11,266 square km) with an average discharge at Bewdley of 2,170 cubic feet (61.5 cubic m) per second. River Severn at Shrewsbury, Shropshire, Eng. Chris Bayley The river’s course is at first southeasterly, descending from an elevation of 2,000 feet (600 m) at its source to 500 feet (150 m) at the Welsh town of Llanidloes. There it turns sharply northeastward, following the Vale of Powys past Newtown and Welshpool . At Llanymynech the...</code> \| \| <code>Which band's name was inspired by a novel by Herman Hesse?</code> \| <code>23 Band Names Inspired by Literature :: Books :: Lists :: Paste 23 Band Names Inspired by Literature By Wyndham Wyeth \| April 24, 2011 \| 10:52pm Share Tweet Submit Pin At Paste, we look for “Signs of Life” in all forms of art. And while we value each artform for its unique merits, it’s always a treat when they overlap. So we decided to take a look at bands that derived their names from literature. The works that inspired several of the entries are probably obvious, but a few of them will most certainly surprise you. It may also surprise you to see which genres favor the written word. (Who knew metalheads were such scholars?) Photo by Max Blau New Jersey punks Titus Andronicus take their name from the greatest wordsmith of them all, William Shakespeare . Titus Andronicus is thought to be the famous playwright’s first tragedy. It is also his bloodiest and most violent work. 2. The Doors Source: The Doors of Perception by Aldous Huxley When The Doors formed in 1965, they decided to na...</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco_10m</summary> * Dataset: [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2) * Size: 10,000,000 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 characters</li><li>mean: 33.47 characters</li><li>max: 158 characters</li></ul> \| <ul><li>min: 53 characters</li><li>mean: 353.76 characters</li><li>max: 948 characters</li></ul> \| <ul><li>min: 67 characters</li><li>mean: 343.74 characters</li><li>max: 1063 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:-------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is ged equivalent</code> \| <code>A GED is equivalent to a high school diploma however colleges do not look at it the same. A huge part of high school is commitment and dedication, therefore if you choose to drop out of high school you will probably not get accepted into any top colleges. However, you could always start at a community college and work your way up. Good Luck</code> \| <code>If you are not far along in your Army career, education is an especially good way to boost your chances of promotion. Continuing your education at any level can earn you points. For completing your GED or bachelor's degree, you earn 10 points.</code> \| \| <code>foods that help with diverticulitis</code> \| <code>Diet for Diverticulitis. Gradually you can ease back into a regular diet. Your doctor may advise you to start with low-fiber foods (white bread, meat, poultry, fish, eggs, and dairy products) before introducing high-fiber foods. Fiber softens and adds bulk to stools, helping them pass more easily through the colon.</code> \| <code>During an attack of diverticulitis, your doctor may recommend a clear liquid diet or a low-fiber diet. This helps the area of infection to heal. Foods allowed on a clear-liquid diet include: 1 Plain water.iverticulitis occurs when small, bulging pouches (diverticula) in your colon become infected and inflamed â causing severe abdominal pain, nausea, and fever. The treatment of a diverticulitis attack will depend on the severity of the symptoms and whether this is your first attack.</code> \| \| <code>calories burned in turbo kick class</code> \| <code>The American Council on Exercise did a study to find out how many calories are burned during a turbo kick class. The study looked at 15 women in a turbo kick class who weighed about 135 lbs and found that they burned between 6.45 to 8.3 calories a minute. This comes out to about 350 to 450 calories an hour.</code> \| <code>Popular Calories Burned Searches: 1 Calories Burned For Intervals of run/walk or walk/jog: 6 mph or slower (> 10 minutes per mile) 2 Calories Burned For Walking: 3.5 mph (17 minutes per mile) 3 Calories Burned For Walking: 4 mph (15 minutes per mile) Calories Burned For Walking: 4.6 mph (13 minutes per mile)</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>swim_ir</summary> * Dataset: [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) at [834c20f](https://huggingface.co/datasets/nthakur/swim-ir-monolingual/tree/834c20f0ceef6a68e029fb4447d17d20bb0288c3) * Size: 501,538 training samples * Columns: <code>query</code> and <code>text</code> * Approximate statistics based on the first 1000 samples: \| \| query \| text \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 0 characters</li><li>mean: 59.98 characters</li><li>max: 189 characters</li></ul> \| <ul><li>min: 208 characters</li><li>mean: 525.9 characters</li><li>max: 2743 characters</li></ul> \| * Samples: \| query \| text \| \|:------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>How many blocked kicks did Williams have in his second year at Bowling Green State University?</code> \| <code>Williams accepted a football scholarship from Bowling Green State University, where he became one of the best special teams players in school history. As a redshirt freshman, he was a wide receiver on the scout team. The next year, he played mainly on special teams and had 3 blocked kicks.</code> \| \| <code>How many town councils are there in the metropolitan borough?</code> \| <code>Horwich, Westhoughton and Blackrod are now constituted as civil parishes. There are three town councils in the metropolitan borough, Westhoughton Town Council, Horwich Town Council and Blackrod Town Council. The rest of the metropolitan borough, Bolton, Farnworth, Kearsley, Little Lever, and South Turton, have remained unparished areas since 1974.</code> \| \| <code>What is the name of the person selected to lead BART’s 296-member police force?</code> \| <code>In 2009, the hiring of two independent organizations reviewed BART's policies and procedures in the process of overseeing the BART Police. The two independent firms investigated the matters of BART Police Shooting of Oscar Grant and were charged with making recommendations to the board. Ward Allen formulated and chaired BART's first Police Department Review Committee, and as a result, BART made sweeping changes on many security measures, as well as corrected and implemented several policies and procedures. BPD Review Committee has led to the re-training of all officers on use of force, diversity re-training and other issues. Ward Allen hired Kenton Rainey, the person selected to lead BART’s 296-member police force, to take command as Chief of Police.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>pubmedqa</summary> * Dataset: [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) at [a1ef0b5](https://huggingface.co/datasets/sentence-transformers/pubmedqa/tree/a1ef0b513b16ed490e807ac11da40e436d3a54c3) * Size: 1,660 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, and <code>negative_20</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| negative_3 \| negative_4 \| negative_5 \| negative_6 \| negative_7 \| negative_8 \| negative_9 \| negative_10 \| negative_11 \| negative_12 \| negative_13 \| negative_14 \| negative_15 \| negative_16 \| negative_17 \| negative_18 \| negative_19 \| negative_20 \| \|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| \| details \| <ul><li>min: 25 characters</li><li>mean: 94.06 characters</li><li>max: 213 characters</li></ul> \| <ul><li>min: 5 characters</li><li>mean: 409.42 characters</li><li>max: 1582 characters</li></ul> \| <ul><li>min: 5 characters</li><li>mean: 325.57 characters</li><li>max: 1300 characters</li></ul> \| <ul><li>min: 17 characters</li><li>mean: 299.5 characters</li><li>max: 1352 characters</li></ul> \| <ul><li>min: 21 characters</li><li>mean: 317.37 characters</li><li>max: 1590 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 334.43 characters</li><li>max: 1536 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 335.49 characters</li><li>max: 1247 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 336.16 characters</li><li>max: 1383 characters</li></ul> \| <ul><li>min: 14 characters</li><li>mean: 319.98 characters</li><li>max: 1501 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 337.33 characters</li><li>max: 1493 characters</li></ul> \| <ul><li>min: 15 characters</li><li>mean: 324.82 characters</li><li>max: 1058 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 336.76 characters</li><li>max: 1457 characters</li></ul> \| <ul><li>min: 10 characters</li><li>mean: 355.4 characters</li><li>max: 1748 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 344.26 characters</li><li>max: 1705 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 335.11 characters</li><li>max: 1593 characters</li></ul> \| <ul><li>min: 18 characters</li><li>mean: 353.83 characters</li><li>max: 1374 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 328.01 characters</li><li>max: 1755 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 337.74 characters</li><li>max: 1579 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 336.94 characters</li><li>max: 1325 characters</li></ul> \| <ul><li>min: 15 characters</li><li>mean: 319.49 characters</li><li>max: 1410 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 340.91 characters</li><li>max: 1680 characters</li></ul> \| <ul><li>min: 20 characters</li><li>mean: 330.34 characters</li><li>max: 1509 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| negative_3 \| negative_4 \| negative_5 \| negative_6 \| negative_7 \| negative_8 \| negative_9 \| negative_10 \| negative_11 \| negative_12 \| negative_13 \| negative_14 \| negative_15 \| negative_16 \| negative_17 \| negative_18 \| negative_19 \| negative_20 \| 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\| <code>Visceral adipose tissue area measurement at a single level: can it represent visceral adipose tissue volume?</code> \| <code>Measurement of visceral adipose tissue (VAT) needs to be accurate and sensitive to change for risk monitoring. The purpose of this study is to determine the CT slice location where VAT area can best reflect changes in VAT volume and body weight.</code> \| <code>46 patients with psoriasis and 46 sex- and age-matched control patients were included in this study. The abdominal fat area [visceral fat area (VFA), subcutaneous fat area (SFA) and total fat area (TFA)] at the level of the umbilicus was evaluated by computed tomography.</code> \| <code>A retrospective review of CRC patients who received adjuvant chemotherapy at a single center during the period 2006-2009 identified from a prospectively maintained database. Visceral adiposity was determined by measuring visceral fat area (VFA) on preoperative staging CT. All patients were followed up to study completion or death.</code> \| <code>A total of 1941 participants without known cardiovascular disease were enrolled from the Korean Genome and Epidemiology Study. Visceral fat area (VFA) was assessed by computed tomography. Appendicular skeletal muscle mass (ASM) was estimated by dual-energy X-ray absorptiometry and was used as a percentage of body weight (ASM/Wt). LV structure and function were assessed by tissue Doppler imaging (TDI) echocardiography.</code> \| <code>One hundred and forty nonobese patients (BMI <25 kg/m2) were enrolled. EFV and visceral fat area were measured by MDCT. Patients were classified according to the plaque components (noncalcified, mixed and calcified) and severity of CAD. Inflammatory biomarkers were also measured, and compared with each CT parameter.</code> \| <code>The blood gas level in each pulmonary vein (PV) was measured in supine subjects with diverse body mass index (BMI) values, to determine whether there was a regional insufficiency in gas exchange depending on the subject's BMI.</code> \| <code>Magnetic resonance imaging (MRI) of 163 patients with cholecystolithiasis and 163 non-cholecystolithiasis control subjects admitted to our institution between March 2011 and September 2013 were included in this cross-sectional evaluation. There were 98 women and 65 men in cholecystolithiasis group with an average age of 57±16 years (range 25-86 years). There were 87 women and 76 men in the control group with an average age of 41±16 years (range 14-77 years). Visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (SAT) and total abdominal adipose tissue (TAT) of all the subjects at navel level were measured on abdominal MRI. According to the visceral adipose area (cut-off point VAT = 100 cm2), study subjects were divided into 1) increased accumulation of intra-abdominal fat and 2) normal distribution of intra-abdominal fat. Logistic regression was used to assess the association of fat with the presence of cholecystolithiasis, adjusted for age and sex.</code> \| <code>We reviewed the medical records of 106 patients undergoing LA or LESS-A at our institution. Total fat area (TFA) and visceral fat area (VFA) were measured at the level of the L4 vertebra by computed tomography. To categorize the type of obesity, the VFA/TFA ratio was calculated. Multiple logistic regression analyses were performed to identify independent predictors of prolonged operative time.</code> \| <code>The weight gain was 5.2% greater in rats exposed to fructose than in controls (P = 0.042). Total and visceral adipose tissue volumes were 5.2 cm3 (P = 0.017) and 3.1 cm3 (P = 0.019) greater, respectively, while lean tissue volumes did not differ. The level of triglycerides and apolipoprotein A-I was higher (P = 0.034, P = 0.005, respectively) in fructose-exposed rats.</code> \| <code>A total of 1593 middle-aged to older patients participated in this cross-sectional study. Brachial-to-ankle pulse wave velocity (baPWV) was measured as an index of arterial stiffness. Second PP (PP2) at the second peak of radial SBP was used to estimate central PP. Radial augmentation index was calculated as PP2/PP. Thigh muscle cross-sectional area and abdominal visceral fat area were quantified by computed tomography. Patients were classified as sarcopenic if their hand grip strength or skeletal muscle mass (measured by bioelectrical impedance) was more than 1 SD lower than the mean of those in a reference group aged below 50 years, or in the lowest 20% of the studied population. Visceral obesity was defined as visceral fat area greater than 100 cm.</code> \| <code>Visceral adiposity is linked with sleep-disordered breathing (SDB) (called Syndrome Z), and both correlate with coronary artery disease (CAD). The aim of the present study was to determine the significance of excess visceral fat, SDB and circulating levels of biomarkers in CAD in Japanese men.</code> \| <code>There are no published studies on the impact of visceral adipose tissue (VAT) change on outcomes of restorative proctocolectomy and ileal pouch-anal anastomosis (IPAA). The aim of this historic cohort study was to evaluate the impact of excessive VAT gain on the outcomes of inflammatory bowel disease (IBD) patients with IPAA.</code> \| <code>A subgroup of 46 men (n = 20, aged 29.1-33.4 years) and women (n = 26, aged 29.1-33.8 years) were recruited from an ongoing population study at our institution. Anthropometric variables including weight, height, and waist circumference were measured using standard procedures, and body mass index was calculated (kg/m(2)). Visceral adipose tissue (VAT) was measured with magnetic resonance imaging. Plasma apolipoproteins, lipids, glucose, and insulin were measured after an overnight fasting.</code> \| <code>Our aim was to describe adipose tissue content and distribution in ALS patients.</code> \| <code>This was a cross-sectional study of 140 Japanese patients with type 2 diabetes (mean age 65 ± 11 year; 44.6% women). Visceral fat area (VFA; cm(2) ) and liver attenuation index (LAI) were assessed by abdominal computed tomography. The patients were divided into four groups by VFA and body mass index (BMI; kg/m(2) ) as follows: BMI <25 kg/m(2) and VFA <100 cm(2) (OB[-]VA[-]), BMI ≥25 kg/m(2) and VFA <100 cm(2) (OB[+]VA[-]), BMI <25 kg/m(2) and VFA ≥100 cm(2) (OB[-]VA[+]), and BMI ≥25 kg/m(2) and VFA ≥100 cm(2) (OB[+]VA[+]). Multivariate linear regression and logistic regression analysis were carried out to determine the impact of OB(-)VA(+) on LAI.</code> \| <code>Epicardial adipose tissue represents visceral adiposity, the early detection of which could be helpful for assessing subclinical target organ damage. Although previous studies have reported a relationship between epicardial fat thickness (EFT) and carotid intima-media thickness, there have been no studies detailing the relationship between EFT and brachial-ankle pulse wave velocity (baPWV).</code> \| <code>Higher adiponectin levels were associated with lower risk of diabetes (P < 0.001). Visceral fat was the only adiposity measure associated with diabetes after adjusting for BMI (odds ratio 3.0 [2.1-4.3] in women and 1.3 [1.0-1.6] in men, P < 0.001 between-sex comparison). Adipocytokines attenuated the association between visceral fat and diabetes for both sexes but more strongly in men (women 2.3 [1.5-3.3], men 1.1 [0.9-1.4]). In men, adiponectin, IL-6, and PAI-1 remained independently associated with diabetes after adjusting for fat depots; in women, adiponectin was the only independently associated adipocytokine. Controlling for insulin, HDL, triglycerides, and blood pressure did not change these results.</code> \| <code>Ninety obesity patients and 95 non-obesity Uygur individuals were enrolled in this study. CD68 levels in abdominal subcutaneous and omental adipose tissues were detected by immunohistochemistry. The cytokine expression levels of adiponectin (APMI) and visfatin in serum were measured by enzyme-linked immunosorbent assay. Infection of 3T3-L1 cells with Ad36 was performed. Real-time PCR was performed to determine expression levels of APMI and Visfatin genes in the 3T3-L1 preadipocytes infected with Ad36.</code> \| <code>Serum CEA levels correlated with visceral fat area, fasting glucose, and triglyceride levels after adjusting for age and BMI. The mean visceral fat area increased significantly with the increasing CEA tirtiles. In a step-wise multiple regression analysis, age (β = 0.26, p<0.01) and visceral fat area (β = 0.19, p = 0.03) were identified as explanatory variables for serum CEA level.</code> \| <code>The visceral adiposity index (VAI) has proved to be a marker of visceral adipose dysfunction, strongly associated with insulin sensitivity in both the general and specific populations of patients at metabolic risk.</code> \| <code>Visceral adiposity is associated with hepatic steatosis, inflammation, and fibrosis in non-alcoholic fatty liver disease (NAFLD). The visceral adiposity index (VAI), a novel marker of visceral fat distribution and dysfunction, has been correlated with histology in hepatitis C. We assessed the ability of VAI to predict disease severity in NAFLD and hence its role as a non-invasive marker of liver damage.</code> \| \| <code>Do general practitioner hospitals reduce the utilisation of general hospital beds?</code> \| <code>Observational study comparing the total rates of admissions and of occupied bed days in general hospitals between populations with and without access to GP hospitals. Comparisons were also made separately for diagnoses commonly encountered in GP hospitals.</code> \| <code>To study the prevalence of GERD comorbidities in a tertiary care hospital.</code> \| <code>The population studied was a sample of 10% of the patients 65 years or older registered with a general practitioner contributing to the General Practice Research Database between 1988 and 1996.</code> \| <code>Tertiary University Hospitals.</code> \| <code>University hospital and district general hospital.</code> \| <code>Inpatient rehabilitation facilities.</code> \| <code>Inpatient rehabilitation facilities.</code> \| <code>General medical service at a teaching hospital.</code> \| <code>To examine the hypothesis that nursing homes responding to these changes in demand shifted the balance of resources from hotel to clinical activities.</code> \| <code>Outpatient practices of general practitioners in the United Kingdom who contribute to the General Practice Research Database.</code> \| <code>Hospital rehabilitation programs.</code> \| <code>Surgical department of a large district general hospital.</code> \| <code>Hospital-based case-control study.</code> \| <code>Patients' homes.</code> \| <code>the relationship between proximity to death and the amount of care provided by general practitioners (GPs) is largely unknown.</code> \| <code>Public hospital, primary care clinic.</code> \| <code>A teaching hospital and a district general hospital.</code> \| <code>District General Hospital in the UK.</code> \| <code>Academic general internal medicine practice.</code> \| <code>A hospital-based study was conducted.</code> \| <code>To evaluate the impact of participation in a trial on General Practitioners management and patient behaviour.</code> \| \| <code>"Occult" posttraumatic lesions of the knee: can magnetic resonance substitute for diagnostic arthroscopy?</code> \| <code>We identified three types of occult post-traumatic injuries by morpho-topographic and signal intensity patterns: bone bruises (no. 25), subchondral (no. 33) and osteochondral (no. 35) injuries. Arthroscopy depicted 45 osteochondral and 19 chondral injuries. A bone bruise was defined as a typical subcortical area of signal loss, with various shapes, on T1-weighted images and of increased signal intensity on T2-weighted and FIR images. The cortical bone and articular cartilage were normal in all cases, while osteochondral injuries exhibited associated bone and cartilage damage with the same abnormal MR signal intensity. Sprain was the mechanism of injury in 52 cases, bruise in 12 and stress in 6. In 52 sprains (30 in valgus), the injury site was the lateral compartment in 92.3% of cases (100% in valgus), associated with meniscal damage in 73% of cases (90% in valgus) and with ligament injury in 90.4% (100% in valgus). In 12 bruises, the injury site was the lateral compartment in 58.3% of...</code> \| <code>Preoperative range of motion (ROM) has been regarded as one of the most important factors in predicting postoperative ROM following total knee arthroplasty (TKA). Mobile-bearing TKA designs have been suggested to possibly improve the knee kinematics compared to fixed-bearing designs. The purpose of this study was to examine the difference in postoperative flexion as a function of preoperative flexion in a consecutive series of TKAs done using a posterior-stabilized rotating-platform prosthesis.</code> \| <code>To assess the association of underlying diagnosis with outcomes after revision total knee arthroplasty (TKA).</code> \| <code>We identified 37 knees diagnosed with osteoarthritis with a preoperative knee flexion ≥120° but a 12-month postoperative range of motion (ROM) ≤110°. A random sample of 111 patients (1:3) from the same database, whose knees had a preoperative and 12-month postoperative ROM ≥120°, based on a diagnosis of primary osteoarthritis and no previous open knee surgery, were selected as the controls.</code> \| <code>This study reports a series of patients operated on by anterior cruciate ligament (ACL) reconstruction combined with valgus high tibial osteotomy (HTO) for chronic anterior knee instability associated with medial tibiofemoral osteoarthritis. It was hypothesized that the combined surgery would enable return to sport, stabilize the knee and relieve medial pain.</code> \| <code>To determine the prevalence and factors associated with knee osteoarthritis (OA) defined by magnetic resonance imaging (MRI) and specific OA features on MRI 1 year after anterior cruciate ligament reconstruction (ACLR).</code> \| <code>In anterior ankle arthroscopy, the anterior working area (AWA) is restricted by the presence of the dorsalis pedis artery (DPA) and tendons. Pseudoaneurysms caused by iatrogenic damage to the DPA are difficult to identify intraoperatively. In knee arthroscopy, risk of popliteal artery damage is reduced in the flexed position [1]. This study investigates how DPA movement is affected by dorsiflexion and plantarflexion with the aim of identifying the positions providing the greatest AWA.</code> \| <code>To investigate whether sex affects the trajectory of functional recovery after total knee arthroplasty (TKA).</code> \| <code>To determine whether magnetic resonance imaging (MRI) evidence of tendinopathy in early rheumatoid arthritis (RA) could be used to predict the course of tendon involvement in later disease and specifically the risk of tendon rupture.</code> \| <code>With the advent of MRI (Magnetic Resonance Imaging), Synovial lesions around knee are being more and more easily detected. Synovial lesions of knee present with boggy swelling, effusion, pain, and restriction of motion. Differential diagnoses of such lesions include pigmented villonodular synovitis, synovial lipoma, synovial chondromatosis, rheumatoid arthritis, synovial hemangioma, amyloid arthropathy, xanthomata and lipoma arborescens. CT and MRI often help in diagnosis of such lesions. MRI of Lipoma Arborescens has been regarded to have characteristic diagnostic appearance - it includes a synovial mass with frond-like architecture and fat signal intensity on all pulse sequences. Sometimes Lipoma Arborescens can present in conjunction with inflammatory arthritis. Synovectomy is often curative for such conditions.</code> \| <code>Joint trauma can lead to a spectrum of acute lesions, including cartilage degradation, ligament or meniscus tears, and synovitis, all potentially associated with osteoarthritis (OA). This study was undertaken to generate and validate a murine model of knee joint trauma following noninvasive controlled injurious compression in vivo.</code> \| <code>To determine which subregions of the knee joint have a high prevalence of pre-radiographic osteoarthritic changes, i.e., cartilage damage and osteophytes that can only be detected by magnetic resonance imaging (MRI), in radiographically normal knees.</code> \| <code>78 of initially 84 patients (80 of 86 knees) were clinically and radiographically reassessed 5 (5.1-5.9) years after conventional, image-based, and image-free total knee arthroplasty. The methodology was identical to that used preoperatively and at 2 years, including the Knee Society score (KSS) and the functional score (FS), and AP and true lateral standard radiographs.</code> \| <code>In adult patients with trauma, an increase in the thickness of the retropharyngeal soft tissues is commonly used as a potential indicator of occult injury, but no studies have examined this parameter using computed tomography (CT) as a screening modality.</code> \| <code>To evaluate the thickness of cartilage at the posterior aspect of the medial and lateral condyle in Osteoarthritis (OA) knees compared to non-OA knees using computed tomography arthrography (CTA).</code> \| <code>Total knee arthroplasty (TKA) successfully alleviates pain from knee osteoarthritis; but deficits in function can persist long term. Despite these well-known deficits, there is little evidence supporting the use of rehabilitation interventions following TKA.</code> \| <code>The anterior intermeniscal ligament of the knee is at risk during knee arthroscopy, anterior cruciate ligament reconstruction, and tibial nail insertion.</code> \| <code>It has been previously demonstrated that radiographic severity of arthritis predicts outcome following knee replacement. In certain circumstances, patients may undergo arthroplasty without severe radiographic disease. An example may be the patient with significant chondral damage unsuccessfully treated with arthroscopy. This patient may proceed to joint replacement when their radiographs would not normally merit such intervention. We investigated whether these findings were also applicable to total ankle replacements (TARs).</code> \| <code>There is an increasing body of evidence that magnetic resonance imaging-occult tissue damage is an important component of primary progressive multiple sclerosis (PPMS) pathology. Proton magnetic resonance spectroscopy (1H-MRS) can be used to measure in vivo whole-brain N-acetylaspartate (WBNAA) concentrations, the decrease of whose levels is considered a marker of neuronal-axonal injury.</code> \| <code>Meniscectomy and articular cartilage damage have been found to increase the prevalence of osteoarthritis after anterior cruciate ligament reconstruction, but the effect of knee range of motion has not been extensively studied.</code> \| <code>Patients 50 years and older with knee osteoarthritis who underwent arthroscopy between 1998 and 2010 were retrospectively identified and an annual arthroscopy rate was calculated from 1998 through 2002 and from 2006 through 2010. Patients who underwent knee arthroplasty within 2 years of arthroscopy during each period were identified, and a 2-year conversion to arthroplasty rate was calculated.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>miracl</summary> * Dataset: [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) at [07e2b62](https://huggingface.co/datasets/sentence-transformers/miracl/tree/07e2b629250bf4185f4c87f640fac15949b8aa73) * Size: 789,900 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 11 characters</li><li>mean: 39.23 characters</li><li>max: 129 characters</li></ul> \| <ul><li>min: 72 characters</li><li>mean: 745.86 characters</li><li>max: 4292 characters</li></ul> \| <ul><li>min: 18 characters</li><li>mean: 649.52 characters</li><li>max: 3570 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Who created The Walking Dead comic books?</code> \| <code>Days Gone Bye (The Walking Dead)<br>Robert Kirkman, the creator of the eponymous series of comic books, considered the idea of creating a television show based on the comic series, but did not move forward. Frank Darabont expressed interest in developing the series for television. In January 2010, AMC formally announced that it had ordered a pilot for a possible series adapted from "The Walking Dead" comic book. In the announcement, the executives stated that Darabont would serve as writer, director, and an executive producer alongside Gale Anne Hurd.</code> \| <code>Living Dead<br>The Walking Dead</code> \| \| <code>When was the first car invented?</code> \| <code>Car<br>In 1879, Benz was granted a patent for his first engine, which had been designed in 1878. Many of his other inventions made the use of the internal combustion engine feasible for powering a vehicle. His first "Motorwagen" was built in 1885 in Mannheim, Germany. He was awarded the patent for its invention as of his application on 29 January 1886 (under the auspices of his major company, Benz & Cie., which was founded in 1883). Benz began promotion of the vehicle on 3 July 1886, and about 25 Benz vehicles were sold between 1888 and 1893, when his first four-wheeler was introduced along with a model intended for affordability. They also were powered with four-stroke engines of his own design. Emile Roger of France, already producing Benz engines under license, now added the Benz car to his line of products. Because France was more open to the early cars, initially more were built and sold in France through Roger than Benz sold in Germany. In August 1888 Bertha Benz, the wife of Karl B...</code> \| <code>Elwood Haynes<br>In 1905, three years after the Apperson brothers split from Haynes, Haynes-Apperson was renamed the Haynes Automobile Company and Haynes launched a series of publicity campaigns. A parade of 2,000 cars was organized in New York City during 1908 and Haynes, whom many recognized as the inventor of the American automobile, led the parade down Broadway riding in the "Pioneer". He was followed by ten Haynes cars, a model from each year to display the advancement in technology. On his way to the parade, Haynes was unaware of the city's newly established speeding laws and was arrested for driving too fast—in a car with a top speed of 15 mph (17 km/h)—and taken to jail. He was soon able to see a magistrate who released him after learning that he was Elwood Haynes and had come to lead the parade. The celebration was intended to be a ten-year commemoration of the invention of the automobile, although earlier self-vehicles dated back nearly twenty years in Europe. Haynes donated the...</code> \| \| <code>How many doctors are in Doctor Who?</code> \| <code>The Doctor (Doctor Who)<br>The Doctor is the title character in the long-running BBC science fiction television programme "Doctor Who". Since the show's inception in 1963, the character has been portrayed by thirteen lead actors. In the programme, "the Doctor" is the alias assumed by a centuries-old alien—a Time Lord from the planet Gallifrey—who travels through space and time in the TARDIS, frequently with companions. The transition to each succeeding actor is explained within the show's narrative through the plot device of "regeneration", a biological function of the Time Lord race that allows a change of cellular structure and appearance with recovery following a potentially fatal injury.</code> \| <code>Sixth Doctor<br>The Sixth Doctor is an incarnation of the Doctor, the protagonist of the BBC science fiction television series "Doctor Who". He is portrayed by Colin Baker. Although his televisual time on the series was comparatively brief and turbulent, Baker has continued as the Sixth Doctor in Big Finish's range of original "Doctor Who" audio adventures. Within the series' narrative, the Doctor is a centuries-old Time Lord alien from the planet Gallifrey who travels in time and space in his TARDIS, frequently with companions. At the end of life, the Doctor can regenerate his body; in doing so, his physical appearance and personality change. Baker portrays the sixth such incarnation, an arrogant, flamboyant character in brightly coloured, mismatched clothes whose brash, often patronising personality set him apart from all his previous incarnations.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mldr</summary> * Dataset: [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) at [40ad767](https://huggingface.co/datasets/sentence-transformers/mldr/tree/40ad7672817ebee49e00dd25aed00e1c401881d6) * Size: 200,000 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 17 characters</li><li>mean: 65.31 characters</li><li>max: 210 characters</li></ul> \| <ul><li>min: 2432 characters</li><li>mean: 20354.29 characters</li><li>max: 123500 characters</li></ul> \| <ul><li>min: 3035 characters</li><li>mean: 16236.77 characters</li><li>max: 166364 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-----------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>When was the art museum in Santa Barbara first opened to the public?</code> \| <code>The Santa Barbara Museum of Art (SBMA) is an art museum located in downtown Santa Barbara, California.<br><br>Founded in 1941, it is home to both permanent and special collections, the former of which includes Asian, American, and European art that spans 4,000 years from ancient to modern.<br><br>History<br><br>The Santa Barbara Museum of Art opened to the public on June 5, 1941, in a building that was at one time the Santa Barbara Post Office (1914–1932). The idea for an art museum first came from the local artist Colin Campbell Cooper when he learned that the post office was going to be sold. In a letter to the editor published in the Santa Barbara News-Press in July 1937, Cooper proposed that the impressive Italianate structure should be transformed into a museum. After gaining momentum in town and with the support of local businesses, politicians and art collectors the Santa Barbara Museum of Art was officially established just four years after Cooper's letter was published. The renowned Chicago arc...</code> \| <code>The Knott's Berry Farm amusement park in Orange County, California, originated from a berry farm owned by Walter Knott (1889–1981). In the 1920s, Knott and his wife, Cordelia, sold berries, berry preserves and pies from a roadside stand beside State Route 39, near the small town of Buena Park.<br>In 1932, on a visit to Rudolph Boysen's farm in nearby Anaheim, Walter Knott was introduced to a new hybrid berry of a blackberry, a red raspberry, and a loganberry cross-bred by Boysen, who gave Walter his last six wilted berry-hybrid plants. Walter planted and cultivated them, then the family sold the berries at their roadside stand. When people asked what kind they were, he called them "boysenberries".<br><br>In 1934, to make ends meet, Knott's wife Cordelia (1890–1974) reluctantly began serving fried chicken dinners on their wedding china. For dessert, Knott's signature Boysenberry Pie was also served to guests dining in the small tea room. As Southern California developed, Highway 39 became the ma...</code> \| \| <code>What is the objective of Opération Chammal?</code> \| <code>Opération Chammal is a French military operation in Iraq and Syria in an attempt to contain the expansion of the Islamic State of Iraq and the Levant and to support the Iraqi Army. Its name comes from the Shamal (Chammal in French), a northwesterly wind that blows over Iraq and the Persian Gulf states.<br><br>Airstrikes over Iraq started 19 September 2014 and airstrikes over Syria started by the end of September 2015. The French operation is limited to airstrikes; French president François Hollande has reiterated that no ground troops would be used in the conflict. Additionally, the French frigate has joined the United States Navy's Commander Task Force 50 (CTF 50) as an escort.<br><br>On 14 November 2015, ISIL claimed that the attacks that took place in Paris the previous day were retaliation for Opération Chammal. In response, French forces increased their attacks against ISIL in Syria.<br><br>Background <br><br>On 10 June 2014, the terrorist group of the Islamic State of Iraq and the Levant and several ot...</code> \| <code>CMA CGM S.A. is a French container transportation and shipping company. It is the world’s 3rd largest container shipping company, using 257 shipping routes between 420 ports in 160 different countries. Its headquarters are in Marseille, France and its North American headquarters are in Norfolk, Virginia, United States.<br><br>The name is an acronym of two predecessor companies, Compagnie Maritime d'Affrètement (CMA) and Compagnie Générale Maritime (CGM), which translate as "Maritime Freighting Company" and "General Maritime Company".<br><br>History<br><br>The history of CMA CGM can be traced back to the middle of the 19th century, when two major French shipping lines were created, respectively Messageries Maritimes (MM) in 1851 and Compagnie Générale Maritime (CGM) in 1855, soon renamed Compagnie Générale Transatlantique in 1861. Both companies were created partly with the backing of the French State, through the award of mail contracts to various destinations, French colonies and overseas territories a...</code> \| \| <code>What was the reason for Williams-Franklin's decision to become a vegan during her time as a player?</code> \| <code>Taj McWilliams-Franklin (born October 20, 1970) is a former American professional women's basketball player.<br><br>A two-time WNBA champion with the Detroit Shock and Minnesota Lynx and six-time all-star, McWilliams-Franklin's professional career has spanned three decades, and began before the WNBA was founded. She retired from the WNBA after the 2012 season.<br><br>College years<br>After attending T. W. Josey High School in Augusta, Georgia, McWilliams-Franklin attended Georgia State University in 1989 and played on the school's basketball team for one season. However, she had become pregnant during her senior year in high school, and after the coach who recruited her to Georgia State was let go, the incoming staff told her "school was no place for kids." McWilliams-Franklin moved to Austin, Texas, where a friend connected her with St. Edward's University coach Dave McKey. She enrolled at St. Edwards as a Rhetoric major.<br><br>While at St. Edward's, she set school records and individual achievements, in...</code> \| <code>Spider-Woman (Gwendolyne Maxine Stacy; colloquial: "Spider-Gwen" or "Ghost-Spider") is a superhero appearing in American comic books published by Marvel Comics. She was created by Jason Latour and Robbi Rodriguez. The character debuted in Edge of Spider-Verse issue #2 as part of the 2014–15 "Spider-Verse" comic book storyline, leading to the ongoing series Spider-Gwen that began in 2015.<br><br>Spider-Woman is a variant of Spider-Man and an alternate-universe version of Gwen Stacy. She lives on Earth-65, where Gwen Stacy is bitten by a radioactive spider and becomes a superhero instead of Peter Parker becoming Spider-Man. The character's various enemies include Earth-65 versions of Matt Murdock and Frank Castle. Gwen Stacy's Spider-Woman harbors much of Peter's personality and conflicts along with his powers and abilities.<br><br>Spider-Woman was met with positive reviews from critics, with them applauding her design—cited as a popular choice for cosplay—and a feminist perspective. For promotion, ...</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mr_tydi</summary> * Dataset: [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) at [abbdf55](https://huggingface.co/datasets/sentence-transformers/mr-tydi/tree/abbdf55c630352da943f779610c3ce6268118351) * Size: 354,700 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 12 characters</li><li>mean: 38.85 characters</li><li>max: 95 characters</li></ul> \| <ul><li>min: 64 characters</li><li>mean: 645.85 characters</li><li>max: 4067 characters</li></ul> \| <ul><li>min: 21 characters</li><li>mean: 626.85 characters</li><li>max: 2870 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:----------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Is amnesia real?</code> \| <code>Amnesia<br>Amnesia is a deficit in memory caused by brain damage, disease, or psychological trauma.[1] Amnesia can also be caused temporarily by the use of various sedatives and hypnotic drugs. The memory can be either wholly or partially lost due to the extent of damage that was caused.[2] There are two main types of amnesia: retrograde amnesia and anterograde amnesia. Retrograde amnesia is the inability to retrieve information that was acquired before a particular date, usually the date of an accident or operation.[3] In some cases the memory loss can extend back decades, while in others the person may lose only a few months of memory. Anterograde amnesia is the inability to transfer new information from the short-term store into the long-term store. People with this type of amnesia cannot remember things for long periods of time. These two types are not mutually exclusive; both can occur simultaneously.</code> \| <code>Amnesia<br>Head trauma is a very broad range as it deals with any kind of injury or active action toward the brain which might cause amnesia. Retrograde and anterograde amnesia is more often seen from events like this, an exact example of a cause of the two would be electroconvulsive therapy, which would cause both briefly for the receiving patient. Traumatic events are more subjective. What is traumatic is dependent on what the person finds to be traumatic. Regardless, a traumatic event is an event where something so distressing occurs that the mind chooses to forget rather than deal with the stress. A common example of amnesia that is caused by traumatic events is dissociative amnesia, which occurs when the person forgets an event that has deeply disturbed them.[8] An example would be a person forgetting a fatal and graphic car accident involving their loved ones. Physical deficiencies are different from head trauma because physical deficiencies lean more toward passive physical issues.</code> \| \| <code>What is the largest naval base in the world?</code> \| <code>Naval Station Norfolk<br>Naval Station Norfolk, is a United States Navy base in Norfolk, Virginia. It supports naval forces in the United States Fleet Forces Command,[1] those operating in the Atlantic Ocean, Mediterranean Sea, and the Indian Ocean. The installation occupies about 4 miles (6.4km) of waterfront space and 11 miles (18km) of pier and wharf space of the Hampton Roads peninsula known as Sewell's Point. It is the world's largest naval station, with the largest concentration of U.S. Navy forces through 75 ships alongside 14 piers and with 134 aircraft and 11 aircraft hangars at the adjacently operated Chambers Field and [2] Port Services controls more than 3,100 ships' movements annually as they arrive and depart their berths.</code> \| <code>Clark Air Base<br>Clark Air Base was arguably the most urbanized military facility in history and was the largest American base overseas. At its peak around 1990, it had a permanent population of 15,000. It had a base exchange, a large commissary, a small shopping arcade, a branch department store, cafeterias, teen centers, a hotel, miniature golf, riding stables, zoo, and other concessions.</code> \| \| <code>What is the power of the Red Lantern?</code> \| <code>Red Lantern Corps<br>In Final Crisis: Rage of the Red Lanterns, Atrocitus is shown in a flashback as having apparently formed a central power battery by using the blood of the other Inversions in blood magic rituals. The battery stands before a great lake of blood from which he forms his red power ring (crystallized by his anger), as well as other rings and batteries used to form the Red Lantern Corps. Harnessing the red light of rage, he sends his rings out into the universe; however, upon accepting the rings, his recruits' hearts are rendered useless. Their blood spoils from within, forcing them to expel the violently flammable and corrosive material from their mouths. Additionally, the Red Lanterns are reduced to an almost animalistic state, with only Atrocitus appearing to be in full control of himself. Once Atrocitus assembles a sufficient force, he leads them on a mission to capture Sinestro (who is being transferred to Korugar for his execution). Coincidentally, the Sinestro Corps ...</code> \| <code>Green Lantern in other media<br>John Stewart is a member of the Justice League in the "Justice League" animated series. In this series, Stewart's ring was initially constrained to permitting him to fly, generating a protective force field, creating walls, and firing energy blasts; this limitation was established as being due to Stewart's mindset, not an inherent limitation of the ring itself (the series' version of Stewart is a former U.S. Marine, not an architect). After being berated by Katma Tui for his unimaginative use of the ring, Stewart has learned to generate complex tools (to defuse a bomb in one instance) and weapons. (He was also shown to be more creative when transformed into a child in the episode "Kids Stuff".) In a development not seen in any other version of the Green Lantern mythos, Stewart's eyes glow green when wearing his charged power ring. The glow fades when the ring runs out of power. The series has been inconsistent about the ring's effectiveness against yellow; ...</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> ### Evaluation Datasets <details><summary>gooaq</summary> * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco</summary> * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 characters</li><li>mean: 33.36 characters</li><li>max: 137 characters</li></ul> \| <ul><li>min: 67 characters</li><li>mean: 347.87 characters</li><li>max: 906 characters</li></ul> \| <ul><li>min: 57 characters</li><li>mean: 318.18 characters</li><li>max: 906 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:-------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>is cabinet refacing worth the cost?</code> \| <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> \| <code>Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.</code> \| \| <code>is the fovea ethmoidalis a bone</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \| \| <code>average pitches per inning</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>squad</summary> * Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) * Size: 87,599 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 1 characters</li><li>mean: 60.25 characters</li><li>max: 161 characters</li></ul> \| <ul><li>min: 152 characters</li><li>mean: 761.88 characters</li><li>max: 2525 characters</li></ul> \| * Samples: \| question \| answer \| \|:----------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>When did the Russian Empire begin to question the existence of the Ottoman Empire?</code> \| <code>In 1853 the Russian Empire on behalf of the Slavic Balkan states began to question the very existence of the Ottoman Empire. The result was the Crimean War, 1853–1856, in which the British Empire and the French Empire supported the Ottoman Empire in its struggle against the incursions of the Russian Empire. Eventually, the Ottoman Empire lost control of the Balkan region.</code> \| \| <code>How would one describe the control of universities before nation-states in the 17th century?</code> \| <code>The propagation of universities was not necessarily a steady progression, as the 17th century was rife with events that adversely affected university expansion. Many wars, and especially the Thirty Years' War, disrupted the university landscape throughout Europe at different times. War, plague, famine, regicide, and changes in religious power and structure often adversely affected the societies that provided support for universities. Internal strife within the universities themselves, such as student brawling and absentee professors, acted to destabilize these institutions as well. Universities were also reluctant to give up older curricula, and the continued reliance on the works of Aristotle defied contemporary advancements in science and the arts. This era was also affected by the rise of the nation-state. As universities increasingly came under state control, or formed under the auspices of the state, the faculty governance model (begun by the University of Paris) became more and m...</code> \| \| <code>When did Jewish law recognize copyright?</code> \| <code>The concept's origins can potentially be traced back further. Jewish law includes several considerations whose effects are similar to those of modern intellectual property laws, though the notion of intellectual creations as property does not seem to exist – notably the principle of Hasagat Ge'vul (unfair encroachment) was used to justify limited-term publisher (but not author) copyright in the 16th century. In 500 BCE, the government of the Greek state of Sybaris offered one year's patent "to all who should discover any new refinement in luxury".</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>s2orc</summary> * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 10,000 evaluation samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: \| \| title \| abstract \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 31 characters</li><li>mean: 80.04 characters</li><li>max: 198 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 653.93 characters</li><li>max: 1023 characters</li></ul> \| * Samples: \| title \| abstract \| \|:-------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna</code> \| <code>The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.</code> \| \| <code>Subclinical organ damage and cardiovascular risk prediction</code> \| <code>AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...</code> \| \| <code>A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan</code> \| <code>In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>allnli</summary> * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 6,584 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 15 characters</li><li>mean: 72.82 characters</li><li>max: 300 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 34.11 characters</li><li>max: 126 characters</li></ul> \| <ul><li>min: 11 characters</li><li>mean: 36.38 characters</li><li>max: 121 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\| \| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \| \| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \| \| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>paq</summary> * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 25 characters</li><li>mean: 51.3 characters</li><li>max: 108 characters</li></ul> \| <ul><li>min: 504 characters</li><li>mean: 623.09 characters</li><li>max: 835 characters</li></ul> \| * Samples: \| query \| answer \| \|:---------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>when did season 3 of the voice brasil start</code> \| <code>The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on</code> \| \| <code>when did the little ranger first come out</code> \| <code>Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures</code> \| \| <code>what is the name of rachel's sister in ninjaaiden</code> \| <code>her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>trivia_qa</summary> * Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 73,346 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 26 characters</li><li>mean: 77.62 characters</li><li>max: 258 characters</li></ul> \| <ul><li>min: 135 characters</li><li>mean: 3169.71 characters</li><li>max: 4096 characters</li></ul> \| * Samples: \| query \| answer \| \|:-----------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>In which country is 'Ninety Mile Beach'?</code> \| <code>Ninety (90) Mile Beach, Gippsland, Victoria - Tourism Australia Gippsland Find travel information on Ninety Mile Beach, one of the longest uninterrupted beaches in the world, located outside of Melbourne at Gippsland Lakes. Ninety Mile Beach, located in the Gippsland region on Victoria's south-eastern coastline, is one of the longest uninterrupted beaches in the world. Stand on the beach and watch the beach disappear into the salty sea spray in the distance. You might find that your footprints are the only ones in the sand that day. This is one of the most natural and unspoilt beaches in the world and is ideal for activities from beach fishing and swimming to walking, whale and dolphin-spotting or just lazing in the sun. Sun, sand and lush national parks all create the perfect holiday environment. Victoria's Ninety Mile Beach is a 90-mile long stretch of pristine golden sand that separates the Gippsland Lakes from Bass Strait. Stretching as far as the eye can see it is one of the most ...</code> \| \| <code>What country gets nearly 75% of its electricity from nuclear power?</code> \| <code>Nuclear Power in France \| French Nuclear Energy - World Nuclear Association Nuclear Power in France (Updated November 2016) France derives about 75% of its electricity from nuclear energy, due to a long-standing policy based on energy security. This share may be reduced to 50% by 2025. France is the world's largest net exporter of electricity due to its very low cost of generation, and gains over €3 billion per year from this. France has been very active in developing nuclear technology. Reactors and especially fuel products and services are a significant export. About 17% of France's electricity is from recycled nuclear fuel. In 2014 French electricity generation was 541 TWh gross. Consumption in 2012 was 454 TWh – 6600 kWh per person. Winter demand varies by 2300 MWe per degree C. Over the last decade France has exported up to 70 billion kWh net each year and EdF expects exports to continue at 55-70 TWh/yr. In 2014 they were principally to Italy, UK, Switzerland, and Belgium, as ...</code> \| \| <code>Which Spaniard led an expedition which reached Tenochtitlan, the Aztec capital in 1519?</code> \| <code>The Spanish Conquest (1519-1521) : Mexico History History \| See all articles tagged history The Spanish Conquest (1519-1521) Tweet April 21, 1519--the year Ce Acatl (One Reed) by Aztec reckoning-- marked the opening of a short but decisive chapter in Mexico's history. On that day a fleet of 11 Spanish galleons sailing along the eastern gulf coast dropped anchor just off the wind-swept beach on the island of San Juan de Ulúa. Under the command of the wily, daring Hernán Cortés, the vessels bore 550 Spanish soldiers and sailors, as well as 16 horses, the first of the species to tread the American continent. The party disembarked to set up camp on the dunes behind the beach. In a friendly reception from the native Totonac Indians, greetings and gifts were exchanged. Cognizant of the existence of a great inland Empire, Cortés promptly dispatched a message requesting an audience with Aztec ruler Moctezuma II . (The term "Aztec" will be used throughout, although some historians prefer the ...</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco_10m</summary> * Dataset: [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2) * Size: 10,000,000 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 characters</li><li>mean: 33.98 characters</li><li>max: 131 characters</li></ul> \| <ul><li>min: 56 characters</li><li>mean: 353.39 characters</li><li>max: 1029 characters</li></ul> \| <ul><li>min: 85 characters</li><li>mean: 339.79 characters</li><li>max: 983 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:----------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what does a dental hygienist do</code> \| <code>During a dental appointment, a hygienist typically removes soft and hard deposits from a patient's teeth; examines the gums and teeth to discern the presence of disease or oral abnormality; and strips the teeth of calculus (tartar), stains and plaque. dental hygienist takes on a somewhat academic role as well; he or she educates dental patients on how to establish and maintain suitable oral hygiene, often with the aid of teeth models to give the patient a visual sense.</code> \| <code>Michelly in Tennessee said: Can anyone tell me how much you get paid hourly working as a dental hygienist. I make $36 dollars an hour and I just graduated from school. Should I be making more. Try to visit this link www.payscale.com/research/US/Job=Dental_Hygienist/Hourly_Rate so you will know how much dental hygienist usually get based on experience.</code> \| \| <code>average annual temperature by florida county</code> \| <code>Lake County Weather. The average temperature of Lake County is 70.90Â°F, which is about the same as the Florida average temperature of 71.80Â°F and is much higher than the national average temperature of 54.45Â°F. Historical Weather. Heating Cost Index, #29.</code> \| <code>average rn salary in fl the average annual salary for a registered nurse in the state of florida in 2011 was $ 64020 the average does fluctuate throughout the state with median rn salaries at their highest in metro areas</code> \| \| <code>what does amortization mean</code> \| <code>What is 'Amortization'. Amortization is the paying off of debt with a fixed repayment schedule in regular installments over a period of time for example with a mortgage or a car loan.</code> \| <code>First off, your EBIT is the same as your operating profit, but you can also calculate it by subtracting interest and tax from net income: $100,000 / ($10,000 + $25,000) = $65,000 To get EBITDA, you need to add back in depreciation and amortization:</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>swim_ir</summary> * Dataset: [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) at [834c20f](https://huggingface.co/datasets/nthakur/swim-ir-monolingual/tree/834c20f0ceef6a68e029fb4447d17d20bb0288c3) * Size: 501,538 evaluation samples * Columns: <code>query</code> and <code>text</code> * Approximate statistics based on the first 1000 samples: \| \| query \| text \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 4 characters</li><li>mean: 59.74 characters</li><li>max: 165 characters</li></ul> \| <ul><li>min: 206 characters</li><li>mean: 522.53 characters</li><li>max: 3079 characters</li></ul> \| * Samples: \| query \| text \| \|:------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Where was he born?</code> \| <code>He was born in Brownsville, Edmonson County, Kentucky, March 28, 1890; attended the public schools, Western Kentucky State Teachers College at Bowling Green, and the law department of the University of Kentucky at Lexington; was admitted to the bar in 1915 and commenced practice in Brownsville, Ky.; county judge of Edmonson County, Ky., 1916-1918.</code> \| \| <code>What was Channon's National Service?</code> \| <code>Channon completed his National Service in the Royal Horse Guards (the Blues) from 1955 to 1956, serving in Cyprus during the 1956 Cyprus emergency. In London, he was a member of the set around Princess Margaret, and then attended Christ Church, Oxford from 1956. He was president of the Oxford University Conservative Association.</code> \| \| <code>What is the role of Immunoglobulin A in the immune system?</code> \| <code>Immunoglobulin heavy constant alpha 1 is a immunoglobulin gene with symbol "IGHA1". It encodes a constant (C) segment of Immunoglobulin A heavy chain. Immunoglobulin A is an antibody that plays a critical role in immune function in the mucous membranes. IgA shows the same typical structure of other antibody classes, with two heavy chains and two light chains, and four distinct domains: one variable region, and three variable regions. As a major class of immunoglobulin in body secretions, IgA plays a role in defending against infection, as well as preventing the access of foreign antigens to the immunologic system.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>pubmedqa</summary> * Dataset: [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) at [a1ef0b5](https://huggingface.co/datasets/sentence-transformers/pubmedqa/tree/a1ef0b513b16ed490e807ac11da40e436d3a54c3) * Size: 1,660 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, and <code>negative_20</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative_1 \| negative_2 \| negative_3 \| negative_4 \| negative_5 \| negative_6 \| negative_7 \| negative_8 \| negative_9 \| negative_10 \| negative_11 \| negative_12 \| negative_13 \| negative_14 \| negative_15 \| negative_16 \| negative_17 \| negative_18 \| negative_19 \| negative_20 \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| string \| \| details \| <ul><li>min: 38 characters</li><li>mean: 88.92 characters</li><li>max: 140 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 372.72 characters</li><li>max: 1113 characters</li></ul> \| <ul><li>min: 26 characters</li><li>mean: 371.1 characters</li><li>max: 1185 characters</li></ul> \| <ul><li>min: 20 characters</li><li>mean: 326.89 characters</li><li>max: 1084 characters</li></ul> \| <ul><li>min: 23 characters</li><li>mean: 334.62 characters</li><li>max: 1477 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 347.64 characters</li><li>max: 1310 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 337.96 characters</li><li>max: 1221 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 303.39 characters</li><li>max: 953 characters</li></ul> \| <ul><li>min: 14 characters</li><li>mean: 328.25 characters</li><li>max: 1168 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 318.59 characters</li><li>max: 989 characters</li></ul> \| <ul><li>min: 18 characters</li><li>mean: 284.81 characters</li><li>max: 853 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 333.89 characters</li><li>max: 875 characters</li></ul> \| <ul><li>min: 26 characters</li><li>mean: 327.33 characters</li><li>max: 1041 characters</li></ul> \| <ul><li>min: 19 characters</li><li>mean: 354.74 characters</li><li>max: 1705 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 334.33 characters</li><li>max: 1593 characters</li></ul> \| <ul><li>min: 18 characters</li><li>mean: 346.0 characters</li><li>max: 1374 characters</li></ul> \| <ul><li>min: 13 characters</li><li>mean: 367.52 characters</li><li>max: 1625 characters</li></ul> \| <ul><li>min: 17 characters</li><li>mean: 357.96 characters</li><li>max: 1126 characters</li></ul> \| <ul><li>min: 23 characters</li><li>mean: 322.84 characters</li><li>max: 1060 characters</li></ul> \| <ul><li>min: 25 characters</li><li>mean: 312.1 characters</li><li>max: 805 characters</li></ul> \| <ul><li>min: 22 characters</li><li>mean: 362.97 characters</li><li>max: 1002 characters</li></ul> \| <ul><li>min: 22 characters</li><li>mean: 328.62 characters</li><li>max: 1310 characters</li></ul> \| * Samples: \| anchor \| positive \| negative_1 \| negative_2 \| negative_3 \| negative_4 \| negative_5 \| negative_6 \| negative_7 \| negative_8 \| negative_9 \| negative_10 \| negative_11 \| negative_12 \| negative_13 \| negative_14 \| negative_15 \| negative_16 \| negative_17 \| negative_18 \| negative_19 \| negative_20 \| 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\| <code>Chronic functional somatic symptoms: a single syndrome?</code> \| <code>Observational study, with a comparison control group.</code> \| <code>Nasal nitric oxide (NO) and olfactory function are decreased in patients with chronic inflammatory sinonasal disease, suggesting a link between these two parameters. The aim of the study was to investigate nasal NO levels in patients with olfactory dysfunction due to different causes.</code> \| <code>Fibromyalgia (FM) is a worldwide diffuse musculoskeletal chronic pain condition that affects up to 5% of the general population. Many symptoms associated with mitochondrial diseases are reported in patients with FM such as exercise intolerance, fatigue, myopathy and mitochondrial dysfunction. In this study, we report a mutation in cytochrome b gene of mitochondrial DNA (mtDNA) in a family with FM with inflammasome complex activation.</code> \| <code>Chronic fatigue syndrome (CFS) has an uncertain pathogenesis. Allergies have been suggested as one cause.</code> \| <code>Patient's self-reported symptoms on a structured case history questionnaire.</code> \| <code>Chronic multisymptom illness (reporting at least one symptom in at least two of the following symptom constructs: general fatigue; mood and cognition problems; and musculoskeletal discomfort) was assessed, differentiating by potential burn pit exposure, among deployers who completed 2004 and 2007 Millennium Cohort questionnaires.</code> \| <code>functional decline and death.</code> \| <code>Olfactory loss is a debilitating symptom of chronic rhinosinusitis (CRS). The pathophysiology of inflammatory olfactory dysfunction likely involves both conductive and sensorineural components. To study the interaction of CRS-associated inflammatory cytokines with the olfactory epithelium (OE), a transgenic mouse model was developed that allows temporally-controlled local gene expression. Interferon-gamma (IFN-γ) is a prototypical T helper 1 (Th1) cytokine linked to nonpolypoid CRS (CRSsNP), as well as sinonasal viral and bacterial infections. In this study, the effects of chronic IFN-γ expression on olfactory histology and function were investigated.</code> \| <code>Monosomy 1p36 syndrome is the most commonly observed subtelomeric deletion syndrome. Patients with this syndrome typically have common clinical features, such as intellectual disability, epilepsy, and characteristic craniofacial features.</code> \| <code>Migraine is frequently accompanied by symptoms consistent with functional gastrointestinal disorders (FGIDs). This study evaluated the prevalence of functional gastrointestinal symptoms and assessed the symptoms' relationship with the concomitant functional symptoms of anxiety, depression, and headache-related disability.</code> \| <code>The 2003 Canadian Consensus Criteria for chronic fatigue syndrome (CFS) are often assumed to suggest low-grade systemic inflammation, but have never been formally validated. This study explored the content validity of the Criteria in a sample of adolescents with CFS selected according to a wide case definition.</code> \| <code>Irritable pouch syndrome (IPS) is a functional disorder in patients with ileal pouch-anal anastomosis (IPAA), which presents with symptoms in the absence of structural abnormalities of the pouch. Thus, it resembles other functional disorders, such as irritable bowel syndrome characterized by visceral hypersensitivity in the presence of normal rectal biomechanics. The aim was to assess pouch biomechanics and perception of balloon distension in different groups of subjects with IPAA and to correlate the findings with clinical features.</code> \| <code>Fibromyalgia (FM) and chronic fatigue syndrome (CFS) frequently overlap clinically and have been considered variants of one common disorder. We have recently shown that CFS is associated with a short corrected electrocardiographic QT interval (QTc). In the present study, we evaluated whether FM and CFS can be distinguished by QTc.</code> \| <code>Single case with clinical follow-up over 2 years.</code> \| <code>Fibromyalgia syndrome (FMS) is a disease of unknown pathogenesis characterized by chronic musculoskeletal pain. FMS has been also associated with altered endocrinological responses, but findings are inconsistent.</code> \| <code>Primary Sjögren's syndrome (pSS) is a systemic rheumatic disease in which gastrointestinal (GI) symptoms are common. Faecal calprotectin (FC) is a non-invasive biomarker that has been suggested to discriminate organic intestinal disease from functional disorders. The purpose of this study was to explore the usefulness of FC testing in patients with pSS.</code> \| <code>An inactive lifestyle has been associated with functional somatic symptoms (FSS), but findings are contradictory. Moreover, mediating factors in this relationship are unclear. We examined whether low physical activity was related to FSS in adolescents, and whether this association was mediated by low physical fitness.</code> \| <code>Nephrotic syndrome is a common kidney disease in both children and adults that is characterized by dramatic structural changes in the actin-rich foot processes of glomerular podocytes. Although glucocorticoids are the primary treatment for nephrotic syndrome, neither the target cell nor mechanism of action of glucocorticoids in nephrotic syndrome is known. For the last 30 years glucocorticoids have been presumed to act by reducing the release of soluble mediators of disease by circulating lymphocytes. In contrast, we hypothesized that glucocorticoids exert their beneficial effects in nephrotic syndrome by direct action on podocytes.</code> \| <code>Chronic sclerosing sialadenitis is a fibroinflammatory disease of the salivary glands, characteristically of the submandibular gland. One prior Asian study proposed that chronic sclerosing sialadenitis is a part of the spectrum of IgG4-associated disease. This association has not been confirmed in Western populations. We therefore, investigated the relationship between IgG4 and chronic sclerosing sialadenitis, and compared the histomorphologic features of this condition with those of chronic sialadenitis-not otherwise specified, Sjögren syndrome, and lymphoepithelial sialadenitis.</code> \| <code>Presence of GI symptoms.</code> \| <code>Disrupted-in schizophrenia 1 (DISC1), identified in a pedigree with a familial psychosis with the chromosome translocation (1:11), is a putative susceptibility gene for psychoses such as schizophrenia and major depressive disorder (MDD). Patients with chronic fatigue syndrome (CFS) report having continuous severe fatigue and many overlapping symptoms with MDD; however, the mechanism and effective treatment of CFS are still unclear. We focused on the overlapping symptoms between CFS and MDD and performed an association study of the functional single-nucleotide polymorphism (SNP) in the DISC1 gene with CFS.</code> \| \| <code>Does sonographic needle guidance affect the clinical outcome of intraarticular injections?</code> \| <code>In total, 148 painful joints were randomized to IA triamcinolone acetonide injection by conventional palpation-guided anatomic injection or sonographic image-guided injection enhanced with a one-handed control syringe (the reciprocating device). A one-needle, 2-syringe technique was used, where the first syringe was used to introduce the needle, aspirate any effusion, and anesthetize and dilate the IA space with lidocaine. After IA placement and synovial space dilation were confirmed, a syringe exchange was performed, and corticosteroid was injected with the second syringe through the indwelling IA needle. Baseline pain, procedural pain, pain at outcome (2 weeks), and changes in pain scores were measured with a 0-10 cm visual analog pain scale (VAS).</code> \| <code>We used a blinded, longitudinal observational design of effectiveness in an effort to determine the accuracy of intra-articular injections and the effect of that accuracy on pain and functional outcomes in patients with various shoulder pathologies.</code> \| <code>Data from standardized procedure notes and postprocedure chest radiographs were extracted and individually reviewed to verify the presence of pneumothorax or misplacement, and any intervention performed for either complication. The overall success rate of ultrasound-guided right internal jugular vein central venous catheter placement was 96.9% with an average of 1.3 attempts. There was only one pneumothorax (0.1% [95% CI, 0-0.4%]), and the rate of catheter misplacement requiring repositioning or replacement was 1.0% (95% CI, 0.6-1.7%). There were no arterial placements found on chest radiographs. Multivariate regression analysis showed no correlation between high-risk patient characteristics and composite complication rate.</code> \| <code>Real-time ultrasound-guided techniques allow for improved cannulation of the internal jugular vein and femoral vein for hemodialysis; however, these techniques require extra sterilization procedures, specialized probes, or needle guides. A simpler ultrasound vessel localization method was performed to investigate whether this alternative approach would aid in the cannulation of the femoral vein for patients in whom temporary angioaccess was required for hemodialysis.</code> \| <code>To determine whether duration or venue of intravenous antibiotic administration affect lung function.</code> \| <code>Needle electromyography (NEE) would be more valuable if it could predict outcomes after lumbar epidural steroid injections (LESIs) in lumbosacral radiculopathy (LSR).</code> \| <code>Among patients with Wilkes stage III and IV disease undergoing arthroscopic lysis and lavage, does the use of an intra-articular injection of sodium hyaluronate (SH), when compared with Ringer lavage, result in better postoperative pain control and temporomandibular joint (TMJ) function?</code> \| <code>Intraoperative MR imaging and sonography are used for navigation during neurosurgical procedures. The purpose of this experimental study was to evaluate the potential of high-resolution sonography using superparamagnetic iron oxide (SPIO) particles as a contrast medium to delineate brain tumors and to relate these findings with those of MR imaging.</code> \| <code>Academic general internal medicine practice.</code> \| <code>To evaluate how guidance on water-intake impacts the degree of nocturia.</code> \| <code>Intra-articular knee injections are commonly performed in clinical practice for treating various knee joint disorders such as osteoarthritis and rheumatoid arthritis. When selecting the portal for injection, not only intra-articular needle accuracy but also procedural pain should be taken into consideration. The purpose of this study was to determine whether injection through anterolateral portal provokes less pain and provides better pain relief compared to superolateral portal.</code> \| <code>Ultrasound guidance reduces the required local anesthetic volume for successful peripheral nerve blockade, but it is unclear whether this impacts postoperative analgesia. This prospective, randomized, observer-blinded study tested the hypothesis that a low-volume ultrasound-guided ankle block would provide similar analgesia after foot surgery compared with a conventional-volume surface landmark technique.</code> \| <code>The purpose of this study was to evaluate how intravascular ultrasound-determined thickness and reflectivity of the inner echogenic layer of coronary artery plaque is affected by changes in collagen, elastin, proteoglycan, calcium and lipid content in the intima and media.</code> \| <code>The anesthetic requirement is decreased in animals with head injury, but there are no data regarding the effect of intracranial tumor on the potency for intravenous anesthetics. The authors compared the quantal dose-response curves for propofol in patients having large (> or = 30 mm, mass effect) brain tumor with those having smaller (< 30 mm) lesions and with control patients undergoing noncranial surgery.</code> \| <code>To develop digital multimedia instruction on intraoral suturing.</code> \| <code>To evaluate a long term efficiency of a deep sclerectomy with T-Flux implant on intraocular pressure</code> \| <code>In bilateral cochlear implant users, electrodes mapped to the same frequency range in each ear may stimulate different places in each cochlea due to an insertion depth difference of electrode arrays. This interaural place of stimulation mismatch can lead to problems with auditory image fusion and sensitivity to binaural cues, which may explain the large localization errors seen in many patients. Previous work has shown that interaural place of stimulation mismatch can lead to off-centered auditory images being perceived even though interaural time and level differences (ITD and ILD, respectively) were zero. Large interaural mismatches reduced the ability to use ITDs for auditory image lateralization. In contrast, lateralization with ILDs was still possible but the mapping of ILDs to spatial locations was distorted. This study extends the previous work by systematically investigating the effect of interaural place of stimulation mismatch on ITD and ILD sensitivity directly and examining...</code> \| <code>Mathematical modeling and use of real world clinical inputs.</code> \| <code>The use of ultrasonography in regional anesthetic blocks has rapidly evolved over the past few years. It has been speculated that ultrasound guidance might increase success rates and reduce complications. The aim of our study is to compare the success rate and quality of interscalene brachial plexus blocks performed either with direct ultrasound visualization or with the aid of nerve stimulation to guide needle placement.</code> \| <code>Intra-operative nerve monitoring (IONM) of the recurrent laryngeal nerve (RLN) during thyroid and parathyroid surgery is thought to aid in identification and dissection of the RLN. While utilization of IONM is increasing, one area of variability in its application is the assessment of adequate endotracheal tube electrode placement for IONM during the case. The main objective of this study is to assess the overall success of utilizing respiratory variation to confirm proper endotracheal tube placement for RLN monitoring.</code> \| <code>Time-dependent development of intracochlear impedances was investigated in 4 different groups of adult patients up to 4 years after implantation. Additionally, during rehabilitation period just after first fitting, impedances before and after stimulation were measured as to investigate the influence of electrical stimulation on the impedances. Results from standard Nucleus 24 Contour (control), standard Nucleus 24 Contour with intraoperative application of steroids, iridium-coated Nucleus 24 Contour, and iridium-coated Nucleus 24 Contour with intraoperative application of steroids were compared.</code> \| \| <code>Does type 1 diabetes mellitus affect Achilles tendon response to a 10 km run?</code> \| <code>Participants were 7 individuals with T1DM and 10 controls. All regularly ran distances greater than 5 km and VISA-A scores indicated good tendon function (T1DM = 94 ± 11, control = 94 ± 10). There were no diabetic complications and HbA1c was 8.7 ± 2.6 mmol/mol for T1DM and 5.3 ± 0.4 mmol/mol for control groups. Baseline tendon structure was similar in T1DM and control groups - UTC echo-types (I-IV) and anterior-posterior thickness were all p > 0.05. No response to load was seen in either T1DM or control group over the 4-days post exercise.</code> \| <code>The inability to produce insulin endogenously precipitates the clinical symptoms of type 1 diabetes mellitus. However, the dynamic trajectory of beta cell destruction following onset remains unclear. Using model-based inference, the severity of beta cell destruction at onset decreases with age where, on average, a 40% reduction in beta cell mass was sufficient to precipitate clinical symptoms at 20 years of age. While plasma C-peptide provides a surrogate measure of endogenous insulin production post-onset, it is unclear as to whether plasma C-peptide represents changes in beta cell mass or beta cell function. The objective of this paper was to determine the relationship between beta cell mass and endogenous insulin production post-onset.</code> \| <code>Seven healthy individuals with type 1 diabetes were tested on two separate occasions, during which either a 30-min MOD or IHE protocol was performed. MOD consisted of continuous exercise at 40% Vo(2peak), while the IHE protocol involved a combination of continuous exercise at 40% Vo(2peak) interspersed with 4-s sprints performed every 2 min to simulate the activity patterns of team sports.</code> \| <code>It is unclear how genetic type 1 diabetes mellitus (DM) influences infarct size when blood glucose is tightly controlled. The aim of this study was to determine the effect of genetic type 1 DM, as occurs in BB rats, on infarct size after transient unilateral middle cerebral artery occlusion (MCAO) in male and female rats. In addition, studies suggest that male type 1 DM rats have a higher incidence of end-organ complications than do females. A second aim of this study was to determine the effect of chronic 17beta-estradiol (E(2)) administration on infarct size in male BB rats.</code> \| <code>Studies on bone mineral characteristics in children with type 1 diabetes mellitus (T1DM) have generated conflicting results.</code> \| <code>It has recently been reported that the risk of developing nephropathy in patients with insulin dependent (type 1) diabetes mellitus is strongly associated with synergism between poor glycaemic control and carriage of the hypertension associated angiotensin II (type 1) receptor C1166 allele. The same report also revealed an increase in risk of nephropathy in diabetic patients carrying a specific angiotensin II (type 1) receptor haplotype, i.e. C1166/140-bp microsatellite allele (major allele).</code> \| <code>The angiotensin II (type 1) (AT1) receptor mediates many biological effects of the renin-angiotensin system (RAS), leading to remodelling of cardiac tissue. The present study was designed to analyse changes in the function and expression of the AT1 receptor as principal effector of the RAS in myocardium from type 2 diabetic patients compared with non-diabetic myocardium as control. In addition, we determined the effect of treatment with ACE inhibitors or AT1 receptor blockers on expression levels of the receptor in diabetic patients.</code> \| <code>Type 2 diabetes mellitus increases the risk of atherosclerotic cardiovascular disease. Antioxidative properties of high density lipoprotein (HDL) are important for atheroprotection. This study investigated whether the antioxidative functionality of HDL is altered in type 2 diabetes mellitus and aimed to identify potential determinants of this parameter.</code> \| <code>Life-long insulin is the standard treatment for type 1 diabetes mellitus (T1DM). The role of traditional Chinese medicine (TCM) in T1DM is still not clear. The aim of this study is to explore the prescription pattern of TCM and its impact on the risk of diabetic ketoacidosis (DKA) in patients with T1DM.</code> \| <code>In type 1 diabetes, lung diffusing capacity for carbon monoxide (DL(CO)) may be impaired, and insulin has been shown to be beneficial in cases in which near-normal metabolic control is achieved. An influence of insulin, per se, on the alveolar-capillary membrane conductance is unexplored. We aimed at testing this possibility.</code> \| <code>aIMT, but not cIMT, was significantly greater in the children with type 1 diabetes mellitus than in control subjects (P < .001). In children with type 1 diabetes mellitus, aIMT correlated with glycosylated hemoglobin (r = 0.31, P = .01) and was independently associated with age (beta = 0.38, P = .001) and low-density lipoprotein cholesterol level (beta = 0.38, P = .001). Vascular function (GTN) was worse in children with type 1 diabetes mellitus who had an aIMT >95th percentile, as defined with the control subjects.</code> \| <code>Type 1 diabetes mellitus (T1DM) is caused by specific destruction of the pancreatic beta cells in the islets of Langerhans. Increased sensitivity to cytokines, in particular to interleukin-1beta (IL-1beta) seems to be an acquired trait during beta-cell maturation. In response to cytokines both protective and deleterious mechanisms are induced in beta cells, and when the deleterious prevail, T1DM develops. The aims of this study were to identify perturbation in protein patterns (PiPP) associated with beta-cell maturation, and compare these changes to previous analyses of IL-1beta exposed rat islets. For this purpose, proteome analyses were carried out using a cell-line, which matures from a glucagon-producing pre-beta-cell phenotype (NHI-glu) to an insulin-producing beta-cell phenotype (NHI-ins). We have previously shown that this maturation is accompanied by acquired sensitivity to the toxic effects of IL-1beta.</code> \| <code>Type 1 diabetes mellitus is a T-cell-mediated autoimmune disease that leads to a major loss of insulin-secreting beta cells. The further decline of beta-cell function after clinical onset might be prevented by treatment with CD3 monoclonal antibodies, as suggested by the results of a phase 1 study. To provide proof of this therapeutic principle at the metabolic level, we initiated a phase 2 placebo-controlled trial with a humanized antibody, an aglycosylated human IgG1 antibody directed against CD3 (ChAglyCD3).</code> \| <code>Type 2 diabetes mellitus (T2DM) affects approximately 10% of Americans, while 79 million Americans are estimated to have glucose intolerance or prediabetes (pre-DM). The present study was designed to determine whether obese patients with pre-DM or T2DM would lose weight as effectively as obese normoglycemic patients, in a medically supervised high-protein, low-calorie-weight management program.</code> \| <code>We have previously shown that long-term type 1 diabetes affects the structural organization, contractile apparatus and extracellular matrix (ECM) of the myometrium during early pregnancy in mice.</code> \| <code>In Achilles injured participants, there was a significant difference between soleus and lateral gastrocnemius offset times during running compared to the asymptomatic controls (p<0.05). There were no significant differences in triceps surae muscle activity between the footwear only and footwear and orthoses condition in the Achilles injured participants.</code> \| <code>Nine adolescents with type 1 diabetes mellitus (five males, four females, aged 16 +/- 1.8 yr, diabetes duration 8.2 +/- 4.1 yr, hemoglobin A1c 7.8 +/- 0.8%, mean +/- SD) were subjected on two different occasions to a rest or 45 min of exercise at 95% of their lactate threshold. Insulin was administered iv at a rate based on their usual insulin dose, with similar plasma insulin levels for both studies (82.1 +/- 19.0, exercise vs. 82.7 +/- 16.4 pmol/liter, rest). Glucose was infused to maintain euglycemia for 18 h.</code> \| <code>Type 1 diabetes mellitus (T1DM) is influenced by genetic as well as environmental factors. Its incidence has risen considerably since the 1950s.</code> \| <code>Insulin resistance has been associated with hypertension and with renal complications in patients with type 1 diabetes mellitus. Causal relationships have not been fully explained.</code> \| <code>Diabetes mellitus (DM) is a common metabolic disease among the middle-aged and older population, which leads to an increase of stroke incidence and poor stroke recovery. The present study was designed to investigate the impact of DM on brain damage and on ischemic brain repair after stroke in aging animals.</code> \| <code>Type 1 diabetes (T1D) is an autoimmune disease with multiple susceptibility genes. The aim of this study was to determine whether combining IDDM1/HLA and IDDM2/ insulin( INS) 5' variable number of tandem repeat locus (VNTR) genotypes improves T1D risk assessment.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>miracl</summary> * Dataset: [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) at [07e2b62](https://huggingface.co/datasets/sentence-transformers/miracl/tree/07e2b629250bf4185f4c87f640fac15949b8aa73) * Size: 789,900 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 11 characters</li><li>mean: 38.08 characters</li><li>max: 86 characters</li></ul> \| <ul><li>min: 93 characters</li><li>mean: 746.58 characters</li><li>max: 3851 characters</li></ul> \| <ul><li>min: 18 characters</li><li>mean: 648.12 characters</li><li>max: 2554 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What was Paul Heyman's first production?</code> \| <code>Paul Heyman<br>On May 23, 2005, Heyman returned in a segment with Vince McMahon and Eric Bischoff announcing ECW One Night Stand, with Heyman in charge. On the May 22 episode of "Raw", Heyman appeared as ECW Representative promoting "One Night Stand". On May 25, 2006 it was announced that ECW would relaunch, as a third WWE brand. Heyman was in charge of the new brand on-camera but had minimal creative input off-camera as well. On the May 29 episode of "Raw", during a face-off with Mick Foley, Heyman announced that he was granted a draft pick from both Raw and SmackDown! by Vince McMahon. His Raw draft pick was former ECW wrestler (and Money in the Bank contract holder) Rob Van Dam, and his SmackDown! draft pick was Kurt Angle. Heyman predicted that Van Dam would defeat John Cena at "One Night Stand" for the WWE Championship and then declare himself the new ECW World Heavyweight Champion.</code> \| <code>Jay Wolpert<br>Wolpert left Goodson-Todman to form his own production company, and his first game show was the 1979 series "Whew!" for CBS. Wolpert produced the series with Burt Sugarman for most of its run. "Whew!" was canceled in 1980 and Wolpert did not return to television with a series until January 1983, despite shooting several pilots in the interim. On January 3, 1983, Wolpert's "Hit Man" debuted on NBC with Peter Tomarken as its host. "Hit Man" lasted thirteen weeks on the air.</code> \| \| <code>When was Locke born?</code> \| <code>Adria Locke Langley<br>Locke was born in Iowa, 1899, as the youngest of three children. When she was young her family moved to Stanton, Nebraska and that is where she grew up. Her father William Locke, was president of the Omaha livestock market and a Quaker. He had certain ideas of what a woman should be. Because of this Adria's grandfather, Thomas Glendenning, took over in teaching her what would later be a great social consciousness.</code> \| <code>Keith Locke<br>Locke was born and grew up in Christchurch, to Jack and Elsie Locke, prominent lifelong political activists for a wide variety of causes. Their four children were brought up in this environment and followed their parents into a life of activism, (as well as Keith, his sister Maire Leadbeater is a well-known activist and former city councillor for Auckland City Council). His father Jack was under surveillance during the 1951 New Zealand waterfront dispute.<br>Former Prime Minister Robert Muldoon is said to have described the Lockes as the most "notorious Communist family in New Zealand". The Lockes lived in the Avon Loop area of the Christchurch Central City and were very active in the community notably organising Avon River clean-ups and native tree planting and arguing against development of the area, and in favour of retaining the character of the area.</code> \| \| <code>How do plants get blight?</code> \| <code>Bacterial blight of soybean<br>Bacterial blight of soybeans can enter leaves through wounds or natural openings such as stomata. After gaining entrance to the host leaves, "Pseudomonas syringae pv. glycinea" multiplies in the leaf intercellular fluid. The pathogen must then overcome the plants defenses. "Pseudomonas syringae pv. glycinea" accomplishes this by using the type three secretion system to inject a variety of pathogenicity effector proteins (Hrp proteins) into the plant cell cytoplasm. These proteins act by interfering with effector-triggered immunity and producing phytohormones/toxins that suppress plant defenses. The expression of these virulence factors depends on the environmental conditions at the time of infection (see "environment section). Furthermore, expression of virulence factors will only take place when a sufficiently large population of bacteria is present, which is determined through quorum sensing. When successful, the common symptoms of bacterial blight will be...</code> \| <code>Gummy stem blight<br>Gummy Stem Blight is a cucurbit-rot disease caused by the fungal plant pathogen "Didymella bryoniae" (anamorph "Phoma cucurbitacearum").<br>Gummy Stem Blight can affect a host at any stage of growth in its development and affects all parts of the host including leaves, stems and fruits. Symptoms generally consist of circular dark tan lesions that blight the leaf, water soaked leaves, stem cankers, and gummy brown ooze that exudes from cankers, giving it the name Gummy Stem Blight. Gummy Stem Blight reduces yields of edible cucurbits by devastating the vines and leaves and rotting the fruits.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mldr</summary> * Dataset: [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) at [40ad767](https://huggingface.co/datasets/sentence-transformers/mldr/tree/40ad7672817ebee49e00dd25aed00e1c401881d6) * Size: 200,000 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 16 characters</li><li>mean: 66.52 characters</li><li>max: 527 characters</li></ul> \| <ul><li>min: 3223 characters</li><li>mean: 20386.58 characters</li><li>max: 131268 characters</li></ul> \| <ul><li>min: 3611 characters</li><li>mean: 15772.93 characters</li><li>max: 145135 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What is an Aboriginal reserve and how was it created?</code> \| <code>An Aboriginal reserve, also called simply reserve, was a government-sanctioned settlement for Aboriginal Australians, created under various state and federal legislation. Along with missions and other institutions, they were used from the 19th century to the 1960s to keep Aboriginal people separate from the white Australian population, for various reasons perceived by the government of the day. The Aboriginal reserve laws gave governments much power over all aspects of Aboriginal people’s lives.<br><br>Protectors of Aborigines and (later) Aboriginal Protection Boards were appointed to look after the interests of the Aboriginal people.<br><br>History<br>Aboriginal reserves were used from the nineteenth century to keep Aboriginal people separate from the white Australian population, often ostensibly for their protection.<br><br>Protectors of Aborigines had been appointed from as early as 1836 in South Australia (with Matthew Moorhouse as the first permanent appointment as Chief Protector in 1839), with the G...</code> \| <code>The Nature Reserve () is a nature reserve located in the municipalities of Sorsele and Storuman in Västerbotten County of Swedish Lapland. It is the largest natural reserve in Sweden and one of the largest protected areas in Europe, totaling 562,772 ha (approx. 5,628 km2).<br><br>Most of the reserve is made up of several Scandinavian Mountains, the main ones being Artfjället, Norra Storfjället, Ammarfjället and Björkfjället. Most of the landscapes of the Swedish mountains are represented. This ranges from the pronounced alpine character of Norra Storfjället, which includes the highlight of the reserve, the Norra Sytertoppen (1,768 m), to the plateau and plains near the base of the mountains. The differences in elevation highlight the diversity of rocks in the mountains. Among the mountains are the valleys and waterways of the Ume River drainage basin. This includes a portion of the Vindel River, after which the reserve is named. Towards the east, the elevation decreases and the mountains gi...</code> \| \| <code>What is the English translation of the song "Bosanac"?</code> \| <code>This is a list of Bosnian patriotic songs.<br><br>{\| class="wikitable sortable"<br>\|+<br>!Title!!English translation!!Lyricist!!Composer!!Arranger!!Year!!scope="col" class="unsortable"\|Description<br>\|-<br>\|"Bosanac"\|\|'Bosnian Man'\|\|\|\|\|\|\|\|style="text-align:center"\|1984\|\|song sung by Bosnian singer Lepa Brena and her band Slatki Greh on their 1984 album Bato, Bato<br>\|-<br>\|"Bosanac"\|\|'Bosnian [Man]'\|\|\|\|\|\|\|\|style="text-align:center"\|1991\|\|folk song sung by singer Enes Begović<br>\|-<br>\|"Bosanac"\|\|'Bosnian [Man]'\|\|Mirko Šenkovski\|\|Mirko Šenkovski\|\|Mirko Šenkovski\|\|style="text-align:center"\|2012\|\|turbo-folk song sung by singer Elvira Rahić and DJ Deny<br>\|-<br>\|"Bosna"\|\|'Bosnia'\|\|\|\|\|\|\|\|\|\|song about the Bosnia and Herzegovina national basketball team, also used when the KK Bosna team plays games<br>\|-<br>\|"Bosna će vječno živjeti u nama"\|\|'Bosnia Will Live in Us Forever'\|\|Besim Spahić\|\|Besim Spahić\|\|Besim Spahić\|\|\|\|song sung by Bosnian musician and scientist <br>\|-<br>\|"Bosna ima jedna samo"\|\|'Bosnia [There] Is One [And] Only'\|\|Enver Ša...</code> \| <code>"Quiéreme mucho" is a criolla-bolero composed in 1911 by Gonzalo Roig with lyrics by Ramón Gollury and Agustín Rodríguez. The song was inspired by Roig's wife, Blanca Becerra, and premiered in Havana in 1911 without much success. In 1917, it was included in the sainete El servicio militar obligatorio and performed by Becerra and Rafael Llorens to critical acclaim. Roig published and sold the rights to the song in 1921, and the first recording was made in the United States by singer Tito Schipa in 1923. The English version, "Yours", was published in 1931 in the United States. It featured lyrics in English written by Albert Gamse and Jack Sherr. Both versions have been extensively recorded and arranged by different musicians, becoming Latin music standards.<br><br>Composition<br>"Quiéreme mucho" was composed by Gonzalo Roig at 21 years of age in 1911, before he had finished his music studies. He wrote the melody and played it on his piano, without making any further arrangements. Roig had been co...</code> \| \| <code>Who were the representatives that declined to run for reelection to the Vermont House of Representatives in 2016?</code> \| <code>Selene Colburn is an American politician currently serving in the Vermont House of Representatives from the Chittenden-6-4 district since 2017 as a member of the Vermont Progressive Party. Prior to her tenure in the State House she served on the city council in Burlington, Vermont. She is the first female chair of the House Progressive Caucus.<br><br>Colburn was born in Burlington, and educated at Burlington High School, Bennington College, and Simmons University. She became active in politics in her youth when she joined anti-war demonstrations.<br><br>Colburn was first elected to office with her election to the Burlington city council in the 2014 election and she won reelection in the 2015 and 2017 elections. She was elected to the state house alongside Brian Cina in the 2016 election with the nominations of the Progressive and Democratic parties and was reelected in the 2018 and 2020 elections. She was selected to serve as assistant chair of the Vermont Progressive Party's caucus in the state h...</code> \| <code>The 2016 United States Senate election in Florida was held November 8, 2016 to elect a member of the United States Senate to represent the State of Florida, concurrently with the 2016 U.S. presidential election, as well as other elections to the United States Senate in other states and elections to the United States House of Representatives and various state and local elections. The primary elections for both the Republicans and Democrats took place on August 30, 2016.<br><br>Incumbent Republican Senator Marco Rubio ran for another term but faced well-funded Republican primary opposition after initially announcing he would not seek re-election to his Senate seat. He had openly considered whether to seek re-election or run for president in 2016. He stated in April 2014 that he would not run for both the Senate and president in 2016, as Florida law prohibits a candidate from simultaneously appearing twice on a ballot, but did not rule out running for either office.<br><br>However, in April 2015, Ru...</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mr_tydi</summary> * Dataset: [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) at [abbdf55](https://huggingface.co/datasets/sentence-transformers/mr-tydi/tree/abbdf55c630352da943f779610c3ce6268118351) * Size: 354,700 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 13 characters</li><li>mean: 38.6 characters</li><li>max: 101 characters</li></ul> \| <ul><li>min: 71 characters</li><li>mean: 667.4 characters</li><li>max: 21297 characters</li></ul> \| <ul><li>min: 10 characters</li><li>mean: 619.16 characters</li><li>max: 3134 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-----------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What is the largest city in Sardinia?</code> \| <code>Sardinia<br>Sardinia is politically a region of Italy, whose official name is Regione Autonoma della Sardegna / Regione Autònoma de Sardigna (Autonomous Region of Sardinia),[3] and enjoys some degree of domestic autonomy granted by a specific Statute.[4] It is divided into four provinces and a metropolitan city, with Cagliari being the region's capital and its largest city as well. Sardinia's indigenous language and the other minority languages (Sassarese, Corsican Gallurese, Algherese Catalan and Ligurian Tabarchino) spoken on the island are recognized by the regional law and enjoy "equal dignity" with Italian.[5]</code> \| <code>History of Cagliari<br>Caralis (or Karales) was the capital of the Roman province of Sardinia and Corsica and was elevated to the rank of "Municipium", a result of the civil war between Julius Caesar and Pompey when Caesar himself granted this status in gratitude to the city for its fidelity during the bloody war. All Caralitani obtained Roman citizenship and were enrolled in the tribe Quirina. The territory of the city included the campidano plain, likely becoming Sanluri.<br>With about 20,000 inhabitants Caralis was the largest and most populous city of the island and the most important of the western Mediterranean basin of the Republic, and later of the Roman Empire. The city was equipped with important road links to the main towns of the island such as Sulki, with the coastal road and with that running through the valley of the Cixerri, Olbia and Tibula along the east coast, and Turris and Tibula along the road modeled on the current "Carlo Felice", and finally a road through the centre ...</code> \| \| <code>What activism did Katherine Schmidt participate in?</code> \| <code>Katherine Schmidt<br>Katherine Schmidt (February 6, 1899 – April 18, 1978) was an American artist and art activist. Early in her career the figure studies, landscapes, and still lifes she painted drew praise for their "purity and clarity of color," "sound draftsmanship," and "individual choice of subject and its handling."[3] During the 1930s she was known mainly for the quality of her still life paintings which showed, one critic said, "impeccable artistry."[4] At the end of her career, in the 1960s and 1970s, she produced specialized and highly disciplined still lifes of objects such as dead leaves and pieces of crumpled paper, which, said a critic, approached a "magical realism."[5] As an art activist she helped promote the rights of artists for fair remuneration.[6]</code> \| <code>Caroline Katzenstein<br>Caroline Katzenstein (1888–1968) was an American suffragist, activist, advocate for equal rights, insurance agent, and author. She was active in the local Philadelphia suffragist movement through the Pennsylvania branch of the National American Woman Suffrage Association and the Equal Franchise Society of Philadelphia. She played a role in the formation of the Congressional Union for Women Suffrage, which later became the National Women's Party. Katzenstein was also active in the movement for equal rights, serving on the Women's Joint Legislative Committee with Alice Paul, and championing the cause for the Equal Rights Amendment. She was the author of "Lifting the Curtain: the State and National Woman Suffrage Campaigns in Pennsylvania as I Saw Them" (1955).</code> \| \| <code>Who made the first wristwatch?</code> \| <code>History of watches<br>From the beginning, wristwatches were almost exclusively worn by women, while men used pocketwatches up until the early 20th century. The concept of the wristwatch goes back to the production of the very earliest watches in the 16th century. Some people say the world's first wristwatch was created by Abraham-Louis Breguet for Caroline Murat, Queen of Naples, in 1810.[21][22][23][24][25] Elizabeth I of England received a wristwatch from Robert Dudley in 1571, described as an arm watch. By the mid nineteenth century, most watchmakers produced a range of wristwatches, often marketed as bracelets, for women.[26]</code> \| <code>Rolex<br>In 1908, Wilsdorf registered the trademark "Rolex" and opened an office in La Chaux-de-Fonds, Switzerland.[14][15] The book The Best of Time: Rolex Wristwatches: An Unauthorized History by Jeffrey P. Hess and James Dowling says that the name was just made up.[16] One story, never confirmed by Wilsdorf, recounts that the name came from the French phrase horlogerie exquise, meaning "exquisite clockwork"[9] or as a contraction of "horological excellence". Wilsdorf was said to want his watch brand's name to be easily pronounceable in any language.[17] He also thought that the name "Rolex" was onomatopoeic, sounding like a watch being wound. It is easily pronounceable in many languages and, as all its upper-case letters have the same size, can be written symmetrically. It was also short enough to fit on the face of a watch.[17]</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> \| Epoch \| Step \| Training Loss \| gooaq loss \| msmarco loss \| squad loss \| s2orc loss \| allnli loss \| paq loss \| trivia qa loss \| msmarco 10m loss \| swim ir loss \| pubmedqa loss \| miracl loss \| mldr loss \| mr tydi loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \| \|:------:\|:-----:\|:-------------:\|:----------:\|:------------:\|:----------:\|:----------:\|:-----------:\|:--------:\|:--------------:\|:----------------:\|:------------:\|:-------------:\|:-----------:\|:---------:\|:------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\| \| 0 \| 0 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| 0.0741 \| 0.3518 \| 0.2118 \| 0.0793 \| 0.3538 \| 0.3200 \| 0.1954 \| 0.1589 \| 0.6759 \| 0.1532 \| 0.0945 \| 0.4296 \| 0.1455 \| 0.2495 \| \| 0.0000 \| 1 \| 32.2074 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0064 \| 250 \| 22.7851 \| 8.3992 \| 17.7191 \| 17.6791 \| 16.6296 \| 18.7913 \| 12.1404 \| 18.9992 \| 12.1891 \| 11.6795 \| 26.3440 \| 8.5795 \| 19.3571 \| 9.5985 \| 0.2366 \| 0.5129 \| 0.6004 \| 0.1960 \| 0.6334 \| 0.3941 \| 0.2713 \| 0.3392 \| 0.7977 \| 0.2416 \| 0.3819 \| 0.5448 \| 0.4679 \| 0.4321 \| \| 0.0127 \| 500 \| 9.6296 \| 4.6987 \| 13.6254 \| 11.7605 \| 12.5290 \| 17.2038 \| 6.9342 \| 12.0873 \| 7.3539 \| 9.1374 \| 23.1663 \| 4.2482 \| 14.5991 \| 3.3365 \| 0.2929 \| 0.5509 \| 0.6529 \| 0.2890 \| 0.6495 \| 0.4244 \| 0.2873 \| 0.3690 \| 0.8830 \| 0.2373 \| 0.3815 \| 0.5802 \| 0.5422 \| 0.4723 \| \| 0.0191 \| 750 \| 6.7008 \| 3.6302 \| 11.7061 \| 10.1299 \| 11.2366 \| 15.1612 \| 5.5833 \| 10.6967 \| 5.7074 \| 8.8117 \| 23.2404 \| 3.5115 \| 12.5734 \| 2.4346 \| 0.3101 \| 0.5565 \| 0.6684 \| 0.3406 \| 0.6354 \| 0.4111 \| 0.2972 \| 0.3894 \| 0.8611 \| 0.2513 \| 0.3613 \| 0.5840 \| 0.5578 \| 0.4788 \| \| 0.0255 \| 1000 \| 5.8282 \| 2.9789 \| 11.2050 \| 9.5095 \| 10.6029 \| 14.7717 \| 5.0173 \| 9.6170 \| 5.1146 \| 9.0596 \| 22.3746 \| 3.0912 \| 12.2982 \| 2.2626 \| 0.3066 \| 0.5514 \| 0.6654 \| 0.3252 \| 0.6390 \| 0.4139 \| 0.2917 \| 0.4168 \| 0.8678 \| 0.2590 \| 0.3884 \| 0.6214 \| 0.5614 \| 0.4852 \| \| 0.0318 \| 1250 \| 5.3975 \| 2.8335 \| 11.0393 \| 9.3407 \| 9.6014 \| 14.5350 \| 4.8262 \| 9.4577 \| 4.9009 \| 8.9271 \| 21.8053 \| 3.2513 \| 12.2634 \| 1.6880 \| 0.3090 \| 0.5449 \| 0.6607 \| 0.3432 \| 0.6243 \| 0.4145 \| 0.3026 \| 0.4392 \| 0.8801 \| 0.2608 \| 0.3760 \| 0.6102 \| 0.5768 \| 0.4879 \| \| 0.0382 \| 1500 \| 5.3077 \| 2.7030 \| 10.6366 \| 8.9914 \| 9.8588 \| 14.6669 \| 4.6253 \| 9.3728 \| 4.5863 \| 9.1788 \| 22.0617 \| 2.8378 \| 10.9618 \| 1.8702 \| 0.3240 \| 0.5421 \| 0.7026 \| 0.3507 \| 0.6227 \| 0.4134 \| 0.3150 \| 0.3996 \| 0.8776 \| 0.2493 \| 0.3625 \| 0.6120 \| 0.5642 \| 0.4874 \| \| 0.0445 \| 1750 \| 4.9354 \| 2.6691 \| 10.6339 \| 8.9606 \| 9.7095 \| 14.9174 \| 4.5880 \| 9.3114 \| 4.1786 \| 8.2898 \| 22.8332 \| 2.6850 \| 11.3781 \| 1.6352 \| 0.3092 \| 0.5591 \| 0.6615 \| 0.3253 \| 0.6363 \| 0.3926 \| 0.3165 \| 0.4057 \| 0.9019 \| 0.2600 \| 0.3685 \| 0.6030 \| 0.5563 \| 0.4843 \| \| 0.0509 \| 2000 \| 4.8017 \| 2.5867 \| 10.0547 \| 8.8155 \| 9.6765 \| 14.7973 \| 4.3931 \| 9.2721 \| 4.0193 \| 7.7955 \| 23.4468 \| 1.9884 \| 11.4315 \| 1.8009 \| 0.3274 \| 0.5615 \| 0.7024 \| 0.3531 \| 0.6481 \| 0.3959 \| 0.3134 \| 0.4183 \| 0.8849 \| 0.2505 \| 0.3694 \| 0.5991 \| 0.5664 \| 0.4916 \| \| 0.0573 \| 2250 \| 4.8193 \| 2.4974 \| 9.9855 \| 8.8389 \| 9.6763 \| 14.4220 \| 4.3112 \| 9.1019 \| 4.0176 \| 8.4064 \| 22.7034 \| 2.7534 \| 11.5256 \| 2.3585 \| 0.3149 \| 0.5392 \| 0.6689 \| 0.3344 \| 0.6495 \| 0.4080 \| 0.3058 \| 0.3953 \| 0.8857 \| 0.2588 \| 0.3426 \| 0.5986 \| 0.5756 \| 0.4829 \| \| 0.0636 \| 2500 \| 4.8773 \| 2.7116 \| 10.2180 \| 8.6935 \| 9.7664 \| 14.3161 \| 4.2722 \| 8.9829 \| 4.2454 \| 9.1911 \| 22.8367 \| 2.6666 \| 11.6110 \| 2.0147 \| 0.3377 \| 0.5455 \| 0.6547 \| 0.3130 \| 0.6396 \| 0.4259 \| 0.3256 \| 0.4226 \| 0.8825 \| 0.2491 \| 0.3908 \| 0.5852 \| 0.5656 \| 0.4875 \| \| 0.0700 \| 2750 \| 4.5856 \| 2.7758 \| 9.9754 \| 8.6197 \| 9.6282 \| 14.4828 \| 4.1534 \| 8.8766 \| 4.2312 \| 9.5281 \| 21.9368 \| 2.9119 \| 9.7259 \| 1.9405 \| 0.3384 \| 0.5763 \| 0.6646 \| 0.3068 \| 0.6740 \| 0.4195 \| 0.3155 \| 0.4301 \| 0.8809 \| 0.2366 \| 0.3965 \| 0.5963 \| 0.5714 \| 0.4928 \| \| 0.0764 \| 3000 \| 4.3725 \| 2.5993 \| 10.1291 \| 8.7361 \| 8.9502 \| 14.8227 \| 4.1163 \| 8.8371 \| 4.1014 \| 9.2259 \| 23.4047 \| 3.2715 \| 10.3051 \| 2.3604 \| 0.3040 \| 0.5703 \| 0.6836 \| 0.3006 \| 0.6355 \| 0.3993 \| 0.3318 \| 0.4236 \| 0.9001 \| 0.2579 \| 0.3966 \| 0.5902 \| 0.5770 \| 0.4900 \| \| 0.0827 \| 3250 \| 4.4409 \| 2.5753 \| 10.0879 \| 8.5131 \| 8.7106 \| 14.8015 \| 4.0560 \| 8.8296 \| 4.1868 \| 9.3069 \| 22.5793 \| 2.3810 \| 8.6639 \| 2.0435 \| 0.3147 \| 0.5806 \| 0.6766 \| 0.3342 \| 0.6293 \| 0.4134 \| 0.3208 \| 0.4089 \| 0.8834 \| 0.2656 \| 0.3784 \| 0.6119 \| 0.5731 \| 0.4916 \| \| 0.0891 \| 3500 \| 4.6192 \| 2.4352 \| 9.9932 \| 8.5716 \| 9.2016 \| 14.1559 \| 4.0585 \| 8.9413 \| 3.8278 \| 8.6089 \| 22.9941 \| 2.5541 \| 9.4271 \| 1.7271 \| 0.3052 \| 0.5531 \| 0.6921 \| 0.3284 \| 0.6391 \| 0.4027 \| 0.3288 \| 0.4235 \| 0.8938 \| 0.2565 \| 0.3928 \| 0.5848 \| 0.5741 \| 0.4904 \| \| 0.0955 \| 3750 \| 4.4805 \| 2.5370 \| 10.0723 \| 8.4652 \| 8.8024 \| 14.4678 \| 4.0045 \| 8.8487 \| 3.6855 \| 8.4129 \| 22.5177 \| 2.5961 \| 9.1362 \| 1.6572 \| 0.2996 \| 0.5669 \| 0.7051 \| 0.3007 \| 0.6433 \| 0.3822 \| 0.3127 \| 0.4419 \| 0.8853 \| 0.2741 \| 0.3696 \| 0.5911 \| 0.5796 \| 0.4886 \| \| 0.1018 \| 4000 \| 4.3246 \| 2.4130 \| 10.0235 \| 8.4203 \| 9.1794 \| 14.4445 \| 3.9667 \| 8.8021 \| 3.6692 \| 8.0637 \| 22.1590 \| 2.2690 \| 9.5487 \| 1.4625 \| 0.2987 \| 0.5659 \| 0.6905 \| 0.3103 \| 0.6280 \| 0.4004 \| 0.3025 \| 0.4273 \| 0.9026 \| 0.2728 \| 0.3710 \| 0.6004 \| 0.5746 \| 0.4881 \| \| 0.1082 \| 4250 \| 4.547 \| 2.2318 \| 10.0773 \| 8.4970 \| 8.6540 \| 13.9845 \| 3.9893 \| 8.7805 \| 3.4480 \| 8.1038 \| 21.2066 \| 2.4344 \| 9.2932 \| 1.4761 \| 0.3037 \| 0.5760 \| 0.7118 \| 0.3239 \| 0.6135 \| 0.4056 \| 0.3170 \| 0.4323 \| 0.8860 \| 0.2620 \| 0.3887 \| 0.6211 \| 0.5824 \| 0.4941 \| \| 0.1145 \| 4500 \| 4.3008 \| 2.3243 \| 9.8733 \| 8.5042 \| 9.0158 \| 14.0935 \| 3.9014 \| 8.8306 \| 3.5557 \| 8.4240 \| 21.2823 \| 2.6280 \| 9.4869 \| 1.8310 \| 0.3076 \| 0.5788 \| 0.6867 \| 0.3187 \| 0.6190 \| 0.3936 \| 0.3181 \| 0.4160 \| 0.8895 \| 0.2564 \| 0.3960 \| 0.6148 \| 0.5736 \| 0.4899 \| \| 0.1209 \| 4750 \| 4.2386 \| 2.4259 \| 9.8799 \| 8.3964 \| 9.1116 \| 13.9412 \| 3.8572 \| 8.7955 \| 3.6524 \| 9.6881 \| 21.3812 \| 2.2282 \| 8.9280 \| 1.5408 \| 0.3393 \| 0.5783 \| 0.7190 \| 0.3137 \| 0.6239 \| 0.3953 \| 0.3044 \| 0.4231 \| 0.8768 \| 0.2636 \| 0.3828 \| 0.6043 \| 0.5662 \| 0.4916 \| \| 0.1273 \| 5000 \| 4.141 \| 2.4005 \| 9.9973 \| 8.2741 \| 9.1627 \| 14.4273 \| 3.7931 \| 8.7825 \| 3.6856 \| 9.0001 \| 21.6595 \| 2.2374 \| 9.2771 \| 1.4845 \| 0.3243 \| 0.5705 \| 0.6858 \| 0.3304 \| 0.6328 \| 0.3888 \| 0.3145 \| 0.4096 \| 0.8775 \| 0.2492 \| 0.3769 \| 0.6001 \| 0.5600 \| 0.4862 \| \| 0.1336 \| 5250 \| 4.3221 \| 2.4200 \| 9.8792 \| 8.2559 \| 9.0431 \| 13.9564 \| 3.8055 \| 8.5773 \| 3.6137 \| 8.1900 \| 21.6272 \| 2.2271 \| 8.1229 \| 1.6308 \| 0.3207 \| 0.5876 \| 0.6945 \| 0.3449 \| 0.6232 \| 0.4072 \| 0.3011 \| 0.4084 \| 0.8894 \| 0.2557 \| 0.3668 \| 0.5905 \| 0.5582 \| 0.4883 \| \| 0.1400 \| 5500 \| 4.2121 \| 2.3857 \| 10.1277 \| 8.3257 \| 9.0878 \| 13.8545 \| 3.7696 \| 8.6034 \| 3.5613 \| 8.7845 \| 21.5562 \| 2.3611 \| 7.5145 \| 1.9243 \| 0.3160 \| 0.5683 \| 0.7024 \| 0.3382 \| 0.6244 \| 0.4038 \| 0.3075 \| 0.4316 \| 0.8784 \| 0.2603 \| 0.3876 \| 0.5866 \| 0.5650 \| 0.4900 \| \| 0.1464 \| 5750 \| 4.1071 \| 2.4265 \| 9.9555 \| 8.0947 \| 9.1289 \| 14.0017 \| 3.7337 \| 8.6306 \| 3.4562 \| 8.3132 \| 21.7894 \| 2.1157 \| 8.1967 \| 1.5567 \| 0.3317 \| 0.5604 \| 0.7101 \| 0.3645 \| 0.6460 \| 0.3857 \| 0.2987 \| 0.4236 \| 0.8830 \| 0.2535 \| 0.3867 \| 0.5767 \| 0.5612 \| 0.4909 \| \| 0.1527 \| 6000 \| 4.1189 \| 2.3586 \| 10.0799 \| 8.0905 \| 9.0291 \| 14.0232 \| 3.7064 \| 8.5220 \| 3.4742 \| 8.3858 \| 21.5903 \| 2.1663 \| 7.6242 \| 1.4405 \| 0.3264 \| 0.5614 \| 0.6825 \| 0.3668 \| 0.6296 \| 0.3972 \| 0.2863 \| 0.4296 \| 0.8869 \| 0.2482 \| 0.3809 \| 0.6004 \| 0.5556 \| 0.4886 \| \| 0.1591 \| 6250 \| 4.0873 \| 2.2906 \| 9.9813 \| 8.1351 \| 8.5907 \| 13.8665 \| 3.7028 \| 8.5648 \| 3.5042 \| 8.1623 \| 21.6688 \| 2.2940 \| 7.6652 \| 1.5228 \| 0.3444 \| 0.5666 \| 0.7035 \| 0.3415 \| 0.6188 \| 0.3992 \| 0.2989 \| 0.4318 \| 0.8816 \| 0.2504 \| 0.4014 \| 0.6042 \| 0.5637 \| 0.4928 \| \| 0.1654 \| 6500 \| 3.9586 \| 2.2378 \| 9.9318 \| 8.0887 \| 8.7977 \| 14.1260 \| 3.6614 \| 8.5028 \| 3.3178 \| 8.3118 \| 21.5718 \| 2.2074 \| 8.0905 \| 1.7266 \| 0.3299 \| 0.5828 \| 0.6994 \| 0.3505 \| 0.6375 \| 0.3988 \| 0.3225 \| 0.4173 \| 0.8891 \| 0.2448 \| 0.3930 \| 0.6034 \| 0.5614 \| 0.4946 \| \| 0.1718 \| 6750 \| 4.1981 \| 2.2863 \| 9.5475 \| 7.6881 \| 8.5462 \| 13.6929 \| 3.6704 \| 8.6660 \| 3.3401 \| 8.9262 \| 21.8933 \| 2.0578 \| 8.4832 \| 1.6796 \| 0.3456 \| 0.5600 \| 0.6983 \| 0.3549 \| 0.6445 \| 0.3844 \| 0.3139 \| 0.4263 \| 0.8952 \| 0.2626 \| 0.3877 \| 0.5820 \| 0.5597 \| 0.4935 \| \| 0.1782 \| 7000 \| 4.0528 \| 2.3292 \| 9.5129 \| 7.8273 \| 8.7743 \| 13.6930 \| 3.6284 \| 8.6346 \| 3.3430 \| 8.5204 \| 21.3129 \| 2.3350 \| 8.8695 \| 1.9034 \| 0.3457 \| 0.5673 \| 0.6850 \| 0.3274 \| 0.6321 \| 0.3981 \| 0.3171 \| 0.4252 \| 0.8830 \| 0.2643 \| 0.3901 \| 0.5888 \| 0.5590 \| 0.4910 \| \| 0.1845 \| 7250 \| 4.0547 \| 2.2386 \| 9.5373 \| 7.9214 \| 8.7896 \| 13.6151 \| 3.6172 \| 8.5316 \| 3.3128 \| 9.3566 \| 21.4568 \| 2.3743 \| 9.1696 \| 1.7235 \| 0.3528 \| 0.5597 \| 0.6931 \| 0.3369 \| 0.6327 \| 0.3951 \| 0.3111 \| 0.4368 \| 0.8787 \| 0.2552 \| 0.3758 \| 0.5911 \| 0.5515 \| 0.4900 \| \| 0.1909 \| 7500 \| 4.3005 \| 2.2273 \| 9.4397 \| 7.9013 \| 8.8606 \| 13.3847 \| 3.6401 \| 8.4134 \| 3.2583 \| 8.3415 \| 21.4206 \| 2.4573 \| 9.2348 \| 1.4832 \| 0.3557 \| 0.5642 \| 0.7145 \| 0.3380 \| 0.6412 \| 0.3772 \| 0.3085 \| 0.4278 \| 0.8792 \| 0.2522 \| 0.3738 \| 0.5843 \| 0.5587 \| 0.4904 \| \| 0.1973 \| 7750 \| 4.0054 \| 2.2277 \| 9.4653 \| 7.9297 \| 8.5999 \| 13.6106 \| 3.6049 \| 8.3861 \| 3.2335 \| 9.3198 \| 21.6595 \| 2.4730 \| 8.7335 \| 1.6145 \| 0.3396 \| 0.5535 \| 0.6901 \| 0.3556 \| 0.6311 \| 0.3867 \| 0.3182 \| 0.4308 \| 0.8692 \| 0.2590 \| 0.3654 \| 0.5925 \| 0.5558 \| 0.4883 \| \| 0.2036 \| 8000 \| 3.8426 \| 2.2970 \| 9.4352 \| 7.9532 \| 8.6501 \| 13.9004 \| 3.5835 \| 8.3664 \| 3.2109 \| 8.4302 \| 21.0340 \| 2.1047 \| 9.0103 \| 1.1751 \| 0.3420 \| 0.5695 \| 0.6868 \| 0.3746 \| 0.6434 \| 0.4042 \| 0.3193 \| 0.4259 \| 0.8847 \| 0.2623 \| 0.3785 \| 0.5945 \| 0.5702 \| 0.4966 \| \| 0.2100 \| 8250 \| 3.9404 \| 2.2417 \| 9.5349 \| 7.8978 \| 8.6899 \| 13.8131 \| 3.5697 \| 8.3664 \| 3.1548 \| 8.6003 \| 21.6214 \| 2.0881 \| 9.1829 \| 0.9559 \| 0.3306 \| 0.5651 \| 0.6959 \| 0.3448 \| 0.6455 \| 0.3857 \| 0.3123 \| 0.4431 \| 0.9009 \| 0.2580 \| 0.3981 \| 0.6073 \| 0.5748 \| 0.4971 \| \| 0.2164 \| 8500 \| 3.9522 \| 2.2103 \| 9.6575 \| 7.9030 \| 8.3617 \| 14.0083 \| 3.5433 \| 8.3198 \| 3.2148 \| 8.5004 \| 20.8166 \| 2.3194 \| 8.0428 \| 1.2475 \| 0.3343 \| 0.5688 \| 0.7054 \| 0.3411 \| 0.6625 \| 0.3919 \| 0.3148 \| 0.4213 \| 0.8965 \| 0.2665 \| 0.3454 \| 0.6133 \| 0.5782 \| 0.4954 \| \| 0.2227 \| 8750 \| 3.9665 \| 2.1659 \| 9.7216 \| 7.8772 \| 8.6394 \| 13.9406 \| 3.5289 \| 8.3360 \| 3.1863 \| 8.7835 \| 21.2033 \| 2.1874 \| 8.4683 \| 1.2399 \| 0.3231 \| 0.5715 \| 0.6794 \| 0.3378 \| 0.6684 \| 0.3966 \| 0.3171 \| 0.4048 \| 0.8887 \| 0.2660 \| 0.3461 \| 0.6094 \| 0.5559 \| 0.4896 \| \| 0.2291 \| 9000 \| 4.0217 \| 2.1042 \| 9.6765 \| 7.8951 \| 8.4255 \| 13.8092 \| 3.5314 \| 8.3896 \| 3.1097 \| 8.0204 \| 21.4246 \| 2.0600 \| 8.7244 \| 1.3343 \| 0.3218 \| 0.5696 \| 0.6931 \| 0.3569 \| 0.6654 \| 0.3978 \| 0.3159 \| 0.4193 \| 0.9000 \| 0.2827 \| 0.3750 \| 0.5890 \| 0.5796 \| 0.4974 \| \| 0.2354 \| 9250 \| 4.0008 \| 2.0865 \| 9.4154 \| 7.9689 \| 8.5298 \| 13.6352 \| 3.5371 \| 8.4191 \| 3.0414 \| 8.4828 \| 21.5173 \| 1.9966 \| 7.6465 \| 1.1097 \| 0.3282 \| 0.5675 \| 0.6934 \| 0.3476 \| 0.6559 \| 0.3907 \| 0.3272 \| 0.4132 \| 0.9038 \| 0.2712 \| 0.3891 \| 0.5951 \| 0.5716 \| 0.4965 \| \| 0.2418 \| 9500 \| 3.8041 \| 2.0969 \| 9.4478 \| 7.9720 \| 8.6298 \| 13.7493 \| 3.5003 \| 8.4702 \| 3.0939 \| 8.5108 \| 21.6929 \| 1.9457 \| 7.9947 \| 1.2784 \| 0.3291 \| 0.5655 \| 0.6915 \| 0.3533 \| 0.6495 \| 0.3949 \| 0.3291 \| 0.4313 \| 0.9007 \| 0.2641 \| 0.3910 \| 0.5838 \| 0.5710 \| 0.4965 \| \| 0.2482 \| 9750 \| 3.9483 \| 2.1627 \| 9.5085 \| 7.8994 \| 8.7048 \| 13.4591 \| 3.4941 \| 8.3342 \| 3.1202 \| 8.7011 \| 20.9101 \| 1.8594 \| 7.8214 \| 1.1181 \| 0.3312 \| 0.5665 \| 0.6870 \| 0.3608 \| 0.6530 \| 0.4038 \| 0.3267 \| 0.4511 \| 0.8973 \| 0.2613 \| 0.4063 \| 0.5832 \| 0.5660 \| 0.4996 \| \| 0.2545 \| 10000 \| 3.9843 \| 2.1229 \| 9.4348 \| 7.9006 \| 8.2377 \| 13.5086 \| 3.4905 \| 8.2574 \| 3.0114 \| 8.3314 \| 20.5157 \| 1.9033 \| 6.7485 \| 1.1358 \| 0.3365 \| 0.5704 \| 0.6858 \| 0.3616 \| 0.6561 \| 0.3953 \| 0.3263 \| 0.4322 \| 0.8833 \| 0.2690 \| 0.3995 \| 0.6004 \| 0.5691 \| 0.4989 \| \| 0.2609 \| 10250 \| 3.8779 \| 2.1442 \| 9.4343 \| 7.9411 \| 8.4677 \| 13.5196 \| 3.4558 \| 8.2314 \| 3.0592 \| 8.6126 \| 20.3879 \| 1.9378 \| 6.8287 \| 1.1270 \| 0.3192 \| 0.5688 \| 0.7051 \| 0.3669 \| 0.6577 \| 0.3913 \| 0.3167 \| 0.4479 \| 0.8978 \| 0.2619 \| 0.4030 \| 0.5966 \| 0.5699 \| 0.5002 \| \| 0.2673 \| 10500 \| 3.8708 \| 2.1487 \| 9.6030 \| 7.9352 \| 8.4657 \| 13.3417 \| 3.4574 \| 8.3005 \| 3.0167 \| 8.4265 \| 20.5354 \| 1.9044 \| 6.9217 \| 1.1079 \| 0.3255 \| 0.5730 \| 0.7020 \| 0.3445 \| 0.6527 \| 0.3861 \| 0.3178 \| 0.4322 \| 0.8859 \| 0.2644 \| 0.3997 \| 0.5969 \| 0.5739 \| 0.4965 \| \| 0.2736 \| 10750 \| 3.8153 \| 2.1486 \| 9.2440 \| 7.8432 \| 8.5271 \| 13.4366 \| 3.4702 \| 8.3222 \| 2.9680 \| 8.4780 \| 20.6855 \| 1.8790 \| 6.6261 \| 1.0838 \| 0.3240 \| 0.5727 \| 0.6970 \| 0.3487 \| 0.6546 \| 0.3897 \| 0.3294 \| 0.4421 \| 0.8908 \| 0.2548 \| 0.3802 \| 0.6028 \| 0.5744 \| 0.4970 \| \| 0.2800 \| 11000 \| 3.9693 \| 2.1542 \| 9.4072 \| 7.8122 \| 8.6226 \| 13.1083 \| 3.4426 \| 8.2961 \| 3.0086 \| 8.6255 \| 20.5001 \| 1.9112 \| 6.6305 \| 1.1554 \| 0.3273 \| 0.5631 \| 0.6912 \| 0.3502 \| 0.6431 \| 0.4012 \| 0.3173 \| 0.4422 \| 0.8834 \| 0.2740 \| 0.3829 \| 0.6117 \| 0.5745 \| 0.4971 \| \| 0.2864 \| 11250 \| 3.7596 \| 2.1288 \| 9.4115 \| 7.8479 \| 8.6295 \| 13.1814 \| 3.4282 \| 8.2609 \| 2.9531 \| 8.6024 \| 20.7827 \| 1.8339 \| 7.0503 \| 1.0273 \| 0.3301 \| 0.5714 \| 0.6814 \| 0.3457 \| 0.6447 \| 0.3962 \| 0.3142 \| 0.4545 \| 0.8751 \| 0.2687 \| 0.3827 \| 0.5921 \| 0.5492 \| 0.4928 \| \| 0.2927 \| 11500 \| 3.7377 \| 2.1764 \| 9.2284 \| 7.7482 \| 8.6753 \| 13.2556 \| 3.4186 \| 8.2092 \| 2.9631 \| 8.2251 \| 20.9522 \| 1.8887 \| 6.8783 \| 1.1493 \| 0.3184 \| 0.5710 \| 0.6896 \| 0.3620 \| 0.6411 \| 0.4016 \| 0.3141 \| 0.4598 \| 0.8871 \| 0.2660 \| 0.4035 \| 0.5946 \| 0.5680 \| 0.4982 \| \| 0.2991 \| 11750 \| 3.645 \| 2.1757 \| 9.2386 \| 7.7988 \| 8.4091 \| 13.3105 \| 3.3986 \| 8.1770 \| 3.0392 \| 8.3319 \| 20.8279 \| 1.8464 \| 7.2340 \| 1.2369 \| 0.3066 \| 0.5680 \| 0.6938 \| 0.3413 \| 0.6498 \| 0.4035 \| 0.3119 \| 0.4692 \| 0.8776 \| 0.2697 \| 0.4018 \| 0.5937 \| 0.5725 \| 0.4969 \| \| 0.3054 \| 12000 \| 3.8302 \| 2.1730 \| 9.2542 \| 7.8031 \| 8.3773 \| 13.3083 \| 3.4245 \| 8.2024 \| 2.9451 \| 8.4451 \| 20.3890 \| 1.8992 \| 7.1868 \| 1.3149 \| 0.3022 \| 0.5639 \| 0.7047 \| 0.3580 \| 0.6585 \| 0.3953 \| 0.3193 \| 0.4512 \| 0.8757 \| 0.2667 \| 0.4016 \| 0.5815 \| 0.5653 \| 0.4957 \| \| 0.3118 \| 12250 \| 3.7341 \| 2.1580 \| 9.1449 \| 7.7487 \| 8.2782 \| 13.4871 \| 3.4325 \| 8.1531 \| 2.8524 \| 8.0765 \| 20.4420 \| 1.8084 \| 7.4004 \| 1.1942 \| 0.3217 \| 0.5648 \| 0.7022 \| 0.3658 \| 0.6597 \| 0.4010 \| 0.3204 \| 0.4470 \| 0.8778 \| 0.2668 \| 0.4062 \| 0.5841 \| 0.5764 \| 0.4995 \| \| 0.3182 \| 12500 \| 3.6937 \| 2.2003 \| 9.0298 \| 7.7776 \| 8.3088 \| 13.3345 \| 3.4198 \| 8.0772 \| 2.8139 \| 8.7163 \| 20.4754 \| 1.8802 \| 7.2714 \| 1.2016 \| 0.3086 \| 0.5676 \| 0.6930 \| 0.3609 \| 0.6532 \| 0.4067 \| 0.3228 \| 0.4341 \| 0.8737 \| 0.2723 \| 0.4014 \| 0.5783 \| 0.5799 \| 0.4964 \| \| 0.3245 \| 12750 \| 3.6917 \| 2.1808 \| 9.0285 \| 7.7770 \| 8.3062 \| 13.5737 \| 3.3953 \| 8.1291 \| 2.8232 \| 8.3426 \| 20.9837 \| 1.9420 \| 7.1199 \| 1.2568 \| 0.3288 \| 0.5620 \| 0.6855 \| 0.3512 \| 0.6575 \| 0.4069 \| 0.3309 \| 0.4372 \| 0.8906 \| 0.2709 \| 0.4106 \| 0.5928 \| 0.5874 \| 0.5009 \| \| 0.3309 \| 13000 \| 3.6376 \| 2.1621 \| 9.0474 \| 7.7871 \| 8.1721 \| 13.4874 \| 3.3904 \| 8.1507 \| 2.8109 \| 8.5607 \| 21.0805 \| 1.9734 \| 7.0553 \| 1.3466 \| 0.3250 \| 0.5686 \| 0.6850 \| 0.3520 \| 0.6630 \| 0.4044 \| 0.3258 \| 0.4424 \| 0.8666 \| 0.2684 \| 0.4038 \| 0.5769 \| 0.5725 \| 0.4965 \| \| 0.3373 \| 13250 \| 3.7786 \| 2.1146 \| 9.1181 \| 7.7333 \| 8.2758 \| 13.4782 \| 3.3906 \| 8.2021 \| 2.8320 \| 8.3097 \| 21.1471 \| 1.8529 \| 7.3608 \| 1.2242 \| 0.3282 \| 0.5649 \| 0.6997 \| 0.3761 \| 0.6680 \| 0.4097 \| 0.3242 \| 0.4143 \| 0.8873 \| 0.2784 \| 0.3958 \| 0.5956 \| 0.5745 \| 0.5013 \| \| 0.3436 \| 13500 \| 3.8654 \| 2.2053 \| 9.0632 \| 7.6973 \| 8.4055 \| 13.2312 \| 3.3747 \| 8.1627 \| 2.8245 \| 8.4075 \| 20.2899 \| 1.7553 \| 7.1383 \| 1.1577 \| 0.3316 \| 0.5672 \| 0.6693 \| 0.3901 \| 0.6695 \| 0.4017 \| 0.3213 \| 0.4138 \| 0.8953 \| 0.2703 \| 0.4023 \| 0.5856 \| 0.5821 \| 0.5000 \| \| 0.3500 \| 13750 \| 3.7545 \| 2.1424 \| 9.0522 \| 7.6998 \| 8.3319 \| 13.5322 \| 3.3625 \| 8.1303 \| 2.8320 \| 8.1860 \| 20.4538 \| 1.7997 \| 7.0770 \| 1.2512 \| 0.3385 \| 0.5654 \| 0.6937 \| 0.3856 \| 0.6659 \| 0.4025 \| 0.3225 \| 0.4156 \| 0.8994 \| 0.2675 \| 0.4113 \| 0.5886 \| 0.5770 \| 0.5026 \| \| 0.3564 \| 14000 \| 3.715 \| 2.1826 \| 8.9396 \| 7.6755 \| 8.3111 \| 13.3344 \| 3.3331 \| 8.1973 \| 2.8760 \| 8.8218 \| 20.6306 \| 1.9014 \| 7.3386 \| 1.2366 \| 0.3256 \| 0.5675 \| 0.6903 \| 0.3846 \| 0.6706 \| 0.4032 \| 0.3300 \| 0.4426 \| 0.8876 \| 0.2647 \| 0.4082 \| 0.5874 \| 0.5770 \| 0.5030 \| \| 0.3627 \| 14250 \| 3.6348 \| 2.1393 \| 9.0032 \| 7.7843 \| 8.3942 \| 13.2654 \| 3.3368 \| 8.1124 \| 2.8874 \| 8.5999 \| 20.8261 \| 1.8395 \| 7.5311 \| 1.1380 \| 0.3339 \| 0.5642 \| 0.7175 \| 0.3780 \| 0.6600 \| 0.3965 \| 0.3229 \| 0.4305 \| 0.8915 \| 0.2714 \| 0.3914 \| 0.5816 \| 0.5732 \| 0.5009 \| \| 0.3691 \| 14500 \| 3.604 \| 2.1709 \| 8.9648 \| 7.7166 \| 8.4281 \| 13.5507 \| 3.3138 \| 8.1362 \| 2.8989 \| 8.3940 \| 20.6827 \| 1.9298 \| 7.3468 \| 1.2705 \| 0.3416 \| 0.5594 \| 0.7198 \| 0.3776 \| 0.6605 \| 0.3965 \| 0.3295 \| 0.4341 \| 0.8838 \| 0.2632 \| 0.3995 \| 0.5884 \| 0.5693 \| 0.5018 \| \| 0.3754 \| 14750 \| 3.5398 \| 2.1451 \| 9.0195 \| 7.7407 \| 8.4766 \| 13.6169 \| 3.2839 \| 8.1386 \| 2.9178 \| 8.2585 \| 21.1674 \| 1.9484 \| 7.5143 \| 1.2747 \| 0.3418 \| 0.5592 \| 0.6971 \| 0.3763 \| 0.6539 \| 0.4060 \| 0.3264 \| 0.4214 \| 0.8805 \| 0.2524 \| 0.3855 \| 0.5872 \| 0.5735 \| 0.4970 \| \| 0.3818 \| 15000 \| 3.7153 \| 2.1038 \| 8.9847 \| 7.7279 \| 8.2667 \| 13.4033 \| 3.2862 \| 8.1232 \| 2.8999 \| 8.3224 \| 20.9361 \| 1.9569 \| 7.3440 \| 1.3036 \| 0.3407 \| 0.5687 \| 0.7066 \| 0.3497 \| 0.6532 \| 0.4043 \| 0.3317 \| 0.4300 \| 0.8830 \| 0.2564 \| 0.3884 \| 0.6013 \| 0.5752 \| 0.4992 \| \| 0.3882 \| 15250 \| 3.752 \| 2.0765 \| 9.0293 \| 7.7275 \| 8.3168 \| 13.2782 \| 3.3105 \| 8.0286 \| 2.8404 \| 8.2745 \| 20.9582 \| 1.7465 \| 7.5436 \| 1.2770 \| 0.3313 \| 0.5710 \| 0.6945 \| 0.3742 \| 0.6699 \| 0.4022 \| 0.3327 \| 0.4277 \| 0.8903 \| 0.2640 \| 0.3872 \| 0.5821 \| 0.5662 \| 0.4995 \| \| 0.3945 \| 15500 \| 3.7794 \| 2.0409 \| 9.0264 \| 7.7501 \| 8.3903 \| 13.3169 \| 3.3058 \| 8.0128 \| 2.8463 \| 8.5307 \| 21.5663 \| 1.7179 \| 7.7448 \| 1.1827 \| 0.3286 \| 0.5684 \| 0.6961 \| 0.3692 \| 0.6743 \| 0.4031 \| 0.3267 \| 0.4267 \| 0.8927 \| 0.2549 \| 0.3777 \| 0.5810 \| 0.5686 \| 0.4975 \| \| 0.4009 \| 15750 \| 3.7444 \| 2.0122 \| 9.0629 \| 7.7541 \| 8.0134 \| 13.2091 \| 3.2956 \| 8.0839 \| 2.8528 \| 8.1722 \| 20.9378 \| 1.7628 \| 7.8655 \| 1.2965 \| 0.3282 \| 0.5744 \| 0.6839 \| 0.3807 \| 0.6644 \| 0.4032 \| 0.3277 \| 0.4451 \| 0.8896 \| 0.2706 \| 0.3916 \| 0.5851 \| 0.5685 \| 0.5010 \| \| 0.4073 \| 16000 \| 3.7817 \| 2.0448 \| 9.1787 \| 7.7705 \| 8.0529 \| 13.1694 \| 3.3128 \| 8.1419 \| 2.8104 \| 8.2099 \| 21.0454 \| 1.7436 \| 7.2934 \| 1.2463 \| 0.3137 \| 0.5640 \| 0.6884 \| 0.3692 \| 0.6600 \| 0.3978 \| 0.3215 \| 0.4314 \| 0.8937 \| 0.2719 \| 0.4094 \| 0.6031 \| 0.5697 \| 0.4995 \| \| 0.4136 \| 16250 \| 3.7293 \| 2.0586 \| 9.2379 \| 7.7514 \| 8.1877 \| 13.1981 \| 3.2983 \| 8.0763 \| 2.8564 \| 8.6500 \| 20.9279 \| 1.8403 \| 7.2051 \| 1.1732 \| 0.3405 \| 0.5648 \| 0.6944 \| 0.3620 \| 0.6614 \| 0.3953 \| 0.3286 \| 0.4245 \| 0.8921 \| 0.2698 \| 0.3946 \| 0.5915 \| 0.5770 \| 0.4997 \| \| 0.4200 \| 16500 \| 3.6243 \| 2.0477 \| 9.1718 \| 7.6943 \| 7.9493 \| 13.3019 \| 3.2908 \| 8.0963 \| 2.8306 \| 8.7436 \| 20.6790 \| 1.8745 \| 7.4356 \| 1.1781 \| 0.3272 \| 0.5657 \| 0.6944 \| 0.3726 \| 0.6848 \| 0.3974 \| 0.3318 \| 0.4274 \| 0.8939 \| 0.2636 \| 0.3948 \| 0.5950 \| 0.5746 \| 0.5018 \| \| 0.4263 \| 16750 \| 3.5071 \| 2.0483 \| 9.2054 \| 7.7004 \| 8.1887 \| 13.3662 \| 3.2727 \| 7.9229 \| 2.8256 \| 8.2771 \| 21.1584 \| 1.8469 \| 7.6476 \| 1.2131 \| 0.3318 \| 0.5660 \| 0.7000 \| 0.3801 \| 0.6902 \| 0.3938 \| 0.3224 \| 0.4191 \| 0.8969 \| 0.2643 \| 0.4052 \| 0.6106 \| 0.5684 \| 0.5038 \| \| 0.4327 \| 17000 \| 3.6337 \| 2.0383 \| 9.1228 \| 7.7337 \| 8.2262 \| 13.2250 \| 3.2714 \| 7.9983 \| 2.7662 \| 8.4949 \| 20.8407 \| 1.8184 \| 7.7876 \| 1.2807 \| 0.3251 \| 0.5595 \| 0.6957 \| 0.3897 \| 0.6697 \| 0.3933 \| 0.3123 \| 0.4334 \| 0.8934 \| 0.2663 \| 0.3935 \| 0.5931 \| 0.5700 \| 0.4996 \| \| 0.4391 \| 17250 \| 3.5075 \| 2.0327 \| 8.9777 \| 7.7194 \| 8.2392 \| 13.4002 \| 3.2678 \| 7.9239 \| 2.7551 \| 8.2470 \| 21.1674 \| 1.7744 \| 7.9402 \| 1.3115 \| 0.3418 \| 0.5595 \| 0.7123 \| 0.3790 \| 0.6625 \| 0.3978 \| 0.3174 \| 0.4232 \| 0.8866 \| 0.2683 \| 0.3926 \| 0.5876 \| 0.5764 \| 0.5004 \| \| 0.4454 \| 17500 \| 3.6595 \| 2.0419 \| 8.9509 \| 7.6536 \| 8.3099 \| 13.3537 \| 3.2766 \| 7.9939 \| 2.7604 \| 8.3880 \| 20.8993 \| 1.8358 \| 7.6156 \| 1.2238 \| 0.3311 \| 0.5643 \| 0.7049 \| 0.3564 \| 0.6686 \| 0.3932 \| 0.3099 \| 0.4350 \| 0.8872 \| 0.2687 \| 0.3826 \| 0.5854 \| 0.5665 \| 0.4964 \| \| 0.4518 \| 17750 \| 3.5743 \| 2.0049 \| 9.0019 \| 7.6693 \| 8.3489 \| 13.3261 \| 3.2758 \| 8.0051 \| 2.7881 \| 8.4247 \| 20.8115 \| 1.8714 \| 7.7491 \| 1.1884 \| 0.3341 \| 0.5614 \| 0.6972 \| 0.3571 \| 0.6625 \| 0.3893 \| 0.3036 \| 0.4368 \| 0.8911 \| 0.2625 \| 0.3861 \| 0.5734 \| 0.5681 \| 0.4941 \| \| 0.4582 \| 18000 \| 3.6038 \| 1.9810 \| 9.0106 \| 7.7162 \| 8.3584 \| 13.1195 \| 3.2674 \| 8.0266 \| 2.8184 \| 8.4383 \| 20.5199 \| 1.8854 \| 7.8663 \| 1.1762 \| 0.3318 \| 0.5583 \| 0.7086 \| 0.3591 \| 0.6509 \| 0.3878 \| 0.3137 \| 0.4247 \| 0.8831 \| 0.2601 \| 0.3921 \| 0.5978 \| 0.5648 \| 0.4948 \| \| 0.4645 \| 18250 \| 3.6903 \| 2.0005 \| 9.0187 \| 7.6743 \| 8.4280 \| 13.0108 \| 3.2733 \| 7.8845 \| 2.7810 \| 8.3511 \| 20.1457 \| 1.7802 \| 8.0015 \| 1.0885 \| 0.3407 \| 0.5657 \| 0.7033 \| 0.3626 \| 0.6644 \| 0.3841 \| 0.3299 \| 0.4358 \| 0.8844 \| 0.2642 \| 0.3904 \| 0.5871 \| 0.5632 \| 0.4981 \| \| 0.4709 \| 18500 \| 3.7208 \| 1.9972 \| 9.1020 \| 7.6472 \| 8.1589 \| 13.0717 \| 3.2601 \| 8.0039 \| 2.7673 \| 8.3361 \| 20.0231 \| 1.8054 \| 7.7381 \| 1.1832 \| 0.3328 \| 0.5652 \| 0.7043 \| 0.3473 \| 0.6693 \| 0.3810 \| 0.3313 \| 0.4293 \| 0.8770 \| 0.2633 \| 0.3946 \| 0.5914 \| 0.5634 \| 0.4962 \| \| 0.4773 \| 18750 \| 3.6357 \| 2.0069 \| 9.1473 \| 7.6843 \| 8.2110 \| 13.1578 \| 3.2540 \| 7.9856 \| 2.7390 \| 8.6913 \| 20.3263 \| 1.8252 \| 7.9545 \| 1.0354 \| 0.3285 \| 0.5631 \| 0.7093 \| 0.3648 \| 0.6685 \| 0.3842 \| 0.3285 \| 0.4361 \| 0.8918 \| 0.2744 \| 0.4065 \| 0.5814 \| 0.5610 \| 0.4998 \| \| 0.4836 \| 19000 \| 3.5737 \| 1.9755 \| 9.1397 \| 7.6784 \| 8.2604 \| 13.3462 \| 3.2391 \| 7.9876 \| 2.7643 \| 8.4540 \| 20.2047 \| 1.7528 \| 7.6572 \| 1.0906 \| 0.3284 \| 0.5631 \| 0.7083 \| 0.3657 \| 0.6660 \| 0.3795 \| 0.3186 \| 0.4393 \| 0.8876 \| 0.2613 \| 0.4056 \| 0.5835 \| 0.5637 \| 0.4977 \| \| 0.4900 \| 19250 \| 3.5325 \| 2.0232 \| 9.1987 \| 7.6517 \| 8.2727 \| 13.1941 \| 3.2276 \| 7.9841 \| 2.7238 \| 8.4698 \| 19.9076 \| 1.8015 \| 7.2144 \| 1.1134 \| 0.3337 \| 0.5635 \| 0.7076 \| 0.3570 \| 0.6572 \| 0.3891 \| 0.3297 \| 0.4360 \| 0.8864 \| 0.2689 \| 0.3997 \| 0.5813 \| 0.5608 \| 0.4978 \| \| 0.4963 \| 19500 \| 3.4782 \| 2.0033 \| 9.1235 \| 7.7026 \| 8.3383 \| 13.1817 \| 3.2308 \| 8.0122 \| 2.6934 \| 8.4448 \| 19.7121 \| 1.7192 \| 6.9417 \| 0.9934 \| 0.3222 \| 0.5656 \| 0.7009 \| 0.3562 \| 0.6654 \| 0.3792 \| 0.3292 \| 0.4544 \| 0.8803 \| 0.2703 \| 0.4027 \| 0.5892 \| 0.5611 \| 0.4982 \| \| 0.5027 \| 19750 \| 3.7141 \| 1.9830 \| 9.2166 \| 7.6724 \| 8.3188 \| 13.1514 \| 3.2465 \| 8.0979 \| 2.7005 \| 8.1956 \| 19.8289 \| 1.6910 \| 7.1995 \| 1.0668 \| 0.3323 \| 0.5632 \| 0.7209 \| 0.3442 \| 0.6745 \| 0.3864 \| 0.3287 \| 0.4255 \| 0.8828 \| 0.2751 \| 0.4097 \| 0.5785 \| 0.5597 \| 0.4986 \| \| 0.5091 \| 20000 \| 3.7058 \| 1.9625 \| 9.1729 \| 7.6554 \| 8.3323 \| 13.0269 \| 3.2368 \| 8.0075 \| 2.7159 \| 8.6974 \| 20.1650 \| 1.6862 \| 7.3387 \| 1.1706 \| 0.3414 \| 0.5637 \| 0.7214 \| 0.3498 \| 0.6745 \| 0.3817 \| 0.3309 \| 0.4290 \| 0.8861 \| 0.2711 \| 0.3861 \| 0.5854 \| 0.5757 \| 0.4998 \| \| 0.5154 \| 20250 \| 3.502 \| 1.9983 \| 9.1304 \| 7.6354 \| 8.3832 \| 13.2376 \| 3.2262 \| 7.9229 \| 2.7119 \| 8.7638 \| 20.0759 \| 1.6633 \| 6.9686 \| 1.1490 \| 0.3296 \| 0.5703 \| 0.7116 \| 0.3409 \| 0.6717 \| 0.3847 \| 0.3294 \| 0.4198 \| 0.8859 \| 0.2655 \| 0.3924 \| 0.5836 \| 0.5647 \| 0.4962 \| \| 0.5218 \| 20500 \| 3.6424 \| 1.9867 \| 9.0935 \| 7.6470 \| 8.4280 \| 13.0866 \| 3.2338 \| 7.9608 \| 2.7225 \| 8.4571 \| 20.1605 \| 1.6184 \| 6.8877 \| 1.1519 \| 0.3377 \| 0.5682 \| 0.7128 \| 0.3479 \| 0.6646 \| 0.3900 \| 0.3255 \| 0.4408 \| 0.8863 \| 0.2655 \| 0.4184 \| 0.5862 \| 0.5627 \| 0.5005 \| \| 0.5282 \| 20750 \| 3.6085 \| 1.9589 \| 9.1559 \| 7.6282 \| 8.3675 \| 13.1289 \| 3.2393 \| 8.0050 \| 2.7199 \| 8.3705 \| 20.4945 \| 1.6721 \| 6.8649 \| 1.2063 \| 0.3328 \| 0.5719 \| 0.7054 \| 0.3526 \| 0.6755 \| 0.3974 \| 0.3280 \| 0.4204 \| 0.8926 \| 0.2686 \| 0.4145 \| 0.5783 \| 0.5553 \| 0.4995 \| \| 0.5345 \| 21000 \| 3.5763 \| 1.9392 \| 9.1139 \| 7.6917 \| 8.2745 \| 13.3345 \| 3.2333 \| 7.9848 \| 2.7010 \| 8.4815 \| 20.5463 \| 1.7049 \| 7.0751 \| 1.1276 \| 0.3251 \| 0.5710 \| 0.7128 \| 0.3561 \| 0.6642 \| 0.4022 \| 0.3225 \| 0.4394 \| 0.8997 \| 0.2737 \| 0.4106 \| 0.5732 \| 0.5592 \| 0.5008 \| \| 0.5409 \| 21250 \| 3.5401 \| 1.9744 \| 9.0583 \| 7.5955 \| 8.2868 \| 13.2877 \| 3.2309 \| 7.9686 \| 2.6641 \| 8.2829 \| 20.3974 \| 1.6843 \| 6.9287 \| 1.0128 \| 0.3322 \| 0.5692 \| 0.7117 \| 0.3348 \| 0.6715 \| 0.3834 \| 0.3269 \| 0.4372 \| 0.8869 \| 0.2662 \| 0.4097 \| 0.5797 \| 0.5587 \| 0.4975 \| \| 0.5473 \| 21500 \| 3.489 \| 1.9417 \| 8.9543 \| 7.6468 \| 8.3612 \| 13.3847 \| 3.2314 \| 7.9631 \| 2.6635 \| 8.4673 \| 20.5367 \| 1.7459 \| 6.7037 \| 1.0989 \| 0.3328 \| 0.5741 \| 0.7097 \| 0.3508 \| 0.6660 \| 0.3916 \| 0.3277 \| 0.4391 \| 0.8860 \| 0.2632 \| 0.4100 \| 0.5910 \| 0.5578 \| 0.5000 \| \| 0.5536 \| 21750 \| 3.555 \| 1.9533 \| 8.9916 \| 7.6360 \| 8.3586 \| 13.3764 \| 3.2284 \| 7.9575 \| 2.7214 \| 8.3429 \| 20.5891 \| 1.7569 \| 6.6890 \| 1.1157 \| 0.3339 \| 0.5744 \| 0.7114 \| 0.3392 \| 0.6636 \| 0.3964 \| 0.3259 \| 0.4504 \| 0.8925 \| 0.2651 \| 0.4031 \| 0.5792 \| 0.5646 \| 0.5000 \| \| 0.5600 \| 22000 \| 3.586 \| 1.9301 \| 8.9806 \| 7.6738 \| 8.3535 \| 13.2453 \| 3.2316 \| 7.9946 \| 2.6907 \| 8.2869 \| 20.5606 \| 1.6573 \| 6.8912 \| 1.1068 \| 0.3239 \| 0.5788 \| 0.7121 \| 0.3537 \| 0.6589 \| 0.4023 \| 0.3243 \| 0.4499 \| 0.8891 \| 0.2644 \| 0.4083 \| 0.5802 \| 0.5616 \| 0.5006 \| \| 0.5663 \| 22250 \| 3.5084 \| 1.9406 \| 9.0040 \| 7.6810 \| 8.3815 \| 13.1792 \| 3.2167 \| 7.9700 \| 2.7316 \| 8.6409 \| 20.6425 \| 1.6145 \| 6.8099 \| 1.1404 \| 0.3174 \| 0.5772 \| 0.7177 \| 0.3519 \| 0.6613 \| 0.3950 \| 0.3324 \| 0.4362 \| 0.8894 \| 0.2648 \| 0.4173 \| 0.5857 \| 0.5626 \| 0.5007 \| \| 0.5727 \| 22500 \| 3.5095 \| 1.9202 \| 8.9619 \| 7.6717 \| 8.3825 \| 13.1359 \| 3.2147 \| 7.9776 \| 2.7114 \| 8.2541 \| 20.5107 \| 1.6855 \| 6.9227 \| 1.1935 \| 0.3335 \| 0.5726 \| 0.7134 \| 0.3574 \| 0.6578 \| 0.3959 \| 0.3262 \| 0.4401 \| 0.8968 \| 0.2604 \| 0.4105 \| 0.5826 \| 0.5620 \| 0.5007 \| \| 0.5791 \| 22750 \| 3.5059 \| 1.9225 \| 8.9956 \| 7.6775 \| 8.3968 \| 13.0329 \| 3.2114 \| 7.9836 \| 2.7107 \| 8.4843 \| 20.6793 \| 1.6821 \| 7.0987 \| 1.0579 \| 0.3273 \| 0.5703 \| 0.7118 \| 0.3689 \| 0.6490 \| 0.3850 \| 0.3257 \| 0.4320 \| 0.8824 \| 0.2622 \| 0.4119 \| 0.5898 \| 0.5605 \| 0.4982 \| \| 0.5854 \| 23000 \| 3.4047 \| 1.9716 \| 9.0017 \| 7.6435 \| 8.4379 \| 13.0467 \| 3.1957 \| 7.9843 \| 2.7017 \| 8.5995 \| 20.8783 \| 1.5818 \| 7.1997 \| 1.0067 \| 0.3272 \| 0.5718 \| 0.7132 \| 0.3728 \| 0.6544 \| 0.3913 \| 0.3255 \| 0.4377 \| 0.8869 \| 0.2583 \| 0.4107 \| 0.5916 \| 0.5539 \| 0.4996 \| \| 0.5918 \| 23250 \| 3.4732 \| 1.9518 \| 8.9827 \| 7.6143 \| 8.3925 \| 13.2800 \| 3.1877 \| 7.9616 \| 2.7122 \| 8.5013 \| 21.0376 \| 1.6291 \| 7.0831 \| 1.0816 \| 0.3316 \| 0.5689 \| 0.7129 \| 0.3705 \| 0.6536 \| 0.3847 \| 0.3211 \| 0.4495 \| 0.8844 \| 0.2622 \| 0.4141 \| 0.5916 \| 0.5551 \| 0.5000 \| \| 0.5982 \| 23500 \| 3.4271 \| 1.9688 \| 9.0092 \| 7.5830 \| 8.3763 \| 13.2765 \| 3.1857 \| 7.9625 \| 2.6794 \| 8.4899 \| 20.8080 \| 1.6519 \| 7.1604 \| 1.1423 \| 0.3346 \| 0.5716 \| 0.7054 \| 0.3637 \| 0.6562 \| 0.3844 \| 0.3249 \| 0.4346 \| 0.8912 \| 0.2577 \| 0.4009 \| 0.5872 \| 0.5697 \| 0.4986 \| \| 0.6045 \| 23750 \| 3.4701 \| 2.0238 \| 9.0036 \| 7.5173 \| 8.4058 \| 13.2881 \| 3.1791 \| 7.9275 \| 2.7149 \| 8.8465 \| 20.6630 \| 1.7025 \| 7.2286 \| 1.1973 \| 0.3308 \| 0.5670 \| 0.7040 \| 0.3754 \| 0.6609 \| 0.3936 \| 0.3252 \| 0.4475 \| 0.8948 \| 0.2542 \| 0.4062 \| 0.5877 \| 0.5717 \| 0.5015 \| \| 0.6109 \| 24000 \| 3.6199 \| 2.0084 \| 8.9869 \| 7.5146 \| 8.3790 \| 13.1350 \| 3.1771 \| 7.9137 \| 2.7032 \| 8.5424 \| 20.5441 \| 1.7652 \| 6.8017 \| 1.1617 \| 0.3224 \| 0.5656 \| 0.7045 \| 0.3643 \| 0.6643 \| 0.3864 \| 0.3174 \| 0.4577 \| 0.8873 \| 0.2571 \| 0.3735 \| 0.5939 \| 0.5611 \| 0.4966 \| \| 0.6173 \| 24250 \| 3.408 \| 2.0137 \| 9.0355 \| 7.5305 \| 8.3597 \| 13.1604 \| 3.1735 \| 7.9201 \| 2.7078 \| 9.0787 \| 20.5226 \| 1.7341 \| 6.7525 \| 1.0237 \| 0.3310 \| 0.5676 \| 0.7061 \| 0.3643 \| 0.6602 \| 0.3995 \| 0.3240 \| 0.4588 \| 0.8936 \| 0.2572 \| 0.3868 \| 0.5928 \| 0.5718 \| 0.5011 \| \| 0.6236 \| 24500 \| 3.4651 \| 1.9564 \| 8.9636 \| 7.5635 \| 8.3731 \| 13.3261 \| 3.1737 \| 7.9529 \| 2.6638 \| 8.5231 \| 20.4787 \| 1.7792 \| 6.7317 \| 1.0516 \| 0.3291 \| 0.5670 \| 0.7060 \| 0.3572 \| 0.6594 \| 0.3982 \| 0.3253 \| 0.4400 \| 0.8917 \| 0.2577 \| 0.3886 \| 0.5951 \| 0.5662 \| 0.4986 \| \| 0.6300 \| 24750 \| 3.6988 \| 1.9668 \| 8.9583 \| 7.5875 \| 8.4316 \| 12.9644 \| 3.1769 \| 7.9801 \| 2.6750 \| 8.5780 \| 20.3883 \| 1.7045 \| 6.9010 \| 1.0783 \| 0.3300 \| 0.5668 \| 0.7040 \| 0.3653 \| 0.6550 \| 0.3957 \| 0.3293 \| 0.4570 \| 0.8878 \| 0.2621 \| 0.3816 \| 0.5932 \| 0.5665 \| 0.4996 \| \| 0.6363 \| 25000 \| 3.4365 \| 1.9782 \| 8.9306 \| 7.5829 \| 8.4123 \| 13.0937 \| 3.1640 \| 7.9870 \| 2.6806 \| 8.6281 \| 20.2045 \| 1.7244 \| 6.9685 \| 1.0365 \| 0.3289 \| 0.5651 \| 0.6956 \| 0.3703 \| 0.6494 \| 0.4025 \| 0.3294 \| 0.4511 \| 0.8841 \| 0.2634 \| 0.3894 \| 0.5970 \| 0.5605 \| 0.4990 \| \| 0.6427 \| 25250 \| 3.6097 \| 1.9653 \| 8.9386 \| 7.5722 \| 8.4185 \| 13.0207 \| 3.1629 \| 7.9910 \| 2.6867 \| 8.7326 \| 20.2092 \| 1.7321 \| 6.8303 \| 1.0569 \| 0.3273 \| 0.5643 \| 0.7024 \| 0.3587 \| 0.6534 \| 0.4018 \| 0.3282 \| 0.4401 \| 0.8872 \| 0.2614 \| 0.3860 \| 0.5966 \| 0.5703 \| 0.4983 \| \| 0.6491 \| 25500 \| 3.5379 \| 1.9518 \| 8.9189 \| 7.5241 \| 8.4156 \| 13.0924 \| 3.1557 \| 7.9747 \| 2.6468 \| 8.6005 \| 20.3848 \| 1.7474 \| 6.9006 \| 1.0193 \| 0.3186 \| 0.5692 \| 0.7032 \| 0.3581 \| 0.6429 \| 0.4068 \| 0.3288 \| 0.4426 \| 0.8937 \| 0.2584 \| 0.4055 \| 0.5963 \| 0.5667 \| 0.4993 \| \| 0.6554 \| 25750 \| 3.6223 \| 1.9609 \| 8.9459 \| 7.5554 \| 8.4227 \| 12.8817 \| 3.1483 \| 7.9813 \| 2.6559 \| 8.8423 \| 20.3294 \| 1.6381 \| 6.8535 \| 1.0140 \| 0.3252 \| 0.5722 \| 0.7104 \| 0.3689 \| 0.6554 \| 0.4087 \| 0.3291 \| 0.4293 \| 0.8894 \| 0.2632 \| 0.3940 \| 0.5995 \| 0.5702 \| 0.5012 \| \| 0.6618 \| 26000 \| 3.402 \| 1.9602 \| 8.9366 \| 7.5236 \| 8.2409 \| 12.9294 \| 3.1424 \| 7.9619 \| 2.6323 \| 8.6640 \| 20.2959 \| 1.6627 \| 6.7331 \| 1.0115 \| 0.3276 \| 0.5676 \| 0.7031 \| 0.3652 \| 0.6493 \| 0.4030 \| 0.3263 \| 0.4298 \| 0.8830 \| 0.2655 \| 0.4030 \| 0.6017 \| 0.5724 \| 0.4998 \| \| 0.6682 \| 26250 \| 3.4876 \| 1.9929 \| 8.9621 \| 7.4947 \| 8.1977 \| 12.8814 \| 3.1350 \| 7.8663 \| 2.6554 \| 8.5629 \| 20.3156 \| 1.7019 \| 6.7602 \| 0.9582 \| 0.3253 \| 0.5639 \| 0.7127 \| 0.3621 \| 0.6544 \| 0.4146 \| 0.3278 \| 0.4350 \| 0.8895 \| 0.2613 \| 0.4056 \| 0.5997 \| 0.5635 \| 0.5012 \| \| 0.6745 \| 26500 \| 3.4301 \| 1.9752 \| 8.9474 \| 7.5246 \| 8.1379 \| 12.7941 \| 3.1369 \| 7.8999 \| 2.6256 \| 8.5223 \| 20.4417 \| 1.6627 \| 6.7999 \| 0.9626 \| 0.3232 \| 0.5661 \| 0.7094 \| 0.3719 \| 0.6474 \| 0.4077 \| 0.3299 \| 0.4533 \| 0.8849 \| 0.2620 \| 0.3993 \| 0.6008 \| 0.5665 \| 0.5017 \| \| 0.6809 \| 26750 \| 3.482 \| 1.9646 \| 8.9353 \| 7.5199 \| 8.2066 \| 12.6512 \| 3.1284 \| 7.9080 \| 2.6464 \| 8.6207 \| 20.4063 \| 1.6852 \| 6.7490 \| 1.0089 \| 0.3172 \| 0.5689 \| 0.7121 \| 0.3734 \| 0.6461 \| 0.4089 \| 0.3287 \| 0.4516 \| 0.8854 \| 0.2601 \| 0.3965 \| 0.5993 \| 0.5634 \| 0.5009 \| \| 0.6873 \| 27000 \| 3.5073 \| 1.9431 \| 8.9478 \| 7.5188 \| 8.0549 \| 12.6486 \| 3.1310 \| 7.9241 \| 2.6508 \| 8.5172 \| 20.4459 \| 1.6855 \| 6.8390 \| 1.0012 \| 0.3222 \| 0.5705 \| 0.6936 \| 0.3675 \| 0.6511 \| 0.3970 \| 0.3286 \| 0.4467 \| 0.8875 \| 0.2587 \| 0.3986 \| 0.6177 \| 0.5690 \| 0.5007 \| \| 0.6936 \| 27250 \| 3.5565 \| 1.9438 \| 8.9610 \| 7.5044 \| 8.0383 \| 12.4879 \| 3.1277 \| 7.9219 \| 2.6321 \| 8.4003 \| 20.6229 \| 1.7421 \| 6.7256 \| 1.0533 \| 0.3327 \| 0.5737 \| 0.6955 \| 0.3653 \| 0.6486 \| 0.4067 \| 0.3277 \| 0.4368 \| 0.8901 \| 0.2589 \| 0.3877 \| 0.6135 \| 0.5696 \| 0.5005 \| \| 0.7000 \| 27500 \| 3.4506 \| 1.9639 \| 8.9673 \| 7.4644 \| 8.1455 \| 12.4523 \| 3.1171 \| 7.9159 \| 2.6772 \| 8.5339 \| 20.7734 \| 1.7690 \| 6.6677 \| 1.0708 \| 0.3260 \| 0.5730 \| 0.6960 \| 0.3562 \| 0.6419 \| 0.4053 \| 0.3310 \| 0.4395 \| 0.8871 \| 0.2594 \| 0.4010 \| 0.6155 \| 0.5679 \| 0.5000 \| \| 0.7063 \| 27750 \| 3.4875 \| 1.9177 \| 8.9276 \| 7.4754 \| 8.1375 \| 12.5163 \| 3.1315 \| 7.8697 \| 2.6247 \| 8.4426 \| 20.4950 \| 1.7047 \| 6.6303 \| 1.0030 \| 0.3244 \| 0.5690 \| 0.6887 \| 0.3499 \| 0.6471 \| 0.4057 \| 0.3309 \| 0.4464 \| 0.8893 \| 0.2570 \| 0.4038 \| 0.6038 \| 0.5701 \| 0.4989 \| \| 0.7127 \| 28000 \| 3.5298 \| 1.8939 \| 8.9022 \| 7.4889 \| 8.1322 \| 12.5461 \| 3.1409 \| 7.8621 \| 2.5963 \| 8.4173 \| 20.4948 \| 1.7078 \| 6.4516 \| 0.9874 \| 0.3380 \| 0.5721 \| 0.6934 \| 0.3631 \| 0.6439 \| 0.4046 \| 0.3272 \| 0.4486 \| 0.8893 \| 0.2593 \| 0.4020 \| 0.6017 \| 0.5687 \| 0.5009 \| \| 0.7191 \| 28250 \| 3.3329 \| 1.8970 \| 8.9172 \| 7.5001 \| 8.1758 \| 12.5515 \| 3.1333 \| 7.9075 \| 2.5928 \| 8.5025 \| 20.3447 \| 1.6879 \| 6.5795 \| 0.9658 \| 0.3247 \| 0.5729 \| 0.7000 \| 0.3710 \| 0.6468 \| 0.4069 \| 0.3336 \| 0.4564 \| 0.8933 \| 0.2637 \| 0.4105 \| 0.6012 \| 0.5749 \| 0.5043 \| \| 0.7254 \| 28500 \| 3.3897 \| 1.8966 \| 8.9038 \| 7.5265 \| 8.2199 \| 12.6173 \| 3.1244 \| 7.8882 \| 2.5974 \| 8.3728 \| 20.3628 \| 1.6804 \| 6.6887 \| 0.9733 \| 0.3278 \| 0.5750 \| 0.7001 \| 0.3645 \| 0.6528 \| 0.4145 \| 0.3310 \| 0.4669 \| 0.8964 \| 0.2638 \| 0.4032 \| 0.6086 \| 0.5713 \| 0.5058 \| \| 0.7318 \| 28750 \| 3.4588 \| 1.8934 \| 8.8948 \| 7.5026 \| 8.2374 \| 12.4866 \| 3.1287 \| 7.8941 \| 2.5811 \| 8.3781 \| 20.4334 \| 1.7109 \| 6.6195 \| 0.9699 \| 0.3251 \| 0.5707 \| 0.7083 \| 0.3702 \| 0.6411 \| 0.4086 \| 0.3252 \| 0.4553 \| 0.8935 \| 0.2641 \| 0.4068 \| 0.6063 \| 0.5592 \| 0.5026 \| \| 0.7382 \| 29000 \| 3.3675 \| 1.8959 \| 8.8925 \| 7.5043 \| 8.2628 \| 12.6063 \| 3.1174 \| 7.9132 \| 2.5908 \| 8.2436 \| 20.3771 \| 1.6740 \| 6.7151 \| 0.9895 \| 0.3262 \| 0.5725 \| 0.7069 \| 0.3694 \| 0.6495 \| 0.4063 \| 0.3169 \| 0.4622 \| 0.8962 \| 0.2609 \| 0.4124 \| 0.6101 \| 0.5635 \| 0.5041 \| \| 0.7445 \| 29250 \| 3.3886 \| 1.8976 \| 8.9215 \| 7.4975 \| 8.2401 \| 12.4978 \| 3.1127 \| 7.8615 \| 2.5814 \| 8.2715 \| 20.4379 \| 1.6740 \| 6.7705 \| 0.9492 \| 0.3269 \| 0.5683 \| 0.7061 \| 0.3760 \| 0.6554 \| 0.4089 \| 0.3209 \| 0.4508 \| 0.8982 \| 0.2658 \| 0.3982 \| 0.6053 \| 0.5668 \| 0.5037 \| \| 0.7509 \| 29500 \| 3.4826 \| 1.8855 \| 8.9320 \| 7.5047 \| 8.2470 \| 12.5056 \| 3.1168 \| 7.8967 \| 2.5742 \| 8.3762 \| 20.4917 \| 1.7122 \| 6.3650 \| 0.9882 \| 0.3380 \| 0.5681 \| 0.7061 \| 0.3727 \| 0.6521 \| 0.4071 \| 0.3222 \| 0.4771 \| 0.8963 \| 0.2701 \| 0.3943 \| 0.6010 \| 0.5624 \| 0.5052 \| \| 0.7572 \| 29750 \| 3.4268 \| 1.8725 \| 8.9332 \| 7.5158 \| 8.2481 \| 12.4708 \| 3.1146 \| 7.9010 \| 2.5617 \| 8.2478 \| 20.3435 \| 1.6860 \| 6.4246 \| 0.9801 \| 0.3331 \| 0.5691 \| 0.7061 \| 0.3767 \| 0.6523 \| 0.4068 \| 0.3195 \| 0.4589 \| 0.9097 \| 0.2627 \| 0.4113 \| 0.6059 \| 0.5641 \| 0.5059 \| \| 0.7636 \| 30000 \| 3.2621 \| 1.8813 \| 8.9125 \| 7.5203 \| 8.2736 \| 12.5555 \| 3.1094 \| 7.9083 \| 2.5488 \| 8.3690 \| 20.3392 \| 1.7015 \| 6.5219 \| 0.9901 \| 0.3366 \| 0.5686 \| 0.6941 \| 0.3696 \| 0.6465 \| 0.4062 \| 0.3228 \| 0.4570 \| 0.8967 \| 0.2657 \| 0.3975 \| 0.6054 \| 0.5702 \| 0.5028 \| \| 0.7700 \| 30250 \| 3.3289 \| 1.8893 \| 8.9211 \| 7.5322 \| 8.1753 \| 12.4890 \| 3.1063 \| 7.9272 \| 2.5320 \| 8.4628 \| 20.3169 \| 1.6841 \| 6.5986 \| 0.9798 \| 0.3302 \| 0.5719 \| 0.6944 \| 0.3745 \| 0.6471 \| 0.4007 \| 0.3172 \| 0.4741 \| 0.9046 \| 0.2685 \| 0.4126 \| 0.6014 \| 0.5666 \| 0.5049 \| \| 0.7763 \| 30500 \| 3.5363 \| 1.8836 \| 8.8909 \| 7.5323 \| 8.2197 \| 12.4000 \| 3.1064 \| 7.9088 \| 2.5494 \| 8.3271 \| 20.3110 \| 1.7132 \| 6.4502 \| 0.9974 \| 0.3298 \| 0.5705 \| 0.6995 \| 0.3671 \| 0.6511 \| 0.4057 \| 0.3204 \| 0.4560 \| 0.8949 \| 0.2613 \| 0.4153 \| 0.6011 \| 0.5738 \| 0.5036 \| \| 0.7827 \| 30750 \| 3.3557 \| 1.8824 \| 8.9002 \| 7.5249 \| 8.2047 \| 12.4618 \| 3.1055 \| 7.9265 \| 2.5501 \| 8.2708 \| 20.2254 \| 1.7222 \| 6.4927 \| 0.9813 \| 0.3333 \| 0.5678 \| 0.6926 \| 0.3781 \| 0.6575 \| 0.4005 \| 0.3225 \| 0.4497 \| 0.8991 \| 0.2649 \| 0.4018 \| 0.6051 \| 0.5698 \| 0.5033 \| \| 0.7891 \| 31000 \| 3.4481 \| 1.8725 \| 8.9077 \| 7.5043 \| 8.2095 \| 12.5197 \| 3.1095 \| 7.9124 \| 2.5216 \| 8.1396 \| 20.0618 \| 1.6962 \| 6.4808 \| 0.9764 \| 0.3321 \| 0.5691 \| 0.6941 \| 0.3650 \| 0.6464 \| 0.4013 \| 0.3239 \| 0.4522 \| 0.9035 \| 0.2657 \| 0.4058 \| 0.6041 \| 0.5639 \| 0.5021 \| \| 0.7954 \| 31250 \| 3.3888 \| 1.8596 \| 8.9249 \| 7.5248 \| 8.2429 \| 12.4622 \| 3.1054 \| 7.9247 \| 2.5351 \| 8.3011 \| 19.9880 \| 1.7062 \| 6.5598 \| 0.9628 \| 0.3428 \| 0.5694 \| 0.7008 \| 0.3761 \| 0.6564 \| 0.4013 \| 0.3223 \| 0.4589 \| 0.9024 \| 0.2633 \| 0.4111 \| 0.6052 \| 0.5670 \| 0.5059 \| \| 0.8018 \| 31500 \| 3.3989 \| 1.8662 \| 8.9299 \| 7.5206 \| 8.1957 \| 12.4667 \| 3.1043 \| 7.9089 \| 2.5465 \| 8.2149 \| 19.8897 \| 1.6830 \| 6.6131 \| 0.9204 \| 0.3452 \| 0.5658 \| 0.7002 \| 0.3709 \| 0.6570 \| 0.4004 \| 0.3233 \| 0.4588 \| 0.9015 \| 0.2619 \| 0.4044 \| 0.6015 \| 0.5654 \| 0.5043 \| \| 0.8082 \| 31750 \| 3.3603 \| 1.8671 \| 8.9158 \| 7.5135 \| 8.1945 \| 12.5201 \| 3.1009 \| 7.9130 \| 2.5539 \| 8.3876 \| 19.9818 \| 1.6685 \| 6.6359 \| 0.9307 \| 0.3516 \| 0.5659 \| 0.6928 \| 0.3767 \| 0.6534 \| 0.4004 \| 0.3245 \| 0.4627 \| 0.9021 \| 0.2619 \| 0.4252 \| 0.6019 \| 0.5699 \| 0.5068 \| \| 0.8145 \| 32000 \| 3.3783 \| 1.8588 \| 8.8993 \| 7.4994 \| 8.2058 \| 12.5200 \| 3.0980 \| 7.8902 \| 2.5448 \| 8.3444 \| 19.9960 \| 1.6815 \| 6.7156 \| 0.9509 \| 0.3358 \| 0.5665 \| 0.7012 \| 0.3759 \| 0.6551 \| 0.4002 \| 0.3259 \| 0.4505 \| 0.8960 \| 0.2621 \| 0.4113 \| 0.6059 \| 0.5716 \| 0.5045 \| \| 0.8209 \| 32250 \| 3.4379 \| 1.8693 \| 8.8896 \| 7.4639 \| 8.0939 \| 12.5840 \| 3.0894 \| 7.8559 \| 2.5590 \| 8.4227 \| 19.9565 \| 1.6855 \| 6.6835 \| 0.9650 \| 0.3433 \| 0.5663 \| 0.7004 \| 0.3717 \| 0.6527 \| 0.4076 \| 0.3255 \| 0.4491 \| 0.8990 \| 0.2643 \| 0.4127 \| 0.5984 \| 0.5741 \| 0.5050 \| \| 0.8272 \| 32500 \| 3.4962 \| 1.8838 \| 8.8634 \| 7.4636 \| 8.1354 \| 12.5144 \| 3.0866 \| 7.8561 \| 2.5410 \| 8.4169 \| 20.0794 \| 1.6625 \| 6.6897 \| 0.9849 \| 0.3461 \| 0.5678 \| 0.7024 \| 0.3808 \| 0.6564 \| 0.4083 \| 0.3200 \| 0.4531 \| 0.9015 \| 0.2622 \| 0.4116 \| 0.6061 \| 0.5685 \| 0.5065 \| \| 0.8336 \| 32750 \| 3.4927 \| 1.8696 \| 8.8655 \| 7.4274 \| 8.0888 \| 12.5431 \| 3.0874 \| 7.8320 \| 2.5324 \| 8.3181 \| 20.0095 \| 1.6403 \| 6.7182 \| 0.9837 \| 0.3439 \| 0.5657 \| 0.7078 \| 0.3771 \| 0.6580 \| 0.4078 \| 0.3210 \| 0.4543 \| 0.9087 \| 0.2625 \| 0.4184 \| 0.6095 \| 0.5691 \| 0.5080 \| \| 0.8400 \| 33000 \| 3.33 \| 1.8743 \| 8.8776 \| 7.4438 \| 8.1127 \| 12.5571 \| 3.0793 \| 7.8474 \| 2.5423 \| 8.3195 \| 20.1064 \| 1.6546 \| 6.7761 \| 1.0118 \| 0.3400 \| 0.5684 \| 0.6938 \| 0.3760 \| 0.6560 \| 0.4077 \| 0.3248 \| 0.4586 \| 0.8965 \| 0.2613 \| 0.4085 \| 0.6094 \| 0.5704 \| 0.5055 \| \| 0.8463 \| 33250 \| 3.3431 \| 1.8687 \| 8.8074 \| 7.4577 \| 8.1313 \| 12.5392 \| 3.0816 \| 7.8629 \| 2.5306 \| 8.3183 \| 20.1216 \| 1.6653 \| 6.8390 \| 1.0199 \| 0.3233 \| 0.5685 \| 0.7031 \| 0.3770 \| 0.6493 \| 0.4075 \| 0.3243 \| 0.4586 \| 0.9015 \| 0.2600 \| 0.4068 \| 0.6050 \| 0.5659 \| 0.5039 \| \| 0.8527 \| 33500 \| 3.4455 \| 1.8618 \| 8.8108 \| 7.4587 \| 8.1421 \| 12.5672 \| 3.0800 \| 7.8533 \| 2.5364 \| 8.2589 \| 20.0766 \| 1.6537 \| 6.8825 \| 1.0161 \| 0.3346 \| 0.5647 \| 0.7003 \| 0.3761 \| 0.6523 \| 0.4075 \| 0.3239 \| 0.4598 \| 0.9088 \| 0.2641 \| 0.4134 \| 0.5976 \| 0.5702 \| 0.5056 \| \| 0.8591 \| 33750 \| 3.3189 \| 1.8578 \| 8.8046 \| 7.4684 \| 8.1488 \| 12.6076 \| 3.0817 \| 7.8637 \| 2.5249 \| 8.2008 \| 20.1733 \| 1.6486 \| 6.9228 \| 1.0004 \| 0.3428 \| 0.5684 \| 0.7019 \| 0.3782 \| 0.6483 \| 0.4049 \| 0.3229 \| 0.4525 \| 0.9039 \| 0.2641 \| 0.4088 \| 0.6114 \| 0.5669 \| 0.5058 \| \| 0.8654 \| 34000 \| 3.3815 \| 1.8587 \| 8.7921 \| 7.4675 \| 8.0968 \| 12.6359 \| 3.0800 \| 7.8680 \| 2.5266 \| 8.3492 \| 20.1037 \| 1.6352 \| 6.8485 \| 1.0061 \| 0.3412 \| 0.5655 \| 0.6945 \| 0.3806 \| 0.6510 \| 0.4061 \| 0.3178 \| 0.4583 \| 0.9085 \| 0.2640 \| 0.4078 \| 0.6105 \| 0.5689 \| 0.5058 \| \| 0.8718 \| 34250 \| 3.3381 \| 1.8586 \| 8.7828 \| 7.4672 \| 8.1115 \| 12.6341 \| 3.0783 \| 7.8743 \| 2.5275 \| 8.3363 \| 20.0616 \| 1.6445 \| 6.8898 \| 1.0146 \| 0.3255 \| 0.5645 \| 0.6941 \| 0.3784 \| 0.6488 \| 0.4027 \| 0.3190 \| 0.4518 \| 0.9039 \| 0.2637 \| 0.4079 \| 0.6115 \| 0.5730 \| 0.5034 \| \| 0.8782 \| 34500 \| 3.3992 \| 1.8597 \| 8.7906 \| 7.4658 \| 8.1316 \| 12.6647 \| 3.0781 \| 7.8643 \| 2.5249 \| 8.3280 \| 20.0237 \| 1.6348 \| 6.8277 \| 1.0203 \| 0.3283 \| 0.5645 \| 0.6925 \| 0.3744 \| 0.6525 \| 0.4027 \| 0.3250 \| 0.4593 \| 0.9040 \| 0.2625 \| 0.4097 \| 0.6121 \| 0.5676 \| 0.5042 \| \| 0.8845 \| 34750 \| 3.2951 \| 1.8608 \| 8.8033 \| 7.4729 \| 8.1063 \| 12.6896 \| 3.0719 \| 7.8671 \| 2.5243 \| 8.3368 \| 20.0587 \| 1.6455 \| 6.7907 \| 1.0106 \| 0.3274 \| 0.5651 \| 0.6925 \| 0.3738 \| 0.6527 \| 0.4045 \| 0.3219 \| 0.4592 \| 0.9041 \| 0.2619 \| 0.3985 \| 0.6120 \| 0.5679 \| 0.5032 \| \| 0.8909 \| 35000 \| 3.262 \| 1.8677 \| 8.8024 \| 7.4317 \| 8.1170 \| 12.7225 \| 3.0671 \| 7.8519 \| 2.5303 \| 8.4655 \| 20.1156 \| 1.6405 \| 6.7997 \| 1.0141 \| 0.3288 \| 0.5667 \| 0.6935 \| 0.3738 \| 0.6554 \| 0.4049 \| 0.3196 \| 0.4532 \| 0.9041 \| 0.2626 \| 0.4116 \| 0.6123 \| 0.5653 \| 0.5040 \| \| 0.8972 \| 35250 \| 3.3218 \| 1.8698 \| 8.7992 \| 7.4495 \| 8.1390 \| 12.7198 \| 3.0627 \| 7.8528 \| 2.5399 \| 8.4549 \| 20.1652 \| 1.6288 \| 6.7857 \| 1.0261 \| 0.3328 \| 0.5666 \| 0.6951 \| 0.3686 \| 0.6538 \| 0.4043 \| 0.3248 \| 0.4596 \| 0.9042 \| 0.2646 \| 0.4097 \| 0.6123 \| 0.5725 \| 0.5053 \| \| 0.9036 \| 35500 \| 3.3529 \| 1.8633 \| 8.7964 \| 7.4339 \| 8.1347 \| 12.7295 \| 3.0657 \| 7.8262 \| 2.5256 \| 8.4119 \| 20.1363 \| 1.6165 \| 6.7987 \| 1.0260 \| 0.3268 \| 0.5660 \| 0.6951 \| 0.3695 \| 0.6543 \| 0.4042 \| 0.3232 \| 0.4511 \| 0.9068 \| 0.2666 \| 0.4047 \| 0.6112 \| 0.5701 \| 0.5038 \| \| 0.9100 \| 35750 \| 3.2205 \| 1.8626 \| 8.7751 \| 7.4335 \| 8.1346 \| 12.7532 \| 3.0654 \| 7.8318 \| 2.5077 \| 8.3883 \| 20.0738 \| 1.5839 \| 6.7819 \| 1.0282 \| 0.3291 \| 0.5659 \| 0.6951 \| 0.3702 \| 0.6530 \| 0.4045 \| 0.3241 \| 0.4493 \| 0.9067 \| 0.2622 \| 0.4192 \| 0.6112 \| 0.5710 \| 0.5047 \| \| 0.9163 \| 36000 \| 3.3671 \| 1.8521 \| 8.7693 \| 7.4500 \| 8.1155 \| 12.8041 \| 3.0671 \| 7.8451 \| 2.4994 \| 8.3053 \| 20.0666 \| 1.5984 \| 6.7696 \| 1.0377 \| 0.3383 \| 0.5667 \| 0.6946 \| 0.3739 \| 0.6536 \| 0.4049 \| 0.3262 \| 0.4457 \| 0.9065 \| 0.2625 \| 0.4143 \| 0.6112 \| 0.5719 \| 0.5054 \| \| 0.9227 \| 36250 \| 3.4074 \| 1.8555 \| 8.7683 \| 7.4607 \| 8.1465 \| 12.7545 \| 3.0621 \| 7.8488 \| 2.5080 \| 8.3816 \| 20.0420 \| 1.6043 \| 6.7991 \| 1.0539 \| 0.3367 \| 0.5685 \| 0.6935 \| 0.3714 \| 0.6523 \| 0.4068 \| 0.3237 \| 0.4471 \| 0.9065 \| 0.2645 \| 0.4176 \| 0.6110 \| 0.5692 \| 0.5053 \| \| 0.9291 \| 36500 \| 3.4806 \| 1.8543 \| 8.7833 \| 7.4584 \| 8.1070 \| 12.7444 \| 3.0605 \| 7.8475 \| 2.5166 \| 8.3809 \| 19.3340 \| 1.6181 \| 6.7663 \| 1.0555 \| 0.3271 \| 0.5693 \| 0.6935 \| 0.3733 \| 0.6560 \| 0.3990 \| 0.3229 \| 0.4530 \| 0.9065 \| 0.2618 \| 0.4105 \| 0.6129 \| 0.5654 \| 0.5039 \| \| 0.9354 \| 36750 \| 3.4848 \| 1.8558 \| 8.7967 \| 7.4512 \| 8.1175 \| 12.6960 \| 3.0616 \| 7.8420 \| 2.5137 \| 8.4267 \| 19.4008 \| 1.6181 \| 6.7376 \| 1.0629 \| 0.3284 \| 0.5697 \| 0.6922 \| 0.3723 \| 0.6530 \| 0.4048 \| 0.3234 \| 0.4534 \| 0.9065 \| 0.2641 \| 0.4088 \| 0.6115 \| 0.5635 \| 0.5040 \| \| 0.9418 \| 37000 \| 3.1947 \| 1.8574 \| 8.8026 \| 7.4555 \| 8.1222 \| 12.7155 \| 3.0591 \| 7.8391 \| 2.5180 \| 8.4551 \| 19.4094 \| 1.6134 \| 6.7200 \| 1.0657 \| 0.3318 \| 0.5681 \| 0.6922 \| 0.3711 \| 0.6550 \| 0.4043 \| 0.3234 \| 0.4534 \| 0.9037 \| 0.2645 \| 0.4090 \| 0.6113 \| 0.5685 \| 0.5043 \| \| 0.9482 \| 37250 \| 3.3557 \| 1.8569 \| 8.7972 \| 7.4535 \| 8.1200 \| 12.7427 \| 3.0585 \| 7.8460 \| 2.5167 \| 8.5049 \| 19.4395 \| 1.6141 \| 6.7080 \| 1.0700 \| 0.3308 \| 0.5699 \| 0.6930 \| 0.3708 \| 0.6513 \| 0.4048 \| 0.3247 \| 0.4589 \| 0.9039 \| 0.2645 \| 0.4087 \| 0.6113 \| 0.5677 \| 0.5046 \| \| 0.9545 \| 37500 \| 3.369 \| 1.8580 \| 8.7958 \| 7.4558 \| 8.1193 \| 12.7407 \| 3.0579 \| 7.8509 \| 2.5139 \| 8.5231 \| 19.4691 \| 1.6139 \| 6.7228 \| 1.0696 \| 0.3388 \| 0.5683 \| 0.6922 \| 0.3696 \| 0.6556 \| 0.4050 \| 0.3236 \| 0.4460 \| 0.8965 \| 0.2624 \| 0.4145 \| 0.6113 \| 0.5682 \| 0.5040 \| \| 0.9609 \| 37750 \| 3.398 \| 1.8564 \| 8.7955 \| 7.4567 \| 8.1174 \| 12.7478 \| 3.0589 \| 7.8415 \| 2.5086 \| 8.5030 \| 19.4719 \| 1.5989 \| 6.7117 \| 1.0689 \| 0.3381 \| 0.5678 \| 0.6922 \| 0.3652 \| 0.6544 \| 0.4049 \| 0.3234 \| 0.4460 \| 0.9039 \| 0.2599 \| 0.3998 \| 0.6113 \| 0.5658 \| 0.5025 \| \| 0.9672 \| 38000 \| 3.3699 \| 1.8534 \| 8.8011 \| 7.4604 \| 8.1135 \| 12.7689 \| 3.0574 \| 7.8412 \| 2.5097 \| 8.4866 \| 19.4973 \| 1.5966 \| 6.7309 \| 1.0655 \| 0.3372 \| 0.5680 \| 0.6922 \| 0.3651 \| 0.6553 \| 0.4038 \| 0.3224 \| 0.4560 \| 0.9025 \| 0.2675 \| 0.4061 \| 0.6111 \| 0.5712 \| 0.5045 \| \| 0.9736 \| 38250 \| 3.4483 \| 1.8540 \| 8.8045 \| 7.4506 \| 8.0942 \| 12.7725 \| 3.0569 \| 7.8379 \| 2.5135 \| 8.4817 \| 19.5038 \| 1.5995 \| 6.7289 \| 1.0693 \| 0.3378 \| 0.5682 \| 0.6922 \| 0.3724 \| 0.6550 \| 0.4038 \| 0.3243 \| 0.4534 \| 0.9025 \| 0.2645 \| 0.4058 \| 0.6111 \| 0.5710 \| 0.5048 \| \| 0.9800 \| 38500 \| 3.254 \| 1.8546 \| 8.8026 \| 7.4530 \| 8.1004 \| 12.7796 \| 3.0559 \| 7.8378 \| 2.5086 \| 8.4538 \| 19.5061 \| 1.5997 \| 6.7390 \| 1.0724 \| 0.3381 \| 0.5681 \| 0.6922 \| 0.3647 \| 0.6547 \| 0.4041 \| 0.3242 \| 0.4534 \| 0.8951 \| 0.2642 \| 0.4064 \| 0.6111 \| 0.5677 \| 0.5034 \| \| 0.9863 \| 38750 \| 3.2759 \| 1.8549 \| 8.8024 \| 7.4545 \| 8.0996 \| 12.7875 \| 3.0562 \| 7.8400 \| 2.5054 \| 8.4401 \| 19.5192 \| 1.6018 \| 6.7433 \| 1.0737 \| 0.3382 \| 0.5681 \| 0.6922 \| 0.3647 \| 0.6547 \| 0.4041 \| 0.3241 \| 0.4534 \| 0.8951 \| 0.2642 \| 0.4078 \| 0.6111 \| 0.5689 \| 0.5036 \| \| 0.9927 \| 39000 \| 3.3273 \| 1.8548 \| 8.8017 \| 7.4536 \| 8.0908 \| 12.7885 \| 3.0557 \| 7.8396 \| 2.5052 \| 8.4335 \| 19.5169 \| 1.6002 \| 6.7439 \| 1.0716 \| 0.3382 \| 0.5681 \| 0.6922 \| 0.3647 \| 0.6547 \| 0.4041 \| 0.3242 \| 0.4534 \| 0.8951 \| 0.2641 \| 0.4078 \| 0.6111 \| 0.5703 \| 0.5037 \| \| 0.9991 \| 39250 \| 3.3902 \| 1.8540 \| 8.8010 \| 7.4543 \| 8.0872 \| 12.7890 \| 3.0561 \| 7.8412 \| 2.5040 \| 8.3945 \| 19.5165 \| 1.5997 \| 6.7462 \| 1.0718 \| 0.3309 \| 0.5681 \| 0.6922 \| 0.3651 \| 0.6547 \| 0.4041 \| 0.3242 \| 0.4534 \| 0.8951 \| 0.2643 \| 0.4078 \| 0.6111 \| 0.5703 \| 0.5032 \| \| 1.0 \| 39287 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| 0.3309 \| 0.5681 \| 0.6922 \| 0.3651 \| 0.6547 \| 0.4041 \| 0.3242 \| 0.4534 \| 0.8951 \| 0.2643 \| 0.4078 \| 0.6111 \| 0.5703 \| 0.5032 \| </details> ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 2.611 kWh - Carbon Emitted: 1.015 kg of CO2 - Hours Used: 17.883 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.45.2 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.1-dev.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION", "TRANSLATION", "SUMMARIZATION" ]	[ "CAS", "CRAFT", "PCR", "PUBMEDQA", "SCIFACT" ]
sentence-transformers-testing/stsb-bert-tiny-lora	sentence-transformers-testing	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers-testing/stsb-bert-tiny-safetensors", "base_model:finetune:sentence-transformers-testing/stsb-bert-tiny-safetensors", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-11-08T13:55:33	2024-11-11T10:23:08	3	--- base_model: sentence-transformers-testing/stsb-bert-tiny-safetensors datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 9.679189270737199 energy_consumed: 0.024901310697493708 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.15 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: stsb-bert-tiny adapter finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.26 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.38 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.05600000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.056666666666666664 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08666666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.17833333333333332 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1412311142763055 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19938095238095235 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.11363345611144926 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.34 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.344 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.29 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02634308391586433 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06038926804951766 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.10265977040056268 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.19610280190566398 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.34151812101104584 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5504126984126985 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21133731615809154 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.22 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.044000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.18 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.22 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.34 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21218661613500586 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.17491269841269838 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.18857101300669993 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.032 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.044000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06199999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12488888888888887 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.15574603174603174 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10395695406287388 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10821428571428571 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08041090092126037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.31 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.35 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.39 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3504958855767756 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4476349206349205 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.29308037158200173 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.36 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21417075898440763 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16666666666666663 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.19159156983842277 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.44 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.00377949106046741 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.007274949456892388 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.012714784638321257 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.019303285579015287 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.09870502263453415 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2538809523809524 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.018928657854150332 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.42 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.06 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.042 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2051878697694875 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1506904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16101738947158584 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.22399999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.624 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7719999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8993333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7992844609162323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7798333333333335 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7635205205527187 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.036000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.09466666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13466666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1348403477257659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.24209523809523809 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10255365352032365 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2375425714519515 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1856666666666667 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1985205474177431 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.32 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.034 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.195 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.28 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19370675821369307 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16466666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1653693334513147 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.20408163265306123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5102040816326531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7551020408163265 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8775510204081632 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20408163265306123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25170068027210885 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.25306122448979596 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24489795918367346 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.014397370082893721 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.04876234248655414 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.0792610922160282 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.14648888406884147 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2485959675297849 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4082118561710398 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16376385142142616 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.20646781789638935 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.33924646781789636 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.41039246467817886 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5121193092621665 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20646781789638935 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1419256933542648 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11762009419152278 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08822291993720567 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10809127782506864 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.19128922256356135 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2362967591905488 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.30153648743329886 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25241711140675877 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2947898009020458 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2040229677928606 name: Cosine Map@100 --- # stsb-bert-tiny adapter finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using [train_script.py](train_script.py). ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) <!-- at revision f3cb857cba53019a20df283396bcca179cf051a4 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 128 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 128, '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, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-lora") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 128] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| cosine_accuracy@1 \| 0.14 \| 0.42 \| 0.12 \| 0.06 \| 0.36 \| 0.06 \| 0.2 \| 0.08 \| 0.7 \| 0.18 \| 0.08 \| 0.08 \| 0.2041 \| \| cosine_accuracy@3 \| 0.22 \| 0.62 \| 0.18 \| 0.1 \| 0.52 \| 0.26 \| 0.26 \| 0.18 \| 0.82 \| 0.26 \| 0.26 \| 0.22 \| 0.5102 \| \| cosine_accuracy@5 \| 0.26 \| 0.72 \| 0.22 \| 0.2 \| 0.54 \| 0.32 \| 0.3 \| 0.2 \| 0.88 \| 0.32 \| 0.32 \| 0.3 \| 0.7551 \| \| cosine_accuracy@10 \| 0.38 \| 0.86 \| 0.36 \| 0.28 \| 0.62 \| 0.36 \| 0.44 \| 0.42 \| 0.94 \| 0.4 \| 0.4 \| 0.32 \| 0.8776 \| \| cosine_precision@1 \| 0.14 \| 0.42 \| 0.12 \| 0.06 \| 0.36 \| 0.06 \| 0.2 \| 0.08 \| 0.7 \| 0.18 \| 0.08 \| 0.08 \| 0.2041 \| \| cosine_precision@3 \| 0.08 \| 0.34 \| 0.06 \| 0.04 \| 0.2067 \| 0.0867 \| 0.12 \| 0.06 \| 0.32 \| 0.12 \| 0.0867 \| 0.0733 \| 0.2517 \| \| cosine_precision@5 \| 0.056 \| 0.344 \| 0.044 \| 0.048 \| 0.14 \| 0.064 \| 0.096 \| 0.04 \| 0.224 \| 0.092 \| 0.064 \| 0.064 \| 0.2531 \| \| cosine_precision@10 \| 0.05 \| 0.29 \| 0.036 \| 0.032 \| 0.078 \| 0.036 \| 0.08 \| 0.042 \| 0.118 \| 0.066 \| 0.04 \| 0.034 \| 0.2449 \| \| cosine_recall@1 \| 0.0567 \| 0.0263 \| 0.12 \| 0.044 \| 0.18 \| 0.06 \| 0.0038 \| 0.08 \| 0.624 \| 0.036 \| 0.08 \| 0.08 \| 0.0144 \| \| cosine_recall@3 \| 0.0867 \| 0.0604 \| 0.18 \| 0.062 \| 0.31 \| 0.26 \| 0.0073 \| 0.17 \| 0.772 \| 0.0747 \| 0.26 \| 0.195 \| 0.0488 \| \| cosine_recall@5 \| 0.1117 \| 0.1027 \| 0.22 \| 0.1249 \| 0.35 \| 0.32 \| 0.0127 \| 0.19 \| 0.866 \| 0.0947 \| 0.32 \| 0.28 \| 0.0793 \| \| cosine_recall@10 \| 0.1783 \| 0.1961 \| 0.34 \| 0.1557 \| 0.39 \| 0.36 \| 0.0193 \| 0.4 \| 0.8993 \| 0.1347 \| 0.4 \| 0.3 \| 0.1465 \| \| cosine_ndcg@10 \| 0.1412 \| 0.3415 \| 0.2122 \| 0.104 \| 0.3505 \| 0.2142 \| 0.0987 \| 0.2052 \| 0.7993 \| 0.1348 \| 0.2375 \| 0.1937 \| 0.2486 \| \| cosine_mrr@10 \| 0.1994 \| 0.5504 \| 0.1749 \| 0.1082 \| 0.4476 \| 0.1667 \| 0.2539 \| 0.1507 \| 0.7798 \| 0.2421 \| 0.1857 \| 0.1647 \| 0.4082 \| \| cosine_map@100 \| 0.1136 \| 0.2113 \| 0.1886 \| 0.0804 \| 0.2931 \| 0.1916 \| 0.0189 \| 0.161 \| 0.7635 \| 0.1026 \| 0.1985 \| 0.1654 \| 0.1638 \| #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.2065 \| \| cosine_accuracy@3 \| 0.3392 \| \| cosine_accuracy@5 \| 0.4104 \| \| cosine_accuracy@10 \| 0.5121 \| \| cosine_precision@1 \| 0.2065 \| \| cosine_precision@3 \| 0.1419 \| \| cosine_precision@5 \| 0.1176 \| \| cosine_precision@10 \| 0.0882 \| \| cosine_recall@1 \| 0.1081 \| \| cosine_recall@3 \| 0.1913 \| \| cosine_recall@5 \| 0.2363 \| \| cosine_recall@10 \| 0.3015 \| \| cosine_ndcg@10 \| 0.2524 \| \| cosine_mrr@10 \| 0.2948 \| \| cosine_map@100 \| 0.204 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \| \|:------:\|:----:\|:-------------:\|:---------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\| \| 0 \| 0 \| - \| - \| 0.1174 \| 0.3053 \| 0.1405 \| 0.0440 \| 0.2821 \| 0.2297 \| 0.0773 \| 0.1708 \| 0.7830 \| 0.1181 \| 0.2017 \| 0.1447 \| 0.1642 \| 0.2138 \| \| 0.0010 \| 1 \| 3.6449 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0256 \| 25 \| 3.6146 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0512 \| 50 \| 3.6074 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0768 \| 75 \| 3.5997 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1024 \| 100 \| 3.5737 \| 2.0205 \| 0.1178 \| 0.3061 \| 0.1477 \| 0.0461 \| 0.2837 \| 0.2291 \| 0.0804 \| 0.1713 \| 0.7791 \| 0.1205 \| 0.2049 \| 0.1534 \| 0.1731 \| 0.2164 \| \| 0.1279 \| 125 \| 3.5644 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1535 \| 150 \| 3.4792 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1791 \| 175 \| 3.4743 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2047 \| 200 \| 3.4169 \| 1.9114 \| 0.1336 \| 0.3084 \| 0.1446 \| 0.0604 \| 0.2965 \| 0.2350 \| 0.0847 \| 0.1650 \| 0.7806 \| 0.1270 \| 0.2141 \| 0.1633 \| 0.1835 \| 0.2228 \| \| 0.2303 \| 225 \| 3.3535 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2559 \| 250 \| 3.3336 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2815 \| 275 \| 3.3038 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3071 \| 300 \| 3.2576 \| 1.8114 \| 0.1359 \| 0.3260 \| 0.1733 \| 0.0752 \| 0.3167 \| 0.2323 \| 0.0851 \| 0.1753 \| 0.7843 \| 0.1266 \| 0.2218 \| 0.1752 \| 0.2012 \| 0.2330 \| \| 0.3327 \| 325 \| 3.2304 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3582 \| 350 \| 3.2133 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3838 \| 375 \| 3.1369 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4094 \| 400 \| 3.1412 \| 1.7379 \| 0.1389 \| 0.3298 \| 0.1930 \| 0.0934 \| 0.3261 \| 0.2310 \| 0.0852 \| 0.1760 \| 0.7850 \| 0.1349 \| 0.2235 \| 0.1863 \| 0.2118 \| 0.2396 \| \| 0.4350 \| 425 \| 3.0782 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4606 \| 450 \| 3.0948 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4862 \| 475 \| 3.0696 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5118 \| 500 \| 3.0641 \| 1.6850 \| 0.1373 \| 0.3307 \| 0.1945 \| 0.0937 \| 0.3301 \| 0.2365 \| 0.0931 \| 0.1950 \| 0.7933 \| 0.1359 \| 0.2231 \| 0.1885 \| 0.2289 \| 0.2447 \| \| 0.5374 \| 525 \| 3.0224 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5629 \| 550 \| 2.9927 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5885 \| 575 \| 2.9796 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6141 \| 600 \| 2.9624 \| 1.6475 \| 0.1397 \| 0.3321 \| 0.2058 \| 0.0999 \| 0.3422 \| 0.2276 \| 0.1014 \| 0.1901 \| 0.7971 \| 0.1393 \| 0.2258 \| 0.1918 \| 0.2342 \| 0.2482 \| \| 0.6397 \| 625 \| 2.9508 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6653 \| 650 \| 2.958 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6909 \| 675 \| 2.9428 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7165 \| 700 \| 2.9589 \| 1.6209 \| 0.1425 \| 0.3344 \| 0.2061 \| 0.1050 \| 0.3427 \| 0.2295 \| 0.1001 \| 0.1868 \| 0.7955 \| 0.1342 \| 0.2298 \| 0.1922 \| 0.2343 \| 0.2487 \| \| 0.7421 \| 725 \| 2.9152 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7677 \| 750 \| 2.9056 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7932 \| 775 \| 2.9111 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8188 \| 800 \| 2.9107 \| 1.6037 \| 0.1415 \| 0.3401 \| 0.2064 \| 0.1053 \| 0.3523 \| 0.2153 \| 0.1001 \| 0.1934 \| 0.7976 \| 0.1340 \| 0.2302 \| 0.1946 \| 0.2461 \| 0.2505 \| \| 0.8444 \| 825 \| 2.8675 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8700 \| 850 \| 2.9175 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8956 \| 875 \| 2.8592 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9212 \| 900 \| 2.86 \| 1.5941 \| 0.1411 \| 0.3415 \| 0.2180 \| 0.1048 \| 0.3506 \| 0.2210 \| 0.0987 \| 0.2052 \| 0.7988 \| 0.1349 \| 0.2302 \| 0.1946 \| 0.2464 \| 0.2528 \| \| 0.9468 \| 925 \| 2.8603 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9724 \| 950 \| 2.8909 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9980 \| 975 \| 2.8819 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 1.0 \| 977 \| - \| - \| 0.1412 \| 0.3415 \| 0.2122 \| 0.1040 \| 0.3505 \| 0.2142 \| 0.0987 \| 0.2052 \| 0.7993 \| 0.1348 \| 0.2375 \| 0.1937 \| 0.2486 \| 0.2524 \| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.025 kWh - Carbon Emitted: 0.010 kg of CO2 - Hours Used: 0.15 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.46.2 - PyTorch: 2.5.0+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
ilhamdprastyo/jina-embeddings-v3-tei	ilhamdprastyo	feature-extraction	[ "transformers", "pytorch", "onnx", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "sentence-transformers", "custom_code", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2409.10173", "license:cc-by-nc-4.0", "model-index", "text-embeddings-inference", "region:us" ]	2024-11-15T03:45:30	2024-11-15T06:59:50	1	--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - false - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh library_name: transformers license: cc-by-nc-4.0 tags: - feature-extraction - sentence-similarity - mteb - sentence-transformers inference: false model-index: - name: jina-embeddings-v3 results: - task: type: STS dataset: name: MTEB AFQMC (default) type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cosine_pearson value: 41.74237700998808 - type: cosine_spearman value: 43.4726782647566 - type: euclidean_pearson value: 42.244585459479964 - type: euclidean_spearman value: 43.525070045169606 - type: main_score value: 43.4726782647566 - type: manhattan_pearson value: 42.04616728224863 - type: manhattan_spearman value: 43.308828270754645 - type: pearson value: 41.74237700998808 - type: spearman value: 43.4726782647566 - task: type: Retrieval dataset: name: MTEB ArguAna-PL (default) type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics: - type: main_score value: 50.117999999999995 - type: map_at_1 value: 24.253 - type: map_at_10 value: 40.725 - type: map_at_100 value: 41.699999999999996 - type: map_at_1000 value: 41.707 - type: map_at_20 value: 41.467999999999996 - type: map_at_3 value: 35.467 - type: map_at_5 value: 38.291 - type: mrr_at_1 value: 24.751066856330013 - type: mrr_at_10 value: 40.91063808169072 - type: mrr_at_100 value: 41.885497923928675 - type: mrr_at_1000 value: 41.89301098419842 - type: mrr_at_20 value: 41.653552355442514 - type: mrr_at_3 value: 35.656709340919775 - type: mrr_at_5 value: 38.466097676623946 - 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type: ndcg_at_1 value: 39.745000000000005 - type: ndcg_at_10 value: 48.248000000000005 - type: ndcg_at_100 value: 52.956 - type: ndcg_at_1000 value: 54.99699999999999 - type: ndcg_at_20 value: 50.01 - type: ndcg_at_3 value: 43.946000000000005 - type: ndcg_at_5 value: 46.038000000000004 - type: precision_at_1 value: 39.745000000000005 - type: precision_at_10 value: 9.229 - type: precision_at_100 value: 1.5070000000000001 - type: precision_at_1000 value: 0.199 - type: precision_at_20 value: 5.489999999999999 - type: precision_at_3 value: 21.38 - type: precision_at_5 value: 15.274 - type: recall_at_1 value: 31.097 - type: recall_at_10 value: 58.617 - type: recall_at_100 value: 78.55199999999999 - type: recall_at_1000 value: 91.13900000000001 - type: recall_at_20 value: 64.92 - type: recall_at_3 value: 45.672000000000004 - type: recall_at_5 value: 51.669 - type: main_score value: 48.248000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval (default) type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.745000000000005 - type: map_at_10 value: 52.063 - type: map_at_100 value: 53.077 - type: map_at_1000 value: 53.13 - type: map_at_20 value: 52.66 - type: map_at_3 value: 48.662 - type: map_at_5 value: 50.507000000000005 - type: mrr_at_1 value: 45.391999999999996 - type: mrr_at_10 value: 55.528 - type: mrr_at_100 value: 56.16100000000001 - type: mrr_at_1000 value: 56.192 - type: mrr_at_20 value: 55.923 - type: mrr_at_3 value: 52.93600000000001 - type: mrr_at_5 value: 54.435 - type: ndcg_at_1 value: 45.391999999999996 - type: ndcg_at_10 value: 58.019 - type: ndcg_at_100 value: 61.936 - type: ndcg_at_1000 value: 63.015 - type: ndcg_at_20 value: 59.691 - type: ndcg_at_3 value: 52.294 - type: ndcg_at_5 value: 55.017 - type: precision_at_1 value: 45.391999999999996 - type: precision_at_10 value: 9.386 - type: precision_at_100 value: 1.232 - type: precision_at_1000 value: 0.136 - type: precision_at_20 value: 5.223 - type: precision_at_3 value: 23.177 - type: precision_at_5 value: 15.9 - type: recall_at_1 value: 39.745000000000005 - type: recall_at_10 value: 72.08099999999999 - type: recall_at_100 value: 88.85300000000001 - type: recall_at_1000 value: 96.569 - type: recall_at_20 value: 78.203 - type: recall_at_3 value: 56.957 - type: recall_at_5 value: 63.63100000000001 - type: main_score value: 58.019 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval (default) type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 26.651999999999997 - type: map_at_10 value: 35.799 - type: map_at_100 value: 36.846000000000004 - type: map_at_1000 value: 36.931000000000004 - type: map_at_20 value: 36.341 - type: map_at_3 value: 32.999 - type: map_at_5 value: 34.597 - type: mrr_at_1 value: 28.814 - type: mrr_at_10 value: 37.869 - type: mrr_at_100 value: 38.728 - type: mrr_at_1000 value: 38.795 - type: mrr_at_20 value: 38.317 - type: mrr_at_3 value: 35.235 - type: mrr_at_5 value: 36.738 - type: ndcg_at_1 value: 28.814 - type: ndcg_at_10 value: 41.028 - type: ndcg_at_100 value: 46.162 - type: ndcg_at_1000 value: 48.15 - type: ndcg_at_20 value: 42.824 - type: ndcg_at_3 value: 35.621 - type: ndcg_at_5 value: 38.277 - type: precision_at_1 value: 28.814 - type: precision_at_10 value: 6.361999999999999 - type: precision_at_100 value: 0.9450000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_20 value: 3.6159999999999997 - type: precision_at_3 value: 15.140999999999998 - type: precision_at_5 value: 10.712000000000002 - type: recall_at_1 value: 26.651999999999997 - type: recall_at_10 value: 55.038 - type: recall_at_100 value: 78.806 - type: recall_at_1000 value: 93.485 - type: recall_at_20 value: 61.742 - type: recall_at_3 value: 40.682 - type: recall_at_5 value: 46.855000000000004 - type: main_score value: 41.028 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval (default) type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 17.627000000000002 - type: map_at_10 value: 26.436999999999998 - type: map_at_100 value: 27.85 - type: map_at_1000 value: 27.955999999999996 - type: map_at_20 value: 27.233 - type: map_at_3 value: 23.777 - type: map_at_5 value: 25.122 - type: mrr_at_1 value: 22.387999999999998 - type: mrr_at_10 value: 31.589 - type: mrr_at_100 value: 32.641999999999996 - type: mrr_at_1000 value: 32.696999999999996 - type: mrr_at_20 value: 32.201 - type: mrr_at_3 value: 28.98 - type: mrr_at_5 value: 30.342000000000002 - type: ndcg_at_1 value: 22.387999999999998 - type: ndcg_at_10 value: 32.129999999999995 - type: ndcg_at_100 value: 38.562999999999995 - type: ndcg_at_1000 value: 40.903 - type: ndcg_at_20 value: 34.652 - type: ndcg_at_3 value: 27.26 - type: ndcg_at_5 value: 29.235 - type: precision_at_1 value: 22.387999999999998 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 1.068 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_20 value: 3.6999999999999997 - type: precision_at_3 value: 13.267000000000001 - type: precision_at_5 value: 9.403 - type: recall_at_1 value: 17.627000000000002 - type: recall_at_10 value: 44.71 - type: recall_at_100 value: 72.426 - type: recall_at_1000 value: 88.64699999999999 - type: recall_at_20 value: 53.65 - type: recall_at_3 value: 30.989 - type: recall_at_5 value: 36.237 - type: main_score value: 32.129999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval (default) type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 30.891000000000002 - type: map_at_10 value: 41.519 - type: map_at_100 value: 42.896 - type: map_at_1000 value: 42.992999999999995 - type: map_at_20 value: 42.287 - type: map_at_3 value: 37.822 - type: map_at_5 value: 39.976 - type: mrr_at_1 value: 37.921 - type: mrr_at_10 value: 47.260999999999996 - type: mrr_at_100 value: 48.044 - type: mrr_at_1000 value: 48.08 - type: mrr_at_20 value: 47.699999999999996 - type: mrr_at_3 value: 44.513999999999996 - type: mrr_at_5 value: 46.064 - type: ndcg_at_1 value: 37.921 - type: ndcg_at_10 value: 47.806 - type: ndcg_at_100 value: 53.274 - type: ndcg_at_1000 value: 55.021 - type: ndcg_at_20 value: 49.973 - type: ndcg_at_3 value: 42.046 - type: ndcg_at_5 value: 44.835 - type: precision_at_1 value: 37.921 - type: precision_at_10 value: 8.767999999999999 - type: precision_at_100 value: 1.353 - type: precision_at_1000 value: 0.168 - type: precision_at_20 value: 5.135 - type: precision_at_3 value: 20.051 - type: precision_at_5 value: 14.398 - type: recall_at_1 value: 30.891000000000002 - type: recall_at_10 value: 60.897999999999996 - type: recall_at_100 value: 83.541 - type: recall_at_1000 value: 94.825 - type: recall_at_20 value: 68.356 - type: recall_at_3 value: 44.65 - type: recall_at_5 value: 51.919000000000004 - type: main_score value: 47.806 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval (default) type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 27.654 - type: map_at_10 value: 38.025999999999996 - type: map_at_100 value: 39.425 - type: map_at_1000 value: 39.528 - type: map_at_20 value: 38.838 - type: map_at_3 value: 34.745 - type: map_at_5 value: 36.537 - type: mrr_at_1 value: 34.018 - type: mrr_at_10 value: 43.314 - type: mrr_at_100 value: 44.283 - type: mrr_at_1000 value: 44.327 - type: mrr_at_20 value: 43.929 - type: mrr_at_3 value: 40.868 - type: mrr_at_5 value: 42.317 - type: ndcg_at_1 value: 34.018 - type: ndcg_at_10 value: 43.887 - type: ndcg_at_100 value: 49.791000000000004 - type: ndcg_at_1000 value: 51.834 - type: ndcg_at_20 value: 46.376 - type: ndcg_at_3 value: 38.769999999999996 - type: ndcg_at_5 value: 41.144 - type: precision_at_1 value: 34.018 - type: precision_at_10 value: 8.001999999999999 - type: precision_at_100 value: 1.2630000000000001 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.737 - type: precision_at_3 value: 18.417 - type: precision_at_5 value: 13.150999999999998 - type: recall_at_1 value: 27.654 - type: recall_at_10 value: 56.111 - type: recall_at_100 value: 81.136 - type: recall_at_1000 value: 94.788 - type: recall_at_20 value: 65.068 - type: recall_at_3 value: 41.713 - type: recall_at_5 value: 48.106 - type: main_score value: 43.887 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval (default) type: CQADupstackRetrieval_is_a_combined_dataset config: default split: test revision: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 42.58858333333333 - type: ndcg_at_10 value: 42.58858333333333 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval (default) type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.501 - type: map_at_10 value: 32.814 - type: map_at_100 value: 33.754 - type: map_at_1000 value: 33.859 - type: map_at_20 value: 33.324 - type: map_at_3 value: 30.758000000000003 - type: map_at_5 value: 31.936999999999998 - type: mrr_at_1 value: 27.761000000000003 - type: mrr_at_10 value: 35.662 - type: mrr_at_100 value: 36.443999999999996 - type: mrr_at_1000 value: 36.516999999999996 - type: mrr_at_20 value: 36.085 - type: mrr_at_3 value: 33.742 - type: mrr_at_5 value: 34.931 - type: ndcg_at_1 value: 27.761000000000003 - type: ndcg_at_10 value: 37.208000000000006 - type: ndcg_at_100 value: 41.839 - type: ndcg_at_1000 value: 44.421 - type: ndcg_at_20 value: 38.917 - type: ndcg_at_3 value: 33.544000000000004 - type: ndcg_at_5 value: 35.374 - type: precision_at_1 value: 27.761000000000003 - type: precision_at_10 value: 5.92 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 3.4130000000000003 - type: precision_at_3 value: 15.031 - type: precision_at_5 value: 10.306999999999999 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 47.579 - type: recall_at_100 value: 69.045 - type: recall_at_1000 value: 88.032 - type: recall_at_20 value: 54.125 - type: recall_at_3 value: 37.202 - type: recall_at_5 value: 41.927 - type: main_score value: 37.208000000000006 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval (default) type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.29 - type: map_at_10 value: 26.183 - type: map_at_100 value: 27.351999999999997 - type: map_at_1000 value: 27.483999999999998 - type: map_at_20 value: 26.798 - type: map_at_3 value: 23.629 - type: map_at_5 value: 24.937 - type: mrr_at_1 value: 22.299 - type: mrr_at_10 value: 30.189 - type: mrr_at_100 value: 31.098 - type: mrr_at_1000 value: 31.177 - type: mrr_at_20 value: 30.697000000000003 - type: mrr_at_3 value: 27.862 - type: mrr_at_5 value: 29.066 - type: ndcg_at_1 value: 22.299 - type: ndcg_at_10 value: 31.202 - type: ndcg_at_100 value: 36.617 - type: ndcg_at_1000 value: 39.544000000000004 - type: ndcg_at_20 value: 33.177 - type: ndcg_at_3 value: 26.639000000000003 - type: ndcg_at_5 value: 28.526 - type: precision_at_1 value: 22.299 - type: precision_at_10 value: 5.8020000000000005 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_20 value: 3.505 - type: precision_at_3 value: 12.698 - type: precision_at_5 value: 9.174 - type: recall_at_1 value: 18.29 - type: recall_at_10 value: 42.254999999999995 - type: recall_at_100 value: 66.60000000000001 - type: recall_at_1000 value: 87.31400000000001 - type: recall_at_20 value: 49.572 - type: recall_at_3 value: 29.342000000000002 - type: recall_at_5 value: 34.221000000000004 - type: main_score value: 31.202 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval (default) type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 27.722 - type: map_at_10 value: 37.698 - type: map_at_100 value: 38.899 - type: map_at_1000 value: 38.998 - type: map_at_20 value: 38.381 - type: map_at_3 value: 34.244 - type: map_at_5 value: 36.295 - type: mrr_at_1 value: 32.183 - type: mrr_at_10 value: 41.429 - type: mrr_at_100 value: 42.308 - type: mrr_at_1000 value: 42.358000000000004 - type: mrr_at_20 value: 41.957 - type: mrr_at_3 value: 38.401999999999994 - type: mrr_at_5 value: 40.294999999999995 - type: ndcg_at_1 value: 32.183 - type: ndcg_at_10 value: 43.519000000000005 - type: ndcg_at_100 value: 48.786 - type: ndcg_at_1000 value: 50.861999999999995 - type: ndcg_at_20 value: 45.654 - type: ndcg_at_3 value: 37.521 - type: ndcg_at_5 value: 40.615 - type: precision_at_1 value: 32.183 - type: precision_at_10 value: 7.603 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_20 value: 4.408 - type: precision_at_3 value: 17.071 - type: precision_at_5 value: 12.668 - type: recall_at_1 value: 27.722 - type: recall_at_10 value: 57.230000000000004 - type: recall_at_100 value: 79.97999999999999 - type: recall_at_1000 value: 94.217 - type: recall_at_20 value: 64.864 - type: recall_at_3 value: 41.215 - type: recall_at_5 value: 48.774 - type: main_score value: 43.519000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval (default) type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 25.852999999999998 - type: map_at_10 value: 35.394999999999996 - type: map_at_100 value: 37.291999999999994 - type: map_at_1000 value: 37.495 - type: map_at_20 value: 36.372 - type: map_at_3 value: 32.336 - type: map_at_5 value: 34.159 - type: mrr_at_1 value: 31.818 - type: mrr_at_10 value: 40.677 - type: mrr_at_100 value: 41.728 - type: mrr_at_1000 value: 41.778 - type: mrr_at_20 value: 41.301 - type: mrr_at_3 value: 38.208 - type: mrr_at_5 value: 39.592 - type: ndcg_at_1 value: 31.818 - type: ndcg_at_10 value: 41.559000000000005 - type: ndcg_at_100 value: 48.012 - type: ndcg_at_1000 value: 50.234 - type: ndcg_at_20 value: 44.15 - type: ndcg_at_3 value: 36.918 - type: ndcg_at_5 value: 39.227000000000004 - type: precision_at_1 value: 31.818 - type: precision_at_10 value: 8.043 - type: precision_at_100 value: 1.625 - type: precision_at_1000 value: 0.245 - type: precision_at_20 value: 5.2170000000000005 - type: precision_at_3 value: 17.655 - type: precision_at_5 value: 12.845999999999998 - type: recall_at_1 value: 25.852999999999998 - type: recall_at_10 value: 53.093 - type: recall_at_100 value: 81.05799999999999 - type: recall_at_1000 value: 94.657 - type: recall_at_20 value: 62.748000000000005 - type: recall_at_3 value: 39.300000000000004 - type: recall_at_5 value: 45.754 - type: main_score value: 41.559000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval (default) type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 19.23 - type: map_at_10 value: 28.128999999999998 - type: map_at_100 value: 29.195 - type: map_at_1000 value: 29.310000000000002 - type: map_at_20 value: 28.713 - type: map_at_3 value: 25.191000000000003 - type: map_at_5 value: 26.69 - type: mrr_at_1 value: 21.257 - type: mrr_at_10 value: 30.253999999999998 - type: mrr_at_100 value: 31.195 - type: mrr_at_1000 value: 31.270999999999997 - type: mrr_at_20 value: 30.747999999999998 - type: mrr_at_3 value: 27.633999999999997 - type: mrr_at_5 value: 28.937 - type: ndcg_at_1 value: 21.257 - type: ndcg_at_10 value: 33.511 - type: ndcg_at_100 value: 38.733000000000004 - type: ndcg_at_1000 value: 41.489 - type: ndcg_at_20 value: 35.476 - type: ndcg_at_3 value: 27.845 - type: ndcg_at_5 value: 30.264999999999997 - type: precision_at_1 value: 21.257 - type: precision_at_10 value: 5.619 - type: precision_at_100 value: 0.893 - type: precision_at_1000 value: 0.124 - type: precision_at_20 value: 3.29 - type: precision_at_3 value: 12.508 - type: precision_at_5 value: 8.946 - type: recall_at_1 value: 19.23 - type: recall_at_10 value: 48.185 - 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type: ndcg_at_100 value: 80.75 - type: ndcg_at_1000 value: 81.026 - type: ndcg_at_20 value: 79.092 - type: ndcg_at_3 value: 72.81 - type: ndcg_at_5 value: 74.22999999999999 - type: precision_at_1 value: 71.829 - type: precision_at_10 value: 17.717 - type: precision_at_100 value: 2.031 - type: precision_at_1000 value: 0.207 - type: precision_at_20 value: 9.399000000000001 - type: precision_at_3 value: 44.458999999999996 - type: precision_at_5 value: 31.535000000000004 - type: recall_at_1 value: 46.444 - type: recall_at_10 value: 86.275 - type: recall_at_100 value: 98.017 - type: recall_at_1000 value: 99.8 - type: recall_at_20 value: 90.935 - type: recall_at_3 value: 70.167 - type: recall_at_5 value: 78.2 --- <br><br> <p align="center"> <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The embedding model trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> <p align="center"> <b>jina-embeddings-v3: Multilingual Embeddings With Task LoRA</b> </p> ## Quick Start [Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) \| [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3) \| [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) \| [API](https://jina.ai/embeddings) ## Intended Usage & Model Info `jina-embeddings-v3` is a multilingual multi-task text embedding model designed for a variety of NLP applications. Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), this model supports Rotary Position Embeddings to handle long input sequences up to 8192 tokens. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. ### Key Features: - Extended Sequence Length: Supports up to 8192 tokens with RoPE. - Task-Specific Embedding: Customize embeddings through the `task` argument with the following options: - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks - `separation`: Used for embeddings in clustering and re-ranking applications - `classification`: Used for embeddings in classification tasks - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks - Matryoshka Embeddings: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application. ### Supported Languages: While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese. ## Usage <details><summary>Apply mean pooling when integrating the model.</summary> <p> ### Why Use Mean Pooling? Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. This approach has been shown to produce high-quality sentence embeddings. We provide an `encode` function that handles this for you automatically. However, if you're working with the model directly, outside of the `encode` function, you'll need to apply mean pooling manually. Here's how you can do it: ```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 = ["How is the weather today?", "What is the current weather like today?"] tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3") model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") task = 'retrieval.query' task_id = model._adaptation_map[task] adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32) with torch.no_grad(): model_output = model(encoded_input, adapter_mask=adapter_mask) embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) embeddings = F.normalize(embeddings, p=2, dim=1) ``` </p> </details> The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/). Alternatively, you can use `jina-embeddings-v3` directly via Transformers package: ```bash !pip install transformers torch einops !pip install 'numpy<2' ``` If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100), ```bash !pip install flash-attn --no-build-isolation ``` ```python from transformers import AutoModel # Initialize the model model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) texts = [ "Follow the white rabbit.", # English "Sigue al conejo blanco.", # Spanish "Suis le lapin blanc.", # French "跟着白兔走。", # Chinese "اتبع الأرنب الأبيض.", # Arabic "Folge dem weißen Kaninchen.", # German ] # When calling the `encode` function, you can choose a `task` based on the use case: # 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching' # Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used. embeddings = model.encode(texts, task="text-matching") # Compute similarities print(embeddings[0] @ embeddings[1].T) ``` By default, the model supports a maximum sequence length of 8192 tokens. However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode(["Very long ... document"], max_length=2048) ``` In case you want to use Matryoshka embeddings and switch to a different dimension, you can adjust it by passing the `truncate_dim` parameter to the `encode` function: ```python embeddings = model.encode(['Sample text'], truncate_dim=256) ``` The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`: ```bash !pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) task = "retrieval.query" embeddings = model.encode( ["What is the weather like in Berlin today?"], task=task, prompt_name=task, ) ``` You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html). To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'}) ``` Or afterwards: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) model[0].default_task = 'classification' ``` This way you can fine-tune the LoRA adapter for the chosen task. However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True}) ``` This will allow fine-tuning the whole model instead of just the LoRA adapters. <details><summary>ONNX Inference.</summary>** <p> You can use ONNX for efficient inference with `jina-embeddings-v3`: ```python import onnxruntime import numpy as np from transformers import AutoTokenizer, PretrainedConfig # Load tokenizer and model config tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3') config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3') # Tokenize input input_text = tokenizer('sample text', return_tensors='np') # ONNX session model_path = 'jina-embeddings-v3/onnx/model.onnx' session = onnxruntime.InferenceSession(model_path) # Prepare inputs for ONNX model task_type = 'text-matching' task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64) inputs = { 'input_ids': input_text['input_ids'], 'attention_mask': input_text['attention_mask'], 'task_id': task_id } # Run model outputs = session.run(None, inputs)[0] # Apply mean pooling to 'outputs' to get a single representation of each text ``` </p> </details> ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## License `jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/). ## Citation If you find `jina-embeddings-v3` useful in your research, please cite the following paper: ```bibtex @misc{sturua2024jinaembeddingsv3multilingualembeddingstask, title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao}, year={2024}, eprint={2409.10173}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.10173}, } ```	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
oMarquess/nahara-dataset-model	oMarquess	question-answering	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "question-answering", "en", "dataset:oMarquess/nahara-dataset-2010n", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-11-17T16:05:17	2024-11-17T18:29:38	0	--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit datasets: - oMarquess/nahara-dataset-2010n language: - en license: apache-2.0 pipeline_tag: question-answering tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Nahara Dataset Model - Developed by: Redeemer Salami Okekale, BMS - License: apache-2.0 - Finetuned from model: unsloth/meta-llama-3.1-8b-bnb-4bit - Training Loss: 1.181600 Model Description: The nahara-dataset-model is a fine-tuned version of Meta's LLaMA series, specifically optimized for low-precision (4-bit) operations to enhance efficiency in both memory usage and computational resources. It was fine-tuned on the Nahara dataset and achieved a training loss of 1.181600, ensuring strong performance on medical data. - Model Type: Transformer-based Language Model - Size: 8 billion parameters - Precision: 4-bit quantization using bnb (bits and bytes), improving memory efficiency and making the model suitable for resource-constrained environments. Intended Use: This model serves as a highly adaptable AI copilot for medical professionals, ideal for providing real-time recommendations and decision support. It can assist with: - Medical diagnostics and treatment suggestions - Summarization of clinical data - Generation of medical reports and documentation - Assistance with medical coding and research data preparation Performance: - Training Loss: 1.181600 - Fine-tuning Data: Medical and clinical datasets enhanced through data augmentation techniques to handle sparsity and variability, making it applicable across various healthcare contexts. Applications: The nahara-dataset-model is suited for: - Clinical decision support systems - AI copilots for medical professionals - Research data analysis and augmentation - Medical record summarization and automated report generation Limitations and Considerations: - The model is trained on medical data but may not encompass all clinical expertise nuances. It should be used to augment decision-making, not replace professional judgment. - Ethical considerations, including data privacy and bias in healthcare applications, must be strictly followed. - While efficiency is boosted by quantizing to 4-bit, there may be trade-offs in performance for complex tasks compared to higher precision models. Future Improvements: The model will undergo further optimization and refinement in Phase 2, including expanding the dataset, improving real-world adaptability, and fine-tuning the AI copilot for specific medical specializations. Contributors: - Emmanuel Akomanin Asiamah, PhD - Elli Banini - Felix Coker - Philip Attram, BMS - Schandorf Osam-Frimpong, MD - Daniel Mawuenyega Gohoho - Vitus Amenorpe - Aaron Kofi Gayi - Julius Richard Ogbey - Cherryln Asiwome Ahiable - Ama Quashie - Andrew Kojo Mensah-Onumah - Edith Zikpi - Azumah Benson, MD	[ "SUMMARIZATION" ]	[ "MEDICAL DATA" ]
HIT-TMG/KaLM-embedding-multilingual-max-instruct-v1	HIT-TMG	null	[ "mteb", "model-index", "region:us" ]	2024-11-21T06:26:54	2025-01-07T12:09:46	9	--- tags: - mteb model-index: - name: KaLM-Embedding results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 94.89505247376313 - type: ap value: 64.78774888517734 - type: ap_weighted value: 64.78774888517734 - type: f1 value: 88.11460157320857 - type: f1_weighted value: 95.22074397272716 - type: main_score value: 94.89505247376313 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 93.71641791044777 - type: ap value: 75.08750683510948 - type: ap_weighted value: 75.08750683510948 - type: f1 value: 90.83321356354264 - 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Aleph-Alpha/Pharia-1-Embedding-4608-control	Aleph-Alpha	null	[ "license:other", "region:us" ]	2024-11-21T14:47:12	2024-12-19T13:51:34	1	--- license: other license_name: open-aleph-license license_link: LICENSE --- # Model Card for Pharia-1-Embedding-4608-control This model card provides an overview of Pharia-1-Embedding-4608-control, an embedding model developed by Aleph Alpha Research. Pharia-1-Embedding-4608-control has been built on top of Pharia-1-LLM-7B-control. For additional training details, including architecture, tokenization, tokenizer fertility, pre-training, instruction fine-tuning and resource usage we refer to the model card of [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control). Due to being trained with a diverse set of instructions, Pharia-1-Embedding-4608-control can deliver customized embeddings at runtime without further finetuning. Pharia-1-Embedding-4608-control was trained on carefully curated data in compliance with applicable EU and national regulations, including copyright and data privacy laws. Furthermore it shows strong cross-lingual performance allowing for prompting and text to be embedded written in different languages. The finetuning was always performed using English instructions. ## Model Overview - Developed by:* Aleph Alpha Research <!--- Funded by [optional]: [More Information Needed]--> <!--- Shared by [optional]: [More Information Needed]--> - Model type/architecture: Embedding adapter on top of Pharia-1-LLM-7B-control trained with representational instruction-tuning (inspired by the approach of GritLM). - Language(s) (NLP): Trained on English, German, French, Spanish. <!--- License: [More Information Needed]--> <!--- Finetuned from model [optional]: [More Information Needed]--> - USP: Model exhibits superior quality in pure cross-lingual tasks for (German, English, French & Spanish pairings, see evaluation below) ### Model Description \|Model \|Embedding Size\|Description\| \|--------------------------------\|--------------\|-----------\| \|Pharia-1-Embedding-4608-control \|4608\|Pharia-1-Embedding-4608-control is an Embedding model optimized for German, French and Spanish and designed for customizable embeddings at runtime via instructions (prompts)\| <!-- Provide a longer summary of what this model is. --> ### Model Access We provide access to our models through the channels listed below. - On-premise installation: Our customers are supplied with our full LLM and Embedding model stack, including model weights and inference runtime. Contact us for options to deploy Pharia-1-Embedding-4608-control in any cloud or on-premise environment. We provide our customers with open access to our full model checkpoint including weights and code for commercial use. Downloadable from Huggingface: An HF-adapted version of our model can be found in our Huggingface repo (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf) together with code snippets that make the model easy to use. Please refer to the changelog for updates to the models served. We do not deprecate officially released versions of old model generations when we release newer versions, so users can continue to have access to available models. No prompt data is stored when using our systems, which means that we do not collect PII (personally identifiable information) for any of our public API users as detailed in our Terms & Conditions. We do not log user inputs to the models. We do not train on user data. - Note: The same models are made available to users regardless of their geographic location, and the input language but subject to sanction regimes, technology export regulations, and other restrictions that may apply. The same offering is provided to all countries within and external to the European Union if no legal restrictions apply. ### Intended Use Pharia-1-Embedding-4608-control is intended to be deployed as components of AI systems or applications. Use-cases and the model's capabilities include but are not limited to: information retrieval, semantic search, re-ranking and clustering. #### Out-of-Scope Use Pharia-1-Embedding-4608-control is not to be used for illegal or unlawful actions of any kind and with any illegal or unlawful content. This includes in particular prohibited activities such as engaging in terrorism, violence, human trafficking, illegal distribution of materials to minors, sexual solicitation, any other criminal activities, harassment, discrimination, creating or promoting malicious code or activities risking death or harm, including those related to military or nuclear applications, and activities not in compliance with sanction regimes, technology export regulations, and other restrictions that may apply. The models are to be used following ethical standards. The utilization of our technology is always governed by, and may be limited in accordance with, our Terms of Use, the Open Aleph License, or any specific agreement we might have established with you. For non-anonymous reports, we also provide an appeals mechanism for usage policy violations via our dedicated contact address [[email protected]]([email protected]) to communicate with us. Customers and partners are enabled to use our ticketing system [ticketing system](https://servicedesk.aleph-alpha.de/external) for appeals, claims and feedback. ### Use limitations Beyond the risks & limitations stated in the original [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control), the following limitation applies: - Pharia-1-Embedding-4608-control has been optimized on embedding computation only. Therefore, we do not recommend usage for text generation purposes. ## How to Use We provide two access pathways for our Pharia4608 embedding model. The first one leverages the HF ecosystem and can be found here: https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf. The code snippet in the box below demonstrates its use. As soon as the model class is invoked, the model will we loaded from the repo and is ready for use. The other access pathway is through our public scaling code base. In this version the model weights were not converted to HF format and the repo https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control can be cloned as is. The model path has to be adjusted to the local path where the model was downloaded. The model cards in the corresponding repositories only the code snippet which applies to the specific repo. ### Use with scaling inference code base To perform inference with the original model files, you’ll first need to install the [Scaling library](https://github.com/Aleph-Alpha/scaling). Follow the installation instructions provided in the repository's README file. After installation, download the model weights and use the Scaling inference module to load the checkpoint, vocabulary, and configuration files. ``` from pathlib import Path from torch.nn import CosineSimilarity from scaling.transformer.inference import TransformerInferenceModule MODEL_PATH = "/path/to/model" inference_model = TransformerInferenceModule.from_checkpoint( checkpoint_dir=Path(MODEL_PATH), ) # embed the query: query = "Which country is Galileo from?" query_embeddings = inference_model.encode_queries(query, convert_to_tensor=True) print(f"Type of embeddings: {type(query_embeddings)},\n\ shape of query embeddings: {query_embeddings.shape}") # embed the documents: document_1 = "Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql." document_embeddings_1 = inference_model.encode_corpus(document_1, convert_to_tensor=True) document_2 = "Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy." document_embeddings_2 = inference_model.encode_corpus(document_2, convert_to_tensor=True) # customized embeddings steering the query: instruction = "Represent the question about TV shows to find a paragraph that answers it." steered_query_embeddings = inference_model.encode_queries(query, instruction=instruction, convert_to_tensor=True) # compute similarity between steered query and both documents cossim = CosineSimilarity(dim=1, eps=1e-6) sim1 = round(cossim(document_embeddings_1, steered_query_embeddings).item(), 3) sim2 = round(cossim(document_embeddings_2, steered_query_embeddings).item(), 3) print("Steered embedding causes higher similarity of query to TV show:") print(f"Similarity query/TV show ({sim1}) > similarity query/Italian polymath: ({sim2})") ``` Disclaimer: For the official evaluation scores we used the Scaling compatible checkpoint available under Pharia-1-Embedding-4608-control (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control) ### Example for instruction embedding Pharia-1-Embedding-4608-control is useful for any use-case that relates to estimating the similarity/relevance between text fragments. This is relevant for use-cases such as information retrieval, semantic search, re-ranking and clustering. We use the task of information retrieval as a guiding example where we assume the following query: “Which country is Galileo from?” and two documents: - Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql. - Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy. Source: Wikipedia For our guiding example we assume the context of this use-case is a Question-Answer system for movies and TV shows. Step 1: Embed the Query ``` "input": "Which country is Galileo from?" ``` → Embedding: ```[-0.6780134, 0.61449033, 0.102911085, ...]``` Step 2: Embed the Documents "input": "Galileo is a German television program series ..." → Embedding: ```[-0.36119246, 0.7793595, -0.38735497, ...]``` "input": "Galileo di Vincenzo Bonaiuti de' Galilei ..." → Embedding: ```[-0.25108248, 1.0496024, -0.20945309, ...]``` Step 3: Compare the similarity A typical similarity measure between vectors is cosine similarity. Higher numbers indicate more similar vectors and by extension capture the concept of relevance. In a RAG application these scores determine the ranking during the retrieval step. In this example, we obtain the following cosine similarities: Query vs. German TV show: ~0.661 Query vs. Italian polymath: ~0.757 This implies that the paragraph about the Italian polymath would be ranked higher than the paragraph about the German TV show which is the one we’re interested in. #### Customized Embeddings To further improve performance you can use instructions to steer the model. Instructions can help the model understand nuances of your specific data and ultimately lead to embeddings that are more useful for your use-case. In this case, we aim to get embeddings that would lead to ranking the paragraph about the German TV Show higher than the paragraph about the Italian polymath. Step 1: Embed the Query with an Instruction ```"instruction": "Represent the question about TV shows to find a paragraph that answers it."``` ```"input": "input": "Which country is Galileo from?"``` → Embedding: ```[-0.6310919, 1.4309896, -0.85546875, ...]``` Step 2: Compare the similarity We leave the embeddings of the documents untouched and now obtain the following cosine similarities: Query vs. German TV show: ~0.632 Query vs. Italian polymath: ~0.512 These new cosine similarities imply that the ranking has indeed changed and the paragraph about the German TV show is now more relevant. This shows that instructions can help the model understand nuances in the data better and ultimately lead to embeddings that are more useful for your use-case. #### Tips on using the model - First try and ideally evaluate the model on your data without instructions to see whether performance aligns with your expectations out-of-the-box - If you decide to use an instruction with the aim of further boosting performance we suggest using this template as a guideline * ```Template: Represent the [X] to find a [Y] that [describe how the X and Y relate]``` * Examples 1. Represent the newspaper paragraph to find a newspaper paragraph with the same topic 2. Represent the sentence to find another sentence with the same meaning - In cases where the two texts to compare are different in nature (e.g. query and document) – also called “asymmetric” – we suggest to first add an instruction to query texts only. Again, try and ideally evaluate the model in this setting. Then, if your aim is to further boost performance, we suggest that you add instructions to document texts as well where [X] and [Y] are flipped accordingly. ## Evaluation ### Evaluations on cross-lingual capabilities There are important use cases where one wants to retrieve multiple documents on a topic or answering questions that are formulated in a different language than the query. This increases recall and information retrieval coverage. For testing on cross-lingual capabilities we evaluated Pharia-1-Embedding-4608-control, GritLM, Nvidia-Embed-v2 and BGE-Multilingual-Gemma2 on the MLQA-V1 datasets (Facebook) for German/English and English/Spanish language pairings. For German/French we used the CLSD-WMT19 dataset providing correct and adversarial translations of a sentence in the corresponding pair language. In order to check quality over a larger range of sample size we did the accuracy computations for varying number of samples taken from the MLQA-V1 dataset. For the CLSD-WMT19 evaluation we employed the full set of data (2900 samples available). #### MLQA-V1 Ger/Eng cross-lingual accuracies for the considered models \|# of samples\|Pharia4608\|GritLM\|Nvidia-Embed-v2\|BGE-Gemma2\| \|:---:\|:---:\|:---:\|:---:\|:---:\| \|1000\|86.0%\|82.5%\|77.0%\|87.0%\| \|2000\|79.5%\|73.4%\|69.4%\|76.8%\| \|4000\|65.3%\|59.2%\|56.0%\|62.7%\| \|6000\|54.3%\|48.6%\|45.6%\|52.6%\| \|10000\|38.6%\|32.8%\|32.8%\|39.4%\| #### MLQA-V1 Eng/Esp cross-lingual accuracies for the considered models \|# samples\|Pharia4608\|GritLM\|NV-Embed-v2\|BGE-Gemma2\| \|:---:\|:---:\|:---:\|:---:\|:---:\| \|1000\|87.5%\|82.0%\|81.5%\|87.0%\| \|2000\|78.5%\|73.9%\|70.7%\|77.0%\| \|4000\|65.5%\|59.3%\|56.9%\|64.2%\| \|6000\|55.3%\|49.2%\|46.2%\|53.4%\| \|10000\|41.7%\|35.5%\|33.2%\|40.0%\| #### CLSD-WMT19 Ger/Fra (2900 samples) cross-lingual evaluation for the considered models \|Model Name \| accuracy \| \|:-----------------------------:\|:--------------------------------:\| \|Pharia-1-Embedding-4608-control\|95.1% \| \|GritLM-7B \|94.2% \| \|Nvidia-Embed-v2 \|93.4% \| \|BGE-Gemma2 \|95.4% \| ## Evaluations on MTEB tasks To evaluate our models multilingual capabilities we evaluate it against other source-available, high-performing embedding models listen in the MTEB leaderboard. For the following evaluations we compare the following models: - NVEmbed-V2: The highest scoring model in the MTEB leaderboard at time of the release - BGE-Multilingual-Gemma2: The highest scoring multilingual model in the MTEB leaderboard at the time of release. - GritLM: A generative representational instruction tuned language model. #### Methodology for Multilingual Evaluations (European languages) * Context: MTEB is a collection of tasks across many task types (e.g. classification, retrieval etc.). Furthermore, tasks can have N subsets on different languages. Subsets itself can also contain N languages, e.g. translation-related tasks. Base script actually comes from [gritlm/evaluation/eval_mteb.py at main · ContextualAI/gritlm](https://github.com/ContextualAI/gritlm/blob/main/evaluation/eval_mteb.py) and includes Medi2-style instructions for many MTEB Tasks. The instructions are all in English. All evaluations use Medi2-style instructions except for the “no instructions” case (see above). If a task does not have Medi2-style instructions, we skip the task. As European languages for MTEB tests German, Italian, Spanish, Portuguese and French were used. * For our Multilingual Evaluations (European languages) we use the tasks from [mteb/scripts/task_selection/europe_tasks.csv at main · embeddings-benchmark/mteb](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/task_selection/europe_tasks.csv) and then filter for tasks where there is at least one subset with at least one of the European languages. * We skip BibleNLPBitextMining and FloresBitextMining because they don’t have ‘test’ splits, only ‘train’ split which we don’t want to use for evaluation (→ training data contamination likely) * We evaluate subsets which contain at least one of the European languages → that’s why there is also an “English” language column because there are subsets that are e.g. En ↔︎ De and are thus considered * The tasks that remain are - AmazonCounterfactualClassification - BUCC.v2 - DiaBlaBitextMining - MassiveScenarioClassification - NTREXBitextMining - STS17 * For NTREXBitextMining the subsets are further filtered down to only pairs of the European languages instead of at least one European language - i.e. this gives 20-2=18 translation pair subsets between the 5 languages. -2 because Italian ↔︎ German doesn’t exist. - this is done because otherwise there are 250 translation pair subsets which are not as relevant (e.g. they contain Vietnamese ↔︎ Portuguese) We used the official scores reported in MTEB Leaderboard if reported, but for some models and subset we created the scores ourselves with the official Huggingface checkpoints and instructions referenced in the Paper or Model card. #### Europe by task \| Model Name \| AmazonCounterfactualClassification \| BUCC.v2 \| DiaBlaBitextMining \| MassiveScenarioClassification \| NTREXBitextMining \| STS17 \| Average \| \|-------------------------------------------------------\|-------------------------------------:\|----------:\|---------------------:\|--------------------------------:\|--------------------:\|---------:\|----------:\| \| Pharia-1-Embedding-4608-control \| 72.49 \| 99.19 \| 86.51 \| 75.58 \| 98.24 \| 87.67 \| 86.61 \| \| GritLM-7B \| 76.64 \| 99.43 \| 86.45 \| 78.93 \| 98.46 \| 88.07 \| 87.99 \| \| BGE-Multilingual-Gemma2 \| 69.72 \| 99.38 \| 86.90 \| 78.57 \| 98.58 \| 86.69 \| 86.64 \| \| Nvidia-Embed-v2 \| 70.72 \| 99.14 \| 73.22 \| 75.21 \| 96.65 \| 87.36 \| 83.72 \| #### Europe by language \| Model Name \| deu-Latn \| eng-Latn \| fra-Latn \| por-Latn \| ita-Latn \| spa-Latn \| Average \| \|-------------------------------------------------------\|-----------:\|-----------:\|-----------:\|-----------:\|-----------:\|-----------:\|----------:\| \| Pharia-1-Embedding-4608-control \| 0.925309 \| 0.902113 \| 0.937961 \| 0.953719 \| 0.942352 \| 0.945642 \| 0.934516 \| \| GritLM-7B \| 0.934603 \| 0.905669 \| 0.942364 \| 0.962042 \| 0.949731 \| 0.947428 \| 0.940306 \| \| BGE-Multilingual-Gemma2\| 93.07 \| 92.17 \| 94.91 \| 94.64 \| 96.28 \| 94.94 \| 94.35 \| \| Nvidia-Embed-v2 \| 91.58 \| 88.85 \| 90.51 \| 93.94 \| 95.08 \| 93.78\| 92.29 \| #### MTEB – English only \| \|Retrieval\|Classification\|STS\|Summarization\|PairClassification\|Clustering\|Reranking\|Average\| \|---\|--\|--\|--\|--\|--\|--\|--\|--\| \|Nvidia-Embed-v2\|62.65\|90.37\|84.31\|30.7\|88.67\|58.46\|60.65\|72.31\| \|BGE-Multilingual-Gemma2\|59.24\|88.08\|83.88\|31.2\|85.84\|54.65\|59.72\|69.88\| \|GritLM-7B\|57.36\|78.65\|83.35\|30.39\|87.29\|50.61\|60.48\|66.58\| \|Pharia-1-Embedding-4608-control\|39.15 \|74.40\|82.7 \|30.95 \|81.73\|46.23\|57.45\|58.94\| #### Ablation for “No Instruction” case We ablate how performance changes when not using task-specific instructions for the embeddings. \|Model Name\|ArguAna\|AskUbuntuDupQuestions\|BIOSSES\|Banking77Classification\|EmotionClassification\|MedrxivClusteringS2S\|NFCorpus\|STS17\|STSBenchmark\|SciFact\|SummEval\|TwitterSemEval2015\|Average\| \|--\|--\|--\|--\|--\|--\|--\|--\|--\|--\|--\|--\|--\|--\| \|Instruction \|51.09\|61.71\|84.56\|86.37\|51.77\|34.29\|37.82\|89.56\|87.08\|69.7 \|30.95\|70.97\|62.99\| \|No Instruction \|50.23\|60.31\|84.45\|86.36\|50.6 \|31.87\|37.58\|88.75\|86.39\|71.28\|31.00\|68.92\|62.31\| \|Relative Δ\|-1.71%\|-2.32%\|-0.13%\|-0.01%\|-2.31%\|-7.59%\|-0.64%\|-0.91%\|-0.80%\|2.22%\|0.16%\|-2.97%\|-1.09%\| We observe slightly reduced performance across most tasks when not using task-specific instructions with an average loss in performance of roughly 1%. ## Training Details ### Model architecture \| \| \| \|-------\|-------\| \|Number of layers\|27\| \|Number of attention heads\|36\| \|Head size\|128\| \|Number of Key-Value heads\|4\| \|Size hidden dimension\|4608\| \|MLP expansion factor\|4\| \|MLP type\|Standard\| \|Vocabulary size\|128,000\| \|Rotary base\|1,000,000\| \|Total parameter count\|7,041,544,704\| ### Training Pharia-1-Embedding-4608-control is an adapter on top of Pharia-1-LLM-7B-control, trained with a context window of 2048 Tokens. Pharia-1-Embedding-4608-control was trained with representational instruction-tuning (inspired by the approach of GritLM) and a contrastive learning approach. The final layer is an embedding head with weighted mean pooling. The train set consisted of a blend of open-source and proprietary datasets. Further postprocessing was used to optimize for downstream use and multilinguality. ### Tokenization Tokenization taking place in this embedding model takes full advantage of the one in [Pharia-1-LLM-7B-control model](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control)	[ "TRANSLATION", "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
Godefroyduchalard/solone-embedding-final2	Godefroyduchalard	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:19485", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:OrdalieTech/Solon-embeddings-large-0.1", "base_model:finetune:OrdalieTech/Solon-embeddings-large-0.1", "doi:10.57967/hf/3679", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-11-22T16:43:39	2024-12-02T08:35:44	0	--- base_model: OrdalieTech/Solon-embeddings-large-0.1 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:19485 - loss:MultipleNegativesRankingLoss widget: - source_sentence: chef de bord sentences: - Personne responsable du pilotage d'un navire. - Le chef de bord est une personne responsable du contrôle des dépenses et de l'organisation des réceptions dans un établissement hôtelier. - Procédure suivie par une juridiction lorsqu'elle doit trancher un litige par un acte juridictionnel. - source_sentence: dotation de solidarité rurale sentences: - Dispositif de défiscalisation concernant les propriétaires de logements acquis neufs ou en l'état futur d'achèvement, entre le 1er janvier 1999 et le 2 avril 2003, qui peuvent demander à bénéficier d'une déduction au titre de l'amortissement. - La dotation de solidarité rurale est une aide financière attribuée aux communes urbaines pour compenser les coûts supplémentaires liés à l'accueil des populations rurales qui viennent s'installer dans ces villes, en raison de la pénurie de logements disponibles dans leurs villages d'origine. - Dotation attribuée à certaines communes et à certains chefs-lieux d'arrondissement, en fonction du nombre d'habitants, pour tenir compte, d'une part, des charges qu'ils supportent pour contribuer au maintien de la vie sociale en milieu rural, d'autre part, de l'insuffisance de leurs ressources fiscales. - source_sentence: monument commémoratif sentences: - Les pensions de réversion sont destinées à garantir au survivant du couple un niveau de vie correct en lui versant une fraction de la pension principale dont bénéficiait ou aurait bénéficié son conjoint. Tous les régimes de retraite versent des pensions de réversion, à différents taux et sous des conditions variables. - Monument servant à commémorer un événement ou à honorer une ou plusieurs personnes. - Un monument commémoratif est un dispositif administratif permettant de définir et de gérer les budgets alloués à des événements ou des personnalités, sans nécessairement les honorer. - source_sentence: ozonosphère sentences: - Gestion visant à anticiper l’impact des réformes, à adapter les modes de gestion des ressources humaines, à enrichir et valoriser les compétences des agents publics. Dans son approche pluriannuelle de la GRH, elle se fonde en amont sur les orientations stratégiques de la politique RH découlant notamment des évolutions prévisibles des services (missions, organisation, ressources…) et sur l’analyse de données quantitatives et qualitatives relatives à la gestion prévisionnelle des emplois des effectifs et des compétences. Elle conduit à l’élaboration de plans d’actions qui portent sur l’ensemble des actes de la GRH. - Couche de la stratosphère terrestre dans laquelle la concentration d'ozone est la plus importante. - L'ozonosphère désigne la couche de l'économie terrestre où les entreprises sont exemptées de taxes sur leurs émissions de gaz à effet de serre. - source_sentence: développement rural sentences: - Gestion du développement humain et orientation des changements technologiques et institutionnels de façon à améliorer l'inclusion, la longévité, les connaissances et les standards de vie dans les zones rurales, et ce dans un contexte d'équité et de durabilité. - Le développement rural est un processus administratif visant à réduire l'urbanisation et à favoriser le déclin économique des zones rurales en leur attribuant une part de la dette nationale, dans le but d'améliorer les conditions de vie des citadins. - Aide financière réelle, qui n'est ni un prêt ni une avance de trésorerie, accordée par l'Etat, une collectivité territoriale ou un organisme privé pour financer ou favoriser le développement d'une activité d'intérêt général ou, à titre de secours, pour subvenir à un cas pressant. --- # SentenceTransformer based on OrdalieTech/Solon-embeddings-large-0.1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [OrdalieTech/Solon-embeddings-large-0.1](https://huggingface.co/OrdalieTech/Solon-embeddings-large-0.1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [OrdalieTech/Solon-embeddings-large-0.1](https://huggingface.co/OrdalieTech/Solon-embeddings-large-0.1) <!-- at revision 9f6465f6ea2f6d10c6294bc15d84edf87d47cdef --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Godefroyduchalard/solone-embedding-final2") # Run inference sentences = [ 'développement rural', "Gestion du développement humain et orientation des changements technologiques et institutionnels de façon à améliorer l'inclusion, la longévité, les connaissances et les standards de vie dans les zones rurales, et ce dans un contexte d'équité et de durabilité.", "Le développement rural est un processus administratif visant à réduire l'urbanisation et à favoriser le déclin économique des zones rurales en leur attribuant une part de la dette nationale, dans le but d'améliorer les conditions de vie des citadins.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 19,485 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 4.53 tokens</li><li>max: 18 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 28.43 tokens</li><li>max: 84 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 40.14 tokens</li><li>max: 71 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-----------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>primo-immigrant</code> \| <code>Une personne qui déménage dans un nouveau pays pour la première fois et qui n'a jamais vécu auparavant dans ce pays en tant que résident permanent.</code> \| <code>Un primo-immigrant est une personne qui a déjà vécu dans un pays pendant au moins dix ans et qui décide de déménager vers un autre pays pour y acquérir la nationalité.</code> \| \| <code>AAH</code> \| <code>L'Allocation aux Adultes Handicapés (AAH) est une aide financière versée par l'Etat français aux personnes ayant un taux d'incapacité supérieur à 80% ou compris entre 50% et 79% avec une restriction substantielle et durable d'accès à l'emploi.</code> \| <code>L'Allocation aux Adultes Handicapés (AAH) est une aide financière versée par les entreprises privées françaises pour récompenser les employeurs qui ont réussi à intégrer des personnes handicapées dans leur effectif.</code> \| \| <code>ACA</code> \| <code>l'ACA est un document administratif qui accompagne une demande d'aide sociale et qui atteste de la situation administrative et financière de la personne concernée</code> \| <code>L'ACA est un document administratif qui permet de déclarer officiellement l'indépendance financière d'une personne, attestant ainsi sa capacité à supporter ses propres besoins sans recours à l'aide sociale.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 500 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 500 samples: \| \| anchor \| positive \| negative \| \|:--------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 3 tokens</li><li>mean: 6.66 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 46.25 tokens</li><li>max: 360 tokens</li></ul> \| <ul><li>min: 19 tokens</li><li>mean: 44.94 tokens</li><li>max: 96 tokens</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-----------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>commission de surendettement des particuliers</code> \| <code>Organisme public, implanté dans chaque département, qu'un particulier peut saisir lorsqu'il rencontre de graves difficultés financières pour rembourser des dettes non professionnelles. <br>La commission a pour mission de préserver les intérêts des particuliers et de leurs créanciers en établissant, lorsque cela est possible, un plan conventionnel de redressement. Ce plan amiable de remboursement est approuvé par le débiteur et les principaux créanciers. En cas d'échec, elle pourra, si le débiteur la saisit à nouveau, établir un second plan en imposant des mesures aux créanciers. Si la situation financière du débiteur rend manifestement impossible la mise en œuvre de ces mesures, la procédure de rétablissement personnel pourra être engagée.</code> \| <code>L'organisme public chargé de veiller au respect des règles de surendettement est en réalité une commission qui se charge d'évaluer les capacités financières des entreprises pour déterminer si elles sont aptes à emprunter de l'argent.</code> \| \| <code>infrastructure ferroviaire</code> \| <code>Ensemble des installations permettant la circulation de trains (notamment les voies ferrées, caténaires, équipements de transport de l'énergie, système de signalisation ferroviaire, bâtiments, ouvrages d'art, système de communication radio sol-train et télécommunications).</code> \| <code>L'infrastructure ferroviaire désigne l'ensemble des installations permettant aux autorités locales de réguler et de contrôler les mouvements des trains, notamment les voies ferrées, les caténaires, les équipements de transport de l'énergie, le système de signalisation ferroviaire, les bâtiments, les ouvrages d'art, le système de communication radio sol-train et les télécommunications.</code> \| \| <code>Géophysique</code> \| <code>Ensemble de sciences utilisant les techniques de la physique et des sciences de <br>l’ingénieur pour connaître la Terre et principalement ses profondeurs inaccessibles à l’observation directe.</code> \| <code>La géophysique est l'ensemble des sciences qui visent à prévenir et à gérer les catastrophes naturelles en utilisant les techniques de la physique et des sciences de l’ingénieur pour anticiper et contrôler les phénomènes météorologiques.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:-----:\|:-------------:\|:---------------:\| \| 0.8210 \| 1000 \| 1.1789 \| 0.4142 \| \| 1.6420 \| 2000 \| 0.7996 \| 0.2781 \| \| 2.4631 \| 3000 \| 0.6071 \| 0.2901 \| \| 3.2841 \| 4000 \| 0.5536 \| 0.2241 \| \| 4.1051 \| 5000 \| 0.5039 \| 0.2887 \| \| 4.9261 \| 6000 \| 0.5153 \| 0.1972 \| \| 5.7471 \| 7000 \| 0.5812 \| 0.1732 \| \| 6.5681 \| 8000 \| 0.5242 \| 0.1657 \| \| 7.3892 \| 9000 \| 0.4647 \| 0.1542 \| \| 8.2102 \| 10000 \| 0.4202 \| 0.1820 \| \| 9.0312 \| 11000 \| 0.4519 \| 0.1430 \| \| 9.8522 \| 12000 \| 0.4862 \| 0.1488 \| ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.1 - Transformers: 4.44.0 - PyTorch: 2.4.1+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CAS" ]
chandanzeon/setfit_finetuned_iaf_98	chandanzeon	text-classification	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:BAAI/bge-small-en-v1.5", "base_model:finetune:BAAI/bge-small-en-v1.5", "model-index", "region:us" ]	2024-12-16T10:46:03	2024-12-16T10:46:15	0	--- base_model: BAAI/bge-small-en-v1.5 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Review of Administrative and Disciplinary Records Recent administrative evaluations have revealed irregularities within key operational postings. Investigations were launched in key command areas such as Jalandhar and Secunderabad, focusing on personnel movement, access permissions, and communication lapses. Anomalies in operational reports indicated unauthorized sharing of personnel data with external parties, sparking concerns about internal security and discipline. The South Western Command and Central Military Headquarters are overseeing these investigations. Reports highlight a need for increased supervision of personnel involved in administrative roles, as lapses in information sharing protocols pose a significant risk to mission readiness. In response, administrative retraining programs focused on compliance, confidentiality, and secure communication have been implemented across all units. Improved oversight measures, such as enhanced access control protocols and personnel background checks, are being prioritized to prevent such breaches from occurring in the future. Specialized training sessions have been hosted at key logistical hubs to strengthen accountability and ensure all military officials understand their responsibilities.' - text: 'Advanced Technological Integration into Military Strategies To maintain strategic advantages, the military has integrated cutting-edge technological assets into operational strategies. Innovations such as advanced surveillance drones equipped with night vision cameras and AI-assisted threat detection have enhanced the military''s ability to track adversarial movements. These drones are deployed on both border operations and maritime patrols, enabling continuous and real-time intelligence-gathering without compromising operational security. Furthermore, electronic warfare units have been equipped with advanced jamming devices capable of disrupting electronic communication signals used by insurgents. This capability ensures that adversarial communication networks are neutralized during operational missions, reducing the ability of enemy cells to coordinate and launch attacks.' - text: 'Drones in Target Acquisition and Precision Strikes Beyond surveillance and reconnaissance, drones are increasingly being used in target acquisition and precision strike missions. The integration of guided munitions with UAVs allows for highly accurate strikes on key targets, including terrorist camps, weapons caches, and enemy fortifications. Drones like the Harpy and Predator have been used in similar missions, providing high-precision strikes while minimizing the risk to personnel. The use of drones for precision strikes significantly reduces the collateral damage typically associated with traditional airstrikes and ground-based artillery fire.' - text: 'Strengthening Army Resilience through Infrastructure Upgrades Recent initiatives to modernize military infrastructure are focusing on strategic roadways, railway networks, and key logistical hubs across Northern and Eastern theater areas. Troop movement flexibility has become vital as regional border security remains fragile. Construction projects have been prioritized near operational areas like Leh, Arunachal Pradesh, and parts of the Indo-Nepal border. Specialized engineering battalions are spearheading the construction of advanced bridges and all- weather roadways, particularly through challenging terrains such as the Himalayan foothills and desert corridors. The latest developments include high-capacity bridge-building technology, allowing troops and supplies to be moved rapidly even in the most inaccessible locations. The strategic development of these routes ensures the swift mobility of logistical support, troop reinforcements, and rapid response units. Furthermore, advancements in railway infrastructure are underway to support rapid troop deployment. Railway hubs near key operational zones are being modernized, with emphasis on dual-use infrastructure that allows both civilian and military operations to utilize these networks when necessary.' - text: 'Tactical Coordination and Training Joint training exercises involving armored and artillery units have been conducted to refine battlefield tactics. These exercises, held in the Thar Desert, simulated multi-front conflict scenarios, emphasizing coordination between various branches of the armed forces. Feedback from these exercises has led to the adoption of new operational guidelines, such as optimized deployment patterns for tanks and artillery systems. Post-exercise debriefings at Jodhpur Cantonment highlighted the importance of synchronized maneuvers in achieving tactical superiority.' inference: true model-index: - name: SetFit with BAAI/bge-small-en-v1.5 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9193548387096774 name: Accuracy --- # SetFit with BAAI/bge-small-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 4 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 3 \| <ul><li>'Closing Ceremony and Awards Distribution\nThe event concluded with the closing ceremony on August 18, 2024. The ceremony was attended by senior military officials, including the Chief of Naval Staff and Chief of Air Staff. The athletes were recognized for their outstanding performances with awards presented in various categories, such as Best Athlete, Best Team, and Best Sportsmanship. The Indian Air Force won the Best Team Performance trophy, while Captain Aaryan Verma from the Army was named the Best Athlete for his exceptional performance in athletics. The Indian Army was declared the overall winner of the competition, having secured the most points across all events. A highlight of the ceremony was the traditional military drill performed by the three services, showcasing the discipline and precision that is characteristic of the Indian Armed Forces.'</li><li>'Community Engagement Activities\nIn addition to the aerial demonstrations, the IAF organized several community outreach activities. A special education booth was set up for schoolchildren, focusing on the history of the Indian Air Force, aviation careers, and the importance of air defense. The booth also displayed several educational films and interactive content about the Air Forceâ\x80\x99s role in peacekeeping, disaster relief, and national defense. Additionally, a blood donation camp was established near the main entrance, in collaboration with Bangaloreâ\x80\x99s city hospital, to encourage voluntary blood donation. Visitors were encouraged to participate, with the goal of increasing awareness about health and wellness in the community. The camp ran smoothly and successfully collected over 500 units of blood, which would be distributed to regional hospitals.'</li><li>'Environmental Considerations\nWith the ongoing infrastructure developments, the Indian Air Force has taken steps to minimize the environmental impact of the construction. Measures are being implemented to reduce waste and promote sustainability during the projectâ\x80\x99s execution. Additionally, the base has set up an environmental monitoring system to track air and water quality in the vicinity, ensuring that the baseâ\x80\x99s operations do not adversely affect local ecosystems. Moreover, the wastewater treatment facility at the station is being upgraded to ensure that all waste generated from daily operations is properly treated and does not affect the surrounding areas. This initiative is part of the Air Forceâ\x80\x99s broader commitment to environmental responsibility and sustainability.'</li></ul> \| \| 1 \| <ul><li>'Radio Frequency Allocation Updates\nThe communications division recently conducted a comprehensive review of radio frequency allocations across the northern and northeastern sectors. Adjustments were made to avoid overlaps that could interfere with civilian and military operations. A new allocation plan has been implemented for units stationed at Bagdogra and Dimapur, ensuring seamless communication during both routine and emergency operations. Periodic audits of frequency usage continue to safeguard against potential breaches or overlaps.'</li><li>'Signal Frequency Interference Monitoring\nSignal frequency interference has become a growing concern as electronic warfare threats evolve. The monitoring units, based in key strategic areas such as Karnal, Leh, and Udhampur, have observed unauthorized intrusions into radio communication patterns. Advanced detection technologies have been deployed to analyze this data, with initial results highlighting the need for improved counter-electronic warfare capabilities. The Signal Corps has expanded its focus on electronic jamming threats near key tactical airstrips and operational centers. Units in these regions are conducting surveillance with advanced signal detection systems, ensuring they can identify and neutralize attempts at electronic disruption. Military radar units in Jaisalmer and Pathankot are receiving new upgrades to improve signal detection in operationally sensitive regions. Commanders have emphasized the importance of coordinating electronic warfare drills with these signal monitoring operations to enhance response mechanisms. Coordination between signal analysis teams and field operations ensures timely detection and neutralization of electronic threats.'</li><li>'Naval Assets and Maritime Patrolling Operations\nNaval deployments along key trade routes and strategic maritime chokepoints have seen increased patrols and strategic upgrades. Units have been repositioned in response to recent developments in regional waters, focusing on both counter-terror operations and maintaining freedom of navigation. Surveillance assets, such as Indian Navy frigates and long-range maritime reconnaissance aircraft, are actively monitoring the Malabar Sea and Arabian Sea for any irregular ship movements or unauthorized military deployments. Naval units stationed at key operational ports like Visakhapatnam and Karwar are equipped with advanced sonar and radar systems. Recent deployments emphasize anti-submarine warfare capabilities, leveraging advanced underwater detection technology to identify potential threats from hostile assets or insurgent activity. Coordination with air assets, including the Sea King and P-8I aircraft, has improved naval surveillance effectiveness, with regular joint operations enhancing strategic interoperability.'</li></ul> \| \| 2 \| <ul><li>"Training Manuals for Official Use Only\nThe following manuals were distributed among units for use during December 2024 training sessions: 1. Guidelines for Advanced Vehicle Maintenance: ï\x82· This manual provides detailed procedures for troubleshooting and repairing light utility vehicles commonly used in supply operations. Emphasis is placed on maintaining vehicle efficiency in cold-weather conditions. ï\x82· A new section outlines methods for diagnosing electronic systems, a critical aspect as newer models are introduced into service. 2. Basic Communication Protocols: ï\x82· Designed for new recruits, this guide introduces secure communication techniques, including encryption basics and signal relay procedures. ï\x82· The document also includes practical exercises to simulate field scenarios, enhancing recruits' readiness for real-world applications."</li><li>'Routine Procurement Documents\nThe procurement department at Ambala Air Force Station has finalized contracts for the supply of spare parts for MiG-21 aircraft. The document details the scheduled delivery of parts such as hydraulic actuators, brake systems, and navigation units over the next quarter. These supplies are essential for routine maintenance and ensuring that the aircraft remains in operational condition for non-combat purposes. The report also includes internal memos on supplier performance and cost negotiations, which are classified as Restricted to prevent unauthorized access and ensure smooth contract execution. Highlights of the Competition\nThe athletics events were among the most anticipated, with the fastest runners from each branch competing for medals. The 100m sprint final featured a thrilling race between the top sprinters from the Army, Navy, and Air Force, with Captain Aaryan Verma of the Army securing the gold medal with a time of 10.87 seconds, followed by Lieutenant Neha Mehra of the Navy, who claimed the silver with 11.03 seconds. In the football tournament, the Indian Army emerged as the champions after a tense final match against the Indian Air Force. The game ended with a score of 2-1 in favor of the Army, with Subedar Major Vikram Singh scoring the winning goal in the final minutes. The Army team displayed exceptional teamwork and strategic play, which ultimately led them to victory. The cricket matches were highly competitive, with the Indian Navy defeating the Air Force team in a closely contested T20 match. The final was a nail-biting affair, with Navyâ\x80\x99s Lieutenant Commander Rahul Mehta hitting the winning six in the last over of the game.'</li><li>'Supply Chain and Procurement Documents\nRoutine procurement activities continue to fuel military preparedness. The most recent batch of documents contains procurement orders for various operational materials needed in peripheral zones. These orders range from vehicles used in reconnaissance missions to tactical gear for military units that are not directly involved in combat but are still crucial for maintaining defense capabilities. For example, a recent procurement request was made for a series of high-powered satellite phones that will be issued to units deployed in isolated locations. These phones are essential for ensuring that communication lines remain open in areas where traditional communication infrastructure is unavailable. Similarly, there are ongoing negotiations for acquiring medical supplies, such as portable surgical kits and trauma care equipment, specifically for units working in non-conflict zones where medical infrastructure might be limited. The documentation detailing these procurements includes specifics on supplier agreements, delivery schedules, and operational requirements. This is sensitive data, as it could potentially reveal gaps in military supply chains if accessed by unauthorized individuals. Suppliers are carefully vetted, and any leak of information regarding these supply chains could jeopardize the mission\'s success in certain strategic areas. Cipher Message: Cipher Text: "NQ5P7 QXZ8T 7J6B2 P1M9Y." â\x80\x93 Encrypted procurement details, listing authorized suppliers and material quantities for internal distribution only.'</li></ul> \| \| 0 \| <ul><li>'Enhancement of Aerial Surveillance\nUnmanned Aerial Vehicles (UAVs) have been deployed from the Jorhat Air Force Station to maintain constant surveillance over disputed areas. These UAVs, equipped with high-resolution cameras and thermal sensors, provide real-time imagery of adversarial activities. Regular patrol missions conducted over regions like Kibithu and Walong have been instrumental in identifying unauthorized constructions. The data gathered is relayed to command centers in Shillong for detailed analysis. AI-powered algorithms help in detecting anomalies, ensuring swift decision-making in case of any potential threats. These proactive measures have significantly improved situational awareness. Implementation of AI-Based Border Surveillance\nThe recent deployment of artificial intelligence-driven surveillance mechanisms has introduced cutting-edge technology into border operations. Surveillance drones and AI-powered detection sensors have been positioned along key border regions, including the North Eastern States and the Indian-Pakistani border. These assets are leveraging machine learning algorithms to identify patterns of unusual activity, unauthorized crossings, and changes in terrain anomalies. The AI systems are capable of processing vast quantities of real-time data collected from UAVs, thermal imaging cameras, and ground-based radar installations. Machine learning analysis identifies trends that may go unnoticed by conventional monitoring, such as small troop movements or unauthorized infiltration attempts across the porous Indo-Bangladesh border. These capabilities have already proven effective in detecting early signs of infiltration and cross-border activity. Additionally, intelligence teams are collaborating with AI experts to fine-tune these tools for real- time decision-making support. Advanced signal detection and image recognition capabilities are improving response times and ensuring that border patrols can intercept threats with enhanced accuracy and minimal delay.'</li><li>'Conclusion\nThe integration of advanced technology with strategic realignments across operational zones highlights the dynamic and robust approach adopted by the armed forces. These measures not only bolster defensive capabilities but also reinforce the nationâ\x80\x99s readiness to respond to evolving threats.'</li><li>'New Munitions Deployment\nTo enhance combat effectiveness, advanced munitions tailored for specific operational conditions have been introduced. The recent deployment of guided mortar systems to units stationed in the Siachen Glacier highlights this focus. These munitions, tested under extreme conditions, provide unmatched accuracy and reliability. Additionally, countermeasure systems designed to neutralize enemy drones have been distributed across critical sectors. These systems employ directed energy technology, effectively disrupting the electronic controls of hostile UAVs.'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.9194 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("chandanzeon/setfit_finetuned_iaf_98") # Run inference preds = model("Tactical Coordination and Training Joint training exercises involving armored and artillery units have been conducted to refine battlefield tactics. These exercises, held in the Thar Desert, simulated multi-front conflict scenarios, emphasizing coordination between various branches of the armed forces. Feedback from these exercises has led to the adoption of new operational guidelines, such as optimized deployment patterns for tanks and artillery systems. Post-exercise debriefings at Jodhpur Cantonment highlighted the importance of synchronized maneuvers in achieving tactical superiority.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:---------\|:----\| \| Word count \| 39 \| 130.3317 \| 475 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 49 \| \| 1 \| 56 \| \| 2 \| 49 \| \| 3 \| 51 \| ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0010 \| 1 \| 0.267 \| - \| \| 0.0508 \| 50 \| 0.2533 \| - \| \| 0.1016 \| 100 \| 0.2342 \| - \| \| 0.1524 \| 150 \| 0.2272 \| - \| \| 0.2033 \| 200 \| 0.2065 \| - \| \| 0.2541 \| 250 \| 0.1573 \| - \| \| 0.3049 \| 300 \| 0.1051 \| - \| \| 0.3557 \| 350 \| 0.0546 \| - \| \| 0.4065 \| 400 \| 0.011 \| - \| \| 0.4573 \| 450 \| 0.004 \| - \| \| 0.5081 \| 500 \| 0.0028 \| - \| \| 0.5589 \| 550 \| 0.0023 \| - \| \| 0.6098 \| 600 \| 0.0019 \| - \| \| 0.6606 \| 650 \| 0.0015 \| - \| \| 0.7114 \| 700 \| 0.0014 \| - \| \| 0.7622 \| 750 \| 0.0014 \| - \| \| 0.8130 \| 800 \| 0.0013 \| - \| \| 0.8638 \| 850 \| 0.0012 \| - \| \| 0.9146 \| 900 \| 0.0011 \| - \| \| 0.9654 \| 950 \| 0.001 \| - \| \| 1.0 \| 984 \| - \| 0.0731 \| \| 1.0163 \| 1000 \| 0.001 \| - \| \| 1.0671 \| 1050 \| 0.0009 \| - \| \| 1.1179 \| 1100 \| 0.0009 \| - \| \| 1.1687 \| 1150 \| 0.0008 \| - \| \| 1.2195 \| 1200 \| 0.0008 \| - \| \| 1.2703 \| 1250 \| 0.0008 \| - \| \| 1.3211 \| 1300 \| 0.0008 \| - \| \| 1.3720 \| 1350 \| 0.0007 \| - \| \| 1.4228 \| 1400 \| 0.0007 \| - \| \| 1.4736 \| 1450 \| 0.0007 \| - \| \| 1.5244 \| 1500 \| 0.0007 \| - \| \| 1.5752 \| 1550 \| 0.0006 \| - \| \| 1.6260 \| 1600 \| 0.0006 \| - \| \| 1.6768 \| 1650 \| 0.0006 \| - \| \| 1.7276 \| 1700 \| 0.0006 \| - \| \| 1.7785 \| 1750 \| 0.0006 \| - \| \| 1.8293 \| 1800 \| 0.0006 \| - \| \| 1.8801 \| 1850 \| 0.0006 \| - \| \| 1.9309 \| 1900 \| 0.0006 \| - \| \| 1.9817 \| 1950 \| 0.0005 \| - \| \| 2.0 \| 1968 \| - \| 0.0762 \| \| 2.0325 \| 2000 \| 0.0005 \| - \| \| 2.0833 \| 2050 \| 0.0005 \| - \| \| 2.1341 \| 2100 \| 0.0005 \| - \| \| 2.1850 \| 2150 \| 0.0005 \| - \| \| 2.2358 \| 2200 \| 0.0005 \| - \| \| 2.2866 \| 2250 \| 0.0005 \| - \| \| 2.3374 \| 2300 \| 0.0005 \| - \| \| 2.3882 \| 2350 \| 0.0005 \| - \| \| 2.4390 \| 2400 \| 0.0005 \| - \| \| 2.4898 \| 2450 \| 0.0005 \| - \| \| 2.5407 \| 2500 \| 0.0005 \| - \| \| 2.5915 \| 2550 \| 0.0004 \| - \| \| 2.6423 \| 2600 \| 0.0004 \| - \| \| 2.6931 \| 2650 \| 0.0004 \| - \| \| 2.7439 \| 2700 \| 0.0004 \| - \| \| 2.7947 \| 2750 \| 0.0004 \| - \| \| 2.8455 \| 2800 \| 0.0004 \| - \| \| 2.8963 \| 2850 \| 0.0004 \| - \| \| 2.9472 \| 2900 \| 0.0004 \| - \| \| 2.9980 \| 2950 \| 0.0004 \| - \| \| 3.0 \| 2952 \| - \| 0.0786 \| \| 3.0488 \| 3000 \| 0.0004 \| - \| \| 3.0996 \| 3050 \| 0.0004 \| - \| \| 3.1504 \| 3100 \| 0.0004 \| - \| \| 3.2012 \| 3150 \| 0.0004 \| - \| \| 3.2520 \| 3200 \| 0.0004 \| - \| \| 3.3028 \| 3250 \| 0.0004 \| - \| \| 3.3537 \| 3300 \| 0.0004 \| - \| \| 3.4045 \| 3350 \| 0.0004 \| - \| \| 3.4553 \| 3400 \| 0.0004 \| - \| \| 3.5061 \| 3450 \| 0.0004 \| - \| \| 3.5569 \| 3500 \| 0.0003 \| - \| \| 3.6077 \| 3550 \| 0.0004 \| - \| \| 3.6585 \| 3600 \| 0.0004 \| - \| \| 3.7093 \| 3650 \| 0.0004 \| - \| \| 3.7602 \| 3700 \| 0.0003 \| - \| \| 3.8110 \| 3750 \| 0.0003 \| - \| \| 3.8618 \| 3800 \| 0.0004 \| - \| \| 3.9126 \| 3850 \| 0.0003 \| - \| \| 3.9634 \| 3900 \| 0.0003 \| - \| \| 4.0 \| 3936 \| - \| 0.0813 \| \| 4.0142 \| 3950 \| 0.0003 \| - \| \| 4.0650 \| 4000 \| 0.0003 \| - \| \| 4.1159 \| 4050 \| 0.0003 \| - \| \| 4.1667 \| 4100 \| 0.0003 \| - \| \| 4.2175 \| 4150 \| 0.0003 \| - \| \| 4.2683 \| 4200 \| 0.0003 \| - \| \| 4.3191 \| 4250 \| 0.0003 \| - \| \| 4.3699 \| 4300 \| 0.0003 \| - \| \| 4.4207 \| 4350 \| 0.0003 \| - \| \| 4.4715 \| 4400 \| 0.0003 \| - \| \| 4.5224 \| 4450 \| 0.0003 \| - \| \| 4.5732 \| 4500 \| 0.0003 \| - \| \| 4.6240 \| 4550 \| 0.0003 \| - \| \| 4.6748 \| 4600 \| 0.0003 \| - \| \| 4.7256 \| 4650 \| 0.0003 \| - \| \| 4.7764 \| 4700 \| 0.0003 \| - \| \| 4.8272 \| 4750 \| 0.0003 \| - \| \| 4.8780 \| 4800 \| 0.0003 \| - \| \| 4.9289 \| 4850 \| 0.0003 \| - \| \| 4.9797 \| 4900 \| 0.0003 \| - \| \| 5.0 \| 4920 \| - \| 0.0804 \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.2.1 - Transformers: 4.42.2 - PyTorch: 2.5.1+cu121 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "MEDAL" ]
Maxthemacaque/onnx-gte-multilingual-base	Maxthemacaque	sentence-similarity	[ "sentence-transformers", "onnx", "mteb", "transformers", "multilingual", "sentence-similarity", "af", "ar", "az", "be", "bg", "bn", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "ht", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ky", "lo", "lt", "lv", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "pa", "pl", "pt", "qu", "ro", "ru", "si", "sk", "sl", "so", "sq", "sr", "sv", "sw", "ta", "te", "th", "tl", "tr", "uk", "ur", "vi", "yo", "zh", "arxiv:2407.19669", "arxiv:2210.09984", "arxiv:2402.03216", "arxiv:2007.15207", "arxiv:2104.08663", "arxiv:2402.07440", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-12-22T10:14:40	2024-12-22T10:19:11	0	--- language: - af - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - es - et - eu - fa - fi - fr - gl - gu - he - hi - hr - ht - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ky - lo - lt - lv - mk - ml - mn - mr - ms - my - ne - nl - 'no' - pa - pl - pt - qu - ro - ru - si - sk - sl - so - sq - sr - sv - sw - ta - te - th - tl - tr - uk - ur - vi - yo - zh license: apache-2.0 tags: - mteb - sentence-transformers - transformers - multilingual - sentence-similarity model-index: - name: gte-multilingual-base (dense) results: - task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics: - type: v_measure value: 33.66681726329994 - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_spearman value: 43.54760696384009 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_spearman value: 48.91186363417501 - task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics: - type: accuracy value: 41.689860834990064 - task: type: Clustering dataset: name: MTEB AlloProfClusteringP2P type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: v_measure value: 54.20241337977897 - type: v_measure value: 44.34083695608643 - task: type: Reranking dataset: name: MTEB AlloprofReranking type: lyon-nlp/mteb-fr-reranking-alloprof-s2p config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics: - type: map value: 64.91495250072002 - task: type: Retrieval dataset: name: MTEB AlloprofRetrieval type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: ndcg_at_10 value: 53.638 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - 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type: accuracy value: 85.36038961038962 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.5903826674123 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.21474277151329 - task: type: Classification dataset: name: MTEB CBD type: PL-MTEB/cbd config: default split: test revision: None metrics: - type: accuracy value: 62.519999999999996 - task: type: PairClassification dataset: name: MTEB CDSC-E type: PL-MTEB/cdsce-pairclassification config: default split: test revision: None metrics: - type: cos_sim_ap value: 74.90132799162956 - task: type: STS dataset: name: MTEB CDSC-R type: PL-MTEB/cdscr-sts config: default split: test revision: None metrics: - type: cos_sim_spearman value: 90.30727955142524 - 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task: type: Retrieval dataset: name: MTEB VideoRetrieval type: C-MTEB/VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: ndcg_at_10 value: 72.792 - task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 82.58000000000001 - task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics: - type: ndcg_at_10 value: 67.327 --- ## gte-multilingual-base The gte-multilingual-base model is the latest in the [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) (General Text Embedding) family of models, featuring several key attributes: - High Performance: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size. - Training Architecture: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. - Long Context: Supports text lengths up to 8192 tokens. - Multilingual Capability: Supports over 70 languages. - Elastic Dense Embedding: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. - Sparse Vectors: In addition to dense representations, it can also generate sparse vectors. Paper: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) ## Model Information - Model Size: 305M - Embedding Dimension: 768 - Max Input Tokens: 8192 ## Usage - It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers). - How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3) - How to use with [TEI](https://github.com/huggingface/text-embeddings-inference): [refs/pr/7](https://huggingface.co/Alibaba-NLP/gte-multilingual-base/discussions/7#66bfb82ea03b764ca92a2221) ### Get Dense Embeddings with Transformers ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(*batch_dict) dimension=768 # The output dimension of the output embedding, should be in [128, 768] embeddings = outputs.last_hidden_state[:, 0][:dimension] embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) 100 print(scores.tolist()) # [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]] ``` ### Use with sentence-transformers ```python # Requires sentence-transformers>=3.0.0 from sentence_transformers import SentenceTransformer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path="Alibaba-NLP/gte-multilingual-base" model = SentenceTransformer(model_name_or_path, trust_remote_code=True) embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768) # sim scores scores = model.similarity(embeddings[:1], embeddings[1:]) print(scores.tolist()) # [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]] ``` ### Use with infinity Usage via docker and [infinity](https://github.com/michaelfeil/infinity), MIT Licensed. ``` docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ michaelf34/infinity:0.0.69 \ v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997 ``` ### Use with custom code to get dense embeddigns and sparse token weights ```python # You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py from gte_embedding import GTEEmbeddidng model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' model = GTEEmbeddidng(model_name_or_path) query = "中国的首都在哪儿" docs = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] embs = model.encode(docs, return_dense=True,return_sparse=True) print('dense_embeddings vecs', embs['dense_embeddings']) print('token_weights', embs['token_weights']) pairs = [(query, doc) for doc in docs] dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0) sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0) hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3) print('dense_scores', dense_scores) print('sparse_scores', sparse_scores) print('hybrid_scores', hybrid_scores) # dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125] # sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084] # hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252] ``` ## Evaluation We validated the performance of the gte-multilingual-base model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), among others. ### Retrieval Task Retrieval results on [MIRACL](https://arxiv.org/abs/2210.09984) and [MLDR](https://arxiv.org/abs/2402.03216) (multilingual), [MKQA](https://arxiv.org/abs/2007.15207) (crosslingual), [BEIR](https://arxiv.org/abs/2104.08663) and [LoCo](https://arxiv.org/abs/2402.07440) (English). ![image](./images/mgte-retrieval.png) - Detail results on [MLDR](https://arxiv.org/abs/2402.03216) ![image](./images/mgte-retrieval.png) - Detail results on [LoCo](https://arxiv.org/abs/2402.07440) ### MTEB Results on MTEB English, Chinese, French, Polish ![image](./images/mgte-mteb.png) More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669). ## Cloud API Services In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud. - [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. ## Citation If you find our paper or models helpful, please consider cite: ``` @misc{zhang2024mgte, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang}, year={2024}, eprint={2407.19669}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.19669}, } ```	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1433	Lots-of-LoRAs	null	[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "license:mit", "region:us" ]	2024-12-30T23:11:25	2024-12-30T23:11:31	0	--- language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1433 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1433_head_qa_language_translation_es_to_en - Developed by: bruel - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: LoRA - Language(s) (NLP): en - License: mit - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/bruel-gabrielsson - Paper [optional]: "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1433_head_qa_language_translation_es_to_en sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "TRANSLATION" ]	[ "HEAD-QA" ]
Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1432	Lots-of-LoRAs	null	[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:mit", "region:us" ]	2025-01-01T14:25:49	2025-01-01T14:25:54	0	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1432 <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> LoRA trained on task1432_head_qa_language_translation_en_to_es - Developed by: bruel - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: LoRA - Language(s) (NLP): en - License: mit - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/bruel-gabrielsson - Paper [optional]: "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> https://huggingface.co/datasets/Lots-of-LoRAs/task1432_head_qa_language_translation_en_to_es sourced from https://github.com/allenai/natural-instructions ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	[ "TRANSLATION" ]	[ "HEAD-QA" ]
ThunderJaw/hu_fasttext_resume_sections	ThunderJaw	text-classification	[ "text-classification", "hu", "dataset:ganchengguang/resume_seven_class", "base_model:facebook/fasttext-hu-vectors", "base_model:finetune:facebook/fasttext-hu-vectors", "region:us" ]	2025-01-03T11:53:48	2025-01-03T12:11:09	0	--- base_model: - facebook/fasttext-hu-vectors datasets: - ganchengguang/resume_seven_class language: - hu pipeline_tag: text-classification --- # Model Card for Resume Section Classifier This model is designed to classify sections within Hungarian resumes into categories such as Skills, Education, Experience, and others. It utilizes the `facebook/fasttext-hu-vectors` model as its base and has been fine-tuned on the `ganchengguang/resume_seven_class` dataset. The dataaset was in English so I translated it into Hungarian. It's not the best approach but it still works. ## Model Details ### Model Description This model leverages the `facebook/fasttext-hu-vectors` pre-trained embeddings to classify Hungarian resume sections into predefined categories. It has been fine-tuned on the `ganchengguang/resume_seven_class` dataset, which includes seven categories: Experience, Education, Knowledge, Project, and others. - Model type: Text Classification - Language(s): Hungarian - Finetuned from model: facebook/fasttext-hu-vectors ## Uses ### Direct Use This model can be used directly to classify sections of Hungarian resumes into categories such as Skills, Education, Experience, and others. It is suitable for applications in recruitment and resume analysis. ### Downstream Use The model can be integrated into larger systems for automated resume screening, assisting HR professionals in efficiently processing and categorizing resume information. ### Out-of-Scope Use This model is not intended for use with resumes in languages other than Hungarian. It may not perform accurately on resumes with non-standard formats or those containing significant amounts of non-Hungarian text. ## Bias, Risks, and Limitations The model has been trained on a specific dataset and may not generalize well to resumes with formats or content significantly different from those in the training data. Users should be aware of potential biases in the training data and the model's limitations in handling diverse resume formats. ### Recommendations Users should validate the model's predictions and consider incorporating human oversight, especially when dealing with resumes that deviate from the standard formats present in the training data. ## How to Get Started with the Model - https://github.com/ssobii2/Wozify-CV-Parser - Check Fasttext Website ## Training Details ### Training Data The model was fine-tuned on the `ganchengguang/resume_seven_class` dataset, which contains English resume sections labeled into seven categories: Experience, Education, Knowledge, Project, and others. I translated the dataset into Hungarian. ### Training Procedure The model was fine-tuned using standard text classification procedures, adjusting hyperparameters to optimize performance on the resume classification task. ## Evaluation ### Testing Data, Factors & Metrics The model's performance was evaluated on a held-out test set from the `ganchengguang/resume_seven_class` dataset, using accuracy and F1-score as evaluation metrics. #### Metrics - Accuracy: Measures the proportion of correctly classified sections. - F1-score: Harmonic mean of precision and recall, providing a balance between the two. ## Environmental Impact The training of this model was conducted on standard hardware, resulting in minimal carbon emissions. Users should consider the environmental impact of training large models and explore options for model distillation or quantization to reduce energy consumption. ## Technical Specifications ### Model Architecture and Objective The model is based on the `facebook/fasttext-hu-vectors` architecture, fine-tuned for the task of classifying Hungarian resume sections into predefined categories. ### Compute Infrastructure The model was trained my personal gaming laptop. #### Hardware - GPU: RTX 4070 Laptop GPU 8GB VRAM - CPI: Intel Core-i7-13620H - RAM: 16GB	[ "TEXT_CLASSIFICATION" ]	[ "CPI" ]
voyageai/voyage-3-m-exp	voyageai	null	[ "mteb", "model-index", "region:us" ]	2025-01-07T05:56:08	2025-01-21T19:16:04	12	--- tags: - mteb model-index: - name: voyageai/voyage-3-m-exp results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 95.7761 - type: f1 value: 93.8227 - type: f1_weighted value: 95.9368 - type: ap value: 82.63589999999999 - type: ap_weighted value: 82.63589999999999 - type: main_score value: 95.7761 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification (default) type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.7143 - type: f1 value: 97.7143 - type: f1_weighted value: 97.7143 - type: ap value: 96.5356 - type: ap_weighted value: 96.5356 - type: main_score value: 97.7143 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 63.617999999999995 - type: f1 value: 61.487199999999994 - type: f1_weighted value: 61.487199999999994 - type: main_score value: 63.617999999999995 - task: type: Retrieval dataset: name: MTEB ArguAna (default) type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: ndcg_at_1 value: 75.39099999999999 - type: ndcg_at_3 value: 88.795 - type: ndcg_at_5 value: 89.634 - type: ndcg_at_10 value: 89.786 - type: ndcg_at_20 value: 89.786 - type: ndcg_at_100 value: 89.786 - type: ndcg_at_1000 value: 89.786 - type: map_at_1 value: 75.39099999999999 - type: map_at_3 value: 85.835 - type: map_at_5 value: 86.311 - type: map_at_10 value: 86.382 - type: map_at_20 value: 86.382 - type: map_at_100 value: 86.382 - type: map_at_1000 value: 86.382 - type: recall_at_1 value: 75.39099999999999 - type: recall_at_3 value: 97.226 - type: recall_at_5 value: 99.21799999999999 - type: recall_at_10 value: 99.644 - type: recall_at_20 value: 99.644 - 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type: similarity_accuracy value: 89.4749 - type: similarity_accuracy_threshold value: 73.03659999999999 - type: similarity_f1 value: 79.4416 - type: similarity_f1_threshold value: 70.7413 - type: similarity_precision value: 75.9696 - type: similarity_recall value: 83.2461 - type: similarity_ap value: 87.1156 - type: cosine_accuracy value: 89.4749 - type: cosine_accuracy_threshold value: 73.03659999999999 - type: cosine_f1 value: 79.4416 - type: cosine_f1_threshold value: 70.7413 - type: cosine_precision value: 75.9696 - type: cosine_recall value: 83.2461 - type: cosine_ap value: 87.1156 - type: manhattan_accuracy value: 89.471 - type: manhattan_accuracy_threshold value: 2630.4434 - type: manhattan_f1 value: 79.4078 - type: manhattan_f1_threshold value: 2764.4342 - type: manhattan_precision value: 75.7675 - type: manhattan_recall value: 83.4155 - type: manhattan_ap value: 87.0938 - type: euclidean_accuracy value: 89.4749 - type: euclidean_accuracy_threshold value: 73.4349 - type: euclidean_f1 value: 79.4416 - type: euclidean_f1_threshold value: 76.4966 - type: euclidean_precision value: 75.9696 - type: euclidean_recall value: 83.2461 - type: euclidean_ap value: 87.1156 - type: dot_accuracy value: 89.4749 - type: dot_accuracy_threshold value: 73.03659999999999 - type: dot_f1 value: 79.4416 - type: dot_f1_threshold value: 70.7413 - type: dot_precision value: 75.9696 - type: dot_recall value: 83.2461 - type: dot_ap value: 87.1156 - type: max_accuracy value: 89.4749 - type: max_f1 value: 79.4416 - type: max_precision value: 75.9696 - type: max_recall value: 83.4155 - type: max_ap value: 87.1156 - type: main_score value: 87.1156 --- # voyage-3-m-exp This repo contains the tokenizer and evaluation results of the `voyage-3-m-exp` embedding model. `voyage-3-m-exp` is an intermediate snapshot of voyage general-purpose embeddings that are tailored to datasets similar to MTEB. The training sets of MTEB datasets are used in training `voyage-3-m-exp`. We note that for production use cases, `voyage-3-large` is highly recommended and likey strictly better than `voyage-3-m-exp`. Please see the [blogpost](https://blog.voyageai.com/2025/01/07/voyage-3-large) for more information about `voyage-3-large`. `voyage-3-m-exp` can be accessed via the [Voyage API](https://docs.voyageai.com/docs/embeddings) with the model name `"voyage-3-m-exp"`. ## Model Information \| Dimension \| Model Size \| Context Length \| \|-----------------------\|------------\|---------------------\| \| 2048 \| 6918M \| 32000 \| ## Reproduction of MTEB results Similar to most open source models on top of the leaderboard, `voyage-3-m-exp` uses task-specific prompts. To reproduce MTEB results on the leaderboard, please set `input_type` of the API to `None` and prepend the following prompts to the input. For retrieval tasks, please use only `text` without adding the `title` field in front of the text. ```python { "BIOSSES" : { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "SICK-R": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS12": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS13": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS14": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS15": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS16": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STSBenchmark": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS17": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS22": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "AmazonCounterfactualClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonPolarityClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonReviewsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "Banking77Classification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "EmotionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ImdbClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveScenarioClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPDomainClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ToxicConversationsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "TweetSentimentExtractionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ArxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "ArxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "TwentyNewsgroupsClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "SprintDuplicateQuestions": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterSemEval2015": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterURLCorpus": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'StackOverflowDupQuestions': { 'query': 'Represent the sentence to retrieve similar sentences: ', 'document': 'Represent the sentence to retrieve similar sentences: ', }, 'SciDocsRR': { 'query': 'Represent the paper title to retrieve relevant paper titles: ', 'document': 'Represent the paper title to retrieve relevant paper titles: ', }, 'MindSmallReranking': { 'query': 'Represent the query to retrieve relevant documents: ', 'document': 'Represent the document for retrieval: ', }, 'AskUbuntuDupQuestions': { 'query': 'Represent the question to retrieve similar questions: ', 'document': 'Represent the question to retrieve similar questions: ', }, "SummEval": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'ClimateFEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'HotpotQA': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'FEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'MSMARCO': { 'query': 'Represent the question for retrieving evidence documents: ', 'document': 'Represent the document for retrieval: ', }, 'DBPedia': { 'query': 'Represent the entity-based query for retrieving relevant articles: ', 'document': 'Represent the article for retrieval: ', }, 'NQ': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'QuoraRetrieval': { 'query': 'Represent the quora question for retrieving similar questions: ', 'document': 'Represent the question for retrieval: ', }, 'SCIDOCS': { 'query': 'Represent the paper title for retrieving possible citation documents: ', 'document': 'Represent the scitific paper document for retrieval: ', }, 'TRECCOVID': { 'query': 'Represent the query for retrieving supporting articles: ', 'document': 'Represent the article for retrieval: ', }, 'Touche2020': { 'query': 'Represent the question for retrieving supporting documents: ', 'document': 'Represent the document for retrieval: ', }, "SciFact": { "query": "Represent the scientific fact for retrieving supporting document: ", "document": "Represent the document for retrieval: " }, 'NFCorpus': { 'query': 'Represent the nutrition fact for retrieving evidence documents: ', 'document': 'Represent the pubmed document for retrieval: ', }, 'ArguAna': { 'query': 'Represent the argument for retrieving counterarguments: ', 'document': 'Represent the conterargument for retrieval: ', }, 'FiQA2018': { 'query': "Represent the query for retrieving supporting documents: ", 'document': "Represent the document for retrieval: ", } } ```	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/GTE512_sw	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:24:09	2025-01-09T11:24:19	0	--- tags: - mteb model-index: - name: gte-base-en-v1.5_embs_nofiltering_sortlenTrue_phrase2sent_512_15epoch__adam0.001_accum1_best_epoch_2611200_bs128_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.02985074626866 - type: ap value: 38.2391132433526 - type: f1 value: 69.06974168824816 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 74.00564999999999 - type: ap value: 68.12255640587608 - type: f1 value: 73.88644324572483 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.46000000000001 - type: f1 value: 38.77579430134605 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.337 - type: map_at_10 value: 36.104 - type: map_at_100 value: 37.363 - type: map_at_1000 value: 37.38 - type: map_at_3 value: 31.46 - type: map_at_5 value: 33.861000000000004 - type: mrr_at_1 value: 22.119 - type: mrr_at_10 value: 36.379 - type: mrr_at_100 value: 37.644 - type: mrr_at_1000 value: 37.662 - type: mrr_at_3 value: 31.745 - type: mrr_at_5 value: 34.12 - type: ndcg_at_1 value: 21.337 - type: ndcg_at_10 value: 44.557 - type: ndcg_at_100 value: 50.072 - type: ndcg_at_1000 value: 50.499 - type: ndcg_at_3 value: 34.794000000000004 - type: ndcg_at_5 value: 39.125 - type: precision_at_1 value: 21.337 - type: precision_at_10 value: 7.176 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 14.817 - type: precision_at_5 value: 10.996 - type: recall_at_1 value: 21.337 - type: recall_at_10 value: 71.764 - type: recall_at_100 value: 96.23 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 44.452000000000005 - type: recall_at_5 value: 54.979 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 38.36878876355172 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 28.19433994044647 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.16001797554904 - type: mrr value: 67.81130457723256 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.1086837226076 - type: cos_sim_spearman value: 80.60966807127197 - type: euclidean_pearson value: 80.73535719827952 - type: euclidean_spearman value: 80.60966807127197 - type: manhattan_pearson value: 79.10544477221981 - type: manhattan_spearman value: 79.59759681777079 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.1525974025974 - type: f1 value: 74.45181803662257 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.568758810321945 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.645931603960374 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.36 - type: map_at_10 value: 29.034 - type: map_at_100 value: 30.197000000000003 - type: map_at_1000 value: 30.36 - type: map_at_3 value: 26.334999999999997 - type: map_at_5 value: 27.894999999999996 - type: mrr_at_1 value: 27.325 - type: mrr_at_10 value: 34.975 - type: mrr_at_100 value: 35.787 - type: mrr_at_1000 value: 35.864000000000004 - type: mrr_at_3 value: 32.761 - type: mrr_at_5 value: 34.083999999999996 - type: ndcg_at_1 value: 27.325 - type: ndcg_at_10 value: 34.302 - type: ndcg_at_100 value: 39.35 - type: ndcg_at_1000 value: 42.516999999999996 - type: ndcg_at_3 value: 30.336000000000002 - type: ndcg_at_5 value: 32.234 - type: precision_at_1 value: 27.325 - type: precision_at_10 value: 6.694999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 14.926 - type: precision_at_5 value: 11.044 - type: recall_at_1 value: 21.36 - type: recall_at_10 value: 43.64 - type: recall_at_100 value: 66.219 - type: recall_at_1000 value: 87.675 - type: recall_at_3 value: 31.34 - type: recall_at_5 value: 36.896 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 19.008 - type: map_at_10 value: 25.762 - type: map_at_100 value: 26.819 - type: map_at_1000 value: 26.944000000000003 - type: map_at_3 value: 23.688000000000002 - type: map_at_5 value: 24.844 - type: mrr_at_1 value: 24.204 - type: mrr_at_10 value: 30.325999999999997 - type: mrr_at_100 value: 31.151 - type: mrr_at_1000 value: 31.22 - type: mrr_at_3 value: 28.311999999999998 - type: mrr_at_5 value: 29.424 - type: ndcg_at_1 value: 24.204 - type: ndcg_at_10 value: 30.020999999999997 - type: ndcg_at_100 value: 34.632000000000005 - type: ndcg_at_1000 value: 37.462 - type: ndcg_at_3 value: 26.607999999999997 - type: ndcg_at_5 value: 28.105999999999998 - type: precision_at_1 value: 24.204 - type: precision_at_10 value: 5.624 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 12.887 - type: precision_at_5 value: 9.159 - type: recall_at_1 value: 19.008 - type: recall_at_10 value: 38.156 - type: recall_at_100 value: 58.158 - type: recall_at_1000 value: 77.471 - type: recall_at_3 value: 27.964 - type: recall_at_5 value: 32.221 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.833999999999996 - type: map_at_10 value: 36.896 - type: map_at_100 value: 38.002 - type: map_at_1000 value: 38.088 - type: map_at_3 value: 34.283 - type: map_at_5 value: 35.754999999999995 - type: mrr_at_1 value: 32.351 - type: mrr_at_10 value: 40.275 - type: mrr_at_100 value: 41.152 - type: mrr_at_1000 value: 41.204 - type: mrr_at_3 value: 37.973 - type: mrr_at_5 value: 39.242 - type: ndcg_at_1 value: 32.351 - type: ndcg_at_10 value: 41.867 - type: ndcg_at_100 value: 47.073 - type: ndcg_at_1000 value: 49.125 - type: ndcg_at_3 value: 37.129 - type: ndcg_at_5 value: 39.361000000000004 - type: precision_at_1 value: 32.351 - type: precision_at_10 value: 6.765000000000001 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 16.489 - type: precision_at_5 value: 11.398 - type: recall_at_1 value: 27.833999999999996 - type: recall_at_10 value: 53.668000000000006 - type: recall_at_100 value: 77.114 - type: recall_at_1000 value: 92.131 - type: recall_at_3 value: 40.745 - type: recall_at_5 value: 46.375 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 12.863 - type: map_at_10 value: 17.881 - type: map_at_100 value: 18.742 - type: map_at_1000 value: 18.86 - type: map_at_3 value: 16.485 - type: map_at_5 value: 17.262 - type: mrr_at_1 value: 13.898 - type: mrr_at_10 value: 19.152 - type: mrr_at_100 value: 20.007 - type: mrr_at_1000 value: 20.116 - type: mrr_at_3 value: 17.759 - type: mrr_at_5 value: 18.544 - type: ndcg_at_1 value: 13.898 - type: ndcg_at_10 value: 20.818 - type: ndcg_at_100 value: 25.342 - type: ndcg_at_1000 value: 28.895 - type: ndcg_at_3 value: 18.034 - type: ndcg_at_5 value: 19.367 - type: precision_at_1 value: 13.898 - type: precision_at_10 value: 3.254 - type: precision_at_100 value: 0.582 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.8340000000000005 - type: precision_at_5 value: 5.446 - type: recall_at_1 value: 12.863 - type: recall_at_10 value: 28.636 - type: recall_at_100 value: 50.112 - type: recall_at_1000 value: 77.828 - type: recall_at_3 value: 21.087 - type: recall_at_5 value: 24.307000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.8020000000000005 - type: map_at_10 value: 11.673 - type: map_at_100 value: 12.462 - type: map_at_1000 value: 12.589 - type: map_at_3 value: 10.035 - type: map_at_5 value: 10.699 - type: mrr_at_1 value: 9.826 - type: mrr_at_10 value: 14.248 - type: mrr_at_100 value: 15.057 - type: mrr_at_1000 value: 15.156 - type: mrr_at_3 value: 12.5 - type: mrr_at_5 value: 13.221 - type: ndcg_at_1 value: 9.826 - type: ndcg_at_10 value: 14.818999999999999 - type: ndcg_at_100 value: 19.309 - type: ndcg_at_1000 value: 22.954 - type: ndcg_at_3 value: 11.535 - type: ndcg_at_5 value: 12.577 - type: precision_at_1 value: 9.826 - type: precision_at_10 value: 2.923 - type: precision_at_100 value: 0.618 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 5.5969999999999995 - type: precision_at_5 value: 4.08 - type: recall_at_1 value: 7.8020000000000005 - type: recall_at_10 value: 22.141 - type: recall_at_100 value: 42.653999999999996 - type: recall_at_1000 value: 70.02199999999999 - type: recall_at_3 value: 13.020000000000001 - type: recall_at_5 value: 15.645999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.532 - type: map_at_10 value: 24.692 - type: map_at_100 value: 26.023000000000003 - type: map_at_1000 value: 26.165 - type: map_at_3 value: 22.522000000000002 - type: map_at_5 value: 23.694000000000003 - type: mrr_at_1 value: 22.618 - type: mrr_at_10 value: 29.334 - type: mrr_at_100 value: 30.348999999999997 - type: mrr_at_1000 value: 30.435000000000002 - type: mrr_at_3 value: 26.997 - type: mrr_at_5 value: 28.282 - type: ndcg_at_1 value: 22.618 - type: ndcg_at_10 value: 29.188 - type: ndcg_at_100 value: 35.213 - type: ndcg_at_1000 value: 38.471 - type: ndcg_at_3 value: 25.313999999999997 - type: ndcg_at_5 value: 27.057 - type: precision_at_1 value: 22.618 - type: precision_at_10 value: 5.38 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 11.741999999999999 - type: precision_at_5 value: 8.662 - type: recall_at_1 value: 18.532 - type: recall_at_10 value: 38.164 - type: recall_at_100 value: 64.197 - type: recall_at_1000 value: 86.75399999999999 - type: recall_at_3 value: 27.262999999999998 - type: recall_at_5 value: 31.651 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.257000000000001 - type: map_at_10 value: 20.762 - type: map_at_100 value: 21.956999999999997 - type: map_at_1000 value: 22.102 - type: map_at_3 value: 18.826999999999998 - type: map_at_5 value: 19.911 - type: mrr_at_1 value: 18.836 - type: mrr_at_10 value: 24.484 - type: mrr_at_100 value: 25.561 - type: mrr_at_1000 value: 25.651000000000003 - type: mrr_at_3 value: 22.546 - type: mrr_at_5 value: 23.613 - type: ndcg_at_1 value: 18.836 - type: ndcg_at_10 value: 24.465999999999998 - type: ndcg_at_100 value: 30.337999999999997 - type: ndcg_at_1000 value: 33.775 - type: ndcg_at_3 value: 21.029 - type: ndcg_at_5 value: 22.576 - type: precision_at_1 value: 18.836 - type: precision_at_10 value: 4.5089999999999995 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 9.893 - type: precision_at_5 value: 7.146 - type: recall_at_1 value: 15.257000000000001 - type: recall_at_10 value: 32.062000000000005 - type: recall_at_100 value: 57.577 - type: recall_at_1000 value: 81.75 - type: recall_at_3 value: 22.579 - type: recall_at_5 value: 26.613999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 15.333916666666667 - type: map_at_10 value: 20.908583333333333 - type: map_at_100 value: 21.891333333333332 - type: map_at_1000 value: 22.02225 - type: map_at_3 value: 19.053833333333333 - type: map_at_5 value: 20.053916666666666 - type: mrr_at_1 value: 18.5485 - type: mrr_at_10 value: 24.15733333333333 - type: mrr_at_100 value: 25.01325 - type: mrr_at_1000 value: 25.101000000000003 - type: mrr_at_3 value: 22.34708333333333 - type: mrr_at_5 value: 23.317833333333336 - type: ndcg_at_1 value: 18.5485 - type: ndcg_at_10 value: 24.614 - type: ndcg_at_100 value: 29.431166666666662 - type: ndcg_at_1000 value: 32.6675 - type: ndcg_at_3 value: 21.285083333333336 - type: ndcg_at_5 value: 22.751416666666664 - type: precision_at_1 value: 18.5485 - type: precision_at_10 value: 4.4013333333333335 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.12825 - type: precision_at_3 value: 9.847916666666666 - type: precision_at_5 value: 7.069166666666668 - type: recall_at_1 value: 15.333916666666667 - type: recall_at_10 value: 32.5695 - type: recall_at_100 value: 54.50375 - type: recall_at_1000 value: 78.02300000000001 - type: recall_at_3 value: 23.115000000000002 - type: recall_at_5 value: 26.968416666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.745 - type: map_at_10 value: 16.406000000000002 - type: map_at_100 value: 17.157 - type: map_at_1000 value: 17.249 - type: map_at_3 value: 14.835999999999999 - type: map_at_5 value: 15.803 - type: mrr_at_1 value: 13.804 - type: mrr_at_10 value: 18.55 - type: mrr_at_100 value: 19.306 - type: mrr_at_1000 value: 19.38 - type: mrr_at_3 value: 17.05 - type: mrr_at_5 value: 17.947 - type: ndcg_at_1 value: 13.804 - type: ndcg_at_10 value: 19.339000000000002 - type: ndcg_at_100 value: 23.624000000000002 - type: ndcg_at_1000 value: 26.301999999999996 - type: ndcg_at_3 value: 16.454 - type: ndcg_at_5 value: 17.999000000000002 - type: precision_at_1 value: 13.804 - type: precision_at_10 value: 3.282 - type: precision_at_100 value: 0.598 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 7.413 - type: precision_at_5 value: 5.428999999999999 - type: recall_at_1 value: 11.745 - type: recall_at_10 value: 26.255 - type: recall_at_100 value: 46.888000000000005 - type: recall_at_1000 value: 67.131 - type: recall_at_3 value: 18.434 - type: recall_at_5 value: 22.328 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.119 - type: map_at_10 value: 11.944 - type: map_at_100 value: 12.647 - type: map_at_1000 value: 12.770000000000001 - type: map_at_3 value: 10.612 - type: map_at_5 value: 11.292 - type: mrr_at_1 value: 10.22 - type: mrr_at_10 value: 14.496 - type: mrr_at_100 value: 15.18 - type: mrr_at_1000 value: 15.279000000000002 - type: mrr_at_3 value: 12.979 - type: mrr_at_5 value: 13.755 - type: ndcg_at_1 value: 10.22 - type: ndcg_at_10 value: 14.687 - type: ndcg_at_100 value: 18.543000000000003 - type: ndcg_at_1000 value: 22.099 - type: ndcg_at_3 value: 12.076 - type: ndcg_at_5 value: 13.161999999999999 - type: precision_at_1 value: 10.22 - type: precision_at_10 value: 2.822 - type: precision_at_100 value: 0.565 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 5.781 - type: precision_at_5 value: 4.301 - type: recall_at_1 value: 8.119 - type: recall_at_10 value: 20.527 - type: recall_at_100 value: 38.719 - type: recall_at_1000 value: 65.16300000000001 - type: recall_at_3 value: 13.275 - type: recall_at_5 value: 15.998999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.334999999999999 - type: map_at_10 value: 18.194 - type: map_at_100 value: 19.049 - type: map_at_1000 value: 19.17 - type: map_at_3 value: 16.625 - type: map_at_5 value: 17.509 - type: mrr_at_1 value: 16.231 - type: mrr_at_10 value: 21.308 - type: mrr_at_100 value: 22.154 - type: mrr_at_1000 value: 22.25 - type: mrr_at_3 value: 19.761 - type: mrr_at_5 value: 20.567 - type: ndcg_at_1 value: 16.231 - type: ndcg_at_10 value: 21.525 - type: ndcg_at_100 value: 26.008 - type: ndcg_at_1000 value: 29.351 - type: ndcg_at_3 value: 18.54 - type: ndcg_at_5 value: 19.916 - type: precision_at_1 value: 16.231 - type: precision_at_10 value: 3.6470000000000002 - type: precision_at_100 value: 0.655 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.488999999999999 - type: precision_at_5 value: 6.007 - type: recall_at_1 value: 13.334999999999999 - type: recall_at_10 value: 28.804999999999996 - type: recall_at_100 value: 49.303000000000004 - type: recall_at_1000 value: 73.95 - type: recall_at_3 value: 20.531 - type: recall_at_5 value: 24.067 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.002 - type: map_at_10 value: 21.282 - type: map_at_100 value: 22.518 - type: map_at_1000 value: 22.728 - type: map_at_3 value: 19.463 - type: map_at_5 value: 20.314 - type: mrr_at_1 value: 19.96 - type: mrr_at_10 value: 25.084 - type: mrr_at_100 value: 26.028000000000002 - type: mrr_at_1000 value: 26.119999999999997 - type: mrr_at_3 value: 23.352999999999998 - type: mrr_at_5 value: 24.203 - type: ndcg_at_1 value: 19.96 - type: ndcg_at_10 value: 25.275 - type: ndcg_at_100 value: 30.574 - type: ndcg_at_1000 value: 34.359 - type: ndcg_at_3 value: 22.281000000000002 - type: ndcg_at_5 value: 23.32 - type: precision_at_1 value: 19.96 - type: precision_at_10 value: 4.920999999999999 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.20400000000000001 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.352 - type: recall_at_1 value: 16.002 - type: recall_at_10 value: 32.452 - type: recall_at_100 value: 57.297 - type: recall_at_1000 value: 83.332 - type: recall_at_3 value: 23.101 - type: recall_at_5 value: 26.395999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.15 - type: map_at_10 value: 16.377 - type: map_at_100 value: 17.122999999999998 - type: map_at_1000 value: 17.241999999999997 - type: map_at_3 value: 14.935 - type: map_at_5 value: 15.669 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.656 - type: mrr_at_100 value: 18.427 - type: mrr_at_1000 value: 18.537 - type: mrr_at_3 value: 16.174 - type: mrr_at_5 value: 16.932 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 19.061 - type: ndcg_at_100 value: 23.168 - type: ndcg_at_1000 value: 26.700000000000003 - type: ndcg_at_3 value: 16.085 - type: ndcg_at_5 value: 17.342 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.994 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 6.715999999999999 - type: precision_at_5 value: 4.806 - type: recall_at_1 value: 12.15 - type: recall_at_10 value: 26.328000000000003 - type: recall_at_100 value: 45.806999999999995 - type: recall_at_1000 value: 73.06899999999999 - type: recall_at_3 value: 18.041 - type: recall_at_5 value: 21.121000000000002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.675999999999999 - type: map_at_10 value: 13.600000000000001 - type: map_at_100 value: 15.287999999999998 - type: map_at_1000 value: 15.508 - type: map_at_3 value: 11.256 - type: map_at_5 value: 12.312 - type: mrr_at_1 value: 17.459 - type: mrr_at_10 value: 27.166 - type: mrr_at_100 value: 28.406 - type: mrr_at_1000 value: 28.464 - type: mrr_at_3 value: 23.931 - type: mrr_at_5 value: 25.66 - type: ndcg_at_1 value: 17.459 - type: ndcg_at_10 value: 20.146 - type: ndcg_at_100 value: 27.625 - type: ndcg_at_1000 value: 31.819999999999997 - type: ndcg_at_3 value: 15.870999999999999 - type: ndcg_at_5 value: 17.158 - type: precision_at_1 value: 17.459 - type: precision_at_10 value: 6.638 - type: precision_at_100 value: 1.4569999999999999 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 12.074 - type: precision_at_5 value: 9.407 - type: recall_at_1 value: 7.675999999999999 - type: recall_at_10 value: 25.267 - type: recall_at_100 value: 51.69200000000001 - type: recall_at_1000 value: 75.58 - type: recall_at_3 value: 14.901 - type: recall_at_5 value: 18.543000000000003 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.424 - type: map_at_10 value: 10.278 - type: map_at_100 value: 14.516000000000002 - type: map_at_1000 value: 15.584999999999999 - type: map_at_3 value: 7.179 - type: map_at_5 value: 8.556 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 52.653000000000006 - type: mrr_at_100 value: 53.33599999999999 - type: mrr_at_1000 value: 53.364999999999995 - type: mrr_at_3 value: 50.542 - type: mrr_at_5 value: 51.803999999999995 - type: ndcg_at_1 value: 31.624999999999996 - type: ndcg_at_10 value: 25.167 - type: ndcg_at_100 value: 28.766000000000002 - type: ndcg_at_1000 value: 35.959 - type: ndcg_at_3 value: 27.807 - type: ndcg_at_5 value: 26.569 - type: precision_at_1 value: 42.0 - type: precision_at_10 value: 22.5 - type: precision_at_100 value: 7.295 - type: precision_at_1000 value: 1.543 - type: precision_at_3 value: 33.5 - type: precision_at_5 value: 29.099999999999998 - type: recall_at_1 value: 4.424 - type: recall_at_10 value: 15.359 - type: recall_at_100 value: 35.99 - type: recall_at_1000 value: 60.707 - type: recall_at_3 value: 8.803999999999998 - type: recall_at_5 value: 11.349 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.99 - type: f1 value: 44.72383731785718 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.753 - type: map_at_10 value: 26.762999999999998 - type: map_at_100 value: 27.773999999999997 - type: map_at_1000 value: 27.845 - type: map_at_3 value: 24.096 - type: map_at_5 value: 25.6 - type: mrr_at_1 value: 19.022 - type: mrr_at_10 value: 28.46 - type: mrr_at_100 value: 29.462 - type: mrr_at_1000 value: 29.520999999999997 - type: mrr_at_3 value: 25.679999999999996 - type: mrr_at_5 value: 27.272999999999996 - type: ndcg_at_1 value: 19.022 - type: ndcg_at_10 value: 32.063 - type: ndcg_at_100 value: 37.169999999999995 - type: ndcg_at_1000 value: 39.048 - type: ndcg_at_3 value: 26.558999999999997 - type: ndcg_at_5 value: 29.266 - type: precision_at_1 value: 19.022 - type: precision_at_10 value: 5.119 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 11.571 - type: precision_at_5 value: 8.368 - type: recall_at_1 value: 17.753 - type: recall_at_10 value: 47.061 - type: recall_at_100 value: 70.75200000000001 - type: recall_at_1000 value: 85.134 - type: recall_at_3 value: 32.049 - type: recall_at_5 value: 38.556000000000004 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 6.845 - type: map_at_10 value: 11.806999999999999 - type: map_at_100 value: 13.104 - type: map_at_1000 value: 13.317 - type: map_at_3 value: 9.746 - type: map_at_5 value: 10.806000000000001 - type: mrr_at_1 value: 13.889000000000001 - type: mrr_at_10 value: 20.456 - type: mrr_at_100 value: 21.572 - type: mrr_at_1000 value: 21.666 - type: mrr_at_3 value: 18.184 - type: mrr_at_5 value: 19.387999999999998 - type: ndcg_at_1 value: 13.889000000000001 - type: ndcg_at_10 value: 16.552 - type: ndcg_at_100 value: 22.817999999999998 - type: ndcg_at_1000 value: 27.401999999999997 - type: ndcg_at_3 value: 13.527000000000001 - type: ndcg_at_5 value: 14.6 - type: precision_at_1 value: 13.889000000000001 - type: precision_at_10 value: 4.984999999999999 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 9.208 - type: precision_at_5 value: 7.13 - type: recall_at_1 value: 6.845 - type: recall_at_10 value: 22.012999999999998 - type: recall_at_100 value: 46.75 - type: recall_at_1000 value: 74.945 - type: recall_at_3 value: 12.352 - type: recall_at_5 value: 16.217000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.886 - type: map_at_10 value: 26.729999999999997 - type: map_at_100 value: 27.732 - type: map_at_1000 value: 27.85 - type: map_at_3 value: 24.57 - type: map_at_5 value: 25.774 - type: mrr_at_1 value: 37.772 - type: mrr_at_10 value: 45.239000000000004 - type: mrr_at_100 value: 45.972 - type: mrr_at_1000 value: 46.027 - type: mrr_at_3 value: 43.279 - type: mrr_at_5 value: 44.397 - type: ndcg_at_1 value: 37.772 - type: ndcg_at_10 value: 33.973 - type: ndcg_at_100 value: 38.456 - type: ndcg_at_1000 value: 41.178 - type: ndcg_at_3 value: 29.988999999999997 - type: ndcg_at_5 value: 31.935999999999996 - type: precision_at_1 value: 37.772 - type: precision_at_10 value: 7.465 - type: precision_at_100 value: 1.1039999999999999 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 18.902 - type: precision_at_5 value: 12.883 - type: recall_at_1 value: 18.886 - type: recall_at_10 value: 37.326 - type: recall_at_100 value: 55.186 - type: recall_at_1000 value: 73.309 - type: recall_at_3 value: 28.352 - type: recall_at_5 value: 32.208 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.6808 - type: ap value: 64.78268083902698 - type: f1 value: 70.48410216634053 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.300999999999999 - type: map_at_10 value: 11.068999999999999 - type: map_at_100 value: 12.0 - type: map_at_1000 value: 12.113999999999999 - type: map_at_3 value: 9.381 - type: map_at_5 value: 10.265 - type: mrr_at_1 value: 6.476 - type: mrr_at_10 value: 11.357000000000001 - type: mrr_at_100 value: 12.293 - type: mrr_at_1000 value: 12.403 - type: mrr_at_3 value: 9.62 - type: mrr_at_5 value: 10.544 - type: ndcg_at_1 value: 6.461 - type: ndcg_at_10 value: 14.058000000000002 - type: ndcg_at_100 value: 19.156000000000002 - type: ndcg_at_1000 value: 22.570999999999998 - type: ndcg_at_3 value: 10.475 - type: ndcg_at_5 value: 12.092 - type: precision_at_1 value: 6.461 - type: precision_at_10 value: 2.431 - type: precision_at_100 value: 0.508 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 4.6080000000000005 - type: precision_at_5 value: 3.5900000000000003 - type: recall_at_1 value: 6.300999999999999 - type: recall_at_10 value: 23.378 - type: recall_at_100 value: 48.258 - type: recall_at_1000 value: 75.652 - type: recall_at_3 value: 13.422 - type: recall_at_5 value: 17.316000000000003 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.34290925672595 - type: f1 value: 90.45651851550997 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 63.57045143638851 - type: f1 value: 44.02606500037181 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.63214525891057 - type: f1 value: 63.33629303043603 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.65635507733691 - type: f1 value: 71.52506282204605 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.593804768886113 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.41249151566158 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.0983658178549 - type: mrr value: 32.18857446274346 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.79 - type: map_at_10 value: 9.095 - type: map_at_100 value: 11.738999999999999 - type: map_at_1000 value: 13.203000000000001 - type: map_at_3 value: 6.68 - type: map_at_5 value: 7.924 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 46.283 - type: mrr_at_100 value: 46.983999999999995 - type: mrr_at_1000 value: 47.046 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 45.268 - type: ndcg_at_1 value: 35.604 - type: ndcg_at_10 value: 27.249000000000002 - type: ndcg_at_100 value: 26.215 - type: ndcg_at_1000 value: 35.867 - type: ndcg_at_3 value: 30.330000000000002 - type: ndcg_at_5 value: 29.574 - type: precision_at_1 value: 37.152 - type: precision_at_10 value: 20.031 - type: precision_at_100 value: 7.217 - type: precision_at_1000 value: 2.072 - type: precision_at_3 value: 27.761000000000003 - type: precision_at_5 value: 25.448999999999998 - type: recall_at_1 value: 4.79 - type: recall_at_10 value: 13.197000000000001 - type: recall_at_100 value: 28.816999999999997 - type: recall_at_1000 value: 63.010999999999996 - type: recall_at_3 value: 7.53 - type: recall_at_5 value: 10.234 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.345 - type: map_at_10 value: 16.655 - type: map_at_100 value: 17.991 - type: map_at_1000 value: 18.093999999999998 - type: map_at_3 value: 13.825000000000001 - type: map_at_5 value: 15.445 - type: mrr_at_1 value: 10.834000000000001 - type: mrr_at_10 value: 18.533 - type: mrr_at_100 value: 19.750999999999998 - type: mrr_at_1000 value: 19.837 - type: mrr_at_3 value: 15.623999999999999 - type: mrr_at_5 value: 17.307 - type: ndcg_at_1 value: 10.834000000000001 - type: ndcg_at_10 value: 21.503 - type: ndcg_at_100 value: 28.141 - type: ndcg_at_1000 value: 30.951 - type: ndcg_at_3 value: 15.7 - type: ndcg_at_5 value: 18.608 - type: precision_at_1 value: 10.834000000000001 - type: precision_at_10 value: 4.09 - type: precision_at_100 value: 0.782 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.474 - type: precision_at_5 value: 6.089 - type: recall_at_1 value: 9.345 - type: recall_at_10 value: 34.760000000000005 - type: recall_at_100 value: 65.455 - type: recall_at_1000 value: 87.008 - type: recall_at_3 value: 19.397000000000002 - type: recall_at_5 value: 26.205000000000002 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.864 - type: map_at_10 value: 76.823 - type: map_at_100 value: 77.58699999999999 - type: map_at_1000 value: 77.619 - type: map_at_3 value: 73.834 - type: map_at_5 value: 75.703 - type: mrr_at_1 value: 73.55000000000001 - type: mrr_at_10 value: 81.077 - type: mrr_at_100 value: 81.296 - type: mrr_at_1000 value: 81.3 - type: mrr_at_3 value: 79.647 - type: mrr_at_5 value: 80.601 - type: ndcg_at_1 value: 73.63 - type: ndcg_at_10 value: 81.526 - type: ndcg_at_100 value: 83.544 - type: ndcg_at_1000 value: 83.86200000000001 - type: ndcg_at_3 value: 77.96300000000001 - type: ndcg_at_5 value: 79.888 - type: precision_at_1 value: 73.63 - type: precision_at_10 value: 12.325 - type: precision_at_100 value: 1.468 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.857 - type: precision_at_5 value: 22.428 - type: recall_at_1 value: 63.864 - type: recall_at_10 value: 90.537 - type: recall_at_100 value: 97.985 - type: recall_at_1000 value: 99.679 - type: recall_at_3 value: 80.351 - type: recall_at_5 value: 85.697 - type: map_at_1 value: 2.8979999999999997 - type: map_at_10 value: 7.376 - type: map_at_100 value: 8.902000000000001 - type: map_at_1000 value: 9.174 - type: map_at_3 value: 5.47 - type: map_at_5 value: 6.432 - type: mrr_at_1 value: 14.2 - type: mrr_at_10 value: 22.966 - type: mrr_at_100 value: 24.117 - type: mrr_at_1000 value: 24.209 - type: mrr_at_3 value: 20.033 - type: mrr_at_5 value: 21.532999999999998 - type: ndcg_at_1 value: 14.2 - type: ndcg_at_10 value: 13.016 - type: ndcg_at_100 value: 19.804 - type: ndcg_at_1000 value: 25.251 - type: ndcg_at_3 value: 12.395 - type: ndcg_at_5 value: 10.793999999999999 - type: precision_at_1 value: 14.2 - type: precision_at_10 value: 6.800000000000001 - type: precision_at_100 value: 1.6709999999999998 - type: precision_at_1000 value: 0.298 - type: precision_at_3 value: 11.767 - type: precision_at_5 value: 9.56 - type: recall_at_1 value: 2.8979999999999997 - type: recall_at_10 value: 13.753000000000002 - type: recall_at_100 value: 33.92 - type: recall_at_1000 value: 60.592 - type: recall_at_3 value: 7.163 - type: recall_at_5 value: 9.678 - type: map_at_1 value: 0.155 - type: map_at_10 value: 0.8330000000000001 - type: map_at_100 value: 4.590000000000001 - type: map_at_1000 value: 11.683 - type: map_at_3 value: 0.334 - type: map_at_5 value: 0.466 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 68.136 - type: mrr_at_100 value: 68.703 - type: mrr_at_1000 value: 68.703 - type: mrr_at_3 value: 66.0 - type: mrr_at_5 value: 66.4 - type: ndcg_at_1 value: 54.0 - type: ndcg_at_10 value: 44.658 - type: ndcg_at_100 value: 33.977000000000004 - type: ndcg_at_1000 value: 30.621 - type: ndcg_at_3 value: 48.939 - type: ndcg_at_5 value: 45.396 - type: precision_at_1 value: 57.99999999999999 - type: precision_at_10 value: 47.4 - type: precision_at_100 value: 35.82 - type: precision_at_1000 value: 14.876000000000001 - type: precision_at_3 value: 50.0 - type: precision_at_5 value: 45.6 - type: recall_at_1 value: 0.155 - type: recall_at_10 value: 1.0670000000000002 - type: recall_at_100 value: 7.651 - type: recall_at_1000 value: 29.537000000000003 - type: recall_at_3 value: 0.35500000000000004 - type: recall_at_5 value: 0.518 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 44.25321653229755 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 49.93875732877625 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 76.86234724246499 - type: cos_sim_spearman value: 67.59298171796401 - type: euclidean_pearson value: 72.34370409015565 - type: euclidean_spearman value: 67.59294254877997 - type: manhattan_pearson value: 70.76123243638206 - type: manhattan_spearman value: 66.86233305574997 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 75.2193174164991 - type: cos_sim_spearman value: 66.95885463258551 - type: euclidean_pearson value: 70.69637254317986 - type: euclidean_spearman value: 66.95991031425478 - type: manhattan_pearson value: 67.25988575290648 - type: manhattan_spearman value: 64.94406492662402 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.42103332836258 - type: cos_sim_spearman value: 78.48875534932043 - type: euclidean_pearson value: 78.1930584097837 - type: euclidean_spearman value: 78.48879315793262 - type: manhattan_pearson value: 75.7705791679418 - type: manhattan_spearman value: 76.01194506942352 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 78.58218995046416 - type: cos_sim_spearman value: 75.61279190671051 - type: euclidean_pearson value: 77.58820759180631 - type: euclidean_spearman value: 75.61278221440635 - type: manhattan_pearson value: 76.12440001778819 - type: manhattan_spearman value: 74.4269498969252 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.2891641302121 - type: cos_sim_spearman value: 82.73098262647434 - type: euclidean_pearson value: 82.5188483930312 - type: euclidean_spearman value: 82.73097334698637 - type: manhattan_pearson value: 81.05168739270556 - type: manhattan_spearman value: 81.10750061837136 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.4670953467407 - type: cos_sim_spearman value: 78.53536279187583 - type: euclidean_pearson value: 77.6227824619736 - type: euclidean_spearman value: 78.53591292409315 - type: manhattan_pearson value: 76.24243879772493 - type: manhattan_spearman value: 77.00775260881191 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.19161153621691 - type: cos_sim_spearman value: 86.86584556712556 - type: euclidean_pearson value: 86.08114835853017 - type: euclidean_spearman value: 86.86671808346402 - type: manhattan_pearson value: 85.71042782796158 - type: manhattan_spearman value: 86.76481453820853 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.80818689506919 - type: cos_sim_spearman value: 59.903534431363916 - type: euclidean_pearson value: 60.967975393911466 - type: euclidean_spearman value: 59.903534431363916 - type: manhattan_pearson value: 59.348745545947104 - type: manhattan_spearman value: 58.506942610232116 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.61911955925173 - type: cos_sim_spearman value: 79.18748066540941 - type: euclidean_pearson value: 80.06976231938555 - type: euclidean_spearman value: 79.18749912961366 - type: manhattan_pearson value: 78.3922696264025 - type: manhattan_spearman value: 77.68224365306664 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.36063395414824 - type: mrr value: 91.81411453470277 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.139 - type: map_at_10 value: 47.508 - type: map_at_100 value: 48.631 - type: map_at_1000 value: 48.691 - type: map_at_3 value: 44.926 - type: map_at_5 value: 46.093 - type: mrr_at_1 value: 41.333 - type: mrr_at_10 value: 49.289 - type: mrr_at_100 value: 50.209 - type: mrr_at_1000 value: 50.261 - type: mrr_at_3 value: 46.944 - type: mrr_at_5 value: 47.978 - type: ndcg_at_1 value: 41.333 - type: ndcg_at_10 value: 52.306 - type: ndcg_at_100 value: 57.403999999999996 - type: ndcg_at_1000 value: 58.733999999999995 - type: ndcg_at_3 value: 47.113 - type: ndcg_at_5 value: 48.966 - type: precision_at_1 value: 41.333 - type: precision_at_10 value: 7.167 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 39.139 - type: recall_at_10 value: 65.84400000000001 - type: recall_at_100 value: 88.94999999999999 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 51.222 - type: recall_at_5 value: 55.72200000000001 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66831683168317 - type: cos_sim_ap value: 89.56758407722171 - type: cos_sim_f1 value: 82.97029702970298 - type: cos_sim_precision value: 82.15686274509804 - type: cos_sim_recall value: 83.8 - type: dot_accuracy value: 99.66831683168317 - type: dot_ap value: 89.56758407722171 - type: dot_f1 value: 82.97029702970298 - type: dot_precision value: 82.15686274509804 - type: dot_recall value: 83.8 - type: euclidean_accuracy value: 99.66831683168317 - type: euclidean_ap value: 89.56758407722171 - type: euclidean_f1 value: 82.97029702970298 - type: euclidean_precision value: 82.15686274509804 - type: euclidean_recall value: 83.8 - type: manhattan_accuracy value: 99.65445544554456 - type: manhattan_ap value: 88.81637821295462 - type: manhattan_f1 value: 81.9047619047619 - type: manhattan_precision value: 82.1105527638191 - type: manhattan_recall value: 81.69999999999999 - type: max_accuracy value: 99.66831683168317 - type: max_ap value: 89.56758407722171 - type: max_f1 value: 82.97029702970298 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 47.34055809539011 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.658298502445096 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.989840235812004 - type: mrr value: 44.506899350649356 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.29667664654959 - type: cos_sim_spearman value: 29.818596667773882 - type: dot_pearson value: 31.296676647072026 - type: dot_spearman value: 29.779857187330265 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.426 - type: map_at_10 value: 8.38 - type: map_at_100 value: 14.308000000000002 - type: map_at_1000 value: 15.956000000000001 - type: map_at_3 value: 4.596 - type: map_at_5 value: 6.1339999999999995 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 44.577 - type: mrr_at_100 value: 45.754 - type: mrr_at_1000 value: 45.754 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 43.401 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 21.116 - type: ndcg_at_100 value: 35.193000000000005 - type: ndcg_at_1000 value: 46.989 - type: ndcg_at_3 value: 24.708 - type: ndcg_at_5 value: 23.594 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 19.592000000000002 - type: precision_at_100 value: 8.265 - type: precision_at_1000 value: 1.5939999999999999 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 25.306 - type: recall_at_1 value: 2.426 - type: recall_at_10 value: 13.691 - type: recall_at_100 value: 49.446 - type: recall_at_1000 value: 86.124 - type: recall_at_3 value: 5.67 - type: recall_at_5 value: 8.506 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 79.2904 - type: ap value: 19.73734798884487 - type: f1 value: 61.89018130098357 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.97906055461234 - type: f1 value: 61.25225658586279 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.859245341604115 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.4714192048638 - type: cos_sim_ap value: 64.50474781834589 - type: cos_sim_f1 value: 60.58070866141732 - type: cos_sim_precision value: 56.75426463808206 - type: cos_sim_recall value: 64.96042216358839 - type: dot_accuracy value: 83.4714192048638 - type: dot_ap value: 64.50474781834589 - type: dot_f1 value: 60.58070866141732 - type: dot_precision value: 56.75426463808206 - type: dot_recall value: 64.96042216358839 - type: euclidean_accuracy value: 83.4714192048638 - type: euclidean_ap value: 64.50474781834589 - type: euclidean_f1 value: 60.58070866141732 - type: euclidean_precision value: 56.75426463808206 - type: euclidean_recall value: 64.96042216358839 - type: manhattan_accuracy value: 83.48334028729809 - type: manhattan_ap value: 64.6227449383717 - type: manhattan_f1 value: 60.88942307692308 - type: manhattan_precision value: 55.916114790286976 - type: manhattan_recall value: 66.83377308707124 - type: max_accuracy value: 83.48334028729809 - type: max_ap value: 64.6227449383717 - type: max_f1 value: 60.88942307692308 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.58295494236815 - type: cos_sim_ap value: 83.22579648788027 - type: cos_sim_f1 value: 75.27357054859048 - type: cos_sim_precision value: 71.09514031485284 - type: cos_sim_recall value: 79.97382198952879 - type: dot_accuracy value: 87.58295494236815 - type: dot_ap value: 83.22579579937255 - type: dot_f1 value: 75.27357054859048 - type: dot_precision value: 71.09514031485284 - type: dot_recall value: 79.97382198952879 - type: euclidean_accuracy value: 87.58295494236815 - type: euclidean_ap value: 83.22580643443949 - type: euclidean_f1 value: 75.27357054859048 - type: euclidean_precision value: 71.09514031485284 - type: euclidean_recall value: 79.97382198952879 - type: manhattan_accuracy value: 87.57325260992742 - type: manhattan_ap value: 83.05240665725778 - type: manhattan_f1 value: 75.09726237641432 - type: manhattan_precision value: 69.99800385920554 - type: manhattan_recall value: 80.99784416384355 - type: max_accuracy value: 87.58295494236815 - type: max_ap value: 83.22580643443949 - type: max_f1 value: 75.27357054859048 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/GTE256_sw	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:24:56	2025-01-09T11:25:04	0	--- tags: - mteb model-index: - name: gte-base-en-v1.5_embs_nofiltering_sortlenTrue_phrase2sent_15epoch_15epoch__adam0.001_accum1_best_epoch_3863037_bs128_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.71641791044777 - type: ap value: 35.599140186230734 - type: f1 value: 66.72372354326045 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 72.7823 - type: ap value: 66.88980427652794 - type: f1 value: 72.60105018624591 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.483999999999995 - type: f1 value: 37.867826932045745 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.195 - type: map_at_10 value: 35.876999999999995 - type: map_at_100 value: 37.147999999999996 - type: map_at_1000 value: 37.165 - type: map_at_3 value: 31.342 - type: map_at_5 value: 33.764 - type: mrr_at_1 value: 21.906 - type: mrr_at_10 value: 36.128 - type: mrr_at_100 value: 37.397999999999996 - type: mrr_at_1000 value: 37.416 - type: mrr_at_3 value: 31.555 - type: mrr_at_5 value: 34.001999999999995 - type: ndcg_at_1 value: 21.195 - type: ndcg_at_10 value: 44.207 - type: ndcg_at_100 value: 49.88 - type: ndcg_at_1000 value: 50.298 - type: ndcg_at_3 value: 34.755 - type: ndcg_at_5 value: 39.135 - type: precision_at_1 value: 21.195 - type: precision_at_10 value: 7.091 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 14.889 - type: precision_at_5 value: 11.067 - type: recall_at_1 value: 21.195 - type: recall_at_10 value: 70.91 - type: recall_at_100 value: 96.30199999999999 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 44.666 - type: recall_at_5 value: 55.334 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 38.289190023047105 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 28.15017802770073 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.677327183831046 - type: mrr value: 68.2003253748406 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 81.79485923763309 - type: cos_sim_spearman value: 79.71265968052003 - type: euclidean_pearson value: 80.78575386279923 - type: euclidean_spearman value: 79.71265968052003 - type: manhattan_pearson value: 81.12300703450198 - type: manhattan_spearman value: 81.23377867759768 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.04870129870129 - type: f1 value: 74.29090714638184 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.18202629628483 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.419352316577875 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.305 - type: map_at_10 value: 28.046 - type: map_at_100 value: 29.174 - type: map_at_1000 value: 29.326999999999998 - type: map_at_3 value: 25.464 - type: map_at_5 value: 26.874 - type: mrr_at_1 value: 26.179999999999996 - type: mrr_at_10 value: 34.0 - type: mrr_at_100 value: 34.797 - type: mrr_at_1000 value: 34.864 - type: mrr_at_3 value: 31.784000000000002 - type: mrr_at_5 value: 32.992 - type: ndcg_at_1 value: 26.179999999999996 - type: ndcg_at_10 value: 33.46 - type: ndcg_at_100 value: 38.539 - type: ndcg_at_1000 value: 41.619 - type: ndcg_at_3 value: 29.471000000000004 - type: ndcg_at_5 value: 31.169999999999998 - type: precision_at_1 value: 26.179999999999996 - type: precision_at_10 value: 6.680999999999999 - type: precision_at_100 value: 1.15 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 14.591999999999999 - type: precision_at_5 value: 10.73 - type: recall_at_1 value: 20.305 - type: recall_at_10 value: 43.199 - type: recall_at_100 value: 66.46 - type: recall_at_1000 value: 87.469 - type: recall_at_3 value: 30.94 - type: recall_at_5 value: 35.927 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 18.265 - type: map_at_10 value: 24.661 - type: map_at_100 value: 25.739 - type: map_at_1000 value: 25.86 - type: map_at_3 value: 22.775000000000002 - type: map_at_5 value: 23.814 - type: mrr_at_1 value: 23.185 - type: mrr_at_10 value: 29.067 - type: mrr_at_100 value: 29.939 - type: mrr_at_1000 value: 30.007 - type: mrr_at_3 value: 27.197 - type: mrr_at_5 value: 28.248 - type: ndcg_at_1 value: 23.185 - type: ndcg_at_10 value: 28.638 - type: ndcg_at_100 value: 33.341 - type: ndcg_at_1000 value: 36.11 - type: ndcg_at_3 value: 25.599 - type: ndcg_at_5 value: 26.901999999999997 - type: precision_at_1 value: 23.185 - type: precision_at_10 value: 5.306 - type: precision_at_100 value: 0.959 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.442 - type: precision_at_5 value: 8.764 - type: recall_at_1 value: 18.265 - type: recall_at_10 value: 36.055 - type: recall_at_100 value: 56.419 - type: recall_at_1000 value: 75.25500000000001 - type: recall_at_3 value: 26.906999999999996 - type: recall_at_5 value: 30.637999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.065 - type: map_at_10 value: 35.952 - type: map_at_100 value: 37.092999999999996 - type: map_at_1000 value: 37.19 - type: map_at_3 value: 33.410000000000004 - type: map_at_5 value: 34.743 - type: mrr_at_1 value: 31.223 - type: mrr_at_10 value: 39.174 - type: mrr_at_100 value: 40.091 - type: mrr_at_1000 value: 40.152 - type: mrr_at_3 value: 37.011 - type: mrr_at_5 value: 38.115 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 40.871 - type: ndcg_at_100 value: 46.068 - type: ndcg_at_1000 value: 48.295 - type: ndcg_at_3 value: 36.285000000000004 - type: ndcg_at_5 value: 38.25 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 6.639 - type: precision_at_100 value: 1.014 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.092000000000002 - type: precision_at_5 value: 11.047 - type: recall_at_1 value: 27.065 - type: recall_at_10 value: 52.605000000000004 - type: recall_at_100 value: 75.653 - type: recall_at_1000 value: 91.724 - type: recall_at_3 value: 40.150999999999996 - type: recall_at_5 value: 44.979 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 12.692999999999998 - type: map_at_10 value: 17.427 - type: map_at_100 value: 18.235 - type: map_at_1000 value: 18.355 - type: map_at_3 value: 16.144 - type: map_at_5 value: 16.81 - type: mrr_at_1 value: 13.672 - type: mrr_at_10 value: 18.633 - type: mrr_at_100 value: 19.447 - type: mrr_at_1000 value: 19.554 - type: mrr_at_3 value: 17.401 - type: mrr_at_5 value: 17.983 - type: ndcg_at_1 value: 13.672 - type: ndcg_at_10 value: 20.212 - type: ndcg_at_100 value: 24.66 - type: ndcg_at_1000 value: 28.265 - type: ndcg_at_3 value: 17.625 - type: ndcg_at_5 value: 18.728 - type: precision_at_1 value: 13.672 - type: precision_at_10 value: 3.141 - type: precision_at_100 value: 0.569 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 7.5329999999999995 - type: precision_at_5 value: 5.220000000000001 - type: recall_at_1 value: 12.692999999999998 - type: recall_at_10 value: 27.656 - type: recall_at_100 value: 48.927 - type: recall_at_1000 value: 77.113 - type: recall_at_3 value: 20.54 - type: recall_at_5 value: 23.177 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.814 - type: map_at_10 value: 11.472 - type: map_at_100 value: 12.283 - type: map_at_1000 value: 12.407 - type: map_at_3 value: 9.892 - type: map_at_5 value: 10.525 - type: mrr_at_1 value: 9.950000000000001 - type: mrr_at_10 value: 13.947999999999999 - type: mrr_at_100 value: 14.790000000000001 - type: mrr_at_1000 value: 14.893999999999998 - type: mrr_at_3 value: 12.189 - type: mrr_at_5 value: 12.91 - type: ndcg_at_1 value: 9.950000000000001 - type: ndcg_at_10 value: 14.481 - type: ndcg_at_100 value: 18.999 - type: ndcg_at_1000 value: 22.519 - type: ndcg_at_3 value: 11.212 - type: ndcg_at_5 value: 12.238 - type: precision_at_1 value: 9.950000000000001 - type: precision_at_10 value: 2.861 - type: precision_at_100 value: 0.607 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 5.224 - type: precision_at_5 value: 3.856 - type: recall_at_1 value: 7.814 - type: recall_at_10 value: 21.507 - type: recall_at_100 value: 42.067 - type: recall_at_1000 value: 68.059 - type: recall_at_3 value: 12.489 - type: recall_at_5 value: 14.973 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.572 - type: map_at_10 value: 24.854000000000003 - type: map_at_100 value: 26.029000000000003 - type: map_at_1000 value: 26.177 - type: map_at_3 value: 22.417 - type: map_at_5 value: 23.612 - type: mrr_at_1 value: 22.907 - type: mrr_at_10 value: 29.643000000000004 - type: mrr_at_100 value: 30.499 - type: mrr_at_1000 value: 30.586999999999996 - type: mrr_at_3 value: 27.108999999999998 - type: mrr_at_5 value: 28.355999999999998 - type: ndcg_at_1 value: 22.907 - type: ndcg_at_10 value: 29.601 - type: ndcg_at_100 value: 35.11 - type: ndcg_at_1000 value: 38.433 - type: ndcg_at_3 value: 25.068 - type: ndcg_at_5 value: 26.828000000000003 - type: precision_at_1 value: 22.907 - type: precision_at_10 value: 5.525 - type: precision_at_100 value: 1.002 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 11.389000000000001 - type: precision_at_5 value: 8.354000000000001 - type: recall_at_1 value: 18.572 - type: recall_at_10 value: 39.499 - type: recall_at_100 value: 63.46000000000001 - type: recall_at_1000 value: 86.52499999999999 - type: recall_at_3 value: 26.699 - type: recall_at_5 value: 31.175000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.918000000000001 - type: map_at_10 value: 20.223 - type: map_at_100 value: 21.429000000000002 - type: map_at_1000 value: 21.578 - type: map_at_3 value: 18.278 - type: map_at_5 value: 19.312 - type: mrr_at_1 value: 18.037 - type: mrr_at_10 value: 23.75 - type: mrr_at_100 value: 24.804000000000002 - type: mrr_at_1000 value: 24.898 - type: mrr_at_3 value: 21.842 - type: mrr_at_5 value: 22.755 - type: ndcg_at_1 value: 18.037 - type: ndcg_at_10 value: 23.907 - type: ndcg_at_100 value: 29.663 - type: ndcg_at_1000 value: 33.245000000000005 - type: ndcg_at_3 value: 20.379 - type: ndcg_at_5 value: 21.799 - type: precision_at_1 value: 18.037 - type: precision_at_10 value: 4.452 - type: precision_at_100 value: 0.881 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 9.513 - type: precision_at_5 value: 6.895 - type: recall_at_1 value: 14.918000000000001 - type: recall_at_10 value: 31.503999999999998 - type: recall_at_100 value: 56.354000000000006 - type: recall_at_1000 value: 81.774 - type: recall_at_3 value: 21.819 - type: recall_at_5 value: 25.459 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.668083333333332 - type: map_at_10 value: 20.24666666666667 - type: map_at_100 value: 21.21025 - type: map_at_1000 value: 21.340666666666664 - type: map_at_3 value: 18.417083333333334 - type: map_at_5 value: 19.366833333333332 - type: mrr_at_1 value: 17.777833333333334 - type: mrr_at_10 value: 23.403333333333336 - type: mrr_at_100 value: 24.25408333333333 - type: mrr_at_1000 value: 24.34333333333333 - type: mrr_at_3 value: 21.6155 - type: mrr_at_5 value: 22.521 - type: ndcg_at_1 value: 17.777833333333334 - type: ndcg_at_10 value: 23.933500000000002 - type: ndcg_at_100 value: 28.714749999999995 - type: ndcg_at_1000 value: 31.968833333333336 - type: ndcg_at_3 value: 20.60758333333333 - type: ndcg_at_5 value: 21.982416666666666 - type: precision_at_1 value: 17.777833333333334 - type: precision_at_10 value: 4.3180000000000005 - type: precision_at_100 value: 0.8045833333333333 - type: precision_at_1000 value: 0.12691666666666668 - type: precision_at_3 value: 9.535000000000002 - type: precision_at_5 value: 6.825916666666666 - type: recall_at_1 value: 14.668083333333332 - type: recall_at_10 value: 31.930916666666665 - type: recall_at_100 value: 53.753249999999994 - type: recall_at_1000 value: 77.43366666666667 - type: recall_at_3 value: 22.524250000000002 - type: recall_at_5 value: 26.094916666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.096 - type: map_at_10 value: 15.190999999999999 - type: map_at_100 value: 15.922 - type: map_at_1000 value: 16.017 - type: map_at_3 value: 13.664000000000001 - type: map_at_5 value: 14.446 - type: mrr_at_1 value: 12.117 - type: mrr_at_10 value: 17.294 - type: mrr_at_100 value: 18.074 - type: mrr_at_1000 value: 18.153 - type: mrr_at_3 value: 15.823 - type: mrr_at_5 value: 16.59 - type: ndcg_at_1 value: 12.117 - type: ndcg_at_10 value: 18.248 - type: ndcg_at_100 value: 22.418 - type: ndcg_at_1000 value: 25.271 - type: ndcg_at_3 value: 15.368 - type: ndcg_at_5 value: 16.614 - type: precision_at_1 value: 12.117 - type: precision_at_10 value: 3.206 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 7.106 - type: precision_at_5 value: 5.061 - type: recall_at_1 value: 10.096 - type: recall_at_10 value: 25.624000000000002 - type: recall_at_100 value: 45.49 - type: recall_at_1000 value: 67.392 - type: recall_at_3 value: 17.68 - type: recall_at_5 value: 20.823 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.7780000000000005 - type: map_at_10 value: 11.493 - type: map_at_100 value: 12.200999999999999 - type: map_at_1000 value: 12.324 - type: map_at_3 value: 10.244 - type: map_at_5 value: 10.899000000000001 - type: mrr_at_1 value: 9.876 - type: mrr_at_10 value: 14.001 - type: mrr_at_100 value: 14.701 - type: mrr_at_1000 value: 14.799999999999999 - type: mrr_at_3 value: 12.583 - type: mrr_at_5 value: 13.325000000000001 - type: ndcg_at_1 value: 9.876 - type: ndcg_at_10 value: 14.158000000000001 - type: ndcg_at_100 value: 18.038999999999998 - type: ndcg_at_1000 value: 21.58 - type: ndcg_at_3 value: 11.722000000000001 - type: ndcg_at_5 value: 12.769 - type: precision_at_1 value: 9.876 - type: precision_at_10 value: 2.705 - type: precision_at_100 value: 0.555 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 5.666 - type: precision_at_5 value: 4.178 - type: recall_at_1 value: 7.7780000000000005 - type: recall_at_10 value: 19.86 - type: recall_at_100 value: 38.0 - type: recall_at_1000 value: 64.331 - type: recall_at_3 value: 13.117999999999999 - type: recall_at_5 value: 15.783 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.088 - type: map_at_10 value: 17.759 - type: map_at_100 value: 18.597 - type: map_at_1000 value: 18.718 - type: map_at_3 value: 16.232 - type: map_at_5 value: 17.129 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.676 - type: mrr_at_100 value: 21.505 - type: mrr_at_1000 value: 21.605 - type: mrr_at_3 value: 18.999 - type: mrr_at_5 value: 19.932 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 21.035 - type: ndcg_at_100 value: 25.52 - type: ndcg_at_1000 value: 28.875 - type: ndcg_at_3 value: 18.015 - type: ndcg_at_5 value: 19.476 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.535 - type: precision_at_100 value: 0.652 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.24 - type: precision_at_5 value: 5.821 - type: recall_at_1 value: 13.088 - type: recall_at_10 value: 28.414 - type: recall_at_100 value: 48.949999999999996 - type: recall_at_1000 value: 73.67399999999999 - type: recall_at_3 value: 19.893 - type: recall_at_5 value: 23.718 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.433 - type: map_at_10 value: 19.926 - type: map_at_100 value: 21.11 - type: map_at_1000 value: 21.302 - type: map_at_3 value: 17.991 - type: map_at_5 value: 19.078999999999997 - type: mrr_at_1 value: 17.391000000000002 - type: mrr_at_10 value: 23.433999999999997 - type: mrr_at_100 value: 24.41 - type: mrr_at_1000 value: 24.501 - type: mrr_at_3 value: 21.706 - type: mrr_at_5 value: 22.684 - type: ndcg_at_1 value: 17.391000000000002 - type: ndcg_at_10 value: 24.11 - type: ndcg_at_100 value: 29.500999999999998 - type: ndcg_at_1000 value: 33.093 - type: ndcg_at_3 value: 21.037 - type: ndcg_at_5 value: 22.439 - type: precision_at_1 value: 17.391000000000002 - type: precision_at_10 value: 4.881 - type: precision_at_100 value: 1.138 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 10.277 - type: precision_at_5 value: 7.549 - type: recall_at_1 value: 13.433 - type: recall_at_10 value: 32.029 - type: recall_at_100 value: 57.727 - type: recall_at_1000 value: 82.536 - type: recall_at_3 value: 22.914 - type: recall_at_5 value: 26.844 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.99 - type: map_at_10 value: 15.956000000000001 - type: map_at_100 value: 16.711000000000002 - type: map_at_1000 value: 16.833000000000002 - type: map_at_3 value: 14.494000000000002 - type: map_at_5 value: 15.159 - type: mrr_at_1 value: 13.123999999999999 - type: mrr_at_10 value: 17.22 - type: mrr_at_100 value: 17.992 - type: mrr_at_1000 value: 18.105 - type: mrr_at_3 value: 15.742 - type: mrr_at_5 value: 16.362 - type: ndcg_at_1 value: 13.123999999999999 - type: ndcg_at_10 value: 18.481 - type: ndcg_at_100 value: 22.719 - type: ndcg_at_1000 value: 26.321 - type: ndcg_at_3 value: 15.509999999999998 - type: ndcg_at_5 value: 16.576 - type: precision_at_1 value: 13.123999999999999 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.436 - type: recall_at_1 value: 11.99 - type: recall_at_10 value: 25.219 - type: recall_at_100 value: 45.532000000000004 - type: recall_at_1000 value: 73.35199999999999 - type: recall_at_3 value: 17.141000000000002 - type: recall_at_5 value: 19.643 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.6240000000000006 - type: map_at_10 value: 13.211999999999998 - type: map_at_100 value: 14.82 - type: map_at_1000 value: 15.039 - type: map_at_3 value: 10.793999999999999 - type: map_at_5 value: 12.035 - type: mrr_at_1 value: 17.459 - type: mrr_at_10 value: 26.590000000000003 - type: mrr_at_100 value: 27.792 - type: mrr_at_1000 value: 27.851 - type: mrr_at_3 value: 23.268 - type: mrr_at_5 value: 25.192999999999998 - type: ndcg_at_1 value: 17.459 - type: ndcg_at_10 value: 19.606 - type: ndcg_at_100 value: 26.87 - type: ndcg_at_1000 value: 31.080000000000002 - type: ndcg_at_3 value: 15.190000000000001 - type: ndcg_at_5 value: 16.85 - type: precision_at_1 value: 17.459 - type: precision_at_10 value: 6.45 - type: precision_at_100 value: 1.421 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 11.488 - type: precision_at_5 value: 9.316 - type: recall_at_1 value: 7.6240000000000006 - type: recall_at_10 value: 24.593 - type: recall_at_100 value: 50.300999999999995 - type: recall_at_1000 value: 74.439 - type: recall_at_3 value: 14.097000000000001 - type: recall_at_5 value: 18.362000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.456 - type: map_at_10 value: 9.995 - type: map_at_100 value: 14.196 - type: map_at_1000 value: 15.284 - type: map_at_3 value: 7.02 - type: map_at_5 value: 8.341 - type: mrr_at_1 value: 43.25 - type: mrr_at_10 value: 52.626 - type: mrr_at_100 value: 53.361000000000004 - type: mrr_at_1000 value: 53.396 - type: mrr_at_3 value: 50.208 - type: mrr_at_5 value: 51.696 - type: ndcg_at_1 value: 31.75 - type: ndcg_at_10 value: 24.557000000000002 - type: ndcg_at_100 value: 28.179 - type: ndcg_at_1000 value: 35.42 - type: ndcg_at_3 value: 27.05 - type: ndcg_at_5 value: 25.938 - type: precision_at_1 value: 43.25 - type: precision_at_10 value: 21.95 - type: precision_at_100 value: 7.21 - type: precision_at_1000 value: 1.5310000000000001 - type: precision_at_3 value: 32.25 - type: precision_at_5 value: 28.050000000000004 - type: recall_at_1 value: 4.456 - type: recall_at_10 value: 14.808 - type: recall_at_100 value: 35.062 - type: recall_at_1000 value: 60.111000000000004 - type: recall_at_3 value: 8.333 - type: recall_at_5 value: 10.847999999999999 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.275 - type: f1 value: 44.11697299626323 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.512 - type: map_at_10 value: 25.102000000000004 - type: map_at_100 value: 26.14 - type: map_at_1000 value: 26.212000000000003 - type: map_at_3 value: 22.531000000000002 - type: map_at_5 value: 23.959 - type: mrr_at_1 value: 17.642 - type: mrr_at_10 value: 26.665 - type: mrr_at_100 value: 27.700000000000003 - type: mrr_at_1000 value: 27.762999999999998 - type: mrr_at_3 value: 24.03 - type: mrr_at_5 value: 25.501 - type: ndcg_at_1 value: 17.642 - type: ndcg_at_10 value: 30.162 - type: ndcg_at_100 value: 35.393 - type: ndcg_at_1000 value: 37.370999999999995 - type: ndcg_at_3 value: 24.878 - type: ndcg_at_5 value: 27.426000000000002 - type: precision_at_1 value: 17.642 - type: precision_at_10 value: 4.845 - type: precision_at_100 value: 0.765 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 10.876 - type: precision_at_5 value: 7.864 - type: recall_at_1 value: 16.512 - type: recall_at_10 value: 44.528 - type: recall_at_100 value: 68.794 - type: recall_at_1000 value: 84.055 - type: recall_at_3 value: 30.151 - type: recall_at_5 value: 36.244 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 6.548 - type: map_at_10 value: 11.365 - type: map_at_100 value: 12.659 - type: map_at_1000 value: 12.870999999999999 - type: map_at_3 value: 9.238 - type: map_at_5 value: 10.295 - type: mrr_at_1 value: 13.735 - type: mrr_at_10 value: 19.666 - type: mrr_at_100 value: 20.848 - type: mrr_at_1000 value: 20.951 - type: mrr_at_3 value: 17.335 - type: mrr_at_5 value: 18.616 - type: ndcg_at_1 value: 13.735 - type: ndcg_at_10 value: 15.923000000000002 - type: ndcg_at_100 value: 22.23 - type: ndcg_at_1000 value: 26.893 - type: ndcg_at_3 value: 12.756 - type: ndcg_at_5 value: 13.883999999999999 - type: precision_at_1 value: 13.735 - type: precision_at_10 value: 4.7379999999999995 - type: precision_at_100 value: 1.086 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 8.436 - type: precision_at_5 value: 6.7589999999999995 - type: recall_at_1 value: 6.548 - type: recall_at_10 value: 21.267 - type: recall_at_100 value: 46.07 - type: recall_at_1000 value: 74.868 - type: recall_at_3 value: 11.611 - type: recall_at_5 value: 15.284 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 17.387 - type: map_at_10 value: 24.564 - type: map_at_100 value: 25.503999999999998 - type: map_at_1000 value: 25.619999999999997 - type: map_at_3 value: 22.496 - type: map_at_5 value: 23.646 - type: mrr_at_1 value: 34.774 - type: mrr_at_10 value: 41.935 - type: mrr_at_100 value: 42.679 - type: mrr_at_1000 value: 42.737 - type: mrr_at_3 value: 39.883 - type: mrr_at_5 value: 41.063 - type: ndcg_at_1 value: 34.774 - type: ndcg_at_10 value: 31.456 - type: ndcg_at_100 value: 35.827 - type: ndcg_at_1000 value: 38.627 - type: ndcg_at_3 value: 27.534999999999997 - type: ndcg_at_5 value: 29.452 - type: precision_at_1 value: 34.774 - type: precision_at_10 value: 6.97 - type: precision_at_100 value: 1.048 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.349 - type: precision_at_5 value: 11.924 - type: recall_at_1 value: 17.387 - type: recall_at_10 value: 34.848 - type: recall_at_100 value: 52.384 - type: recall_at_1000 value: 71.134 - type: recall_at_3 value: 26.023000000000003 - type: recall_at_5 value: 29.811 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 69.98119999999999 - type: ap value: 64.17725086855937 - type: f1 value: 69.78928359055172 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.372999999999999 - type: map_at_10 value: 11.145 - type: map_at_100 value: 12.055 - type: map_at_1000 value: 12.17 - type: map_at_3 value: 9.391 - type: map_at_5 value: 10.270999999999999 - type: mrr_at_1 value: 6.561999999999999 - type: mrr_at_10 value: 11.446000000000002 - type: mrr_at_100 value: 12.359 - type: mrr_at_1000 value: 12.47 - type: mrr_at_3 value: 9.654 - type: mrr_at_5 value: 10.566 - type: ndcg_at_1 value: 6.5329999999999995 - type: ndcg_at_10 value: 14.174000000000001 - type: ndcg_at_100 value: 19.168 - type: ndcg_at_1000 value: 22.579 - type: ndcg_at_3 value: 10.465 - type: ndcg_at_5 value: 12.057 - type: precision_at_1 value: 6.5329999999999995 - type: precision_at_10 value: 2.451 - type: precision_at_100 value: 0.506 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 4.58 - type: precision_at_5 value: 3.553 - type: recall_at_1 value: 6.372999999999999 - type: recall_at_10 value: 23.639 - type: recall_at_100 value: 48.012 - type: recall_at_1000 value: 75.368 - type: recall_at_3 value: 13.333 - type: recall_at_5 value: 17.147000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.3953488372093 - type: f1 value: 90.47618297254341 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.249430004559954 - type: f1 value: 42.242289025471344 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.15131136516476 - type: f1 value: 62.8508450491576 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.9670477471419 - type: f1 value: 70.83719077833712 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.27049656570754 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.8992311215977 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.216583547389536 - type: mrr value: 32.31147129597184 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.651000000000001 - type: map_at_10 value: 8.924999999999999 - type: map_at_100 value: 11.43 - type: map_at_1000 value: 12.879999999999999 - type: map_at_3 value: 6.718 - type: map_at_5 value: 7.727 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 46.018 - type: mrr_at_100 value: 46.649 - type: mrr_at_1000 value: 46.713 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 44.928000000000004 - type: ndcg_at_1 value: 36.378 - type: ndcg_at_10 value: 27.193 - type: ndcg_at_100 value: 25.840000000000003 - type: ndcg_at_1000 value: 35.382999999999996 - type: ndcg_at_3 value: 31.054 - type: ndcg_at_5 value: 29.523 - type: precision_at_1 value: 37.461 - type: precision_at_10 value: 19.875999999999998 - type: precision_at_100 value: 7.198 - type: precision_at_1000 value: 2.069 - type: precision_at_3 value: 28.38 - type: precision_at_5 value: 25.386999999999997 - type: recall_at_1 value: 4.651000000000001 - type: recall_at_10 value: 13.517999999999999 - type: recall_at_100 value: 28.475 - type: recall_at_1000 value: 61.861999999999995 - type: recall_at_3 value: 7.657 - type: recall_at_5 value: 9.76 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.456000000000001 - type: map_at_10 value: 16.392 - type: map_at_100 value: 17.730999999999998 - type: map_at_1000 value: 17.835 - type: map_at_3 value: 13.743 - type: map_at_5 value: 15.262999999999998 - type: mrr_at_1 value: 10.776 - type: mrr_at_10 value: 18.163999999999998 - type: mrr_at_100 value: 19.403000000000002 - type: mrr_at_1000 value: 19.489 - type: mrr_at_3 value: 15.464 - type: mrr_at_5 value: 17.035 - type: ndcg_at_1 value: 10.776 - type: ndcg_at_10 value: 20.959 - type: ndcg_at_100 value: 27.589000000000002 - type: ndcg_at_1000 value: 30.416999999999998 - type: ndcg_at_3 value: 15.552 - type: ndcg_at_5 value: 18.275 - type: precision_at_1 value: 10.776 - type: precision_at_10 value: 3.94 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 7.396999999999999 - type: precision_at_5 value: 5.933 - type: recall_at_1 value: 9.456000000000001 - type: recall_at_10 value: 33.394 - type: recall_at_100 value: 63.915 - type: recall_at_1000 value: 85.598 - type: recall_at_3 value: 19.098000000000003 - type: recall_at_5 value: 25.466 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.744 - type: map_at_10 value: 77.86 - type: map_at_100 value: 78.58800000000001 - type: map_at_1000 value: 78.617 - type: map_at_3 value: 74.788 - type: map_at_5 value: 76.716 - type: mrr_at_1 value: 74.49 - type: mrr_at_10 value: 81.843 - type: mrr_at_100 value: 82.035 - type: mrr_at_1000 value: 82.038 - type: mrr_at_3 value: 80.39 - type: mrr_at_5 value: 81.372 - type: ndcg_at_1 value: 74.59 - type: ndcg_at_10 value: 82.459 - type: ndcg_at_100 value: 84.34899999999999 - type: ndcg_at_1000 value: 84.626 - type: ndcg_at_3 value: 78.821 - type: ndcg_at_5 value: 80.83500000000001 - type: precision_at_1 value: 74.59 - type: precision_at_10 value: 12.494 - type: precision_at_100 value: 1.477 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.233000000000004 - type: precision_at_5 value: 22.747999999999998 - type: recall_at_1 value: 64.744 - type: recall_at_10 value: 91.355 - type: recall_at_100 value: 98.30799999999999 - type: recall_at_1000 value: 99.766 - type: recall_at_3 value: 81.109 - type: recall_at_5 value: 86.572 - type: map_at_1 value: 2.868 - type: map_at_10 value: 7.155 - type: map_at_100 value: 8.651 - type: map_at_1000 value: 8.921 - type: map_at_3 value: 5.197 - type: map_at_5 value: 6.168 - type: mrr_at_1 value: 14.099999999999998 - type: mrr_at_10 value: 22.528000000000002 - type: mrr_at_100 value: 23.730999999999998 - type: mrr_at_1000 value: 23.827 - type: mrr_at_3 value: 19.683 - type: mrr_at_5 value: 21.233 - type: ndcg_at_1 value: 14.099999999999998 - type: ndcg_at_10 value: 12.756 - type: ndcg_at_100 value: 19.49 - type: ndcg_at_1000 value: 24.942 - type: ndcg_at_3 value: 11.905000000000001 - type: ndcg_at_5 value: 10.474 - type: precision_at_1 value: 14.099999999999998 - type: precision_at_10 value: 6.7299999999999995 - type: precision_at_100 value: 1.657 - type: precision_at_1000 value: 0.297 - type: precision_at_3 value: 11.200000000000001 - type: precision_at_5 value: 9.3 - type: recall_at_1 value: 2.868 - type: recall_at_10 value: 13.613 - type: recall_at_100 value: 33.645 - type: recall_at_1000 value: 60.372 - type: recall_at_3 value: 6.808 - type: recall_at_5 value: 9.418 - type: map_at_1 value: 0.157 - type: map_at_10 value: 0.989 - type: map_at_100 value: 5.3580000000000005 - type: map_at_1000 value: 13.614999999999998 - type: map_at_3 value: 0.391 - type: map_at_5 value: 0.557 - type: mrr_at_1 value: 57.99999999999999 - type: mrr_at_10 value: 69.039 - type: mrr_at_100 value: 69.618 - type: mrr_at_1000 value: 69.618 - type: mrr_at_3 value: 67.667 - type: mrr_at_5 value: 68.56700000000001 - type: ndcg_at_1 value: 55.00000000000001 - type: ndcg_at_10 value: 48.394 - type: ndcg_at_100 value: 37.158 - type: ndcg_at_1000 value: 34.204 - type: ndcg_at_3 value: 53.754000000000005 - type: ndcg_at_5 value: 50.712999999999994 - type: precision_at_1 value: 57.99999999999999 - type: precision_at_10 value: 51.800000000000004 - type: precision_at_100 value: 39.26 - type: precision_at_1000 value: 16.503999999999998 - type: precision_at_3 value: 57.333 - type: precision_at_5 value: 52.800000000000004 - type: recall_at_1 value: 0.157 - type: recall_at_10 value: 1.238 - type: recall_at_100 value: 8.674 - type: recall_at_1000 value: 33.222 - type: recall_at_3 value: 0.436 - type: recall_at_5 value: 0.643 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 46.254882422464526 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 50.94989318333412 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.98965504956827 - type: cos_sim_spearman value: 68.28460263921258 - type: euclidean_pearson value: 73.50270698016448 - type: euclidean_spearman value: 68.28468403646217 - type: manhattan_pearson value: 72.8261914195885 - type: manhattan_spearman value: 67.86873546122553 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 77.14830947681742 - type: cos_sim_spearman value: 68.60266030636393 - type: euclidean_pearson value: 72.88451477994006 - type: euclidean_spearman value: 68.60389167221209 - type: manhattan_pearson value: 71.89880964464528 - type: manhattan_spearman value: 68.11051648970675 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.72037928360238 - type: cos_sim_spearman value: 79.74389537608737 - type: euclidean_pearson value: 79.39980926218213 - type: euclidean_spearman value: 79.74393317465844 - type: manhattan_pearson value: 78.7481714360194 - type: manhattan_spearman value: 79.05784658583435 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.20839429694983 - type: cos_sim_spearman value: 75.75758249233702 - type: euclidean_pearson value: 78.2593144118954 - type: euclidean_spearman value: 75.7575727998599 - type: manhattan_pearson value: 77.98797449902915 - type: manhattan_spearman value: 75.58570762607603 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.38797626966284 - type: cos_sim_spearman value: 83.0821006142509 - type: euclidean_pearson value: 82.89995084283936 - type: euclidean_spearman value: 83.08209908184749 - type: manhattan_pearson value: 82.6019409098804 - type: manhattan_spearman value: 82.76534947735776 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.80219740466768 - type: cos_sim_spearman value: 79.07336247296158 - type: euclidean_pearson value: 78.34175159212086 - type: euclidean_spearman value: 79.07335507859334 - type: manhattan_pearson value: 78.146156004842 - type: manhattan_spearman value: 78.85783029933849 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.74773705987958 - type: cos_sim_spearman value: 85.73402749289298 - type: euclidean_pearson value: 85.18510280404286 - type: euclidean_spearman value: 85.73490066116952 - type: manhattan_pearson value: 84.93638596678905 - type: manhattan_spearman value: 85.5315548466084 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.17015437324628 - type: cos_sim_spearman value: 59.75467857816752 - type: euclidean_pearson value: 60.812443155269534 - type: euclidean_spearman value: 59.75467857816752 - type: manhattan_pearson value: 59.950493146979255 - type: manhattan_spearman value: 58.932105528273645 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.46948132600193 - type: cos_sim_spearman value: 79.10069645170242 - type: euclidean_pearson value: 80.31463403998292 - type: euclidean_spearman value: 79.10071491600597 - type: manhattan_pearson value: 80.01917165738134 - type: manhattan_spearman value: 78.86150076844012 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.0368453616957 - type: mrr value: 91.42105987694224 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 37.083 - type: map_at_10 value: 45.626 - type: map_at_100 value: 46.741 - type: map_at_1000 value: 46.796 - type: map_at_3 value: 43.397999999999996 - type: map_at_5 value: 44.098 - type: mrr_at_1 value: 39.333 - type: mrr_at_10 value: 47.424 - type: mrr_at_100 value: 48.365 - type: mrr_at_1000 value: 48.413000000000004 - type: mrr_at_3 value: 45.444 - type: mrr_at_5 value: 46.011 - type: ndcg_at_1 value: 39.333 - type: ndcg_at_10 value: 50.324999999999996 - type: ndcg_at_100 value: 55.74400000000001 - type: ndcg_at_1000 value: 57.092 - type: ndcg_at_3 value: 45.805 - type: ndcg_at_5 value: 46.826 - type: precision_at_1 value: 39.333 - type: precision_at_10 value: 6.9 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.111 - type: precision_at_5 value: 11.466999999999999 - type: recall_at_1 value: 37.083 - type: recall_at_10 value: 63.444 - type: recall_at_100 value: 88.617 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 50.556 - type: recall_at_5 value: 53.056000000000004 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66039603960397 - type: cos_sim_ap value: 89.16346887114837 - type: cos_sim_f1 value: 83.18072289156628 - type: cos_sim_precision value: 80.27906976744185 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 89.16346887114837 - type: dot_f1 value: 83.18072289156628 - type: dot_precision value: 80.27906976744185 - type: dot_recall value: 86.3 - type: euclidean_accuracy value: 99.66039603960397 - type: euclidean_ap value: 89.16346887114837 - type: euclidean_f1 value: 83.18072289156628 - type: euclidean_precision value: 80.27906976744185 - type: euclidean_recall value: 86.3 - type: manhattan_accuracy value: 99.66930693069307 - type: manhattan_ap value: 89.13276894140405 - type: manhattan_f1 value: 83.46534653465346 - type: manhattan_precision value: 82.6470588235294 - type: manhattan_recall value: 84.3 - type: max_accuracy value: 99.66930693069307 - type: max_ap value: 89.16346887114837 - type: max_f1 value: 83.46534653465346 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 49.394155025012324 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.32321222461949 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.523517787741575 - type: mrr value: 44.07447638146168 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.28542082978425 - type: cos_sim_spearman value: 30.039804865964005 - type: dot_pearson value: 31.28542082880828 - type: dot_spearman value: 30.051397798547818 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.385 - type: map_at_10 value: 7.414 - type: map_at_100 value: 13.084999999999999 - type: map_at_1000 value: 14.765 - type: map_at_3 value: 3.5909999999999997 - type: map_at_5 value: 5.402 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 37.669000000000004 - type: mrr_at_100 value: 38.823 - type: mrr_at_1000 value: 38.823 - type: mrr_at_3 value: 33.672999999999995 - type: mrr_at_5 value: 35.612 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 19.288 - type: ndcg_at_100 value: 33.376 - type: ndcg_at_1000 value: 45.28 - type: ndcg_at_3 value: 20.511 - type: ndcg_at_5 value: 21.182000000000002 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 18.776 - type: precision_at_100 value: 8.061 - type: precision_at_1000 value: 1.5779999999999998 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.385 - type: recall_at_10 value: 13.113 - type: recall_at_100 value: 48.345 - type: recall_at_1000 value: 85.087 - type: recall_at_3 value: 4.932 - type: recall_at_5 value: 8.4 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 79.2658 - type: ap value: 19.45051650328674 - type: f1 value: 61.721255030714005 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.19524617996604 - type: f1 value: 60.47726202926952 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.20230019842334 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.32240567443525 - type: cos_sim_ap value: 63.95052535841297 - type: cos_sim_f1 value: 60.78094302554028 - type: cos_sim_precision value: 56.844281120808446 - type: cos_sim_recall value: 65.30343007915567 - type: dot_accuracy value: 83.32240567443525 - type: dot_ap value: 63.95052535841297 - type: dot_f1 value: 60.78094302554028 - type: dot_precision value: 56.844281120808446 - type: dot_recall value: 65.30343007915567 - type: euclidean_accuracy value: 83.32240567443525 - type: euclidean_ap value: 63.95052535841297 - type: euclidean_f1 value: 60.78094302554028 - type: euclidean_precision value: 56.844281120808446 - type: euclidean_recall value: 65.30343007915567 - type: manhattan_accuracy value: 83.30452405078381 - type: manhattan_ap value: 63.82521079916541 - type: manhattan_f1 value: 60.567750833237554 - type: manhattan_precision value: 53.65506006923234 - type: manhattan_recall value: 69.52506596306068 - type: max_accuracy value: 83.32240567443525 - type: max_ap value: 63.95052535841297 - type: max_f1 value: 60.78094302554028 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.51309814879497 - type: cos_sim_ap value: 83.04143677647984 - type: cos_sim_f1 value: 75.14412661109682 - type: cos_sim_precision value: 71.82871182871183 - type: cos_sim_recall value: 78.78041268863566 - type: dot_accuracy value: 87.51309814879497 - type: dot_ap value: 83.0414382592019 - type: dot_f1 value: 75.14412661109682 - type: dot_precision value: 71.82871182871183 - type: dot_recall value: 78.78041268863566 - type: euclidean_accuracy value: 87.51309814879497 - type: euclidean_ap value: 83.04144849399968 - type: euclidean_f1 value: 75.14412661109682 - type: euclidean_precision value: 71.82871182871183 - type: euclidean_recall value: 78.78041268863566 - type: manhattan_accuracy value: 87.50921721581868 - type: manhattan_ap value: 82.97187030449552 - type: manhattan_f1 value: 74.93584260051325 - type: manhattan_precision value: 72.48003453485863 - type: manhattan_recall value: 77.56390514320911 - type: max_accuracy value: 87.51309814879497 - type: max_ap value: 83.04144849399968 - type: max_f1 value: 75.14412661109682 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/mv_sw	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:25:19	2025-01-09T11:25:26	0	--- tags: - mteb model-index: - name: model2vec_result_fixed results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7910447761194 - type: ap value: 33.038020188116036 - type: f1 value: 65.03799728338926 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 72.47644999999999 - type: ap value: 66.91002822830875 - type: f1 value: 72.2600863044581 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 36.012 - type: f1 value: 35.38209336470206 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.124000000000002 - type: map_at_10 value: 34.329 - type: map_at_100 value: 35.612 - type: map_at_1000 value: 35.647 - type: map_at_3 value: 30.263 - type: map_at_5 value: 32.358 - type: mrr_at_1 value: 21.764 - type: mrr_at_10 value: 34.558 - type: mrr_at_100 value: 35.848 - type: mrr_at_1000 value: 35.882999999999996 - type: mrr_at_3 value: 30.441000000000003 - type: mrr_at_5 value: 32.621 - type: ndcg_at_1 value: 21.124000000000002 - type: ndcg_at_10 value: 41.961 - type: ndcg_at_100 value: 47.746 - type: ndcg_at_1000 value: 48.63 - type: ndcg_at_3 value: 33.469 - type: ndcg_at_5 value: 37.261 - type: precision_at_1 value: 21.124000000000002 - type: precision_at_10 value: 6.643000000000001 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.272000000000002 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.124000000000002 - type: recall_at_10 value: 66.43 - type: recall_at_100 value: 92.461 - type: recall_at_1000 value: 99.289 - type: recall_at_3 value: 42.817 - type: recall_at_5 value: 52.063 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.422522812555265 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 25.271555965391595 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.11180788298141 - type: mrr value: 68.73587477465594 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.11612347924923 - type: cos_sim_spearman value: 75.85775256673794 - type: euclidean_pearson value: 77.46080567383865 - type: euclidean_spearman value: 75.85775256673794 - type: manhattan_pearson value: 77.7319143671074 - type: manhattan_spearman value: 75.98908086034702 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.63636363636363 - type: f1 value: 71.69751597573539 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.861094091770546 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 20.222365644637257 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 19.939 - type: map_at_10 value: 26.924 - type: map_at_100 value: 28.16 - type: map_at_1000 value: 28.316999999999997 - type: map_at_3 value: 24.45 - type: map_at_5 value: 25.751 - type: mrr_at_1 value: 25.894000000000002 - type: mrr_at_10 value: 32.652 - type: mrr_at_100 value: 33.584 - type: mrr_at_1000 value: 33.664 - type: mrr_at_3 value: 30.520000000000003 - type: mrr_at_5 value: 31.671 - type: ndcg_at_1 value: 25.894000000000002 - type: ndcg_at_10 value: 31.835 - type: ndcg_at_100 value: 37.325 - type: ndcg_at_1000 value: 40.586 - type: ndcg_at_3 value: 28.143 - type: ndcg_at_5 value: 29.648999999999997 - type: precision_at_1 value: 25.894000000000002 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.126 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 13.543 - type: precision_at_5 value: 9.757 - type: recall_at_1 value: 19.939 - type: recall_at_10 value: 40.537 - type: recall_at_100 value: 64.717 - type: recall_at_1000 value: 87.01299999999999 - type: recall_at_3 value: 29.301 - type: recall_at_5 value: 33.918 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.601 - type: map_at_10 value: 22.07 - type: map_at_100 value: 22.958000000000002 - type: map_at_1000 value: 23.074 - type: map_at_3 value: 20.137 - type: map_at_5 value: 21.315 - type: mrr_at_1 value: 20.382 - type: mrr_at_10 value: 25.954 - type: mrr_at_100 value: 26.723000000000003 - type: mrr_at_1000 value: 26.791999999999998 - type: mrr_at_3 value: 24.098 - type: mrr_at_5 value: 25.27 - type: ndcg_at_1 value: 20.382 - type: ndcg_at_10 value: 25.734 - type: ndcg_at_100 value: 29.952 - type: ndcg_at_1000 value: 32.618 - type: ndcg_at_3 value: 22.445999999999998 - type: ndcg_at_5 value: 24.162 - type: precision_at_1 value: 20.382 - type: precision_at_10 value: 4.662 - type: precision_at_100 value: 0.8580000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.446 - type: precision_at_5 value: 7.682 - type: recall_at_1 value: 16.601 - type: recall_at_10 value: 32.882 - type: recall_at_100 value: 51.273 - type: recall_at_1000 value: 69.33200000000001 - type: recall_at_3 value: 23.54 - type: recall_at_5 value: 28.054000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.386999999999997 - type: map_at_10 value: 34.183 - type: map_at_100 value: 35.198 - type: map_at_1000 value: 35.292 - type: map_at_3 value: 31.466 - type: map_at_5 value: 33.037 - type: mrr_at_1 value: 29.404000000000003 - type: mrr_at_10 value: 37.519000000000005 - type: mrr_at_100 value: 38.305 - type: mrr_at_1000 value: 38.365 - type: mrr_at_3 value: 35.152 - type: mrr_at_5 value: 36.531000000000006 - type: ndcg_at_1 value: 29.404000000000003 - type: ndcg_at_10 value: 39.235 - type: ndcg_at_100 value: 44.072 - type: ndcg_at_1000 value: 46.272999999999996 - type: ndcg_at_3 value: 34.292 - type: ndcg_at_5 value: 36.735 - type: precision_at_1 value: 29.404000000000003 - type: precision_at_10 value: 6.539000000000001 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 15.423 - type: precision_at_5 value: 10.984 - type: recall_at_1 value: 25.386999999999997 - type: recall_at_10 value: 51.256 - type: recall_at_100 value: 73.53699999999999 - type: recall_at_1000 value: 89.522 - type: recall_at_3 value: 37.830999999999996 - type: recall_at_5 value: 43.811 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.832 - type: map_at_10 value: 16.154 - type: map_at_100 value: 16.863 - type: map_at_1000 value: 16.979 - type: map_at_3 value: 14.654 - type: map_at_5 value: 15.634 - type: mrr_at_1 value: 11.751000000000001 - type: mrr_at_10 value: 17.286 - type: mrr_at_100 value: 18.019 - type: mrr_at_1000 value: 18.122 - type: mrr_at_3 value: 15.706000000000001 - type: mrr_at_5 value: 16.774 - type: ndcg_at_1 value: 11.751000000000001 - type: ndcg_at_10 value: 19.197 - type: ndcg_at_100 value: 23.159 - type: ndcg_at_1000 value: 26.453 - type: ndcg_at_3 value: 16.186 - type: ndcg_at_5 value: 17.936 - type: precision_at_1 value: 11.751000000000001 - type: precision_at_10 value: 3.1189999999999998 - type: precision_at_100 value: 0.54 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 7.194000000000001 - type: precision_at_5 value: 5.311 - type: recall_at_1 value: 10.832 - type: recall_at_10 value: 27.472 - type: recall_at_100 value: 46.471000000000004 - type: recall_at_1000 value: 71.91199999999999 - type: recall_at_3 value: 19.417 - type: recall_at_5 value: 23.577 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.019 - type: map_at_10 value: 9.584 - type: map_at_100 value: 10.433 - type: map_at_1000 value: 10.562000000000001 - type: map_at_3 value: 8.351 - type: map_at_5 value: 9.005 - type: mrr_at_1 value: 7.2139999999999995 - type: mrr_at_10 value: 11.62 - type: mrr_at_100 value: 12.469 - type: mrr_at_1000 value: 12.577 - type: mrr_at_3 value: 10.158000000000001 - type: mrr_at_5 value: 10.898 - type: ndcg_at_1 value: 7.2139999999999995 - type: ndcg_at_10 value: 12.145 - type: ndcg_at_100 value: 16.672 - type: ndcg_at_1000 value: 20.342 - type: ndcg_at_3 value: 9.607000000000001 - type: ndcg_at_5 value: 10.712000000000002 - type: precision_at_1 value: 7.2139999999999995 - type: precision_at_10 value: 2.338 - type: precision_at_100 value: 0.5459999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 4.726 - type: precision_at_5 value: 3.5319999999999996 - type: recall_at_1 value: 6.019 - type: recall_at_10 value: 18.102999999999998 - type: recall_at_100 value: 38.482 - type: recall_at_1000 value: 65.436 - type: recall_at_3 value: 11.178 - type: recall_at_5 value: 13.877 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 16.822 - type: map_at_10 value: 22.476 - type: map_at_100 value: 23.69 - type: map_at_1000 value: 23.827 - type: map_at_3 value: 20.441000000000003 - type: map_at_5 value: 21.512 - type: mrr_at_1 value: 20.788999999999998 - type: mrr_at_10 value: 26.674 - type: mrr_at_100 value: 27.675 - type: mrr_at_1000 value: 27.753 - type: mrr_at_3 value: 24.495 - type: mrr_at_5 value: 25.629999999999995 - type: ndcg_at_1 value: 20.788999999999998 - type: ndcg_at_10 value: 26.667999999999996 - type: ndcg_at_100 value: 32.565 - type: ndcg_at_1000 value: 35.634 - type: ndcg_at_3 value: 22.942 - type: ndcg_at_5 value: 24.514 - type: precision_at_1 value: 20.788999999999998 - type: precision_at_10 value: 4.947 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.748000000000001 - type: precision_at_5 value: 7.68 - type: recall_at_1 value: 16.822 - type: recall_at_10 value: 35.237 - type: recall_at_100 value: 61.219 - type: recall_at_1000 value: 82.499 - type: recall_at_3 value: 24.524 - type: recall_at_5 value: 28.787000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 12.416 - type: map_at_10 value: 17.684 - type: map_at_100 value: 18.851000000000003 - type: map_at_1000 value: 18.991 - type: map_at_3 value: 15.770999999999999 - type: map_at_5 value: 16.606 - type: mrr_at_1 value: 15.068000000000001 - type: mrr_at_10 value: 21.288 - type: mrr_at_100 value: 22.306 - type: mrr_at_1000 value: 22.396 - type: mrr_at_3 value: 19.273 - type: mrr_at_5 value: 20.398 - type: ndcg_at_1 value: 15.068000000000001 - type: ndcg_at_10 value: 21.66 - type: ndcg_at_100 value: 27.245 - type: ndcg_at_1000 value: 30.591 - type: ndcg_at_3 value: 17.968999999999998 - type: ndcg_at_5 value: 19.352 - type: precision_at_1 value: 15.068000000000001 - type: precision_at_10 value: 4.326 - type: precision_at_100 value: 0.855 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 8.713999999999999 - type: precision_at_5 value: 6.3469999999999995 - type: recall_at_1 value: 12.416 - type: recall_at_10 value: 30.008000000000003 - type: recall_at_100 value: 54.498999999999995 - type: recall_at_1000 value: 78.32000000000001 - type: recall_at_3 value: 19.79 - type: recall_at_5 value: 23.376 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.36133333333333 - type: map_at_10 value: 18.6895 - type: map_at_100 value: 19.62275 - type: map_at_1000 value: 19.748833333333334 - type: map_at_3 value: 16.8815 - type: map_at_5 value: 17.84133333333334 - type: mrr_at_1 value: 16.093083333333336 - type: mrr_at_10 value: 21.63225 - type: mrr_at_100 value: 22.477333333333334 - type: mrr_at_1000 value: 22.563166666666664 - type: mrr_at_3 value: 19.83 - type: mrr_at_5 value: 20.799166666666668 - type: ndcg_at_1 value: 16.093083333333336 - type: ndcg_at_10 value: 22.30233333333333 - type: ndcg_at_100 value: 27.000333333333337 - type: ndcg_at_1000 value: 30.14883333333333 - type: ndcg_at_3 value: 18.966499999999996 - type: ndcg_at_5 value: 20.425916666666666 - type: precision_at_1 value: 16.093083333333336 - type: precision_at_10 value: 4.062916666666667 - type: precision_at_100 value: 0.7655833333333333 - type: precision_at_1000 value: 0.12208333333333334 - type: precision_at_3 value: 8.848666666666666 - type: precision_at_5 value: 6.400833333333333 - type: recall_at_1 value: 13.36133333333333 - type: recall_at_10 value: 30.32383333333334 - type: recall_at_100 value: 51.808 - type: recall_at_1000 value: 74.64483333333332 - type: recall_at_3 value: 20.884249999999994 - type: recall_at_5 value: 24.67641666666667 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 9.722999999999999 - type: map_at_10 value: 14.280999999999999 - type: map_at_100 value: 15.065000000000001 - type: map_at_1000 value: 15.154 - type: map_at_3 value: 13.004 - type: map_at_5 value: 13.626 - type: mrr_at_1 value: 11.81 - type: mrr_at_10 value: 16.384 - type: mrr_at_100 value: 17.189 - type: mrr_at_1000 value: 17.269000000000002 - type: mrr_at_3 value: 15.082 - type: mrr_at_5 value: 15.711 - type: ndcg_at_1 value: 11.81 - type: ndcg_at_10 value: 17.253 - type: ndcg_at_100 value: 21.404 - type: ndcg_at_1000 value: 24.09 - type: ndcg_at_3 value: 14.716999999999999 - type: ndcg_at_5 value: 15.706000000000001 - type: precision_at_1 value: 11.81 - type: precision_at_10 value: 2.9749999999999996 - type: precision_at_100 value: 0.543 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 6.902 - type: precision_at_5 value: 4.816 - type: recall_at_1 value: 9.722999999999999 - type: recall_at_10 value: 24.569 - type: recall_at_100 value: 43.997 - type: recall_at_1000 value: 64.44 - type: recall_at_3 value: 17.134 - type: recall_at_5 value: 19.72 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.497 - type: map_at_10 value: 10.846 - type: map_at_100 value: 11.498999999999999 - type: map_at_1000 value: 11.618 - type: map_at_3 value: 9.658999999999999 - type: map_at_5 value: 10.298 - type: mrr_at_1 value: 9.119 - type: mrr_at_10 value: 12.992999999999999 - type: mrr_at_100 value: 13.700999999999999 - type: mrr_at_1000 value: 13.797999999999998 - type: mrr_at_3 value: 11.666 - type: mrr_at_5 value: 12.362 - type: ndcg_at_1 value: 9.119 - type: ndcg_at_10 value: 13.308 - type: ndcg_at_100 value: 16.98 - type: ndcg_at_1000 value: 20.488 - type: ndcg_at_3 value: 10.982 - type: ndcg_at_5 value: 12.003 - type: precision_at_1 value: 9.119 - type: precision_at_10 value: 2.4979999999999998 - type: precision_at_100 value: 0.519 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 5.288 - type: precision_at_5 value: 3.8890000000000002 - type: recall_at_1 value: 7.497 - type: recall_at_10 value: 18.817999999999998 - type: recall_at_100 value: 35.893 - type: recall_at_1000 value: 61.966 - type: recall_at_3 value: 12.199 - type: recall_at_5 value: 14.87 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.856 - type: map_at_10 value: 16.685 - type: map_at_100 value: 17.433 - type: map_at_1000 value: 17.558 - type: map_at_3 value: 15.021 - type: map_at_5 value: 15.931999999999999 - type: mrr_at_1 value: 14.179 - type: mrr_at_10 value: 19.398 - type: mrr_at_100 value: 20.153 - type: mrr_at_1000 value: 20.251 - type: mrr_at_3 value: 17.631 - type: mrr_at_5 value: 18.517 - type: ndcg_at_1 value: 14.179 - type: ndcg_at_10 value: 20.061999999999998 - type: ndcg_at_100 value: 24.149 - type: ndcg_at_1000 value: 27.644999999999996 - type: ndcg_at_3 value: 16.794 - type: ndcg_at_5 value: 18.224 - type: precision_at_1 value: 14.179 - type: precision_at_10 value: 3.582 - type: precision_at_100 value: 0.623 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.774 - type: precision_at_5 value: 5.5969999999999995 - type: recall_at_1 value: 11.856 - type: recall_at_10 value: 27.778999999999996 - type: recall_at_100 value: 46.733000000000004 - type: recall_at_1000 value: 72.481 - type: recall_at_3 value: 18.859 - type: recall_at_5 value: 22.435 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.164000000000001 - type: map_at_10 value: 19.317999999999998 - type: map_at_100 value: 20.463 - type: map_at_1000 value: 20.646 - type: map_at_3 value: 17.126 - type: map_at_5 value: 18.056 - type: mrr_at_1 value: 16.601 - type: mrr_at_10 value: 22.62 - type: mrr_at_100 value: 23.601 - type: mrr_at_1000 value: 23.676 - type: mrr_at_3 value: 20.685000000000002 - type: mrr_at_5 value: 21.465999999999998 - type: ndcg_at_1 value: 16.601 - type: ndcg_at_10 value: 23.735999999999997 - type: ndcg_at_100 value: 29.047 - type: ndcg_at_1000 value: 32.323 - type: ndcg_at_3 value: 20.013 - type: ndcg_at_5 value: 21.165 - type: precision_at_1 value: 16.601 - type: precision_at_10 value: 4.7829999999999995 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 9.881 - type: precision_at_5 value: 7.074999999999999 - type: recall_at_1 value: 13.164000000000001 - type: recall_at_10 value: 33.041 - type: recall_at_100 value: 57.907 - type: recall_at_1000 value: 79.887 - type: recall_at_3 value: 21.397 - type: recall_at_5 value: 24.863 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 10.08 - type: map_at_10 value: 14.069 - type: map_at_100 value: 14.860000000000001 - type: map_at_1000 value: 14.968 - type: map_at_3 value: 12.498 - type: map_at_5 value: 13.324 - type: mrr_at_1 value: 10.906 - type: mrr_at_10 value: 15.198999999999998 - type: mrr_at_100 value: 16.003 - type: mrr_at_1000 value: 16.095000000000002 - type: mrr_at_3 value: 13.494 - type: mrr_at_5 value: 14.362 - type: ndcg_at_1 value: 10.906 - type: ndcg_at_10 value: 16.794999999999998 - type: ndcg_at_100 value: 21.434 - type: ndcg_at_1000 value: 24.743000000000002 - type: ndcg_at_3 value: 13.507 - type: ndcg_at_5 value: 14.953 - type: precision_at_1 value: 10.906 - type: precision_at_10 value: 2.791 - type: precision_at_100 value: 0.5559999999999999 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 5.545 - type: precision_at_5 value: 4.14 - type: recall_at_1 value: 10.08 - type: recall_at_10 value: 24.184 - type: recall_at_100 value: 46.967999999999996 - type: recall_at_1000 value: 72.92999999999999 - type: recall_at_3 value: 15.440999999999999 - type: recall_at_5 value: 18.829 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.537 - type: map_at_10 value: 11.465 - type: map_at_100 value: 12.851 - type: map_at_1000 value: 13.045000000000002 - type: map_at_3 value: 9.369 - type: map_at_5 value: 10.331 - type: mrr_at_1 value: 15.244 - type: mrr_at_10 value: 23.593 - type: mrr_at_100 value: 24.772 - type: mrr_at_1000 value: 24.839 - type: mrr_at_3 value: 20.467 - type: mrr_at_5 value: 22.027 - type: ndcg_at_1 value: 15.244 - type: ndcg_at_10 value: 17.288999999999998 - type: ndcg_at_100 value: 23.757 - type: ndcg_at_1000 value: 27.725 - type: ndcg_at_3 value: 13.245000000000001 - type: ndcg_at_5 value: 14.485000000000001 - type: precision_at_1 value: 15.244 - type: precision_at_10 value: 5.733 - type: precision_at_100 value: 1.264 - type: precision_at_1000 value: 0.199 - type: precision_at_3 value: 10.054 - type: precision_at_5 value: 7.9350000000000005 - type: recall_at_1 value: 6.537 - type: recall_at_10 value: 22.046 - type: recall_at_100 value: 44.818000000000005 - type: recall_at_1000 value: 67.676 - type: recall_at_3 value: 12.232 - type: recall_at_5 value: 15.540999999999999 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.304 - type: map_at_10 value: 9.944 - type: map_at_100 value: 14.113000000000001 - type: map_at_1000 value: 15.085 - type: map_at_3 value: 7.228999999999999 - type: map_at_5 value: 8.368 - type: mrr_at_1 value: 43.0 - type: mrr_at_10 value: 53.303999999999995 - type: mrr_at_100 value: 53.979 - type: mrr_at_1000 value: 54.005 - type: mrr_at_3 value: 50.542 - type: mrr_at_5 value: 52.154 - type: ndcg_at_1 value: 31.5 - type: ndcg_at_10 value: 24.235 - type: ndcg_at_100 value: 28.01 - type: ndcg_at_1000 value: 34.724 - type: ndcg_at_3 value: 26.682 - type: ndcg_at_5 value: 25.249 - type: precision_at_1 value: 43.0 - type: precision_at_10 value: 21.65 - type: precision_at_100 value: 6.97 - type: precision_at_1000 value: 1.4449999999999998 - type: precision_at_3 value: 32.25 - type: precision_at_5 value: 27.250000000000004 - type: recall_at_1 value: 4.304 - type: recall_at_10 value: 15.014 - type: recall_at_100 value: 35.115 - type: recall_at_1000 value: 58.52 - type: recall_at_3 value: 8.698 - type: recall_at_5 value: 11.052 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.09 - type: f1 value: 41.3731018097549 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.349 - type: map_at_10 value: 24.917 - type: map_at_100 value: 26.003 - type: map_at_1000 value: 26.072 - type: map_at_3 value: 22.067999999999998 - type: map_at_5 value: 23.610999999999997 - type: mrr_at_1 value: 17.416999999999998 - type: mrr_at_10 value: 26.44 - type: mrr_at_100 value: 27.509 - type: mrr_at_1000 value: 27.57 - type: mrr_at_3 value: 23.422 - type: mrr_at_5 value: 25.063999999999997 - type: ndcg_at_1 value: 17.416999999999998 - type: ndcg_at_10 value: 30.267 - type: ndcg_at_100 value: 35.650999999999996 - type: ndcg_at_1000 value: 37.57 - type: ndcg_at_3 value: 24.303 - type: ndcg_at_5 value: 27.099 - type: precision_at_1 value: 17.416999999999998 - type: precision_at_10 value: 4.9590000000000005 - type: precision_at_100 value: 0.7799999999999999 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 10.536 - type: precision_at_5 value: 7.807 - type: recall_at_1 value: 16.349 - type: recall_at_10 value: 45.678999999999995 - type: recall_at_100 value: 70.541 - type: recall_at_1000 value: 85.36500000000001 - type: recall_at_3 value: 29.42 - type: recall_at_5 value: 36.112 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.478999999999999 - type: map_at_10 value: 11.933 - type: map_at_100 value: 13.078000000000001 - type: map_at_1000 value: 13.267999999999999 - type: map_at_3 value: 9.975000000000001 - type: map_at_5 value: 10.928 - type: mrr_at_1 value: 14.66 - type: mrr_at_10 value: 20.737 - type: mrr_at_100 value: 21.719 - type: mrr_at_1000 value: 21.809 - type: mrr_at_3 value: 18.57 - type: mrr_at_5 value: 19.558 - type: ndcg_at_1 value: 14.66 - type: ndcg_at_10 value: 16.619 - type: ndcg_at_100 value: 22.467000000000002 - type: ndcg_at_1000 value: 26.745 - type: ndcg_at_3 value: 13.547 - type: ndcg_at_5 value: 14.466999999999999 - type: precision_at_1 value: 14.66 - type: precision_at_10 value: 4.8149999999999995 - type: precision_at_100 value: 1.0619999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 9.002 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 7.478999999999999 - type: recall_at_10 value: 21.884 - type: recall_at_100 value: 45.545 - type: recall_at_1000 value: 71.887 - type: recall_at_3 value: 12.485 - type: recall_at_5 value: 15.862000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.628 - type: map_at_10 value: 28.559 - type: map_at_100 value: 29.5 - type: map_at_1000 value: 29.601 - type: map_at_3 value: 26.429000000000002 - type: map_at_5 value: 27.589000000000002 - type: mrr_at_1 value: 41.256 - type: mrr_at_10 value: 48.842999999999996 - type: mrr_at_100 value: 49.523 - type: mrr_at_1000 value: 49.57 - type: mrr_at_3 value: 46.894000000000005 - type: mrr_at_5 value: 48.024 - type: ndcg_at_1 value: 41.256 - type: ndcg_at_10 value: 36.217 - type: ndcg_at_100 value: 40.422000000000004 - type: ndcg_at_1000 value: 42.762 - type: ndcg_at_3 value: 32.275999999999996 - type: ndcg_at_5 value: 34.184 - type: precision_at_1 value: 41.256 - type: precision_at_10 value: 7.838000000000001 - type: precision_at_100 value: 1.119 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 20.207 - type: precision_at_5 value: 13.636999999999999 - type: recall_at_1 value: 20.628 - type: recall_at_10 value: 39.190000000000005 - type: recall_at_100 value: 55.962 - type: recall_at_1000 value: 71.56700000000001 - type: recall_at_3 value: 30.311 - type: recall_at_5 value: 34.092 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.78 - type: ap value: 65.09281598781793 - type: f1 value: 70.56498155979408 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.149 - type: map_at_10 value: 12.494 - type: map_at_100 value: 13.438 - type: map_at_1000 value: 13.544 - type: map_at_3 value: 10.58 - type: map_at_5 value: 11.623 - type: mrr_at_1 value: 7.364 - type: mrr_at_10 value: 12.817 - type: mrr_at_100 value: 13.758000000000001 - type: mrr_at_1000 value: 13.861 - type: mrr_at_3 value: 10.879 - type: mrr_at_5 value: 11.942 - type: ndcg_at_1 value: 7.364 - type: ndcg_at_10 value: 15.787999999999998 - type: ndcg_at_100 value: 20.973 - type: ndcg_at_1000 value: 24.156 - type: ndcg_at_3 value: 11.782 - type: ndcg_at_5 value: 13.675 - type: precision_at_1 value: 7.364 - type: precision_at_10 value: 2.702 - type: precision_at_100 value: 0.539 - type: precision_at_1000 value: 0.08099999999999999 - type: precision_at_3 value: 5.148 - type: precision_at_5 value: 4.043 - type: recall_at_1 value: 7.149 - type: recall_at_10 value: 26.039 - type: recall_at_100 value: 51.405 - type: recall_at_1000 value: 76.97500000000001 - type: recall_at_3 value: 14.979000000000001 - type: recall_at_5 value: 19.553 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.95576835385319 - type: f1 value: 88.06364678376042 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.99726402188783 - type: f1 value: 38.19916053247397 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.79287155346336 - type: f1 value: 61.634629394462934 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.30934767989241 - type: f1 value: 68.77914761769519 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.617349409076375 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.802943866708315 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.431263837648547 - type: mrr value: 30.205900793315156 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.479 - type: map_at_10 value: 7.603 - type: map_at_100 value: 9.725999999999999 - type: map_at_1000 value: 10.84 - type: map_at_3 value: 5.844 - type: map_at_5 value: 6.732 - type: mrr_at_1 value: 33.745999999999995 - type: mrr_at_10 value: 43.516 - type: mrr_at_100 value: 44.190000000000005 - type: mrr_at_1000 value: 44.248 - type: mrr_at_3 value: 41.744 - type: mrr_at_5 value: 42.828 - type: ndcg_at_1 value: 31.424000000000003 - type: ndcg_at_10 value: 24.267 - type: ndcg_at_100 value: 22.416 - type: ndcg_at_1000 value: 31.165 - type: ndcg_at_3 value: 28.349999999999998 - type: ndcg_at_5 value: 26.596999999999998 - type: precision_at_1 value: 33.745999999999995 - type: precision_at_10 value: 18.173000000000002 - type: precision_at_100 value: 6.142 - type: precision_at_1000 value: 1.856 - type: precision_at_3 value: 27.141 - type: precision_at_5 value: 22.91 - type: recall_at_1 value: 3.479 - type: recall_at_10 value: 10.838000000000001 - type: recall_at_100 value: 23.817 - type: recall_at_1000 value: 54.910000000000004 - type: recall_at_3 value: 7.236 - type: recall_at_5 value: 9.003 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 8.413 - type: map_at_10 value: 15.137 - type: map_at_100 value: 16.393 - type: map_at_1000 value: 16.492 - type: map_at_3 value: 12.584999999999999 - type: map_at_5 value: 13.963000000000001 - type: mrr_at_1 value: 9.762 - type: mrr_at_10 value: 16.813 - type: mrr_at_100 value: 17.98 - type: mrr_at_1000 value: 18.064 - type: mrr_at_3 value: 14.257 - type: mrr_at_5 value: 15.651000000000002 - type: ndcg_at_1 value: 9.733 - type: ndcg_at_10 value: 19.543 - type: ndcg_at_100 value: 25.965 - type: ndcg_at_1000 value: 28.663 - type: ndcg_at_3 value: 14.308000000000002 - type: ndcg_at_5 value: 16.771 - type: precision_at_1 value: 9.733 - type: precision_at_10 value: 3.7249999999999996 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 6.856 - type: precision_at_5 value: 5.475 - type: recall_at_1 value: 8.413 - type: recall_at_10 value: 31.668000000000003 - type: recall_at_100 value: 61.551 - type: recall_at_1000 value: 82.228 - type: recall_at_3 value: 17.669 - type: recall_at_5 value: 23.488999999999997 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.522 - type: map_at_10 value: 76.068 - type: map_at_100 value: 76.858 - type: map_at_1000 value: 76.89099999999999 - type: map_at_3 value: 73.07000000000001 - type: map_at_5 value: 74.883 - type: mrr_at_1 value: 73.11 - type: mrr_at_10 value: 80.134 - type: mrr_at_100 value: 80.403 - type: mrr_at_1000 value: 80.411 - type: mrr_at_3 value: 78.728 - type: mrr_at_5 value: 79.60000000000001 - type: ndcg_at_1 value: 73.1 - type: ndcg_at_10 value: 80.595 - type: ndcg_at_100 value: 82.749 - type: ndcg_at_1000 value: 83.14099999999999 - type: ndcg_at_3 value: 77.021 - type: ndcg_at_5 value: 78.846 - type: precision_at_1 value: 73.1 - type: precision_at_10 value: 12.206999999999999 - type: precision_at_100 value: 1.459 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.36 - type: precision_at_5 value: 22.09 - type: recall_at_1 value: 63.522 - type: recall_at_10 value: 89.32600000000001 - type: recall_at_100 value: 97.35000000000001 - type: recall_at_1000 value: 99.613 - type: recall_at_3 value: 79.074 - type: recall_at_5 value: 84.143 - type: map_at_1 value: 3.053 - type: map_at_10 value: 6.912999999999999 - type: map_at_100 value: 8.261000000000001 - type: map_at_1000 value: 8.530999999999999 - type: map_at_3 value: 5.094 - type: map_at_5 value: 5.997 - type: mrr_at_1 value: 15.0 - type: mrr_at_10 value: 22.795 - type: mrr_at_100 value: 24.008 - type: mrr_at_1000 value: 24.099999999999998 - type: mrr_at_3 value: 20.1 - type: mrr_at_5 value: 21.685 - type: ndcg_at_1 value: 15.0 - type: ndcg_at_10 value: 12.386999999999999 - type: ndcg_at_100 value: 18.533 - type: ndcg_at_1000 value: 23.955000000000002 - type: ndcg_at_3 value: 11.75 - type: ndcg_at_5 value: 10.285 - type: precision_at_1 value: 15.0 - type: precision_at_10 value: 6.36 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.28300000000000003 - type: precision_at_3 value: 10.767 - type: precision_at_5 value: 8.9 - type: recall_at_1 value: 3.053 - type: recall_at_10 value: 12.873000000000001 - type: recall_at_100 value: 30.982 - type: recall_at_1000 value: 57.489999999999995 - type: recall_at_3 value: 6.553000000000001 - type: recall_at_5 value: 9.013 - type: map_at_1 value: 0.148 - type: map_at_10 value: 0.971 - type: map_at_100 value: 4.65 - type: map_at_1000 value: 11.509 - type: map_at_3 value: 0.366 - type: map_at_5 value: 0.5599999999999999 - type: mrr_at_1 value: 62.0 - type: mrr_at_10 value: 70.069 - type: mrr_at_100 value: 70.455 - type: mrr_at_1000 value: 70.455 - type: mrr_at_3 value: 68.0 - type: mrr_at_5 value: 69.19999999999999 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 45.729 - type: ndcg_at_100 value: 32.757 - type: ndcg_at_1000 value: 29.631999999999998 - type: ndcg_at_3 value: 50.407999999999994 - type: ndcg_at_5 value: 48.208 - type: precision_at_1 value: 62.0 - type: precision_at_10 value: 47.8 - type: precision_at_100 value: 33.72 - type: precision_at_1000 value: 14.238000000000001 - type: precision_at_3 value: 53.333 - type: precision_at_5 value: 50.8 - type: recall_at_1 value: 0.148 - type: recall_at_10 value: 1.143 - type: recall_at_100 value: 7.219 - type: recall_at_1000 value: 28.294999999999998 - type: recall_at_3 value: 0.392 - type: recall_at_5 value: 0.628 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.546512756347916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.07923662495948 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.6733681207629 - type: cos_sim_spearman value: 64.67529822790183 - type: euclidean_pearson value: 69.13481548437119 - type: euclidean_spearman value: 64.67521597440148 - type: manhattan_pearson value: 69.01619022585454 - type: manhattan_spearman value: 64.8728374071917 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.06681953798454 - type: cos_sim_spearman value: 62.247506425866405 - type: euclidean_pearson value: 68.05816014766324 - type: euclidean_spearman value: 62.24902354181767 - type: manhattan_pearson value: 66.68543187933726 - type: manhattan_spearman value: 61.438544148098664 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 76.53983672284885 - type: cos_sim_spearman value: 77.2760080817994 - type: euclidean_pearson value: 76.7796065728204 - type: euclidean_spearman value: 77.27600787572996 - type: manhattan_pearson value: 76.37651419577129 - type: manhattan_spearman value: 76.85568457177312 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 76.2085441120845 - type: cos_sim_spearman value: 71.91409062241355 - type: euclidean_pearson value: 74.52730472762947 - type: euclidean_spearman value: 71.91409512725335 - type: manhattan_pearson value: 74.53275469819042 - type: manhattan_spearman value: 71.9720930787841 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.2427339046162 - type: cos_sim_spearman value: 79.75345017876988 - type: euclidean_pearson value: 79.31395774152486 - type: euclidean_spearman value: 79.75345672749796 - type: manhattan_pearson value: 79.24199253925532 - type: manhattan_spearman value: 79.64057053536243 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.64452384480809 - type: cos_sim_spearman value: 76.26343905510407 - type: euclidean_pearson value: 75.64112078051633 - type: euclidean_spearman value: 76.26343823222666 - type: manhattan_pearson value: 75.32718790811802 - type: manhattan_spearman value: 75.9420892784719 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.67406953406964 - type: cos_sim_spearman value: 85.96709815630739 - type: euclidean_pearson value: 84.71863724469544 - type: euclidean_spearman value: 85.96709815630739 - type: manhattan_pearson value: 85.07894738833434 - type: manhattan_spearman value: 86.57110045700985 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.318066667301615 - type: cos_sim_spearman value: 63.07956002739231 - type: euclidean_pearson value: 62.464248268498814 - type: euclidean_spearman value: 63.07956002739231 - type: manhattan_pearson value: 62.04813588964373 - type: manhattan_spearman value: 61.83898606879604 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 77.25982574948274 - type: cos_sim_spearman value: 75.4051305973876 - type: euclidean_pearson value: 77.1987828515963 - type: euclidean_spearman value: 75.40516069202422 - type: manhattan_pearson value: 77.04099633595793 - type: manhattan_spearman value: 75.32222510947251 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.10127087089839 - type: mrr value: 90.62288020621355 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 35.5 - type: map_at_10 value: 45.238 - type: map_at_100 value: 46.135999999999996 - type: map_at_1000 value: 46.181 - type: map_at_3 value: 42.329 - type: map_at_5 value: 44.054 - type: mrr_at_1 value: 37.667 - type: mrr_at_10 value: 46.661 - type: mrr_at_100 value: 47.378 - type: mrr_at_1000 value: 47.418 - type: mrr_at_3 value: 43.944 - type: mrr_at_5 value: 45.528 - type: ndcg_at_1 value: 37.667 - type: ndcg_at_10 value: 50.63999999999999 - type: ndcg_at_100 value: 54.885 - type: ndcg_at_1000 value: 56.274 - type: ndcg_at_3 value: 44.891999999999996 - type: ndcg_at_5 value: 47.788000000000004 - type: precision_at_1 value: 37.667 - type: precision_at_10 value: 7.3 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.6 - type: recall_at_1 value: 35.5 - type: recall_at_10 value: 66.178 - type: recall_at_100 value: 85.9 - type: recall_at_1000 value: 97.1 - type: recall_at_3 value: 50.306 - type: recall_at_5 value: 57.443999999999996 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71386138613862 - type: cos_sim_ap value: 90.20131932554314 - type: cos_sim_f1 value: 84.7749114820435 - type: cos_sim_precision value: 85.7727737973388 - type: cos_sim_recall value: 83.8 - type: dot_accuracy value: 99.71386138613862 - type: dot_ap value: 90.20131927652947 - type: dot_f1 value: 84.7749114820435 - type: dot_precision value: 85.7727737973388 - type: dot_recall value: 83.8 - type: euclidean_accuracy value: 99.71386138613862 - type: euclidean_ap value: 90.20131927652946 - type: euclidean_f1 value: 84.7749114820435 - type: euclidean_precision value: 85.7727737973388 - type: euclidean_recall value: 83.8 - type: manhattan_accuracy value: 99.7059405940594 - type: manhattan_ap value: 90.00682250828238 - type: manhattan_f1 value: 84.44211629125196 - type: manhattan_precision value: 88.66886688668868 - type: manhattan_recall value: 80.60000000000001 - type: max_accuracy value: 99.71386138613862 - type: max_ap value: 90.20131932554314 - type: max_f1 value: 84.7749114820435 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 48.18939518021159 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.748387331082416 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.24644967679195 - type: mrr value: 43.66944126135303 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.88359913790285 - type: cos_sim_spearman value: 29.20319307230353 - type: dot_pearson value: 29.883592420103206 - type: dot_spearman value: 29.228231500970136 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.22 - type: map_at_10 value: 6.635000000000001 - type: map_at_100 value: 10.873 - type: map_at_1000 value: 12.415 - type: map_at_3 value: 2.8240000000000003 - type: map_at_5 value: 4.111 - type: mrr_at_1 value: 14.285999999999998 - type: mrr_at_10 value: 31.857999999999997 - type: mrr_at_100 value: 33.049 - type: mrr_at_1000 value: 33.049 - type: mrr_at_3 value: 25.85 - type: mrr_at_5 value: 29.218 - type: ndcg_at_1 value: 12.245000000000001 - type: ndcg_at_10 value: 18.618000000000002 - type: ndcg_at_100 value: 28.488000000000003 - type: ndcg_at_1000 value: 41.208 - type: ndcg_at_3 value: 15.045 - type: ndcg_at_5 value: 16.359 - type: precision_at_1 value: 14.285999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 6.5920000000000005 - type: precision_at_1000 value: 1.471 - type: precision_at_3 value: 18.367 - type: precision_at_5 value: 18.776 - type: recall_at_1 value: 1.22 - type: recall_at_10 value: 13.763 - type: recall_at_100 value: 40.107 - type: recall_at_1000 value: 79.06800000000001 - type: recall_at_3 value: 4.2540000000000004 - type: recall_at_5 value: 7.142999999999999 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.82600000000001 - type: ap value: 14.59656193783295 - type: f1 value: 55.237720537754875 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 55.387662705149964 - type: f1 value: 55.62292803889264 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.53590896395144 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 81.57000655659535 - type: cos_sim_ap value: 57.187256107173354 - type: cos_sim_f1 value: 54.94480738905159 - type: cos_sim_precision value: 47.93632075471698 - type: cos_sim_recall value: 64.35356200527704 - type: dot_accuracy value: 81.57000655659535 - type: dot_ap value: 57.187234074371496 - type: dot_f1 value: 54.94480738905159 - type: dot_precision value: 47.93632075471698 - type: dot_recall value: 64.35356200527704 - type: euclidean_accuracy value: 81.57000655659535 - type: euclidean_ap value: 57.18724422350816 - type: euclidean_f1 value: 54.94480738905159 - type: euclidean_precision value: 47.93632075471698 - type: euclidean_recall value: 64.35356200527704 - type: manhattan_accuracy value: 81.71902008702389 - type: manhattan_ap value: 57.51605309414705 - type: manhattan_f1 value: 55.16339869281046 - type: manhattan_precision value: 50.18378378378379 - type: manhattan_recall value: 61.24010554089709 - type: max_accuracy value: 81.71902008702389 - type: max_ap value: 57.51605309414705 - type: max_f1 value: 55.16339869281046 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.09977878682035 - type: cos_sim_ap value: 81.948747937846 - type: cos_sim_f1 value: 74.04089724292375 - type: cos_sim_precision value: 70.7599466704091 - type: cos_sim_recall value: 77.64089929165382 - type: dot_accuracy value: 87.09977878682035 - type: dot_ap value: 81.94874861792225 - type: dot_f1 value: 74.04089724292375 - type: dot_precision value: 70.7599466704091 - type: dot_recall value: 77.64089929165382 - type: euclidean_accuracy value: 87.09977878682035 - type: euclidean_ap value: 81.94875280390386 - type: euclidean_f1 value: 74.04089724292375 - type: euclidean_precision value: 70.7599466704091 - type: euclidean_recall value: 77.64089929165382 - type: manhattan_accuracy value: 87.19292117825125 - type: manhattan_ap value: 82.13752985145429 - type: manhattan_f1 value: 74.36426623424485 - type: manhattan_precision value: 71.32051463311183 - type: manhattan_recall value: 77.6793963658762 - type: max_accuracy value: 87.19292117825125 - type: max_ap value: 82.13752985145429 - type: max_f1 value: 74.36426623424485 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/l3_wl	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:25:40	2025-01-09T11:25:45	0	--- tags: - mteb model-index: - name: l3_wordllama_fixed results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.40298507462687 - type: ap value: 28.677454675181384 - type: f1 value: 60.58324071299079 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.75847499999999 - type: ap value: 59.00482910406265 - type: f1 value: 63.59920748914567 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 32.09 - type: f1 value: 31.527306414565835 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.413 - type: map_at_10 value: 35.176 - type: map_at_100 value: 36.489 - type: map_at_1000 value: 36.507 - type: map_at_3 value: 30.69 - type: map_at_5 value: 32.859 - type: mrr_at_1 value: 21.124000000000002 - type: mrr_at_10 value: 35.44 - type: mrr_at_100 value: 36.753 - type: mrr_at_1000 value: 36.77 - type: mrr_at_3 value: 30.915 - type: mrr_at_5 value: 33.113 - type: ndcg_at_1 value: 20.413 - type: ndcg_at_10 value: 43.565 - type: ndcg_at_100 value: 49.329 - type: ndcg_at_1000 value: 49.757 - type: ndcg_at_3 value: 34.143 - type: ndcg_at_5 value: 38.046 - type: precision_at_1 value: 20.413 - type: precision_at_10 value: 7.048 - type: precision_at_100 value: 0.9610000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.723 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 20.413 - type: recall_at_10 value: 70.48400000000001 - type: recall_at_100 value: 96.088 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.168 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.885229242790935 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.49720713710708 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.61953366105678 - type: mrr value: 70.12344457635315 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 76.26075421266883 - type: cos_sim_spearman value: 71.32873370732024 - type: euclidean_pearson value: 74.59312194402976 - type: euclidean_spearman value: 71.32873370732024 - type: manhattan_pearson value: 74.5892678336525 - type: manhattan_spearman value: 71.02450990790472 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.68506493506494 - type: f1 value: 72.88555102531198 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.29089107203252 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.19965378718348 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.508 - type: map_at_10 value: 29.088 - type: map_at_100 value: 30.279 - type: map_at_1000 value: 30.445 - type: map_at_3 value: 26.552999999999997 - type: map_at_5 value: 27.939000000000004 - type: mrr_at_1 value: 26.466 - type: mrr_at_10 value: 34.171 - type: mrr_at_100 value: 35.059000000000005 - type: mrr_at_1000 value: 35.137 - type: mrr_at_3 value: 31.855 - type: mrr_at_5 value: 33.093 - type: ndcg_at_1 value: 26.466 - type: ndcg_at_10 value: 34.097 - type: ndcg_at_100 value: 39.612 - type: ndcg_at_1000 value: 42.819 - type: ndcg_at_3 value: 29.918 - type: ndcg_at_5 value: 31.683 - type: precision_at_1 value: 26.466 - type: precision_at_10 value: 6.422999999999999 - type: precision_at_100 value: 1.15 - type: precision_at_1000 value: 0.17700000000000002 - type: precision_at_3 value: 13.972000000000001 - type: precision_at_5 value: 10.129000000000001 - type: recall_at_1 value: 21.508 - type: recall_at_10 value: 43.699 - type: recall_at_100 value: 68.404 - type: recall_at_1000 value: 89.687 - type: recall_at_3 value: 31.773 - type: recall_at_5 value: 36.687 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.865 - type: map_at_10 value: 23.164 - type: map_at_100 value: 24.15 - type: map_at_1000 value: 24.288 - type: map_at_3 value: 20.97 - type: map_at_5 value: 22.277 - type: mrr_at_1 value: 21.401 - type: mrr_at_10 value: 27.614 - type: mrr_at_100 value: 28.395 - type: mrr_at_1000 value: 28.469 - type: mrr_at_3 value: 25.594 - type: mrr_at_5 value: 26.735 - type: ndcg_at_1 value: 21.401 - type: ndcg_at_10 value: 27.343 - type: ndcg_at_100 value: 31.726 - type: ndcg_at_1000 value: 34.586 - type: ndcg_at_3 value: 23.723 - type: ndcg_at_5 value: 25.524 - type: precision_at_1 value: 21.401 - type: precision_at_10 value: 5.236 - type: precision_at_100 value: 0.9650000000000001 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 11.444 - type: precision_at_5 value: 8.497 - type: recall_at_1 value: 16.865 - type: recall_at_10 value: 35.209 - type: recall_at_100 value: 54.371 - type: recall_at_1000 value: 73.651 - type: recall_at_3 value: 24.943 - type: recall_at_5 value: 29.634 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.424000000000003 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.461999999999996 - type: map_at_1000 value: 35.551 - type: map_at_3 value: 31.694 - type: map_at_5 value: 33.111000000000004 - type: mrr_at_1 value: 29.215999999999998 - type: mrr_at_10 value: 37.333 - type: mrr_at_100 value: 38.223 - type: mrr_at_1000 value: 38.282 - type: mrr_at_3 value: 35.004999999999995 - type: mrr_at_5 value: 36.272 - type: ndcg_at_1 value: 29.215999999999998 - type: ndcg_at_10 value: 39.309 - type: ndcg_at_100 value: 44.718999999999994 - type: ndcg_at_1000 value: 46.877 - type: ndcg_at_3 value: 34.449999999999996 - type: ndcg_at_5 value: 36.675999999999995 - type: precision_at_1 value: 29.215999999999998 - type: precision_at_10 value: 6.483 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 15.298 - type: precision_at_5 value: 10.734 - type: recall_at_1 value: 25.424000000000003 - type: recall_at_10 value: 51.464 - type: recall_at_100 value: 75.87 - type: recall_at_1000 value: 91.77300000000001 - type: recall_at_3 value: 38.396 - type: recall_at_5 value: 43.759 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 14.674000000000001 - type: map_at_10 value: 18.984 - type: map_at_100 value: 19.867 - type: map_at_1000 value: 19.975 - type: map_at_3 value: 17.488999999999997 - type: map_at_5 value: 18.412 - type: mrr_at_1 value: 15.818999999999999 - type: mrr_at_10 value: 20.472 - type: mrr_at_100 value: 21.342 - type: mrr_at_1000 value: 21.431 - type: mrr_at_3 value: 18.908 - type: mrr_at_5 value: 19.811999999999998 - type: ndcg_at_1 value: 15.818999999999999 - type: ndcg_at_10 value: 21.823 - type: ndcg_at_100 value: 27.0 - type: ndcg_at_1000 value: 30.064999999999998 - type: ndcg_at_3 value: 18.776 - type: ndcg_at_5 value: 20.395 - type: precision_at_1 value: 15.818999999999999 - type: precision_at_10 value: 3.367 - type: precision_at_100 value: 0.649 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.797 - type: precision_at_5 value: 5.582 - type: recall_at_1 value: 14.674000000000001 - type: recall_at_10 value: 29.087000000000003 - type: recall_at_100 value: 54.52 - type: recall_at_1000 value: 78.27 - type: recall_at_3 value: 21.075 - type: recall_at_5 value: 24.92 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 8.123 - type: map_at_10 value: 13.483 - type: map_at_100 value: 14.457999999999998 - type: map_at_1000 value: 14.579 - type: map_at_3 value: 11.271 - type: map_at_5 value: 12.418 - type: mrr_at_1 value: 10.323 - type: mrr_at_10 value: 16.244 - type: mrr_at_100 value: 17.186 - type: mrr_at_1000 value: 17.27 - type: mrr_at_3 value: 13.91 - type: mrr_at_5 value: 15.116 - type: ndcg_at_1 value: 10.323 - type: ndcg_at_10 value: 17.366999999999997 - type: ndcg_at_100 value: 22.553 - type: ndcg_at_1000 value: 25.817 - type: ndcg_at_3 value: 12.895000000000001 - type: ndcg_at_5 value: 14.856 - type: precision_at_1 value: 10.323 - type: precision_at_10 value: 3.5069999999999997 - type: precision_at_100 value: 0.711 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 6.3020000000000005 - type: precision_at_5 value: 5.0 - type: recall_at_1 value: 8.123 - type: recall_at_10 value: 26.889000000000003 - type: recall_at_100 value: 50.397999999999996 - type: recall_at_1000 value: 74.244 - type: recall_at_3 value: 14.691 - type: recall_at_5 value: 19.503 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.607000000000003 - type: map_at_10 value: 25.596000000000004 - type: map_at_100 value: 26.984 - type: map_at_1000 value: 27.125 - type: map_at_3 value: 22.917 - type: map_at_5 value: 24.201 - type: mrr_at_1 value: 22.907 - type: mrr_at_10 value: 30.384 - type: mrr_at_100 value: 31.432 - type: mrr_at_1000 value: 31.5 - type: mrr_at_3 value: 27.703 - type: mrr_at_5 value: 29.137 - type: ndcg_at_1 value: 22.907 - type: ndcg_at_10 value: 30.824 - type: ndcg_at_100 value: 37.265 - type: ndcg_at_1000 value: 40.191 - type: ndcg_at_3 value: 25.913000000000004 - type: ndcg_at_5 value: 27.849 - type: precision_at_1 value: 22.907 - type: precision_at_10 value: 5.9479999999999995 - type: precision_at_100 value: 1.094 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 12.384 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 18.607000000000003 - type: recall_at_10 value: 42.082 - type: recall_at_100 value: 70.018 - type: recall_at_1000 value: 90.003 - type: recall_at_3 value: 27.932000000000002 - type: recall_at_5 value: 32.975 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.52 - type: map_at_10 value: 21.61 - type: map_at_100 value: 22.827 - type: map_at_1000 value: 22.964000000000002 - type: map_at_3 value: 19.500999999999998 - type: map_at_5 value: 20.798 - type: mrr_at_1 value: 17.694 - type: mrr_at_10 value: 25.161 - type: mrr_at_100 value: 26.180999999999997 - type: mrr_at_1000 value: 26.269 - type: mrr_at_3 value: 23.116 - type: mrr_at_5 value: 24.412 - type: ndcg_at_1 value: 17.694 - type: ndcg_at_10 value: 25.924000000000003 - type: ndcg_at_100 value: 31.615 - type: ndcg_at_1000 value: 34.955000000000005 - type: ndcg_at_3 value: 22.161 - type: ndcg_at_5 value: 24.16 - type: precision_at_1 value: 17.694 - type: precision_at_10 value: 4.874 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 10.731 - type: precision_at_5 value: 8.014000000000001 - type: recall_at_1 value: 14.52 - type: recall_at_10 value: 35.369 - type: recall_at_100 value: 60.0 - type: recall_at_1000 value: 83.66799999999999 - type: recall_at_3 value: 25.058999999999997 - type: recall_at_5 value: 30.131999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.35675 - type: map_at_10 value: 21.087750000000007 - type: map_at_100 value: 22.12925 - type: map_at_1000 value: 22.262 - type: map_at_3 value: 19.156249999999996 - type: map_at_5 value: 20.202916666666663 - type: mrr_at_1 value: 18.301583333333333 - type: mrr_at_10 value: 24.283083333333334 - type: mrr_at_100 value: 25.176583333333337 - type: mrr_at_1000 value: 25.262083333333337 - type: mrr_at_3 value: 22.38533333333333 - type: mrr_at_5 value: 23.408 - type: ndcg_at_1 value: 18.301583333333333 - type: ndcg_at_10 value: 24.931416666666667 - type: ndcg_at_100 value: 30.107249999999997 - type: ndcg_at_1000 value: 33.292500000000004 - type: ndcg_at_3 value: 21.380833333333335 - type: ndcg_at_5 value: 22.965416666666663 - type: precision_at_1 value: 18.301583333333333 - type: precision_at_10 value: 4.475583333333334 - type: precision_at_100 value: 0.84875 - type: precision_at_1000 value: 0.13066666666666668 - type: precision_at_3 value: 9.858500000000001 - type: precision_at_5 value: 7.125333333333334 - type: recall_at_1 value: 15.35675 - type: recall_at_10 value: 33.385666666666665 - type: recall_at_100 value: 57.03541666666667 - type: recall_at_1000 value: 80.00874999999999 - type: recall_at_3 value: 23.440833333333337 - type: recall_at_5 value: 27.48841666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 13.023000000000001 - type: map_at_10 value: 17.116999999999997 - type: map_at_100 value: 18.016 - type: map_at_1000 value: 18.124000000000002 - type: map_at_3 value: 15.654000000000002 - type: map_at_5 value: 16.494 - type: mrr_at_1 value: 14.877 - type: mrr_at_10 value: 19.061 - type: mrr_at_100 value: 19.933 - type: mrr_at_1000 value: 20.027 - type: mrr_at_3 value: 17.740000000000002 - type: mrr_at_5 value: 18.384 - type: ndcg_at_1 value: 14.877 - type: ndcg_at_10 value: 19.991999999999997 - type: ndcg_at_100 value: 24.836 - type: ndcg_at_1000 value: 27.922000000000004 - type: ndcg_at_3 value: 17.221 - type: ndcg_at_5 value: 18.496000000000002 - type: precision_at_1 value: 14.877 - type: precision_at_10 value: 3.298 - type: precision_at_100 value: 0.629 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 7.617999999999999 - type: precision_at_5 value: 5.428999999999999 - type: recall_at_1 value: 13.023000000000001 - type: recall_at_10 value: 27.064 - type: recall_at_100 value: 49.971 - type: recall_at_1000 value: 73.195 - type: recall_at_3 value: 19.273 - type: recall_at_5 value: 22.465 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.86 - type: map_at_10 value: 12.806999999999999 - type: map_at_100 value: 13.55 - type: map_at_1000 value: 13.684 - type: map_at_3 value: 11.368 - type: map_at_5 value: 12.106 - type: mrr_at_1 value: 10.943 - type: mrr_at_10 value: 15.397 - type: mrr_at_100 value: 16.139 - type: mrr_at_1000 value: 16.242 - type: mrr_at_3 value: 13.805 - type: mrr_at_5 value: 14.601 - type: ndcg_at_1 value: 10.943 - type: ndcg_at_10 value: 15.693999999999999 - type: ndcg_at_100 value: 19.869 - type: ndcg_at_1000 value: 23.579 - type: ndcg_at_3 value: 12.920000000000002 - type: ndcg_at_5 value: 14.054 - type: precision_at_1 value: 10.943 - type: precision_at_10 value: 2.97 - type: precision_at_100 value: 0.609 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 6.148 - type: precision_at_5 value: 4.529 - type: recall_at_1 value: 8.86 - type: recall_at_10 value: 22.041 - type: recall_at_100 value: 41.528 - type: recall_at_1000 value: 68.917 - type: recall_at_3 value: 14.257 - type: recall_at_5 value: 17.191000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 14.508 - type: map_at_10 value: 19.814999999999998 - type: map_at_100 value: 20.761 - type: map_at_1000 value: 20.899 - type: map_at_3 value: 17.959 - type: map_at_5 value: 18.877 - type: mrr_at_1 value: 17.444000000000003 - type: mrr_at_10 value: 23.067 - type: mrr_at_100 value: 23.906 - type: mrr_at_1000 value: 24.015 - type: mrr_at_3 value: 21.191 - type: mrr_at_5 value: 22.124 - type: ndcg_at_1 value: 17.444000000000003 - type: ndcg_at_10 value: 23.519000000000002 - type: ndcg_at_100 value: 28.546 - type: ndcg_at_1000 value: 32.243 - type: ndcg_at_3 value: 19.958000000000002 - type: ndcg_at_5 value: 21.391 - type: precision_at_1 value: 17.444000000000003 - type: precision_at_10 value: 4.104 - type: precision_at_100 value: 0.758 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 9.142 - type: precision_at_5 value: 6.474 - type: recall_at_1 value: 14.508 - type: recall_at_10 value: 31.788 - type: recall_at_100 value: 55.047999999999995 - type: recall_at_1000 value: 82.155 - type: recall_at_3 value: 21.857 - type: recall_at_5 value: 25.549 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.733 - type: map_at_10 value: 21.721 - type: map_at_100 value: 22.986 - type: map_at_1000 value: 23.198 - type: map_at_3 value: 20.229 - type: map_at_5 value: 21.066 - type: mrr_at_1 value: 19.96 - type: mrr_at_10 value: 25.683 - type: mrr_at_100 value: 26.662000000000003 - type: mrr_at_1000 value: 26.749000000000002 - type: mrr_at_3 value: 24.209 - type: mrr_at_5 value: 25.049 - type: ndcg_at_1 value: 19.96 - type: ndcg_at_10 value: 25.413999999999998 - type: ndcg_at_100 value: 30.916 - type: ndcg_at_1000 value: 34.678 - type: ndcg_at_3 value: 23.138 - type: ndcg_at_5 value: 24.169 - type: precision_at_1 value: 19.96 - type: precision_at_10 value: 4.743 - type: precision_at_100 value: 1.126 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 10.935 - type: precision_at_5 value: 7.707999999999999 - type: recall_at_1 value: 16.733 - type: recall_at_10 value: 31.512 - type: recall_at_100 value: 57.079 - type: recall_at_1000 value: 82.661 - type: recall_at_3 value: 24.252000000000002 - type: recall_at_5 value: 27.317000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.436 - type: map_at_10 value: 15.35 - type: map_at_100 value: 16.211000000000002 - type: map_at_1000 value: 16.311999999999998 - type: map_at_3 value: 14.27 - type: map_at_5 value: 14.735999999999999 - type: mrr_at_1 value: 12.568999999999999 - type: mrr_at_10 value: 16.81 - type: mrr_at_100 value: 17.660999999999998 - type: mrr_at_1000 value: 17.754 - type: mrr_at_3 value: 15.588 - type: mrr_at_5 value: 16.161 - type: ndcg_at_1 value: 12.568999999999999 - type: ndcg_at_10 value: 17.871000000000002 - type: ndcg_at_100 value: 22.63 - type: ndcg_at_1000 value: 25.778000000000002 - type: ndcg_at_3 value: 15.497 - type: ndcg_at_5 value: 16.332 - type: precision_at_1 value: 12.568999999999999 - type: precision_at_10 value: 2.754 - type: precision_at_100 value: 0.551 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.531000000000001 - type: precision_at_5 value: 4.399 - type: recall_at_1 value: 11.436 - type: recall_at_10 value: 24.424 - type: recall_at_100 value: 47.217999999999996 - type: recall_at_1000 value: 71.881 - type: recall_at_3 value: 17.782 - type: recall_at_5 value: 19.729 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.822 - type: map_at_10 value: 12.872 - type: map_at_100 value: 14.504 - type: map_at_1000 value: 14.712 - type: map_at_3 value: 10.357 - type: map_at_5 value: 11.700000000000001 - type: mrr_at_1 value: 15.895999999999999 - type: mrr_at_10 value: 26.407999999999998 - type: mrr_at_100 value: 27.528999999999996 - type: mrr_at_1000 value: 27.586 - type: mrr_at_3 value: 22.714000000000002 - type: mrr_at_5 value: 24.762999999999998 - type: ndcg_at_1 value: 15.895999999999999 - type: ndcg_at_10 value: 19.643 - type: ndcg_at_100 value: 26.863999999999997 - type: ndcg_at_1000 value: 30.804 - type: ndcg_at_3 value: 14.914 - type: ndcg_at_5 value: 16.723 - type: precision_at_1 value: 15.895999999999999 - type: precision_at_10 value: 6.612 - type: precision_at_100 value: 1.434 - type: precision_at_1000 value: 0.216 - type: precision_at_3 value: 11.488 - type: precision_at_5 value: 9.354999999999999 - type: recall_at_1 value: 6.822 - type: recall_at_10 value: 25.478 - type: recall_at_100 value: 50.94 - type: recall_at_1000 value: 73.264 - type: recall_at_3 value: 14.228 - type: recall_at_5 value: 18.91 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.601999999999999 - type: map_at_10 value: 9.225999999999999 - type: map_at_100 value: 12.692 - type: map_at_1000 value: 13.65 - type: map_at_3 value: 6.883 - type: map_at_5 value: 7.904 - type: mrr_at_1 value: 34.0 - type: mrr_at_10 value: 45.83 - type: mrr_at_100 value: 46.608 - type: mrr_at_1000 value: 46.635 - type: mrr_at_3 value: 42.583 - type: mrr_at_5 value: 44.721 - type: ndcg_at_1 value: 24.75 - type: ndcg_at_10 value: 21.092 - type: ndcg_at_100 value: 25.288 - type: ndcg_at_1000 value: 32.550000000000004 - type: ndcg_at_3 value: 22.808999999999997 - type: ndcg_at_5 value: 21.931 - type: precision_at_1 value: 34.0 - type: precision_at_10 value: 18.525 - type: precision_at_100 value: 6.265 - type: precision_at_1000 value: 1.395 - type: precision_at_3 value: 27.500000000000004 - type: precision_at_5 value: 23.799999999999997 - type: recall_at_1 value: 4.601999999999999 - type: recall_at_10 value: 13.578000000000001 - type: recall_at_100 value: 32.438 - type: recall_at_1000 value: 57.067 - type: recall_at_3 value: 8.013 - type: recall_at_5 value: 10.057 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 39.0 - type: f1 value: 35.038106148143335 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.577 - type: map_at_10 value: 25.397 - type: map_at_100 value: 26.493 - type: map_at_1000 value: 26.56 - type: map_at_3 value: 22.523 - type: map_at_5 value: 24.102 - type: mrr_at_1 value: 17.717 - type: mrr_at_10 value: 26.999000000000002 - type: mrr_at_100 value: 28.084999999999997 - type: mrr_at_1000 value: 28.144999999999996 - type: mrr_at_3 value: 24.01 - type: mrr_at_5 value: 25.669999999999998 - type: ndcg_at_1 value: 17.717 - type: ndcg_at_10 value: 30.836999999999996 - type: ndcg_at_100 value: 36.278 - type: ndcg_at_1000 value: 38.139 - type: ndcg_at_3 value: 24.868000000000002 - type: ndcg_at_5 value: 27.701999999999998 - type: precision_at_1 value: 17.717 - type: precision_at_10 value: 5.0569999999999995 - type: precision_at_100 value: 0.791 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 8.004999999999999 - type: recall_at_1 value: 16.577 - type: recall_at_10 value: 46.451 - type: recall_at_100 value: 71.61800000000001 - type: recall_at_1000 value: 85.902 - type: recall_at_3 value: 30.130000000000003 - type: recall_at_5 value: 36.902 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.0680000000000005 - type: map_at_10 value: 12.424 - type: map_at_100 value: 13.750000000000002 - type: map_at_1000 value: 13.963999999999999 - type: map_at_3 value: 10.41 - type: map_at_5 value: 11.459999999999999 - type: mrr_at_1 value: 14.506 - type: mrr_at_10 value: 21.644 - type: mrr_at_100 value: 22.708000000000002 - type: mrr_at_1000 value: 22.811 - type: mrr_at_3 value: 19.084 - type: mrr_at_5 value: 20.543 - type: ndcg_at_1 value: 14.506 - type: ndcg_at_10 value: 17.485 - type: ndcg_at_100 value: 23.565 - type: ndcg_at_1000 value: 28.177000000000003 - type: ndcg_at_3 value: 14.423 - type: ndcg_at_5 value: 15.536 - type: precision_at_1 value: 14.506 - type: precision_at_10 value: 5.122999999999999 - type: precision_at_100 value: 1.13 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 9.722 - type: precision_at_5 value: 7.623 - type: recall_at_1 value: 7.0680000000000005 - type: recall_at_10 value: 23.423 - type: recall_at_100 value: 46.682 - type: recall_at_1000 value: 75.22999999999999 - type: recall_at_3 value: 13.544999999999998 - type: recall_at_5 value: 17.448 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 21.837 - type: map_at_10 value: 30.614 - type: map_at_100 value: 31.6 - type: map_at_1000 value: 31.71 - type: map_at_3 value: 28.219 - type: map_at_5 value: 29.598000000000003 - type: mrr_at_1 value: 43.673 - type: mrr_at_10 value: 51.627 - type: mrr_at_100 value: 52.323 - type: mrr_at_1000 value: 52.364 - type: mrr_at_3 value: 49.527 - type: mrr_at_5 value: 50.76500000000001 - type: ndcg_at_1 value: 43.673 - type: ndcg_at_10 value: 38.696000000000005 - type: ndcg_at_100 value: 43.124 - type: ndcg_at_1000 value: 45.552 - type: ndcg_at_3 value: 34.338 - type: ndcg_at_5 value: 36.553000000000004 - type: precision_at_1 value: 43.673 - type: precision_at_10 value: 8.432 - type: precision_at_100 value: 1.198 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 21.58 - type: precision_at_5 value: 14.706 - type: recall_at_1 value: 21.837 - type: recall_at_10 value: 42.161 - type: recall_at_100 value: 59.899 - type: recall_at_1000 value: 76.036 - type: recall_at_3 value: 32.37 - type: recall_at_5 value: 36.766 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 65.0232 - type: ap value: 59.81346113056583 - type: f1 value: 64.78827292080608 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.614000000000001 - type: map_at_10 value: 11.733 - type: map_at_100 value: 12.757 - type: map_at_1000 value: 12.873999999999999 - type: map_at_3 value: 9.783999999999999 - type: map_at_5 value: 10.807 - type: mrr_at_1 value: 6.834 - type: mrr_at_10 value: 12.074 - type: mrr_at_100 value: 13.099 - type: mrr_at_1000 value: 13.211 - type: mrr_at_3 value: 10.098 - type: mrr_at_5 value: 11.132 - type: ndcg_at_1 value: 6.834 - type: ndcg_at_10 value: 15.046000000000001 - type: ndcg_at_100 value: 20.657 - type: ndcg_at_1000 value: 24.112000000000002 - type: ndcg_at_3 value: 10.95 - type: ndcg_at_5 value: 12.796 - type: precision_at_1 value: 6.834 - type: precision_at_10 value: 2.633 - type: precision_at_100 value: 0.555 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 4.842 - type: precision_at_5 value: 3.8249999999999997 - type: recall_at_1 value: 6.614000000000001 - type: recall_at_10 value: 25.39 - type: recall_at_100 value: 52.793 - type: recall_at_1000 value: 80.415 - type: recall_at_3 value: 14.033000000000001 - type: recall_at_5 value: 18.496000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.58139534883719 - type: f1 value: 84.72133199480218 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.2608299133607 - type: f1 value: 36.74698315617003 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.993947545393404 - type: f1 value: 59.68762807193991 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.49361129791525 - type: f1 value: 67.16568787114376 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.675655693797378 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.87954369022046 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.47254787311633 - type: mrr value: 31.476216991631862 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 6.99 - type: map_at_100 value: 9.191 - type: map_at_1000 value: 10.385 - type: map_at_3 value: 5.015 - type: map_at_5 value: 5.904 - type: mrr_at_1 value: 30.031000000000002 - type: mrr_at_10 value: 40.001 - type: mrr_at_100 value: 40.724 - type: mrr_at_1000 value: 40.778 - type: mrr_at_3 value: 37.358000000000004 - type: mrr_at_5 value: 38.426 - type: ndcg_at_1 value: 28.483000000000004 - type: ndcg_at_10 value: 23.229 - type: ndcg_at_100 value: 22.115000000000002 - type: ndcg_at_1000 value: 31.263 - type: ndcg_at_3 value: 26.432 - type: ndcg_at_5 value: 25.074999999999996 - type: precision_at_1 value: 30.031000000000002 - type: precision_at_10 value: 17.957 - type: precision_at_100 value: 6.3 - type: precision_at_1000 value: 1.909 - type: precision_at_3 value: 26.006 - type: precision_at_5 value: 22.786 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 11.333 - type: recall_at_100 value: 24.629 - type: recall_at_1000 value: 57.162 - type: recall_at_3 value: 6.148 - type: recall_at_5 value: 7.902000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.904 - type: map_at_10 value: 18.551000000000002 - type: map_at_100 value: 19.913 - type: map_at_1000 value: 20.008 - type: map_at_3 value: 15.8 - type: map_at_5 value: 17.261000000000003 - type: mrr_at_1 value: 12.457 - type: mrr_at_10 value: 20.319000000000003 - type: mrr_at_100 value: 21.532999999999998 - type: mrr_at_1000 value: 21.61 - type: mrr_at_3 value: 17.449 - type: mrr_at_5 value: 19.023 - type: ndcg_at_1 value: 12.457 - type: ndcg_at_10 value: 23.488999999999997 - type: ndcg_at_100 value: 30.109 - type: ndcg_at_1000 value: 32.725 - type: ndcg_at_3 value: 17.73 - type: ndcg_at_5 value: 20.387 - type: precision_at_1 value: 12.457 - type: precision_at_10 value: 4.3709999999999996 - type: precision_at_100 value: 0.8109999999999999 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.333 - type: precision_at_5 value: 6.489000000000001 - type: recall_at_1 value: 10.904 - type: recall_at_10 value: 37.143 - type: recall_at_100 value: 67.432 - type: recall_at_1000 value: 87.59400000000001 - type: recall_at_3 value: 21.734 - type: recall_at_5 value: 27.927999999999997 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.499 - type: map_at_10 value: 77.088 - type: map_at_100 value: 77.91 - type: map_at_1000 value: 77.935 - type: map_at_3 value: 73.88900000000001 - type: map_at_5 value: 75.797 - type: mrr_at_1 value: 73.2 - type: mrr_at_10 value: 80.927 - type: mrr_at_100 value: 81.146 - type: mrr_at_1000 value: 81.148 - type: mrr_at_3 value: 79.427 - type: mrr_at_5 value: 80.363 - type: ndcg_at_1 value: 73.22999999999999 - type: ndcg_at_10 value: 81.926 - type: ndcg_at_100 value: 83.929 - type: ndcg_at_1000 value: 84.127 - type: ndcg_at_3 value: 78.071 - type: ndcg_at_5 value: 80.015 - type: precision_at_1 value: 73.22999999999999 - type: precision_at_10 value: 12.639 - type: precision_at_100 value: 1.5110000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.217 - type: precision_at_5 value: 22.722 - type: recall_at_1 value: 63.499 - type: recall_at_10 value: 91.646 - type: recall_at_100 value: 98.92999999999999 - type: recall_at_1000 value: 99.914 - type: recall_at_3 value: 80.703 - type: recall_at_5 value: 86.048 - type: map_at_1 value: 3.773 - type: map_at_10 value: 9.305 - type: map_at_100 value: 11.469 - type: map_at_1000 value: 11.828 - type: map_at_3 value: 6.675000000000001 - type: map_at_5 value: 7.965 - type: mrr_at_1 value: 18.6 - type: mrr_at_10 value: 28.392 - type: mrr_at_100 value: 29.664 - type: mrr_at_1000 value: 29.724 - type: mrr_at_3 value: 25.183 - type: mrr_at_5 value: 26.893 - type: ndcg_at_1 value: 18.6 - type: ndcg_at_10 value: 16.292 - type: ndcg_at_100 value: 25.0 - type: ndcg_at_1000 value: 31.136000000000003 - type: ndcg_at_3 value: 15.212 - type: ndcg_at_5 value: 13.354 - type: precision_at_1 value: 18.6 - type: precision_at_10 value: 8.57 - type: precision_at_100 value: 2.122 - type: precision_at_1000 value: 0.359 - type: precision_at_3 value: 14.267 - type: precision_at_5 value: 11.799999999999999 - type: recall_at_1 value: 3.773 - type: recall_at_10 value: 17.352999999999998 - type: recall_at_100 value: 43.062 - type: recall_at_1000 value: 72.775 - type: recall_at_3 value: 8.677999999999999 - type: recall_at_5 value: 11.958 - type: map_at_1 value: 0.186 - type: map_at_10 value: 1.304 - type: map_at_100 value: 6.688 - type: map_at_1000 value: 15.162 - type: map_at_3 value: 0.46499999999999997 - type: map_at_5 value: 0.7100000000000001 - type: mrr_at_1 value: 72.0 - type: mrr_at_10 value: 79.51899999999999 - type: mrr_at_100 value: 79.673 - type: mrr_at_1000 value: 79.673 - type: mrr_at_3 value: 77.667 - type: mrr_at_5 value: 78.567 - type: ndcg_at_1 value: 66.0 - type: ndcg_at_10 value: 58.172000000000004 - type: ndcg_at_100 value: 41.583999999999996 - type: ndcg_at_1000 value: 34.916000000000004 - type: ndcg_at_3 value: 62.0 - type: ndcg_at_5 value: 60.104 - type: precision_at_1 value: 72.0 - type: precision_at_10 value: 62.0 - type: precision_at_100 value: 43.32 - type: precision_at_1000 value: 15.962000000000002 - type: precision_at_3 value: 65.333 - type: precision_at_5 value: 63.6 - type: recall_at_1 value: 0.186 - type: recall_at_10 value: 1.525 - type: recall_at_100 value: 9.600999999999999 - type: recall_at_1000 value: 32.72 - type: recall_at_3 value: 0.492 - type: recall_at_5 value: 0.782 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.80644854168368 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 48.708800185514974 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.70369570699015 - type: cos_sim_spearman value: 67.00421728409633 - type: euclidean_pearson value: 71.7303217538682 - type: euclidean_spearman value: 67.00421728409633 - type: manhattan_pearson value: 71.62358736603595 - type: manhattan_spearman value: 66.93696271331966 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.3464707081196 - type: cos_sim_spearman value: 63.91086584602619 - type: euclidean_pearson value: 68.22390430027092 - type: euclidean_spearman value: 63.91086584602619 - type: manhattan_pearson value: 68.14984324829423 - type: manhattan_spearman value: 63.86219497566778 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 72.80276772789091 - type: cos_sim_spearman value: 73.34700075766551 - type: euclidean_pearson value: 72.88415583236083 - type: euclidean_spearman value: 73.34700075766551 - type: manhattan_pearson value: 72.71141307415924 - type: manhattan_spearman value: 73.10626124984765 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 74.00122656955553 - type: cos_sim_spearman value: 69.07090069837032 - type: euclidean_pearson value: 71.79931055857548 - type: euclidean_spearman value: 69.07090069837032 - type: manhattan_pearson value: 71.71577354549707 - type: manhattan_spearman value: 69.0177557195104 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.17450916936498 - type: cos_sim_spearman value: 81.53568053124042 - type: euclidean_pearson value: 81.04779414575466 - type: euclidean_spearman value: 81.53568053124042 - type: manhattan_pearson value: 80.95262960295437 - type: manhattan_spearman value: 81.43365291054681 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.7401837172966 - type: cos_sim_spearman value: 76.13099867057305 - type: euclidean_pearson value: 75.56851096153042 - type: euclidean_spearman value: 76.13099867057305 - type: manhattan_pearson value: 75.4483276223799 - type: manhattan_spearman value: 75.96804558062843 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.0294369233462 - type: cos_sim_spearman value: 85.17543345937065 - type: euclidean_pearson value: 84.55546274084796 - type: euclidean_spearman value: 85.17543345937065 - type: manhattan_pearson value: 84.48053547013386 - type: manhattan_spearman value: 85.1543300887167 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 61.225097153955176 - type: cos_sim_spearman value: 60.16234340521003 - type: euclidean_pearson value: 62.59204214787284 - type: euclidean_spearman value: 60.16234340521003 - type: manhattan_pearson value: 62.17494761193987 - type: manhattan_spearman value: 59.80098747946264 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.76720129845401 - type: cos_sim_spearman value: 77.01581381977705 - type: euclidean_pearson value: 78.25405293225397 - type: euclidean_spearman value: 77.01581381977705 - type: manhattan_pearson value: 78.1737464440924 - type: manhattan_spearman value: 76.98020258619971 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.38429389881968 - type: mrr value: 94.92898441427853 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.306000000000004 - type: map_at_10 value: 49.913000000000004 - type: map_at_100 value: 50.965 - type: map_at_1000 value: 51.022999999999996 - type: map_at_3 value: 47.398 - type: map_at_5 value: 48.962 - type: mrr_at_1 value: 41.0 - type: mrr_at_10 value: 51.147 - type: mrr_at_100 value: 52.022 - type: mrr_at_1000 value: 52.073 - type: mrr_at_3 value: 48.888999999999996 - type: mrr_at_5 value: 50.239 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 55.033 - type: ndcg_at_100 value: 59.364 - type: ndcg_at_1000 value: 60.849 - type: ndcg_at_3 value: 50.159 - type: ndcg_at_5 value: 52.788999999999994 - type: precision_at_1 value: 41.0 - type: precision_at_10 value: 7.632999999999999 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 20.222 - type: precision_at_5 value: 13.667000000000002 - type: recall_at_1 value: 39.306000000000004 - type: recall_at_10 value: 69.45599999999999 - type: recall_at_100 value: 88.022 - type: recall_at_1000 value: 99.6 - type: recall_at_3 value: 56.27799999999999 - type: recall_at_5 value: 62.639 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.73960396039604 - type: cos_sim_ap value: 91.77061379171414 - type: cos_sim_f1 value: 86.49746192893402 - type: cos_sim_precision value: 87.83505154639175 - type: cos_sim_recall value: 85.2 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 91.77061379171414 - type: dot_f1 value: 86.49746192893402 - type: dot_precision value: 87.83505154639175 - type: dot_recall value: 85.2 - type: euclidean_accuracy value: 99.73960396039604 - type: euclidean_ap value: 91.77061379171414 - type: euclidean_f1 value: 86.49746192893402 - type: euclidean_precision value: 87.83505154639175 - type: euclidean_recall value: 85.2 - type: manhattan_accuracy value: 99.73861386138614 - type: manhattan_ap value: 91.73584684604442 - type: manhattan_f1 value: 86.41722193746797 - type: manhattan_precision value: 88.64353312302839 - type: manhattan_recall value: 84.3 - type: max_accuracy value: 99.73960396039604 - type: max_ap value: 91.77061379171414 - type: max_f1 value: 86.49746192893402 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.7931704300123 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.48651577951652 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 41.818447756127505 - type: mrr value: 42.1808155080214 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.799110359832028 - type: cos_sim_spearman value: 30.826213689865888 - type: dot_pearson value: 29.79911097173556 - type: dot_spearman value: 30.8964325010969 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.6100000000000003 - type: map_at_10 value: 10.317 - type: map_at_100 value: 16.651 - type: map_at_1000 value: 18.4 - type: map_at_3 value: 4.952999999999999 - type: map_at_5 value: 7.037 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 51.351 - type: mrr_at_100 value: 51.912000000000006 - type: mrr_at_1000 value: 51.912000000000006 - type: mrr_at_3 value: 46.599000000000004 - type: mrr_at_5 value: 49.762 - type: ndcg_at_1 value: 31.633 - type: ndcg_at_10 value: 27.601 - type: ndcg_at_100 value: 39.080999999999996 - type: ndcg_at_1000 value: 50.308 - type: ndcg_at_3 value: 30.020000000000003 - type: ndcg_at_5 value: 29.465999999999998 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 26.122 - type: precision_at_100 value: 8.530999999999999 - type: precision_at_1000 value: 1.5650000000000002 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 31.019999999999996 - type: recall_at_1 value: 2.6100000000000003 - type: recall_at_10 value: 17.166 - type: recall_at_100 value: 50.480999999999995 - type: recall_at_1000 value: 84.87599999999999 - type: recall_at_3 value: 6.026 - type: recall_at_5 value: 10.165000000000001 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 66.8218 - type: ap value: 11.906071313412117 - type: f1 value: 50.99103419180737 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 50.1188455008489 - type: f1 value: 50.19144196024773 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.550995025713995 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 79.29307981164689 - type: cos_sim_ap value: 48.474835734978406 - type: cos_sim_f1 value: 48.95389383959706 - type: cos_sim_precision value: 38.674625038261404 - type: cos_sim_recall value: 66.6754617414248 - type: dot_accuracy value: 79.29307981164689 - type: dot_ap value: 48.4748345893063 - type: dot_f1 value: 48.95389383959706 - type: dot_precision value: 38.674625038261404 - type: dot_recall value: 66.6754617414248 - type: euclidean_accuracy value: 79.29307981164689 - type: euclidean_ap value: 48.47484295524529 - type: euclidean_f1 value: 48.95389383959706 - type: euclidean_precision value: 38.674625038261404 - type: euclidean_recall value: 66.6754617414248 - type: manhattan_accuracy value: 79.34672468260118 - type: manhattan_ap value: 48.423218655778356 - type: manhattan_f1 value: 48.93181153058239 - type: manhattan_precision value: 38.81752050766135 - type: manhattan_recall value: 66.17414248021109 - type: max_accuracy value: 79.34672468260118 - type: max_ap value: 48.47484295524529 - type: max_f1 value: 48.95389383959706 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 86.65541196103544 - type: cos_sim_ap value: 80.9065470343605 - type: cos_sim_f1 value: 73.7394283267316 - type: cos_sim_precision value: 68.7196541403392 - type: cos_sim_recall value: 79.55035417308285 - type: dot_accuracy value: 86.65541196103544 - type: dot_ap value: 80.90654522467446 - type: dot_f1 value: 73.7394283267316 - type: dot_precision value: 68.7196541403392 - type: dot_recall value: 79.55035417308285 - type: euclidean_accuracy value: 86.65541196103544 - type: euclidean_ap value: 80.90654748736512 - type: euclidean_f1 value: 73.7394283267316 - type: euclidean_precision value: 68.7196541403392 - type: euclidean_recall value: 79.55035417308285 - type: manhattan_accuracy value: 86.61272169829627 - type: manhattan_ap value: 80.85801370403492 - type: manhattan_f1 value: 73.63878299647558 - type: manhattan_precision value: 69.0916452962613 - type: manhattan_recall value: 78.8266091777025 - type: max_accuracy value: 86.65541196103544 - type: max_ap value: 80.90654748736512 - type: max_f1 value: 73.7394283267316 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/gte_wl	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:26:07	2025-01-09T11:26:12	0	--- tags: - mteb model-index: - name: gte_wordllama_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.07462686567165 - type: ap value: 34.03639155919273 - type: f1 value: 65.69832537072352 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 69.453025 - type: ap value: 63.87884877644433 - type: f1 value: 69.23150048939367 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 36.364 - type: f1 value: 35.72067919658383 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 22.546 - type: map_at_10 value: 37.411 - type: map_at_100 value: 38.582 - type: map_at_1000 value: 38.597 - type: map_at_3 value: 32.492 - type: map_at_5 value: 35.141 - type: mrr_at_1 value: 23.186 - type: mrr_at_10 value: 37.651 - type: mrr_at_100 value: 38.822 - type: mrr_at_1000 value: 38.836999999999996 - type: mrr_at_3 value: 32.741 - type: mrr_at_5 value: 35.408 - type: ndcg_at_1 value: 22.546 - type: ndcg_at_10 value: 46.012 - type: ndcg_at_100 value: 51.197 - type: ndcg_at_1000 value: 51.547 - type: ndcg_at_3 value: 35.762 - type: ndcg_at_5 value: 40.567 - type: precision_at_1 value: 22.546 - type: precision_at_10 value: 7.367999999999999 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.078 - type: precision_at_5 value: 11.394 - type: recall_at_1 value: 22.546 - type: recall_at_10 value: 73.68400000000001 - type: recall_at_100 value: 96.799 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 45.235 - type: recall_at_5 value: 56.97 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.643731613769525 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.63510872385387 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.581954717688454 - type: mrr value: 69.65857626522447 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.65184787408168 - type: cos_sim_spearman value: 76.59391391898701 - type: euclidean_pearson value: 78.27369147487082 - type: euclidean_spearman value: 76.59391391898701 - type: manhattan_pearson value: 78.35436546555296 - type: manhattan_spearman value: 76.41258448606804 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.67532467532469 - type: f1 value: 74.96407787263568 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.80818669258118 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 27.110794795227715 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 30.358 - type: map_at_100 value: 31.708 - type: map_at_1000 value: 31.857999999999997 - type: map_at_3 value: 27.721 - type: map_at_5 value: 29.054000000000002 - type: mrr_at_1 value: 29.041 - type: mrr_at_10 value: 36.405 - type: mrr_at_100 value: 37.358000000000004 - type: mrr_at_1000 value: 37.419999999999995 - type: mrr_at_3 value: 34.335 - type: mrr_at_5 value: 35.365 - type: ndcg_at_1 value: 29.041 - type: ndcg_at_10 value: 35.673 - type: ndcg_at_100 value: 41.432 - type: ndcg_at_1000 value: 44.372 - type: ndcg_at_3 value: 31.707 - type: ndcg_at_5 value: 33.147999999999996 - type: precision_at_1 value: 29.041 - type: precision_at_10 value: 6.895999999999999 - type: precision_at_100 value: 1.237 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 15.212 - type: precision_at_5 value: 10.901 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 45.234 - type: recall_at_100 value: 70.658 - type: recall_at_1000 value: 90.70700000000001 - type: recall_at_3 value: 32.729 - type: recall_at_5 value: 37.242 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 18.834 - type: map_at_10 value: 25.796999999999997 - type: map_at_100 value: 26.881 - type: map_at_1000 value: 27.004 - type: map_at_3 value: 23.857999999999997 - type: map_at_5 value: 24.89 - type: mrr_at_1 value: 24.204 - type: mrr_at_10 value: 30.529 - type: mrr_at_100 value: 31.386999999999997 - type: mrr_at_1000 value: 31.456 - type: mrr_at_3 value: 28.715000000000003 - type: mrr_at_5 value: 29.658 - type: ndcg_at_1 value: 24.204 - type: ndcg_at_10 value: 30.053 - type: ndcg_at_100 value: 34.826 - type: ndcg_at_1000 value: 37.557 - type: ndcg_at_3 value: 26.927 - type: ndcg_at_5 value: 28.205999999999996 - type: precision_at_1 value: 24.204 - type: precision_at_10 value: 5.561 - type: precision_at_100 value: 1.011 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 12.994 - type: precision_at_5 value: 9.107999999999999 - type: recall_at_1 value: 18.834 - type: recall_at_10 value: 38.022 - type: recall_at_100 value: 58.587 - type: recall_at_1000 value: 76.953 - type: recall_at_3 value: 28.777 - type: recall_at_5 value: 32.372 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 28.138999999999996 - type: map_at_10 value: 37.378 - type: map_at_100 value: 38.576 - type: map_at_1000 value: 38.673 - type: map_at_3 value: 34.733000000000004 - type: map_at_5 value: 36.083999999999996 - type: mrr_at_1 value: 32.414 - type: mrr_at_10 value: 40.589999999999996 - type: mrr_at_100 value: 41.519 - type: mrr_at_1000 value: 41.577999999999996 - type: mrr_at_3 value: 38.213 - type: mrr_at_5 value: 39.428999999999995 - type: ndcg_at_1 value: 32.414 - type: ndcg_at_10 value: 42.501 - type: ndcg_at_100 value: 47.715 - type: ndcg_at_1000 value: 49.899 - type: ndcg_at_3 value: 37.595 - type: ndcg_at_5 value: 39.653 - type: precision_at_1 value: 32.414 - type: precision_at_10 value: 6.978 - type: precision_at_100 value: 1.054 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 16.761 - type: precision_at_5 value: 11.498 - type: recall_at_1 value: 28.138999999999996 - type: recall_at_10 value: 54.803999999999995 - type: recall_at_100 value: 77.648 - type: recall_at_1000 value: 93.545 - type: recall_at_3 value: 41.323 - type: recall_at_5 value: 46.489999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.864 - type: map_at_10 value: 18.775 - type: map_at_100 value: 19.706000000000003 - type: map_at_1000 value: 19.822 - type: map_at_3 value: 17.314 - type: map_at_5 value: 18.028 - type: mrr_at_1 value: 14.915000000000001 - type: mrr_at_10 value: 20.095 - type: mrr_at_100 value: 20.992 - type: mrr_at_1000 value: 21.092 - type: mrr_at_3 value: 18.587999999999997 - type: mrr_at_5 value: 19.271 - type: ndcg_at_1 value: 14.915000000000001 - type: ndcg_at_10 value: 21.811 - type: ndcg_at_100 value: 26.656000000000002 - type: ndcg_at_1000 value: 30.009000000000004 - type: ndcg_at_3 value: 18.790000000000003 - type: ndcg_at_5 value: 20.009 - type: precision_at_1 value: 14.915000000000001 - type: precision_at_10 value: 3.401 - type: precision_at_100 value: 0.623 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.06 - type: precision_at_5 value: 5.537 - type: recall_at_1 value: 13.864 - type: recall_at_10 value: 29.914 - type: recall_at_100 value: 52.580000000000005 - type: recall_at_1000 value: 78.648 - type: recall_at_3 value: 21.586 - type: recall_at_5 value: 24.58 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.223 - type: map_at_10 value: 12.272 - type: map_at_100 value: 13.252 - type: map_at_1000 value: 13.381000000000002 - type: map_at_3 value: 10.610999999999999 - type: map_at_5 value: 11.505 - type: mrr_at_1 value: 9.203999999999999 - type: mrr_at_10 value: 14.639 - type: mrr_at_100 value: 15.629000000000001 - type: mrr_at_1000 value: 15.733 - type: mrr_at_3 value: 12.852 - type: mrr_at_5 value: 13.797999999999998 - type: ndcg_at_1 value: 9.203999999999999 - type: ndcg_at_10 value: 15.543999999999999 - type: ndcg_at_100 value: 20.89 - type: ndcg_at_1000 value: 24.547 - type: ndcg_at_3 value: 12.264 - type: ndcg_at_5 value: 13.748 - type: precision_at_1 value: 9.203999999999999 - type: precision_at_10 value: 3.085 - type: precision_at_100 value: 0.688 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 6.095 - type: precision_at_5 value: 4.677 - type: recall_at_1 value: 7.223 - type: recall_at_10 value: 23.268 - type: recall_at_100 value: 47.452 - type: recall_at_1000 value: 74.69200000000001 - type: recall_at_3 value: 14.437 - type: recall_at_5 value: 18.007 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 19.661 - type: map_at_10 value: 26.145000000000003 - type: map_at_100 value: 27.477 - type: map_at_1000 value: 27.622999999999998 - type: map_at_3 value: 23.315 - type: map_at_5 value: 24.87 - type: mrr_at_1 value: 24.157999999999998 - type: mrr_at_10 value: 31.035 - type: mrr_at_100 value: 32.011 - type: mrr_at_1000 value: 32.086999999999996 - type: mrr_at_3 value: 28.199999999999996 - type: mrr_at_5 value: 29.769000000000002 - type: ndcg_at_1 value: 24.157999999999998 - type: ndcg_at_10 value: 31.249 - type: ndcg_at_100 value: 37.319 - type: ndcg_at_1000 value: 40.394999999999996 - type: ndcg_at_3 value: 26.184 - type: ndcg_at_5 value: 28.518 - type: precision_at_1 value: 24.157999999999998 - type: precision_at_10 value: 5.9479999999999995 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 12.191 - type: precision_at_5 value: 9.142999999999999 - type: recall_at_1 value: 19.661 - type: recall_at_10 value: 41.959 - type: recall_at_100 value: 68.22399999999999 - type: recall_at_1000 value: 89.071 - type: recall_at_3 value: 27.617000000000004 - type: recall_at_5 value: 33.693 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.714 - type: map_at_10 value: 21.786 - type: map_at_100 value: 23.052 - type: map_at_1000 value: 23.186999999999998 - type: map_at_3 value: 19.286 - type: map_at_5 value: 20.699 - type: mrr_at_1 value: 19.064 - type: mrr_at_10 value: 25.576 - type: mrr_at_100 value: 26.613 - type: mrr_at_1000 value: 26.697 - type: mrr_at_3 value: 23.212 - type: mrr_at_5 value: 24.553 - type: ndcg_at_1 value: 19.064 - type: ndcg_at_10 value: 26.19 - type: ndcg_at_100 value: 32.019 - type: ndcg_at_1000 value: 35.323 - type: ndcg_at_3 value: 21.609 - type: ndcg_at_5 value: 23.747 - type: precision_at_1 value: 19.064 - type: precision_at_10 value: 5.045999999999999 - type: precision_at_100 value: 0.947 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.16 - type: precision_at_5 value: 7.693999999999999 - type: recall_at_1 value: 15.714 - type: recall_at_10 value: 35.846000000000004 - type: recall_at_100 value: 60.885 - type: recall_at_1000 value: 84.437 - type: recall_at_3 value: 23.357 - type: recall_at_5 value: 28.698 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.797416666666667 - type: map_at_10 value: 21.674916666666668 - type: map_at_100 value: 22.73633333333333 - type: map_at_1000 value: 22.868583333333333 - type: map_at_3 value: 19.66508333333333 - type: map_at_5 value: 20.75133333333333 - type: mrr_at_1 value: 19.052333333333333 - type: mrr_at_10 value: 24.958083333333335 - type: mrr_at_100 value: 25.862666666666666 - type: mrr_at_1000 value: 25.95 - type: mrr_at_3 value: 23.02525 - type: mrr_at_5 value: 24.053166666666666 - type: ndcg_at_1 value: 19.052333333333333 - type: ndcg_at_10 value: 25.618249999999996 - type: ndcg_at_100 value: 30.751666666666665 - type: ndcg_at_1000 value: 33.93783333333333 - type: ndcg_at_3 value: 21.966166666666666 - type: ndcg_at_5 value: 23.569333333333333 - type: precision_at_1 value: 19.052333333333333 - type: precision_at_10 value: 4.6321666666666665 - type: precision_at_100 value: 0.8673333333333333 - type: precision_at_1000 value: 0.13283333333333333 - type: precision_at_3 value: 10.15075 - type: precision_at_5 value: 7.330416666666667 - type: recall_at_1 value: 15.797416666666667 - type: recall_at_10 value: 34.28000000000001 - type: recall_at_100 value: 57.498416666666664 - type: recall_at_1000 value: 80.52425000000001 - type: recall_at_3 value: 23.929416666666665 - type: recall_at_5 value: 28.09466666666667 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.323 - type: map_at_10 value: 17.07 - type: map_at_100 value: 17.849999999999998 - type: map_at_1000 value: 17.957 - type: map_at_3 value: 15.414 - type: map_at_5 value: 16.431 - type: mrr_at_1 value: 13.497 - type: mrr_at_10 value: 19.188 - type: mrr_at_100 value: 19.978 - type: mrr_at_1000 value: 20.071 - type: mrr_at_3 value: 17.663999999999998 - type: mrr_at_5 value: 18.538 - type: ndcg_at_1 value: 13.497 - type: ndcg_at_10 value: 20.485999999999997 - type: ndcg_at_100 value: 24.855 - type: ndcg_at_1000 value: 27.773999999999997 - type: ndcg_at_3 value: 17.399 - type: ndcg_at_5 value: 18.988 - type: precision_at_1 value: 13.497 - type: precision_at_10 value: 3.5740000000000003 - type: precision_at_100 value: 0.63 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 8.129 - type: precision_at_5 value: 5.92 - type: recall_at_1 value: 11.323 - type: recall_at_10 value: 28.92 - type: recall_at_100 value: 49.75 - type: recall_at_1000 value: 71.492 - type: recall_at_3 value: 20.452 - type: recall_at_5 value: 24.346 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.625 - type: map_at_10 value: 12.41 - type: map_at_100 value: 13.200999999999999 - type: map_at_1000 value: 13.333999999999998 - type: map_at_3 value: 11.141 - type: map_at_5 value: 11.776 - type: mrr_at_1 value: 10.805 - type: mrr_at_10 value: 14.979999999999999 - type: mrr_at_100 value: 15.759 - type: mrr_at_1000 value: 15.867 - type: mrr_at_3 value: 13.569999999999999 - type: mrr_at_5 value: 14.316 - type: ndcg_at_1 value: 10.805 - type: ndcg_at_10 value: 15.129999999999999 - type: ndcg_at_100 value: 19.339000000000002 - type: ndcg_at_1000 value: 23.034 - type: ndcg_at_3 value: 12.661 - type: ndcg_at_5 value: 13.664000000000001 - type: precision_at_1 value: 10.805 - type: precision_at_10 value: 2.88 - type: precision_at_100 value: 0.5950000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.091 - type: precision_at_5 value: 4.4319999999999995 - type: recall_at_1 value: 8.625 - type: recall_at_10 value: 20.924 - type: recall_at_100 value: 40.343 - type: recall_at_1000 value: 67.60199999999999 - type: recall_at_3 value: 13.963000000000001 - type: recall_at_5 value: 16.558999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 15.116999999999999 - type: map_at_10 value: 20.283 - type: map_at_100 value: 21.181 - type: map_at_1000 value: 21.318 - type: map_at_3 value: 18.528 - type: map_at_5 value: 19.506 - type: mrr_at_1 value: 17.91 - type: mrr_at_10 value: 23.399 - type: mrr_at_100 value: 24.254 - type: mrr_at_1000 value: 24.36 - type: mrr_at_3 value: 21.502 - type: mrr_at_5 value: 22.617 - type: ndcg_at_1 value: 17.91 - type: ndcg_at_10 value: 23.848 - type: ndcg_at_100 value: 28.63 - type: ndcg_at_1000 value: 32.236 - type: ndcg_at_3 value: 20.351 - type: ndcg_at_5 value: 21.992 - type: precision_at_1 value: 17.91 - type: precision_at_10 value: 4.011 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 9.049 - type: precision_at_5 value: 6.455 - type: recall_at_1 value: 15.116999999999999 - type: recall_at_10 value: 31.911 - type: recall_at_100 value: 53.791999999999994 - type: recall_at_1000 value: 79.997 - type: recall_at_3 value: 22.229 - type: recall_at_5 value: 26.366 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.415999999999999 - type: map_at_10 value: 21.364 - type: map_at_100 value: 22.631 - type: map_at_1000 value: 22.832 - type: map_at_3 value: 19.139999999999997 - type: map_at_5 value: 20.549 - type: mrr_at_1 value: 19.368 - type: mrr_at_10 value: 25.218 - type: mrr_at_100 value: 26.135 - type: mrr_at_1000 value: 26.218999999999998 - type: mrr_at_3 value: 23.155 - type: mrr_at_5 value: 24.371000000000002 - type: ndcg_at_1 value: 19.368 - type: ndcg_at_10 value: 25.715 - type: ndcg_at_100 value: 31.291999999999998 - type: ndcg_at_1000 value: 34.757 - type: ndcg_at_3 value: 22.131999999999998 - type: ndcg_at_5 value: 24.018 - type: precision_at_1 value: 19.368 - type: precision_at_10 value: 5.138 - type: precision_at_100 value: 1.229 - type: precision_at_1000 value: 0.209 - type: precision_at_3 value: 10.474 - type: precision_at_5 value: 7.904999999999999 - type: recall_at_1 value: 15.415999999999999 - type: recall_at_10 value: 33.83 - type: recall_at_100 value: 60.19799999999999 - type: recall_at_1000 value: 83.88600000000001 - type: recall_at_3 value: 23.018 - type: recall_at_5 value: 28.37 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.822 - type: map_at_10 value: 16.461000000000002 - type: map_at_100 value: 17.321 - type: map_at_1000 value: 17.434 - type: map_at_3 value: 14.92 - type: map_at_5 value: 15.623999999999999 - type: mrr_at_1 value: 14.048 - type: mrr_at_10 value: 17.843 - type: mrr_at_100 value: 18.717 - type: mrr_at_1000 value: 18.82 - type: mrr_at_3 value: 16.297 - type: mrr_at_5 value: 16.953 - type: ndcg_at_1 value: 14.048 - type: ndcg_at_10 value: 19.219 - type: ndcg_at_100 value: 24.047 - type: ndcg_at_1000 value: 27.351 - type: ndcg_at_3 value: 15.975 - type: ndcg_at_5 value: 17.141000000000002 - type: precision_at_1 value: 14.048 - type: precision_at_10 value: 3.068 - type: precision_at_100 value: 0.5950000000000001 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 6.593 - type: precision_at_5 value: 4.695 - type: recall_at_1 value: 12.822 - type: recall_at_10 value: 26.728 - type: recall_at_100 value: 49.864000000000004 - type: recall_at_1000 value: 75.261 - type: recall_at_3 value: 17.665 - type: recall_at_5 value: 20.413 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 8.301 - type: map_at_10 value: 14.709 - type: map_at_100 value: 16.396 - type: map_at_1000 value: 16.606 - type: map_at_3 value: 11.987 - type: map_at_5 value: 13.401 - type: mrr_at_1 value: 19.088 - type: mrr_at_10 value: 29.421999999999997 - type: mrr_at_100 value: 30.517 - type: mrr_at_1000 value: 30.568 - type: mrr_at_3 value: 25.646 - type: mrr_at_5 value: 27.897 - type: ndcg_at_1 value: 19.088 - type: ndcg_at_10 value: 21.851000000000003 - type: ndcg_at_100 value: 29.093999999999998 - type: ndcg_at_1000 value: 33.101 - type: ndcg_at_3 value: 16.862 - type: ndcg_at_5 value: 18.790000000000003 - type: precision_at_1 value: 19.088 - type: precision_at_10 value: 7.244000000000001 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 12.812000000000001 - type: precision_at_5 value: 10.41 - type: recall_at_1 value: 8.301 - type: recall_at_10 value: 27.49 - type: recall_at_100 value: 52.937999999999995 - type: recall_at_1000 value: 75.79599999999999 - type: recall_at_3 value: 15.603 - type: recall_at_5 value: 20.612 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.576 - type: map_at_10 value: 11.394 - type: map_at_100 value: 16.276 - type: map_at_1000 value: 17.459 - type: map_at_3 value: 8.269 - type: map_at_5 value: 9.711 - type: mrr_at_1 value: 47.25 - type: mrr_at_10 value: 57.201 - type: mrr_at_100 value: 57.727 - type: mrr_at_1000 value: 57.751 - type: mrr_at_3 value: 54.458 - type: mrr_at_5 value: 56.421 - type: ndcg_at_1 value: 35.25 - type: ndcg_at_10 value: 26.617 - type: ndcg_at_100 value: 30.952 - type: ndcg_at_1000 value: 38.287 - type: ndcg_at_3 value: 29.814 - type: ndcg_at_5 value: 28.436 - type: precision_at_1 value: 47.25 - type: precision_at_10 value: 23.175 - type: precision_at_100 value: 7.6450000000000005 - type: precision_at_1000 value: 1.624 - type: precision_at_3 value: 35.667 - type: precision_at_5 value: 30.65 - type: recall_at_1 value: 5.576 - type: recall_at_10 value: 15.804000000000002 - type: recall_at_100 value: 38.086 - type: recall_at_1000 value: 63.034 - type: recall_at_3 value: 9.407 - type: recall_at_5 value: 12.247 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.21 - type: f1 value: 43.021356364911156 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.775 - type: map_at_10 value: 27.131 - type: map_at_100 value: 28.186 - type: map_at_1000 value: 28.255999999999997 - type: map_at_3 value: 24.198 - type: map_at_5 value: 25.907000000000004 - type: mrr_at_1 value: 19.006999999999998 - type: mrr_at_10 value: 28.769 - type: mrr_at_100 value: 29.809 - type: mrr_at_1000 value: 29.866 - type: mrr_at_3 value: 25.773000000000003 - type: mrr_at_5 value: 27.51 - type: ndcg_at_1 value: 19.006999999999998 - type: ndcg_at_10 value: 32.698 - type: ndcg_at_100 value: 37.891999999999996 - type: ndcg_at_1000 value: 39.728 - type: ndcg_at_3 value: 26.680999999999997 - type: ndcg_at_5 value: 29.73 - type: precision_at_1 value: 19.006999999999998 - type: precision_at_10 value: 5.2909999999999995 - type: precision_at_100 value: 0.8049999999999999 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 11.616 - type: precision_at_5 value: 8.554 - type: recall_at_1 value: 17.775 - type: recall_at_10 value: 48.603 - type: recall_at_100 value: 72.465 - type: recall_at_1000 value: 86.509 - type: recall_at_3 value: 32.26 - type: recall_at_5 value: 39.589999999999996 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 8.584 - type: map_at_10 value: 13.774000000000001 - type: map_at_100 value: 15.247 - type: map_at_1000 value: 15.468000000000002 - type: map_at_3 value: 11.779 - type: map_at_5 value: 12.732 - type: mrr_at_1 value: 16.512 - type: mrr_at_10 value: 23.016000000000002 - type: mrr_at_100 value: 24.276 - type: mrr_at_1000 value: 24.362000000000002 - type: mrr_at_3 value: 20.756 - type: mrr_at_5 value: 21.852 - type: ndcg_at_1 value: 16.512 - type: ndcg_at_10 value: 18.604000000000003 - type: ndcg_at_100 value: 25.298 - type: ndcg_at_1000 value: 29.803 - type: ndcg_at_3 value: 15.790000000000001 - type: ndcg_at_5 value: 16.614 - type: precision_at_1 value: 16.512 - type: precision_at_10 value: 5.293 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 10.237 - type: precision_at_5 value: 7.7780000000000005 - type: recall_at_1 value: 8.584 - type: recall_at_10 value: 23.685000000000002 - type: recall_at_100 value: 49.461 - type: recall_at_1000 value: 76.972 - type: recall_at_3 value: 14.657 - type: recall_at_5 value: 17.861 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 19.662 - type: map_at_10 value: 28.195999999999998 - type: map_at_100 value: 29.21 - type: map_at_1000 value: 29.322 - type: map_at_3 value: 25.852999999999998 - type: map_at_5 value: 27.121000000000002 - type: mrr_at_1 value: 39.324999999999996 - type: mrr_at_10 value: 47.083999999999996 - type: mrr_at_100 value: 47.805 - type: mrr_at_1000 value: 47.853 - type: mrr_at_3 value: 44.913 - type: mrr_at_5 value: 46.132 - type: ndcg_at_1 value: 39.324999999999996 - type: ndcg_at_10 value: 35.766999999999996 - type: ndcg_at_100 value: 40.306 - type: ndcg_at_1000 value: 42.870000000000005 - type: ndcg_at_3 value: 31.395 - type: ndcg_at_5 value: 33.469 - type: precision_at_1 value: 39.324999999999996 - type: precision_at_10 value: 7.933999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 19.855999999999998 - type: precision_at_5 value: 13.556000000000001 - type: recall_at_1 value: 19.662 - type: recall_at_10 value: 39.669 - type: recall_at_100 value: 57.833 - type: recall_at_1000 value: 74.929 - type: recall_at_3 value: 29.784 - type: recall_at_5 value: 33.889 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 68.03079999999999 - type: ap value: 62.45465282637356 - type: f1 value: 67.82133366706746 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.297 - type: map_at_10 value: 12.847 - type: map_at_100 value: 13.872000000000002 - type: map_at_1000 value: 13.987 - type: map_at_3 value: 10.741 - type: map_at_5 value: 11.838999999999999 - type: mrr_at_1 value: 7.536 - type: mrr_at_10 value: 13.157 - type: mrr_at_100 value: 14.184 - type: mrr_at_1000 value: 14.295 - type: mrr_at_3 value: 11.020000000000001 - type: mrr_at_5 value: 12.133 - type: ndcg_at_1 value: 7.507 - type: ndcg_at_10 value: 16.374 - type: ndcg_at_100 value: 22.039 - type: ndcg_at_1000 value: 25.380999999999997 - type: ndcg_at_3 value: 11.935 - type: ndcg_at_5 value: 13.919999999999998 - type: precision_at_1 value: 7.507 - type: precision_at_10 value: 2.8449999999999998 - type: precision_at_100 value: 0.581 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 5.191 - type: precision_at_5 value: 4.112 - type: recall_at_1 value: 7.297 - type: recall_at_10 value: 27.450999999999997 - type: recall_at_100 value: 55.215 - type: recall_at_1000 value: 81.878 - type: recall_at_3 value: 15.143 - type: recall_at_5 value: 19.922 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.23347013223893 - type: f1 value: 90.37745005574784 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 60.43775649794802 - type: f1 value: 41.826394298669705 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.53799596503026 - type: f1 value: 63.37514998537075 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.92535305985206 - type: f1 value: 72.01043365342854 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.093053205851135 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.838169401102558 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.012335830272843 - type: mrr value: 32.04656357642063 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.865 - type: map_at_10 value: 9.599 - type: map_at_100 value: 12.466000000000001 - type: map_at_1000 value: 13.935 - type: map_at_3 value: 7.260999999999999 - type: map_at_5 value: 8.526 - type: mrr_at_1 value: 38.080000000000005 - type: mrr_at_10 value: 47.695 - type: mrr_at_100 value: 48.304 - type: mrr_at_1000 value: 48.351 - type: mrr_at_3 value: 45.098 - type: mrr_at_5 value: 46.569 - type: ndcg_at_1 value: 36.223 - type: ndcg_at_10 value: 28.582 - type: ndcg_at_100 value: 27.229 - type: ndcg_at_1000 value: 36.643 - type: ndcg_at_3 value: 32.653 - type: ndcg_at_5 value: 31.215 - type: precision_at_1 value: 38.080000000000005 - type: precision_at_10 value: 21.207 - type: precision_at_100 value: 7.498 - type: precision_at_1000 value: 2.104 - type: precision_at_3 value: 30.65 - type: precision_at_5 value: 27.059 - type: recall_at_1 value: 4.865 - type: recall_at_10 value: 13.614 - type: recall_at_100 value: 29.659999999999997 - type: recall_at_1000 value: 63.172 - type: recall_at_3 value: 8.248 - type: recall_at_5 value: 10.684000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.581 - type: map_at_10 value: 18.221 - type: map_at_100 value: 19.637999999999998 - type: map_at_1000 value: 19.737 - type: map_at_3 value: 15.341 - type: map_at_5 value: 16.943 - type: mrr_at_1 value: 12.051 - type: mrr_at_10 value: 20.102 - type: mrr_at_100 value: 21.385 - type: mrr_at_1000 value: 21.465 - type: mrr_at_3 value: 17.159 - type: mrr_at_5 value: 18.851000000000003 - type: ndcg_at_1 value: 12.051 - type: ndcg_at_10 value: 23.267 - type: ndcg_at_100 value: 30.211 - type: ndcg_at_1000 value: 32.878 - type: ndcg_at_3 value: 17.354 - type: ndcg_at_5 value: 20.247999999999998 - type: precision_at_1 value: 12.051 - type: precision_at_10 value: 4.356999999999999 - type: precision_at_100 value: 0.827 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 8.266 - type: precision_at_5 value: 6.553000000000001 - type: recall_at_1 value: 10.581 - type: recall_at_10 value: 37.119 - type: recall_at_100 value: 68.89699999999999 - type: recall_at_1000 value: 89.354 - type: recall_at_3 value: 21.404999999999998 - type: recall_at_5 value: 28.194000000000003 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 66.119 - type: map_at_10 value: 79.611 - type: map_at_100 value: 80.354 - type: map_at_1000 value: 80.38 - type: map_at_3 value: 76.606 - type: map_at_5 value: 78.485 - type: mrr_at_1 value: 76.12 - type: mrr_at_10 value: 83.328 - type: mrr_at_100 value: 83.499 - type: mrr_at_1000 value: 83.502 - type: mrr_at_3 value: 82.00699999999999 - type: mrr_at_5 value: 82.89699999999999 - type: ndcg_at_1 value: 76.22 - type: ndcg_at_10 value: 84.051 - type: ndcg_at_100 value: 85.797 - type: ndcg_at_1000 value: 86.007 - type: ndcg_at_3 value: 80.646 - type: ndcg_at_5 value: 82.50800000000001 - type: precision_at_1 value: 76.22 - type: precision_at_10 value: 12.76 - type: precision_at_100 value: 1.5010000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.160000000000004 - type: precision_at_5 value: 23.264000000000003 - type: recall_at_1 value: 66.119 - type: recall_at_10 value: 92.664 - type: recall_at_100 value: 98.863 - type: recall_at_1000 value: 99.91 - type: recall_at_3 value: 82.994 - type: recall_at_5 value: 88.119 - type: map_at_1 value: 3.2680000000000002 - type: map_at_10 value: 8.579 - type: map_at_100 value: 10.421999999999999 - type: map_at_1000 value: 10.737 - type: map_at_3 value: 6.0040000000000004 - type: map_at_5 value: 7.26 - type: mrr_at_1 value: 16.0 - type: mrr_at_10 value: 26.185000000000002 - type: mrr_at_100 value: 27.439000000000004 - type: mrr_at_1000 value: 27.511999999999997 - type: mrr_at_3 value: 22.917 - type: mrr_at_5 value: 24.642 - type: ndcg_at_1 value: 16.0 - type: ndcg_at_10 value: 15.232000000000001 - type: ndcg_at_100 value: 23.047 - type: ndcg_at_1000 value: 28.774 - type: ndcg_at_3 value: 13.834 - type: ndcg_at_5 value: 12.304 - type: precision_at_1 value: 16.0 - type: precision_at_10 value: 8.19 - type: precision_at_100 value: 1.958 - type: precision_at_1000 value: 0.333 - type: precision_at_3 value: 13.167000000000002 - type: precision_at_5 value: 11.06 - type: recall_at_1 value: 3.2680000000000002 - type: recall_at_10 value: 16.563 - type: recall_at_100 value: 39.708 - type: recall_at_1000 value: 67.60199999999999 - type: recall_at_3 value: 8.018 - type: recall_at_5 value: 11.193 - type: map_at_1 value: 0.161 - type: map_at_10 value: 1.171 - type: map_at_100 value: 6.306000000000001 - type: map_at_1000 value: 16.732 - type: map_at_3 value: 0.432 - type: map_at_5 value: 0.645 - type: mrr_at_1 value: 57.99999999999999 - type: mrr_at_10 value: 72.32499999999999 - type: mrr_at_100 value: 72.458 - type: mrr_at_1000 value: 72.458 - type: mrr_at_3 value: 69.667 - type: mrr_at_5 value: 71.56700000000001 - type: ndcg_at_1 value: 53.0 - type: ndcg_at_10 value: 52.207 - type: ndcg_at_100 value: 40.717 - type: ndcg_at_1000 value: 38.254 - type: ndcg_at_3 value: 57.553 - type: ndcg_at_5 value: 53.795 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 56.599999999999994 - type: precision_at_100 value: 42.84 - type: precision_at_1000 value: 18.386 - type: precision_at_3 value: 63.333 - type: precision_at_5 value: 57.99999999999999 - type: recall_at_1 value: 0.161 - type: recall_at_10 value: 1.434 - type: recall_at_100 value: 9.454 - type: recall_at_1000 value: 37.175000000000004 - type: recall_at_3 value: 0.477 - type: recall_at_5 value: 0.735 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.342566470284666 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.11469484366251 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 78.76771912274579 - type: cos_sim_spearman value: 68.21965433585433 - type: euclidean_pearson value: 73.41725536408647 - type: euclidean_spearman value: 68.21970849513703 - type: manhattan_pearson value: 73.07310010299138 - type: manhattan_spearman value: 68.02842343011922 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 77.24856339472711 - type: cos_sim_spearman value: 68.13233535199409 - type: euclidean_pearson value: 72.83173400932682 - type: euclidean_spearman value: 68.13353961544857 - type: manhattan_pearson value: 72.364020033214 - type: manhattan_spearman value: 67.96817473009628 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.11822706559114 - type: cos_sim_spearman value: 78.82692788488787 - type: euclidean_pearson value: 78.42176146428962 - type: euclidean_spearman value: 78.82696569079468 - type: manhattan_pearson value: 77.94207608371939 - type: manhattan_spearman value: 78.30672557882981 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.37520382719511 - type: cos_sim_spearman value: 75.09236770903914 - type: euclidean_pearson value: 77.94076407783429 - type: euclidean_spearman value: 75.0923580173567 - type: manhattan_pearson value: 77.739191296084 - type: manhattan_spearman value: 74.9480210937594 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.9584878497231 - type: cos_sim_spearman value: 83.58865804953194 - type: euclidean_pearson value: 83.32064366874845 - type: euclidean_spearman value: 83.58865650778534 - type: manhattan_pearson value: 83.17898835151296 - type: manhattan_spearman value: 83.45146824277634 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.40206220271352 - type: cos_sim_spearman value: 78.18587292841029 - type: euclidean_pearson value: 77.63109474603048 - type: euclidean_spearman value: 78.18586561703366 - type: manhattan_pearson value: 77.56336963431791 - type: manhattan_spearman value: 78.13426002359485 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.28987235462407 - type: cos_sim_spearman value: 86.91762382232156 - type: euclidean_pearson value: 86.05340443036164 - type: euclidean_spearman value: 86.91849630883524 - type: manhattan_pearson value: 85.98189959096196 - type: manhattan_spearman value: 86.94471215865201 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 61.248533592592025 - type: cos_sim_spearman value: 61.25674726411208 - type: euclidean_pearson value: 62.668232482670724 - type: euclidean_spearman value: 61.25674726411208 - type: manhattan_pearson value: 62.217580952381915 - type: manhattan_spearman value: 60.77021894786932 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.84077621570408 - type: cos_sim_spearman value: 79.26302777438052 - type: euclidean_pearson value: 80.5028036765331 - type: euclidean_spearman value: 79.26304623849835 - type: manhattan_pearson value: 80.45325721545979 - type: manhattan_spearman value: 79.22021810584245 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.71971528163719 - type: mrr value: 94.15308003543299 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 36.611 - type: map_at_10 value: 46.424 - type: map_at_100 value: 47.347 - type: map_at_1000 value: 47.404 - type: map_at_3 value: 43.153000000000006 - type: map_at_5 value: 45.024 - type: mrr_at_1 value: 39.0 - type: mrr_at_10 value: 48.423 - type: mrr_at_100 value: 49.126 - type: mrr_at_1000 value: 49.179 - type: mrr_at_3 value: 45.389 - type: mrr_at_5 value: 47.221999999999994 - type: ndcg_at_1 value: 39.0 - type: ndcg_at_10 value: 52.142999999999994 - type: ndcg_at_100 value: 56.606 - type: ndcg_at_1000 value: 57.894 - type: ndcg_at_3 value: 45.611000000000004 - type: ndcg_at_5 value: 48.85 - type: precision_at_1 value: 39.0 - type: precision_at_10 value: 7.467 - type: precision_at_100 value: 1.0030000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.111 - type: precision_at_5 value: 12.6 - type: recall_at_1 value: 36.611 - type: recall_at_10 value: 68.289 - type: recall_at_100 value: 89.267 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 50.471999999999994 - type: recall_at_5 value: 58.289 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72475247524753 - type: cos_sim_ap value: 92.0612887387195 - type: cos_sim_f1 value: 85.78528827037775 - type: cos_sim_precision value: 85.27667984189723 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.72475247524753 - type: dot_ap value: 92.0612887387195 - type: dot_f1 value: 85.78528827037775 - type: dot_precision value: 85.27667984189723 - type: dot_recall value: 86.3 - type: euclidean_accuracy value: 99.72475247524753 - type: euclidean_ap value: 92.0612887387195 - type: euclidean_f1 value: 85.78528827037775 - type: euclidean_precision value: 85.27667984189723 - type: euclidean_recall value: 86.3 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 92.11384029855155 - type: manhattan_f1 value: 85.75595527467186 - type: manhattan_precision value: 83.44370860927152 - type: manhattan_recall value: 88.2 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 92.11384029855155 - type: max_f1 value: 85.78528827037775 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 51.43694167734459 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.99750013836291 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 44.11670648850121 - type: mrr value: 44.651265809354044 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.82538139718491 - type: cos_sim_spearman value: 30.223708279486612 - type: dot_pearson value: 29.8253813971849 - type: dot_spearman value: 30.26388644272319 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.144 - type: map_at_10 value: 8.538 - type: map_at_100 value: 14.526 - type: map_at_1000 value: 16.253 - type: map_at_3 value: 3.721 - type: map_at_5 value: 5.979 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 41.553000000000004 - type: mrr_at_100 value: 42.672 - type: mrr_at_1000 value: 42.672 - type: mrr_at_3 value: 35.714 - type: mrr_at_5 value: 40.306 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 21.421 - type: ndcg_at_100 value: 35.417 - type: ndcg_at_1000 value: 47.281 - type: ndcg_at_3 value: 20.107 - type: ndcg_at_5 value: 23.012 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 21.02 - type: precision_at_100 value: 8.245 - type: precision_at_1000 value: 1.608 - type: precision_at_3 value: 22.448999999999998 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.144 - type: recall_at_10 value: 15.318999999999999 - type: recall_at_100 value: 50.608 - type: recall_at_1000 value: 86.652 - type: recall_at_3 value: 4.65 - type: recall_at_5 value: 9.286 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 76.1994 - type: ap value: 17.166874536029024 - type: f1 value: 58.91563395048056 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.56140350877194 - type: f1 value: 59.83462102375279 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.717753205468256 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 82.51177206890385 - type: cos_sim_ap value: 61.880585258206324 - type: cos_sim_f1 value: 59.29389759176994 - type: cos_sim_precision value: 53.232577665827044 - type: cos_sim_recall value: 66.91292875989447 - type: dot_accuracy value: 82.51177206890385 - type: dot_ap value: 61.880585258206324 - type: dot_f1 value: 59.29389759176994 - type: dot_precision value: 53.232577665827044 - type: dot_recall value: 66.91292875989447 - type: euclidean_accuracy value: 82.51177206890385 - type: euclidean_ap value: 61.880585258206324 - type: euclidean_f1 value: 59.29389759176994 - type: euclidean_precision value: 53.232577665827044 - type: euclidean_recall value: 66.91292875989447 - type: manhattan_accuracy value: 82.41044286821243 - type: manhattan_ap value: 61.69366003781778 - type: manhattan_f1 value: 59.267976933035186 - type: manhattan_precision value: 53.494794986190776 - type: manhattan_recall value: 66.43799472295514 - type: max_accuracy value: 82.51177206890385 - type: max_ap value: 61.880585258206324 - type: max_f1 value: 59.29389759176994 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.58683587534443 - type: cos_sim_ap value: 83.41906537158532 - type: cos_sim_f1 value: 75.80436150912658 - type: cos_sim_precision value: 73.01191070537052 - type: cos_sim_recall value: 78.81890976285803 - type: dot_accuracy value: 87.58683587534443 - type: dot_ap value: 83.41906537158532 - type: dot_f1 value: 75.80436150912658 - type: dot_precision value: 73.01191070537052 - type: dot_recall value: 78.81890976285803 - type: euclidean_accuracy value: 87.58683587534443 - type: euclidean_ap value: 83.41906537158532 - type: euclidean_f1 value: 75.80436150912658 - type: euclidean_precision value: 73.01191070537052 - type: euclidean_recall value: 78.81890976285803 - type: manhattan_accuracy value: 87.55190747855785 - type: manhattan_ap value: 83.37075875688966 - type: manhattan_f1 value: 75.71862755868028 - type: manhattan_precision value: 72.19467914251798 - type: manhattan_recall value: 79.60425007699415 - type: max_accuracy value: 87.58683587534443 - type: max_ap value: 83.41906537158532 - type: max_f1 value: 75.80436150912658 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/GTE_wl_mv	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:26:26	2025-01-09T11:26:30	0	--- tags: - mteb model-index: - name: gte_WORDLLAMA_MODEL2VEC_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.13432835820896 - type: ap value: 35.167459200441506 - type: f1 value: 66.74544259725131 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 71.5158 - type: ap value: 65.87290139797425 - type: f1 value: 71.31117308043078 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.032 - type: f1 value: 36.34554421029957 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.172 - type: map_at_100 value: 39.339 - type: map_at_1000 value: 39.353 - type: map_at_3 value: 33.286 - type: map_at_5 value: 35.942 - type: mrr_at_1 value: 24.253 - type: mrr_at_10 value: 38.423 - type: mrr_at_100 value: 39.589 - type: mrr_at_1000 value: 39.604 - type: mrr_at_3 value: 33.559 - type: mrr_at_5 value: 36.169000000000004 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.660000000000004 - type: ndcg_at_100 value: 51.800999999999995 - type: ndcg_at_1000 value: 52.147 - type: ndcg_at_3 value: 36.498000000000005 - type: ndcg_at_5 value: 41.309000000000005 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.396999999999999 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.268 - type: precision_at_5 value: 11.508000000000001 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 73.969 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 45.804 - type: recall_at_5 value: 57.538999999999994 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.8392617925804 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.39147233524174 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.43457632808065 - type: mrr value: 69.7011168271556 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.40924171268267 - type: cos_sim_spearman value: 76.48728498335026 - type: euclidean_pearson value: 78.11322656013188 - type: euclidean_spearman value: 76.48728498335026 - type: manhattan_pearson value: 78.39882365124392 - type: manhattan_spearman value: 76.55837094044142 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.63311688311688 - type: f1 value: 74.89031278068427 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.47759744268641 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 26.72176842867392 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.918000000000003 - type: map_at_10 value: 29.912 - type: map_at_100 value: 31.205 - type: map_at_1000 value: 31.357000000000003 - type: map_at_3 value: 27.206000000000003 - type: map_at_5 value: 28.613 - type: mrr_at_1 value: 27.897 - type: mrr_at_10 value: 35.921 - type: mrr_at_100 value: 36.825 - type: mrr_at_1000 value: 36.894 - type: mrr_at_3 value: 33.858 - type: mrr_at_5 value: 34.881 - type: ndcg_at_1 value: 27.897 - type: ndcg_at_10 value: 35.306 - type: ndcg_at_100 value: 40.955999999999996 - type: ndcg_at_1000 value: 43.909 - type: ndcg_at_3 value: 31.422 - type: ndcg_at_5 value: 32.89 - type: precision_at_1 value: 27.897 - type: precision_at_10 value: 6.9239999999999995 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 11.044 - type: recall_at_1 value: 21.918000000000003 - type: recall_at_10 value: 45.171 - type: recall_at_100 value: 70.226 - type: recall_at_1000 value: 90.279 - type: recall_at_3 value: 32.657000000000004 - type: recall_at_5 value: 37.372 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 20.456 - type: map_at_10 value: 26.596999999999998 - type: map_at_100 value: 27.639999999999997 - type: map_at_1000 value: 27.766000000000002 - type: map_at_3 value: 24.487000000000002 - type: map_at_5 value: 25.683 - type: mrr_at_1 value: 25.605 - type: mrr_at_10 value: 31.326999999999998 - type: mrr_at_100 value: 32.133 - type: mrr_at_1000 value: 32.198 - type: mrr_at_3 value: 29.310000000000002 - type: mrr_at_5 value: 30.431 - type: ndcg_at_1 value: 25.605 - type: ndcg_at_10 value: 30.728 - type: ndcg_at_100 value: 35.318 - type: ndcg_at_1000 value: 38.082 - type: ndcg_at_3 value: 27.226 - type: ndcg_at_5 value: 28.828 - type: precision_at_1 value: 25.605 - type: precision_at_10 value: 5.561 - type: precision_at_100 value: 1.001 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 12.717999999999998 - type: precision_at_5 value: 9.134 - type: recall_at_1 value: 20.456 - type: recall_at_10 value: 38.476 - type: recall_at_100 value: 58.120000000000005 - type: recall_at_1000 value: 76.793 - type: recall_at_3 value: 28.232000000000003 - type: recall_at_5 value: 32.53 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 28.088 - type: map_at_10 value: 37.584 - type: map_at_100 value: 38.75 - type: map_at_1000 value: 38.842999999999996 - type: map_at_3 value: 34.839999999999996 - type: map_at_5 value: 36.352000000000004 - type: mrr_at_1 value: 32.476 - type: mrr_at_10 value: 40.892 - type: mrr_at_100 value: 41.792 - type: mrr_at_1000 value: 41.845 - type: mrr_at_3 value: 38.474000000000004 - type: mrr_at_5 value: 39.818999999999996 - type: ndcg_at_1 value: 32.476 - type: ndcg_at_10 value: 42.811 - type: ndcg_at_100 value: 48.045 - type: ndcg_at_1000 value: 50.09400000000001 - type: ndcg_at_3 value: 37.830000000000005 - type: ndcg_at_5 value: 40.168 - type: precision_at_1 value: 32.476 - type: precision_at_10 value: 7.034 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 16.949 - type: precision_at_5 value: 11.799 - type: recall_at_1 value: 28.088 - type: recall_at_10 value: 55.318 - type: recall_at_100 value: 78.66499999999999 - type: recall_at_1000 value: 93.415 - type: recall_at_3 value: 41.865 - type: recall_at_5 value: 47.675 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.13 - type: map_at_10 value: 18.506 - type: map_at_100 value: 19.405 - type: map_at_1000 value: 19.516 - type: map_at_3 value: 16.821 - type: map_at_5 value: 17.782 - type: mrr_at_1 value: 14.124 - type: mrr_at_10 value: 19.767000000000003 - type: mrr_at_100 value: 20.66 - type: mrr_at_1000 value: 20.755000000000003 - type: mrr_at_3 value: 18.023 - type: mrr_at_5 value: 19.0 - type: ndcg_at_1 value: 14.124 - type: ndcg_at_10 value: 21.728 - type: ndcg_at_100 value: 26.422 - type: ndcg_at_1000 value: 29.73 - type: ndcg_at_3 value: 18.312 - type: ndcg_at_5 value: 19.993 - type: precision_at_1 value: 14.124 - type: precision_at_10 value: 3.4459999999999997 - type: precision_at_100 value: 0.617 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.91 - type: precision_at_5 value: 5.695 - type: recall_at_1 value: 13.13 - type: recall_at_10 value: 30.470000000000002 - type: recall_at_100 value: 52.449 - type: recall_at_1000 value: 78.25 - type: recall_at_3 value: 21.209 - type: recall_at_5 value: 25.281 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.7 - type: map_at_10 value: 12.333 - type: map_at_100 value: 13.367999999999999 - type: map_at_1000 value: 13.492 - type: map_at_3 value: 10.747 - type: map_at_5 value: 11.645999999999999 - type: mrr_at_1 value: 9.826 - type: mrr_at_10 value: 14.81 - type: mrr_at_100 value: 15.854 - type: mrr_at_1000 value: 15.953000000000001 - type: mrr_at_3 value: 13.039000000000001 - type: mrr_at_5 value: 14.046 - type: ndcg_at_1 value: 9.826 - type: ndcg_at_10 value: 15.437000000000001 - type: ndcg_at_100 value: 21.009 - type: ndcg_at_1000 value: 24.515 - type: ndcg_at_3 value: 12.349 - type: ndcg_at_5 value: 13.850000000000001 - type: precision_at_1 value: 9.826 - type: precision_at_10 value: 3.01 - type: precision_at_100 value: 0.692 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 6.053 - type: precision_at_5 value: 4.577 - type: recall_at_1 value: 7.7 - type: recall_at_10 value: 22.546 - type: recall_at_100 value: 47.648 - type: recall_at_1000 value: 73.655 - type: recall_at_3 value: 14.289 - type: recall_at_5 value: 17.994 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 19.886 - type: map_at_10 value: 26.63 - type: map_at_100 value: 27.944999999999997 - type: map_at_1000 value: 28.097 - type: map_at_3 value: 24.077 - type: map_at_5 value: 25.378 - type: mrr_at_1 value: 24.254 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.425 - type: mrr_at_1000 value: 32.501999999999995 - type: mrr_at_3 value: 28.793999999999997 - type: mrr_at_5 value: 30.237000000000002 - type: ndcg_at_1 value: 24.254 - type: ndcg_at_10 value: 31.524 - type: ndcg_at_100 value: 37.658 - type: ndcg_at_1000 value: 40.722 - type: ndcg_at_3 value: 26.953 - type: ndcg_at_5 value: 28.919 - type: precision_at_1 value: 24.254 - type: precision_at_10 value: 5.881 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 12.479999999999999 - type: precision_at_5 value: 9.105 - type: recall_at_1 value: 19.886 - type: recall_at_10 value: 41.593 - type: recall_at_100 value: 68.43599999999999 - type: recall_at_1000 value: 89.041 - type: recall_at_3 value: 28.723 - type: recall_at_5 value: 33.804 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.821 - type: map_at_10 value: 21.898999999999997 - type: map_at_100 value: 23.189 - type: map_at_1000 value: 23.323 - type: map_at_3 value: 19.634999999999998 - type: map_at_5 value: 20.848 - type: mrr_at_1 value: 19.064 - type: mrr_at_10 value: 25.784000000000002 - type: mrr_at_100 value: 26.828999999999997 - type: mrr_at_1000 value: 26.904 - type: mrr_at_3 value: 23.573 - type: mrr_at_5 value: 24.812 - type: ndcg_at_1 value: 19.064 - type: ndcg_at_10 value: 26.229999999999997 - type: ndcg_at_100 value: 32.326 - type: ndcg_at_1000 value: 35.435 - type: ndcg_at_3 value: 22.070999999999998 - type: ndcg_at_5 value: 23.93 - type: precision_at_1 value: 19.064 - type: precision_at_10 value: 4.966 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.54 - type: precision_at_5 value: 7.785 - type: recall_at_1 value: 15.821 - type: recall_at_10 value: 35.516 - type: recall_at_100 value: 61.971 - type: recall_at_1000 value: 83.848 - type: recall_at_3 value: 23.97 - type: recall_at_5 value: 28.662 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.921916666666666 - type: map_at_10 value: 21.780166666666666 - type: map_at_100 value: 22.84433333333333 - type: map_at_1000 value: 22.975916666666667 - type: map_at_3 value: 19.735916666666665 - type: map_at_5 value: 20.860416666666666 - type: mrr_at_1 value: 19.054249999999996 - type: mrr_at_10 value: 25.021333333333335 - type: mrr_at_100 value: 25.93491666666667 - type: mrr_at_1000 value: 26.019166666666667 - type: mrr_at_3 value: 23.03583333333333 - type: mrr_at_5 value: 24.140000000000004 - type: ndcg_at_1 value: 19.054249999999996 - type: ndcg_at_10 value: 25.70233333333334 - type: ndcg_at_100 value: 30.890500000000003 - type: ndcg_at_1000 value: 34.02575 - type: ndcg_at_3 value: 22.017666666666663 - type: ndcg_at_5 value: 23.718666666666664 - type: precision_at_1 value: 19.054249999999996 - type: precision_at_10 value: 4.622083333333333 - type: precision_at_100 value: 0.86825 - type: precision_at_1000 value: 0.13258333333333333 - type: precision_at_3 value: 10.176166666666669 - type: precision_at_5 value: 7.382749999999999 - type: recall_at_1 value: 15.921916666666666 - type: recall_at_10 value: 34.314833333333326 - type: recall_at_100 value: 57.83341666666667 - type: recall_at_1000 value: 80.45625000000001 - type: recall_at_3 value: 23.967166666666667 - type: recall_at_5 value: 28.36841666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 12.857 - type: map_at_10 value: 17.826 - type: map_at_100 value: 18.677 - type: map_at_1000 value: 18.775 - type: map_at_3 value: 16.227 - type: map_at_5 value: 17.168 - type: mrr_at_1 value: 14.877 - type: mrr_at_10 value: 19.784 - type: mrr_at_100 value: 20.662 - type: mrr_at_1000 value: 20.746000000000002 - type: mrr_at_3 value: 18.175 - type: mrr_at_5 value: 19.08 - type: ndcg_at_1 value: 14.877 - type: ndcg_at_10 value: 20.987000000000002 - type: ndcg_at_100 value: 25.654 - type: ndcg_at_1000 value: 28.360000000000003 - type: ndcg_at_3 value: 17.919 - type: ndcg_at_5 value: 19.404 - type: precision_at_1 value: 14.877 - type: precision_at_10 value: 3.528 - type: precision_at_100 value: 0.641 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.129 - type: precision_at_5 value: 5.798 - type: recall_at_1 value: 12.857 - type: recall_at_10 value: 28.864 - type: recall_at_100 value: 50.943000000000005 - type: recall_at_1000 value: 71.158 - type: recall_at_3 value: 20.330000000000002 - type: recall_at_5 value: 24.03 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.823 - type: map_at_10 value: 12.664 - type: map_at_100 value: 13.447000000000001 - type: map_at_1000 value: 13.58 - type: map_at_3 value: 11.372 - type: map_at_5 value: 12.052 - type: mrr_at_1 value: 10.84 - type: mrr_at_10 value: 15.135000000000002 - type: mrr_at_100 value: 15.919 - type: mrr_at_1000 value: 16.026 - type: mrr_at_3 value: 13.702 - type: mrr_at_5 value: 14.496 - type: ndcg_at_1 value: 10.84 - type: ndcg_at_10 value: 15.375 - type: ndcg_at_100 value: 19.612 - type: ndcg_at_1000 value: 23.305 - type: ndcg_at_3 value: 12.879999999999999 - type: ndcg_at_5 value: 13.980999999999998 - type: precision_at_1 value: 10.84 - type: precision_at_10 value: 2.887 - type: precision_at_100 value: 0.599 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.171 - type: precision_at_5 value: 4.522 - type: recall_at_1 value: 8.823 - type: recall_at_10 value: 21.19 - type: recall_at_100 value: 40.843 - type: recall_at_1000 value: 68.118 - type: recall_at_3 value: 14.219000000000001 - type: recall_at_5 value: 17.061 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 14.841999999999999 - type: map_at_10 value: 19.807 - type: map_at_100 value: 20.646 - type: map_at_1000 value: 20.782 - type: map_at_3 value: 17.881 - type: map_at_5 value: 18.94 - type: mrr_at_1 value: 17.631 - type: mrr_at_10 value: 22.949 - type: mrr_at_100 value: 23.727 - type: mrr_at_1000 value: 23.829 - type: mrr_at_3 value: 20.896 - type: mrr_at_5 value: 21.964 - type: ndcg_at_1 value: 17.631 - type: ndcg_at_10 value: 23.544999999999998 - type: ndcg_at_100 value: 28.042 - type: ndcg_at_1000 value: 31.66 - type: ndcg_at_3 value: 19.697 - type: ndcg_at_5 value: 21.467 - type: precision_at_1 value: 17.631 - type: precision_at_10 value: 4.039000000000001 - type: precision_at_100 value: 0.7080000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 8.831 - type: precision_at_5 value: 6.381 - type: recall_at_1 value: 14.841999999999999 - type: recall_at_10 value: 32.144 - type: recall_at_100 value: 52.896 - type: recall_at_1000 value: 79.3 - type: recall_at_3 value: 21.64 - type: recall_at_5 value: 26.127 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.182 - type: map_at_10 value: 21.423000000000002 - type: map_at_100 value: 22.766000000000002 - type: map_at_1000 value: 22.966 - type: map_at_3 value: 19.096 - type: map_at_5 value: 20.514 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.834999999999997 - type: mrr_at_100 value: 25.818 - type: mrr_at_1000 value: 25.893 - type: mrr_at_3 value: 22.628 - type: mrr_at_5 value: 24.032 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 25.766 - type: ndcg_at_100 value: 31.677 - type: ndcg_at_1000 value: 35.024 - type: ndcg_at_3 value: 22.027 - type: ndcg_at_5 value: 24.046 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 5.158 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.211 - type: precision_at_3 value: 10.474 - type: precision_at_5 value: 7.983999999999999 - type: recall_at_1 value: 15.182 - type: recall_at_10 value: 34.008 - type: recall_at_100 value: 61.882000000000005 - type: recall_at_1000 value: 84.635 - type: recall_at_3 value: 23.3 - type: recall_at_5 value: 28.732999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.36 - type: map_at_10 value: 16.181 - type: map_at_100 value: 17.094 - type: map_at_1000 value: 17.214 - type: map_at_3 value: 14.442 - type: map_at_5 value: 15.348999999999998 - type: mrr_at_1 value: 13.678 - type: mrr_at_10 value: 17.636 - type: mrr_at_100 value: 18.575 - type: mrr_at_1000 value: 18.685 - type: mrr_at_3 value: 15.958 - type: mrr_at_5 value: 16.882 - type: ndcg_at_1 value: 13.678 - type: ndcg_at_10 value: 18.991 - type: ndcg_at_100 value: 23.967 - type: ndcg_at_1000 value: 27.473 - type: ndcg_at_3 value: 15.526000000000002 - type: ndcg_at_5 value: 17.148 - type: precision_at_1 value: 13.678 - type: precision_at_10 value: 3.031 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 6.4079999999999995 - type: precision_at_5 value: 4.769 - type: recall_at_1 value: 12.36 - type: recall_at_10 value: 26.482 - type: recall_at_100 value: 49.922 - type: recall_at_1000 value: 76.983 - type: recall_at_3 value: 17.172 - type: recall_at_5 value: 21.152 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 8.464 - type: map_at_10 value: 14.78 - type: map_at_100 value: 16.436999999999998 - type: map_at_1000 value: 16.650000000000002 - type: map_at_3 value: 12.027000000000001 - type: map_at_5 value: 13.428999999999998 - type: mrr_at_1 value: 19.544 - type: mrr_at_10 value: 29.537999999999997 - type: mrr_at_100 value: 30.653000000000002 - type: mrr_at_1000 value: 30.708000000000002 - type: mrr_at_3 value: 25.798 - type: mrr_at_5 value: 28.072000000000003 - type: ndcg_at_1 value: 19.544 - type: ndcg_at_10 value: 21.953 - type: ndcg_at_100 value: 29.188 - type: ndcg_at_1000 value: 33.222 - type: ndcg_at_3 value: 16.89 - type: ndcg_at_5 value: 18.825 - type: precision_at_1 value: 19.544 - type: precision_at_10 value: 7.277 - type: precision_at_100 value: 1.506 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.834000000000001 - type: precision_at_5 value: 10.488999999999999 - type: recall_at_1 value: 8.464 - type: recall_at_10 value: 27.762999999999998 - type: recall_at_100 value: 53.147999999999996 - type: recall_at_1000 value: 76.183 - type: recall_at_3 value: 15.642 - type: recall_at_5 value: 20.593 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.676 - type: map_at_10 value: 11.847000000000001 - type: map_at_100 value: 16.875999999999998 - type: map_at_1000 value: 18.081 - type: map_at_3 value: 8.512 - type: map_at_5 value: 9.956 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 57.928000000000004 - type: mrr_at_100 value: 58.52 - type: mrr_at_1000 value: 58.544 - type: mrr_at_3 value: 55.333 - type: mrr_at_5 value: 56.958 - type: ndcg_at_1 value: 35.875 - type: ndcg_at_10 value: 27.221 - type: ndcg_at_100 value: 31.808999999999997 - type: ndcg_at_1000 value: 39.199 - type: ndcg_at_3 value: 30.274 - type: ndcg_at_5 value: 28.785 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 23.65 - type: precision_at_100 value: 7.818 - type: precision_at_1000 value: 1.651 - type: precision_at_3 value: 35.833 - type: precision_at_5 value: 31.0 - type: recall_at_1 value: 5.676 - type: recall_at_10 value: 16.619 - type: recall_at_100 value: 39.422000000000004 - type: recall_at_1000 value: 64.095 - type: recall_at_3 value: 9.608 - type: recall_at_5 value: 12.277000000000001 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.185 - type: f1 value: 44.87033813298503 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 18.904 - type: map_at_10 value: 28.435 - type: map_at_100 value: 29.498 - type: map_at_1000 value: 29.567 - type: map_at_3 value: 25.319000000000003 - type: map_at_5 value: 27.13 - type: mrr_at_1 value: 20.116999999999997 - type: mrr_at_10 value: 30.112 - type: mrr_at_100 value: 31.155 - type: mrr_at_1000 value: 31.213 - type: mrr_at_3 value: 26.895000000000003 - type: mrr_at_5 value: 28.793000000000003 - type: ndcg_at_1 value: 20.116999999999997 - type: ndcg_at_10 value: 34.244 - type: ndcg_at_100 value: 39.409 - type: ndcg_at_1000 value: 41.195 - type: ndcg_at_3 value: 27.872000000000003 - type: ndcg_at_5 value: 31.128 - type: precision_at_1 value: 20.116999999999997 - type: precision_at_10 value: 5.534 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.965 - type: recall_at_1 value: 18.904 - type: recall_at_10 value: 50.858000000000004 - type: recall_at_100 value: 74.42 - type: recall_at_1000 value: 88.023 - type: recall_at_3 value: 33.675 - type: recall_at_5 value: 41.449999999999996 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 8.892 - type: map_at_10 value: 14.363000000000001 - type: map_at_100 value: 15.75 - type: map_at_1000 value: 15.959000000000001 - type: map_at_3 value: 12.25 - type: map_at_5 value: 13.286999999999999 - type: mrr_at_1 value: 16.821 - type: mrr_at_10 value: 23.425 - type: mrr_at_100 value: 24.556 - type: mrr_at_1000 value: 24.637 - type: mrr_at_3 value: 20.885 - type: mrr_at_5 value: 22.127 - type: ndcg_at_1 value: 16.821 - type: ndcg_at_10 value: 19.412 - type: ndcg_at_100 value: 25.836 - type: ndcg_at_1000 value: 30.131000000000004 - type: ndcg_at_3 value: 16.198 - type: ndcg_at_5 value: 17.185 - type: precision_at_1 value: 16.821 - type: precision_at_10 value: 5.556 - type: precision_at_100 value: 1.1820000000000002 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 10.545 - type: precision_at_5 value: 8.056000000000001 - type: recall_at_1 value: 8.892 - type: recall_at_10 value: 25.249 - type: recall_at_100 value: 50.263000000000005 - type: recall_at_1000 value: 76.43299999999999 - type: recall_at_3 value: 15.094 - type: recall_at_5 value: 18.673000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.831 - type: map_at_10 value: 29.959999999999997 - type: map_at_100 value: 30.959999999999997 - type: map_at_1000 value: 31.069000000000003 - type: map_at_3 value: 27.453 - type: map_at_5 value: 28.838 - type: mrr_at_1 value: 41.661 - type: mrr_at_10 value: 49.647999999999996 - type: mrr_at_100 value: 50.304 - type: mrr_at_1000 value: 50.352 - type: mrr_at_3 value: 47.403 - type: mrr_at_5 value: 48.657000000000004 - type: ndcg_at_1 value: 41.661 - type: ndcg_at_10 value: 37.854 - type: ndcg_at_100 value: 42.248999999999995 - type: ndcg_at_1000 value: 44.756 - type: ndcg_at_3 value: 33.243 - type: ndcg_at_5 value: 35.467 - type: precision_at_1 value: 41.661 - type: precision_at_10 value: 8.386000000000001 - type: precision_at_100 value: 1.1900000000000002 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 21.022 - type: precision_at_5 value: 14.377 - type: recall_at_1 value: 20.831 - type: recall_at_10 value: 41.931000000000004 - type: recall_at_100 value: 59.507 - type: recall_at_1000 value: 76.232 - type: recall_at_3 value: 31.533 - type: recall_at_5 value: 35.942 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.2136 - type: ap value: 64.38274263735502 - type: f1 value: 70.02577813394484 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.542999999999999 - type: map_at_10 value: 13.229 - type: map_at_100 value: 14.283999999999999 - type: map_at_1000 value: 14.396 - type: map_at_3 value: 11.139000000000001 - type: map_at_5 value: 12.259 - type: mrr_at_1 value: 7.808 - type: mrr_at_10 value: 13.577 - type: mrr_at_100 value: 14.625 - type: mrr_at_1000 value: 14.732000000000001 - type: mrr_at_3 value: 11.464 - type: mrr_at_5 value: 12.584999999999999 - type: ndcg_at_1 value: 7.779 - type: ndcg_at_10 value: 16.793 - type: ndcg_at_100 value: 22.564 - type: ndcg_at_1000 value: 25.799 - type: ndcg_at_3 value: 12.431000000000001 - type: ndcg_at_5 value: 14.442 - type: precision_at_1 value: 7.779 - type: precision_at_10 value: 2.894 - type: precision_at_100 value: 0.59 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 5.454 - type: precision_at_5 value: 4.278 - type: recall_at_1 value: 7.542999999999999 - type: recall_at_10 value: 27.907 - type: recall_at_100 value: 56.13399999999999 - type: recall_at_1000 value: 81.877 - type: recall_at_3 value: 15.878999999999998 - type: recall_at_5 value: 20.726 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.68490652074783 - type: f1 value: 90.90009716586837 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.33150934792522 - type: f1 value: 42.414995407585955 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.29455279085406 - type: f1 value: 64.0154454215856 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.91055817081372 - type: f1 value: 72.79505573377739 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.478611587568 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.395691978780366 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.75504868917307 - type: mrr value: 31.723412508217553 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.739 - type: map_at_10 value: 9.419 - type: map_at_100 value: 12.209 - type: map_at_1000 value: 13.653 - type: map_at_3 value: 7.292999999999999 - type: map_at_5 value: 8.291 - type: mrr_at_1 value: 38.7 - type: mrr_at_10 value: 47.934 - type: mrr_at_100 value: 48.605 - type: mrr_at_1000 value: 48.646 - type: mrr_at_3 value: 45.717 - type: mrr_at_5 value: 47.157 - type: ndcg_at_1 value: 36.842000000000006 - type: ndcg_at_10 value: 28.077 - type: ndcg_at_100 value: 26.83 - type: ndcg_at_1000 value: 36.272 - type: ndcg_at_3 value: 32.429 - type: ndcg_at_5 value: 30.823 - type: precision_at_1 value: 38.7 - type: precision_at_10 value: 20.774 - type: precision_at_100 value: 7.331 - type: precision_at_1000 value: 2.085 - type: precision_at_3 value: 30.341 - type: precision_at_5 value: 26.502 - type: recall_at_1 value: 4.739 - type: recall_at_10 value: 13.065999999999999 - type: recall_at_100 value: 28.875 - type: recall_at_1000 value: 62.751000000000005 - type: recall_at_3 value: 8.338 - type: recall_at_5 value: 10.211 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.764 - type: map_at_10 value: 18.582 - type: map_at_100 value: 19.953000000000003 - type: map_at_1000 value: 20.049 - type: map_at_3 value: 15.551 - type: map_at_5 value: 17.143 - type: mrr_at_1 value: 12.283 - type: mrr_at_10 value: 20.507 - type: mrr_at_100 value: 21.724 - type: mrr_at_1000 value: 21.801000000000002 - type: mrr_at_3 value: 17.434 - type: mrr_at_5 value: 19.097 - type: ndcg_at_1 value: 12.254 - type: ndcg_at_10 value: 23.818 - type: ndcg_at_100 value: 30.652 - type: ndcg_at_1000 value: 33.25 - type: ndcg_at_3 value: 17.577 - type: ndcg_at_5 value: 20.43 - type: precision_at_1 value: 12.254 - type: precision_at_10 value: 4.492999999999999 - type: precision_at_100 value: 0.8370000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 8.333 - type: precision_at_5 value: 6.593 - type: recall_at_1 value: 10.764 - type: recall_at_10 value: 38.279999999999994 - type: recall_at_100 value: 69.77600000000001 - type: recall_at_1000 value: 89.75 - type: recall_at_3 value: 21.608 - type: recall_at_5 value: 28.247 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 66.238 - type: map_at_10 value: 79.61 - type: map_at_100 value: 80.339 - type: map_at_1000 value: 80.366 - type: map_at_3 value: 76.572 - type: map_at_5 value: 78.45100000000001 - type: mrr_at_1 value: 76.18 - type: mrr_at_10 value: 83.319 - type: mrr_at_100 value: 83.492 - type: mrr_at_1000 value: 83.49499999999999 - type: mrr_at_3 value: 82.002 - type: mrr_at_5 value: 82.88 - type: ndcg_at_1 value: 76.24 - type: ndcg_at_10 value: 84.048 - type: ndcg_at_100 value: 85.76700000000001 - type: ndcg_at_1000 value: 85.989 - type: ndcg_at_3 value: 80.608 - type: ndcg_at_5 value: 82.45 - type: precision_at_1 value: 76.24 - type: precision_at_10 value: 12.775 - type: precision_at_100 value: 1.498 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.107 - type: precision_at_5 value: 23.198 - type: recall_at_1 value: 66.238 - type: recall_at_10 value: 92.655 - type: recall_at_100 value: 98.79599999999999 - type: recall_at_1000 value: 99.914 - type: recall_at_3 value: 82.818 - type: recall_at_5 value: 87.985 - type: map_at_1 value: 3.3029999999999995 - type: map_at_10 value: 8.534 - type: map_at_100 value: 10.269 - type: map_at_1000 value: 10.569 - type: map_at_3 value: 6.02 - type: map_at_5 value: 7.3 - type: mrr_at_1 value: 16.2 - type: mrr_at_10 value: 26.048 - type: mrr_at_100 value: 27.229 - type: mrr_at_1000 value: 27.307 - type: mrr_at_3 value: 22.8 - type: mrr_at_5 value: 24.555 - type: ndcg_at_1 value: 16.2 - type: ndcg_at_10 value: 15.152 - type: ndcg_at_100 value: 22.692999999999998 - type: ndcg_at_1000 value: 28.283 - type: ndcg_at_3 value: 13.831 - type: ndcg_at_5 value: 12.383 - type: precision_at_1 value: 16.2 - type: precision_at_10 value: 8.15 - type: precision_at_100 value: 1.921 - type: precision_at_1000 value: 0.326 - type: precision_at_3 value: 13.167000000000002 - type: precision_at_5 value: 11.200000000000001 - type: recall_at_1 value: 3.3029999999999995 - type: recall_at_10 value: 16.463 - type: recall_at_100 value: 38.968 - type: recall_at_1000 value: 66.208 - type: recall_at_3 value: 8.023 - type: recall_at_5 value: 11.338 - type: map_at_1 value: 0.154 - type: map_at_10 value: 1.216 - type: map_at_100 value: 6.401 - type: map_at_1000 value: 16.882 - type: map_at_3 value: 0.418 - type: map_at_5 value: 0.7040000000000001 - type: mrr_at_1 value: 62.0 - type: mrr_at_10 value: 75.319 - type: mrr_at_100 value: 75.435 - type: mrr_at_1000 value: 75.435 - type: mrr_at_3 value: 73.333 - type: mrr_at_5 value: 75.033 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 54.176 - type: ndcg_at_100 value: 40.741 - type: ndcg_at_1000 value: 38.385000000000005 - type: ndcg_at_3 value: 57.676 - type: ndcg_at_5 value: 57.867000000000004 - type: precision_at_1 value: 62.0 - type: precision_at_10 value: 57.8 - type: precision_at_100 value: 42.68 - type: precision_at_1000 value: 18.478 - type: precision_at_3 value: 61.333000000000006 - type: precision_at_5 value: 63.6 - type: recall_at_1 value: 0.154 - type: recall_at_10 value: 1.468 - type: recall_at_100 value: 9.541 - type: recall_at_1000 value: 37.218 - type: recall_at_3 value: 0.46299999999999997 - type: recall_at_5 value: 0.8340000000000001 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.96790773164943 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.114201492992976 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 78.21858054391086 - type: cos_sim_spearman value: 67.3365618536054 - 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type: euclidean_spearman value: 60.59580774316736 - type: manhattan_pearson value: 61.43013801720455 - type: manhattan_spearman value: 59.92526461879062 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.58292387813594 - type: cos_sim_spearman value: 78.85975762418589 - type: euclidean_pearson value: 80.28122335716425 - type: euclidean_spearman value: 78.85977608876506 - type: manhattan_pearson value: 80.20419882971093 - type: manhattan_spearman value: 78.79811621332709 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.54383068715617 - type: mrr value: 93.62365031482678 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.111000000000004 - type: map_at_10 value: 47.686 - type: map_at_100 value: 48.722 - type: map_at_1000 value: 48.776 - type: map_at_3 value: 44.625 - type: map_at_5 value: 46.289 - type: mrr_at_1 value: 41.667 - type: mrr_at_10 value: 49.619 - type: mrr_at_100 value: 50.434 - type: mrr_at_1000 value: 50.482000000000006 - type: mrr_at_3 value: 46.833000000000006 - type: mrr_at_5 value: 48.317 - type: ndcg_at_1 value: 41.667 - type: ndcg_at_10 value: 52.819 - type: ndcg_at_100 value: 57.69 - type: ndcg_at_1000 value: 58.965 - type: ndcg_at_3 value: 46.857 - type: ndcg_at_5 value: 49.697 - type: precision_at_1 value: 41.667 - type: precision_at_10 value: 7.367 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.6 - type: recall_at_1 value: 39.111000000000004 - type: recall_at_10 value: 67.039 - type: recall_at_100 value: 89.767 - type: recall_at_1000 value: 99.467 - type: recall_at_3 value: 51.056000000000004 - type: recall_at_5 value: 57.99999999999999 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72772277227723 - type: cos_sim_ap value: 91.98542118937158 - type: cos_sim_f1 value: 85.91691995947316 - type: cos_sim_precision value: 87.06365503080082 - type: cos_sim_recall value: 84.8 - type: dot_accuracy value: 99.72772277227723 - type: dot_ap value: 91.98542118937158 - type: dot_f1 value: 85.91691995947316 - type: dot_precision value: 87.06365503080082 - type: dot_recall value: 84.8 - type: euclidean_accuracy value: 99.72772277227723 - type: euclidean_ap value: 91.98542118937158 - type: euclidean_f1 value: 85.91691995947316 - type: euclidean_precision value: 87.06365503080082 - type: euclidean_recall value: 84.8 - type: manhattan_accuracy value: 99.72574257425742 - type: manhattan_ap value: 91.96773898408213 - type: manhattan_f1 value: 85.8601327207759 - type: manhattan_precision value: 87.69551616266945 - type: manhattan_recall value: 84.1 - type: max_accuracy value: 99.72772277227723 - type: max_ap value: 91.98542118937158 - type: max_f1 value: 85.91691995947316 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 50.974351388709024 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.94724711190474 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.618618519378074 - type: mrr value: 44.19061942959002 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.75942900919329 - type: cos_sim_spearman value: 30.265779375382486 - type: dot_pearson value: 29.759429009193283 - type: dot_spearman value: 30.216316271647514 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.144 - type: map_at_10 value: 8.38 - type: map_at_100 value: 14.482000000000001 - type: map_at_1000 value: 16.179 - type: map_at_3 value: 3.821 - type: map_at_5 value: 5.96 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 41.501 - type: mrr_at_100 value: 42.575 - type: mrr_at_1000 value: 42.575 - type: mrr_at_3 value: 36.054 - type: mrr_at_5 value: 40.238 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 21.644 - type: ndcg_at_100 value: 35.427 - type: ndcg_at_1000 value: 47.116 - type: ndcg_at_3 value: 20.814 - type: ndcg_at_5 value: 22.783 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 21.224 - type: precision_at_100 value: 8.265 - type: precision_at_1000 value: 1.5959999999999999 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.144 - type: recall_at_10 value: 15.278 - type: recall_at_100 value: 50.541000000000004 - type: recall_at_1000 value: 86.144 - type: recall_at_3 value: 5.056 - type: recall_at_5 value: 9.203 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 75.88100000000001 - type: ap value: 17.210410808772743 - type: f1 value: 58.7851360197636 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.68024900962084 - type: f1 value: 59.95386992880734 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.55446050017461 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 82.32699529117244 - type: cos_sim_ap value: 61.49148139881723 - type: cos_sim_f1 value: 59.31940298507462 - type: cos_sim_precision value: 54.17666303162486 - type: cos_sim_recall value: 65.54089709762533 - type: dot_accuracy value: 82.32699529117244 - type: dot_ap value: 61.49148139881723 - type: dot_f1 value: 59.31940298507462 - type: dot_precision value: 54.17666303162486 - type: dot_recall value: 65.54089709762533 - type: euclidean_accuracy value: 82.32699529117244 - type: euclidean_ap value: 61.49148139881723 - type: euclidean_f1 value: 59.31940298507462 - type: euclidean_precision value: 54.17666303162486 - type: euclidean_recall value: 65.54089709762533 - type: manhattan_accuracy value: 82.44024557429815 - type: manhattan_ap value: 61.57050440663527 - type: manhattan_f1 value: 59.36456916800594 - type: manhattan_precision value: 55.8501977204001 - type: manhattan_recall value: 63.35092348284961 - type: max_accuracy value: 82.44024557429815 - type: max_ap value: 61.57050440663527 - type: max_f1 value: 59.36456916800594 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.70714479760935 - type: cos_sim_ap value: 83.52059059692118 - type: cos_sim_f1 value: 75.8043805261034 - type: cos_sim_precision value: 72.40171000070083 - type: cos_sim_recall value: 79.54265475823837 - type: dot_accuracy value: 87.70714479760935 - type: dot_ap value: 83.52059016767844 - type: dot_f1 value: 75.8043805261034 - type: dot_precision value: 72.40171000070083 - type: dot_recall value: 79.54265475823837 - type: euclidean_accuracy value: 87.70714479760935 - type: euclidean_ap value: 83.52059046795347 - type: euclidean_f1 value: 75.8043805261034 - type: euclidean_precision value: 72.40171000070083 - type: euclidean_recall value: 79.54265475823837 - type: manhattan_accuracy value: 87.7187875965382 - type: manhattan_ap value: 83.5377383098018 - type: manhattan_f1 value: 75.87021520062012 - type: manhattan_precision value: 72.87102035028008 - type: manhattan_recall value: 79.12688635663689 - type: max_accuracy value: 87.7187875965382 - type: max_ap value: 83.5377383098018 - type: max_f1 value: 75.87021520062012 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
twadada/wl_sw_256	twadada	null	[ "mteb", "model-index", "region:us" ]	2025-01-09T11:43:11	2025-01-09T11:43:20	0	--- tags: - mteb model-index: - name: l3_wordllama_256 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 65.97014925373134 - type: ap value: 27.33017285839569 - type: f1 value: 59.04330619047924 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.248250000000006 - type: ap value: 58.695642654646576 - type: f1 value: 62.98826255412888 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.689999999999998 - type: f1 value: 31.106666192619258 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 19.986 - type: map_at_10 value: 34.634 - type: map_at_100 value: 35.937000000000005 - type: map_at_1000 value: 35.954 - type: map_at_3 value: 29.742 - type: map_at_5 value: 32.444 - type: mrr_at_1 value: 20.341 - type: mrr_at_10 value: 34.763 - type: mrr_at_100 value: 36.065999999999995 - type: mrr_at_1000 value: 36.083 - type: mrr_at_3 value: 29.872 - type: mrr_at_5 value: 32.574999999999996 - type: ndcg_at_1 value: 19.986 - type: ndcg_at_10 value: 43.074 - type: ndcg_at_100 value: 48.819 - type: ndcg_at_1000 value: 49.26 - type: ndcg_at_3 value: 32.934000000000005 - type: ndcg_at_5 value: 37.830999999999996 - type: precision_at_1 value: 19.986 - type: precision_at_10 value: 7.02 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.059 - type: precision_at_5 value: 10.825 - type: recall_at_1 value: 19.986 - type: recall_at_10 value: 70.199 - type: recall_at_100 value: 95.804 - type: recall_at_1000 value: 99.21799999999999 - type: recall_at_3 value: 42.176 - type: recall_at_5 value: 54.125 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.64176717184799 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.06122250673383 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.808484614132844 - type: mrr value: 71.09121487930351 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 74.96889982129713 - type: cos_sim_spearman value: 70.34256665852179 - type: euclidean_pearson value: 73.59375229907496 - type: euclidean_spearman value: 70.34256665852179 - type: manhattan_pearson value: 72.38820178677287 - type: manhattan_spearman value: 69.3919425882689 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.56818181818181 - type: f1 value: 72.78107232170503 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.10380086081637 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.238238325966222 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.294999999999998 - type: map_at_10 value: 27.535999999999998 - type: map_at_100 value: 28.803 - type: map_at_1000 value: 28.971000000000004 - type: map_at_3 value: 25.029 - type: map_at_5 value: 26.526 - type: mrr_at_1 value: 24.893 - type: mrr_at_10 value: 32.554 - type: mrr_at_100 value: 33.504 - type: mrr_at_1000 value: 33.583 - type: mrr_at_3 value: 30.091 - type: mrr_at_5 value: 31.535999999999998 - type: ndcg_at_1 value: 24.893 - type: ndcg_at_10 value: 32.495000000000005 - type: ndcg_at_100 value: 38.288 - type: ndcg_at_1000 value: 41.559000000000005 - type: ndcg_at_3 value: 28.321 - type: ndcg_at_5 value: 30.401 - type: precision_at_1 value: 24.893 - type: precision_at_10 value: 6.109 - type: precision_at_100 value: 1.142 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 13.447999999999999 - type: precision_at_5 value: 9.927999999999999 - type: recall_at_1 value: 20.294999999999998 - type: recall_at_10 value: 42.129 - type: recall_at_100 value: 67.709 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 30.148999999999997 - type: recall_at_5 value: 35.804 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.426 - type: map_at_10 value: 22.461000000000002 - type: map_at_100 value: 23.424 - type: map_at_1000 value: 23.559 - type: map_at_3 value: 20.643 - type: map_at_5 value: 21.602 - type: mrr_at_1 value: 20.701 - type: mrr_at_10 value: 26.734 - type: mrr_at_100 value: 27.516000000000002 - type: mrr_at_1000 value: 27.594 - type: mrr_at_3 value: 24.936 - type: mrr_at_5 value: 25.901000000000003 - type: ndcg_at_1 value: 20.701 - type: ndcg_at_10 value: 26.381 - type: ndcg_at_100 value: 30.731 - type: ndcg_at_1000 value: 33.603 - type: ndcg_at_3 value: 23.336000000000002 - type: ndcg_at_5 value: 24.644 - type: precision_at_1 value: 20.701 - type: precision_at_10 value: 5.006 - type: precision_at_100 value: 0.9339999999999999 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.315999999999999 - type: precision_at_5 value: 8.14 - type: recall_at_1 value: 16.426 - type: recall_at_10 value: 33.593 - type: recall_at_100 value: 52.746 - type: recall_at_1000 value: 72.15899999999999 - type: recall_at_3 value: 24.712 - type: recall_at_5 value: 28.233000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 24.46 - type: map_at_10 value: 33.292 - type: map_at_100 value: 34.437 - type: map_at_1000 value: 34.534 - type: map_at_3 value: 30.567 - type: map_at_5 value: 32.202 - type: mrr_at_1 value: 28.276 - type: mrr_at_10 value: 36.235 - type: mrr_at_100 value: 37.173 - type: mrr_at_1000 value: 37.234 - type: mrr_at_3 value: 33.783 - type: mrr_at_5 value: 35.237 - type: ndcg_at_1 value: 28.276 - type: ndcg_at_10 value: 38.202000000000005 - type: ndcg_at_100 value: 43.634 - type: ndcg_at_1000 value: 45.894 - type: ndcg_at_3 value: 33.19 - type: ndcg_at_5 value: 35.798 - type: precision_at_1 value: 28.276 - type: precision_at_10 value: 6.332 - type: precision_at_100 value: 1.008 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 14.671000000000001 - type: precision_at_5 value: 10.571 - type: recall_at_1 value: 24.46 - type: recall_at_10 value: 50.156 - type: recall_at_100 value: 74.648 - type: recall_at_1000 value: 91.269 - type: recall_at_3 value: 36.937999999999995 - type: recall_at_5 value: 43.15 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 14.052999999999999 - type: map_at_10 value: 18.287 - type: map_at_100 value: 19.137 - type: map_at_1000 value: 19.258 - type: map_at_3 value: 16.79 - type: map_at_5 value: 17.618000000000002 - type: mrr_at_1 value: 15.254000000000001 - type: mrr_at_10 value: 19.88 - type: mrr_at_100 value: 20.71 - type: mrr_at_1000 value: 20.812 - type: mrr_at_3 value: 18.23 - type: mrr_at_5 value: 19.185 - type: ndcg_at_1 value: 15.254000000000001 - type: ndcg_at_10 value: 21.183 - type: ndcg_at_100 value: 25.972 - type: ndcg_at_1000 value: 29.271 - type: ndcg_at_3 value: 18.046 - type: ndcg_at_5 value: 19.570999999999998 - type: precision_at_1 value: 15.254000000000001 - type: precision_at_10 value: 3.288 - type: precision_at_100 value: 0.614 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.5329999999999995 - type: precision_at_5 value: 5.379 - type: recall_at_1 value: 14.052999999999999 - type: recall_at_10 value: 28.599999999999998 - type: recall_at_100 value: 51.815 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 20.238999999999997 - type: recall_at_5 value: 23.837 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 8.475000000000001 - type: map_at_10 value: 12.898000000000001 - type: map_at_100 value: 13.950000000000001 - type: map_at_1000 value: 14.063999999999998 - type: map_at_3 value: 10.965 - type: map_at_5 value: 11.905000000000001 - type: mrr_at_1 value: 10.323 - type: mrr_at_10 value: 15.431000000000001 - type: mrr_at_100 value: 16.442 - type: mrr_at_1000 value: 16.526 - type: mrr_at_3 value: 13.288 - type: mrr_at_5 value: 14.382 - type: ndcg_at_1 value: 10.323 - type: ndcg_at_10 value: 16.325 - type: ndcg_at_100 value: 21.831999999999997 - type: ndcg_at_1000 value: 25.079 - type: ndcg_at_3 value: 12.372 - type: ndcg_at_5 value: 14.011999999999999 - type: precision_at_1 value: 10.323 - type: precision_at_10 value: 3.197 - type: precision_at_100 value: 0.6930000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 5.970000000000001 - type: precision_at_5 value: 4.627 - type: recall_at_1 value: 8.475000000000001 - type: recall_at_10 value: 24.651999999999997 - type: recall_at_100 value: 49.63 - type: recall_at_1000 value: 73.35000000000001 - type: recall_at_3 value: 13.852 - type: recall_at_5 value: 17.813000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.278 - type: map_at_10 value: 24.852 - type: map_at_100 value: 26.308999999999997 - type: map_at_1000 value: 26.450000000000003 - type: map_at_3 value: 22.183 - type: map_at_5 value: 23.493 - type: mrr_at_1 value: 22.522000000000002 - type: mrr_at_10 value: 29.554000000000002 - type: mrr_at_100 value: 30.705 - type: mrr_at_1000 value: 30.774 - type: mrr_at_3 value: 26.821 - type: mrr_at_5 value: 28.288000000000004 - type: ndcg_at_1 value: 22.522000000000002 - type: ndcg_at_10 value: 29.79 - type: ndcg_at_100 value: 36.473 - type: ndcg_at_1000 value: 39.440999999999995 - type: ndcg_at_3 value: 24.915000000000003 - type: ndcg_at_5 value: 26.941 - type: precision_at_1 value: 22.522000000000002 - type: precision_at_10 value: 5.707 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 11.645999999999999 - type: precision_at_5 value: 8.584999999999999 - type: recall_at_1 value: 18.278 - type: recall_at_10 value: 40.150999999999996 - type: recall_at_100 value: 68.978 - type: recall_at_1000 value: 89.295 - type: recall_at_3 value: 26.548 - type: recall_at_5 value: 31.772 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.634 - type: map_at_10 value: 21.377 - type: map_at_100 value: 22.522000000000002 - type: map_at_1000 value: 22.657 - type: map_at_3 value: 19.292 - type: map_at_5 value: 20.278 - type: mrr_at_1 value: 18.151 - type: mrr_at_10 value: 25.263999999999996 - type: mrr_at_100 value: 26.156000000000002 - type: mrr_at_1000 value: 26.247 - type: mrr_at_3 value: 23.154 - type: mrr_at_5 value: 24.188000000000002 - type: ndcg_at_1 value: 18.151 - type: ndcg_at_10 value: 25.773000000000003 - type: ndcg_at_100 value: 31.130999999999997 - type: ndcg_at_1000 value: 34.452 - type: ndcg_at_3 value: 21.975 - type: ndcg_at_5 value: 23.36 - type: precision_at_1 value: 18.151 - type: precision_at_10 value: 4.829 - type: precision_at_100 value: 0.894 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.693 - type: precision_at_5 value: 7.648000000000001 - type: recall_at_1 value: 14.634 - type: recall_at_10 value: 35.433 - type: recall_at_100 value: 58.617 - type: recall_at_1000 value: 82.364 - type: recall_at_3 value: 24.59 - type: recall_at_5 value: 28.217 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.736583333333334 - type: map_at_10 value: 20.393 - type: map_at_100 value: 21.42775 - type: map_at_1000 value: 21.560666666666666 - type: map_at_3 value: 18.52958333333333 - type: map_at_5 value: 19.509249999999998 - type: mrr_at_1 value: 17.61366666666667 - type: mrr_at_10 value: 23.522250000000003 - type: mrr_at_100 value: 24.424166666666668 - type: mrr_at_1000 value: 24.512166666666666 - type: mrr_at_3 value: 21.64875 - type: mrr_at_5 value: 22.648916666666665 - type: ndcg_at_1 value: 17.61366666666667 - type: ndcg_at_10 value: 24.16458333333333 - type: ndcg_at_100 value: 29.305916666666672 - type: ndcg_at_1000 value: 32.52291666666667 - type: ndcg_at_3 value: 20.732 - type: ndcg_at_5 value: 22.223333333333333 - type: precision_at_1 value: 17.61366666666667 - type: precision_at_10 value: 4.33925 - type: precision_at_100 value: 0.8296666666666666 - type: precision_at_1000 value: 0.12933333333333333 - type: precision_at_3 value: 9.6265 - type: precision_at_5 value: 6.921666666666666 - type: recall_at_1 value: 14.736583333333334 - type: recall_at_10 value: 32.46958333333333 - type: recall_at_100 value: 55.94050000000001 - type: recall_at_1000 value: 79.17466666666667 - type: recall_at_3 value: 22.765749999999997 - type: recall_at_5 value: 26.614583333333336 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.152 - type: map_at_10 value: 16.052 - type: map_at_100 value: 16.892 - type: map_at_1000 value: 17.0 - type: map_at_3 value: 14.677999999999999 - type: map_at_5 value: 15.424 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.871000000000002 - type: mrr_at_100 value: 18.694 - type: mrr_at_1000 value: 18.793000000000003 - type: mrr_at_3 value: 16.641000000000002 - type: mrr_at_5 value: 17.262 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.981 - type: ndcg_at_100 value: 23.704 - type: ndcg_at_1000 value: 26.810000000000002 - type: ndcg_at_3 value: 16.361 - type: ndcg_at_5 value: 17.507 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.221 - type: precision_at_100 value: 0.612 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.4639999999999995 - type: precision_at_5 value: 5.244999999999999 - type: recall_at_1 value: 11.152 - type: recall_at_10 value: 26.22 - type: recall_at_100 value: 48.870000000000005 - type: recall_at_1000 value: 72.328 - type: recall_at_3 value: 18.838 - type: recall_at_5 value: 21.693 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.338 - type: map_at_10 value: 12.315 - type: map_at_100 value: 13.086 - type: map_at_1000 value: 13.214 - type: map_at_3 value: 11.032 - type: map_at_5 value: 11.691 - type: mrr_at_1 value: 10.255 - type: mrr_at_10 value: 14.723 - type: mrr_at_100 value: 15.528 - type: mrr_at_1000 value: 15.626000000000001 - type: mrr_at_3 value: 13.289000000000001 - type: mrr_at_5 value: 14.047 - type: ndcg_at_1 value: 10.255 - type: ndcg_at_10 value: 15.058 - type: ndcg_at_100 value: 19.326 - type: ndcg_at_1000 value: 22.972 - type: ndcg_at_3 value: 12.565999999999999 - type: ndcg_at_5 value: 13.603000000000002 - type: precision_at_1 value: 10.255 - type: precision_at_10 value: 2.815 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.045 - type: precision_at_5 value: 4.405 - type: recall_at_1 value: 8.338 - type: recall_at_10 value: 21.125 - type: recall_at_100 value: 40.936 - type: recall_at_1000 value: 67.984 - type: recall_at_3 value: 14.018 - type: recall_at_5 value: 16.725 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.575000000000001 - type: map_at_10 value: 18.967 - type: map_at_100 value: 19.924 - type: map_at_1000 value: 20.06 - type: map_at_3 value: 17.101 - type: map_at_5 value: 18.142 - type: mrr_at_1 value: 16.418 - type: mrr_at_10 value: 22.131 - type: mrr_at_100 value: 22.993 - type: mrr_at_1000 value: 23.101 - type: mrr_at_3 value: 20.288999999999998 - type: mrr_at_5 value: 21.282999999999998 - type: ndcg_at_1 value: 16.418 - type: ndcg_at_10 value: 22.625 - type: ndcg_at_100 value: 27.676000000000002 - type: ndcg_at_1000 value: 31.41 - type: ndcg_at_3 value: 19.136 - type: ndcg_at_5 value: 20.748 - type: precision_at_1 value: 16.418 - type: precision_at_10 value: 3.9739999999999998 - type: precision_at_100 value: 0.743 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.924 - type: precision_at_5 value: 6.381 - type: recall_at_1 value: 13.575000000000001 - type: recall_at_10 value: 30.794 - type: recall_at_100 value: 54.02400000000001 - type: recall_at_1000 value: 81.634 - type: recall_at_3 value: 21.095 - type: recall_at_5 value: 25.25 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.915999999999999 - type: map_at_10 value: 20.976 - type: map_at_100 value: 22.127 - type: map_at_1000 value: 22.329 - type: map_at_3 value: 19.62 - type: map_at_5 value: 20.247999999999998 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.822 - type: mrr_at_100 value: 25.765 - type: mrr_at_1000 value: 25.852000000000004 - type: mrr_at_3 value: 23.551 - type: mrr_at_5 value: 24.193 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 24.956999999999997 - type: ndcg_at_100 value: 30.224 - type: ndcg_at_1000 value: 33.883 - type: ndcg_at_3 value: 23.094 - type: ndcg_at_5 value: 23.659 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.802 - type: precision_at_100 value: 1.105 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 11.462 - type: precision_at_5 value: 7.826 - type: recall_at_1 value: 14.915999999999999 - type: recall_at_10 value: 31.902 - type: recall_at_100 value: 57.296 - type: recall_at_1000 value: 82.107 - type: recall_at_3 value: 25.013 - type: recall_at_5 value: 27.281 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.237 - type: map_at_10 value: 15.703 - type: map_at_100 value: 16.522000000000002 - type: map_at_1000 value: 16.631999999999998 - type: map_at_3 value: 14.455000000000002 - type: map_at_5 value: 14.982000000000001 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.068 - type: mrr_at_100 value: 17.904 - type: mrr_at_1000 value: 18.004 - type: mrr_at_3 value: 15.712000000000002 - type: mrr_at_5 value: 16.285 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 18.205 - type: ndcg_at_100 value: 22.68 - type: ndcg_at_1000 value: 25.901000000000003 - type: ndcg_at_3 value: 15.472 - type: ndcg_at_5 value: 16.436 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.791 - type: precision_at_100 value: 0.538 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.324999999999999 - type: recall_at_1 value: 12.237 - type: recall_at_10 value: 24.88 - type: recall_at_100 value: 46.017 - type: recall_at_1000 value: 71.029 - type: recall_at_3 value: 17.197000000000003 - type: recall_at_5 value: 19.6 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.732 - type: map_at_10 value: 12.674 - type: map_at_100 value: 14.257 - type: map_at_1000 value: 14.463999999999999 - type: map_at_3 value: 10.355 - type: map_at_5 value: 11.524 - type: mrr_at_1 value: 15.831000000000001 - type: mrr_at_10 value: 25.972 - type: mrr_at_100 value: 27.107999999999997 - type: mrr_at_1000 value: 27.167 - type: mrr_at_3 value: 22.637999999999998 - type: mrr_at_5 value: 24.319 - type: ndcg_at_1 value: 15.831000000000001 - type: ndcg_at_10 value: 19.244 - type: ndcg_at_100 value: 26.329 - type: ndcg_at_1000 value: 30.270999999999997 - type: ndcg_at_3 value: 14.966 - type: ndcg_at_5 value: 16.377 - type: precision_at_1 value: 15.831000000000001 - type: precision_at_10 value: 6.404 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 11.64 - type: precision_at_5 value: 9.134 - type: recall_at_1 value: 6.732 - type: recall_at_10 value: 24.855 - type: recall_at_100 value: 49.730000000000004 - type: recall_at_1000 value: 72.214 - type: recall_at_3 value: 14.299000000000001 - type: recall_at_5 value: 18.363 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.529 - type: map_at_10 value: 9.075999999999999 - type: map_at_100 value: 12.394 - type: map_at_1000 value: 13.272999999999998 - type: map_at_3 value: 6.688 - type: map_at_5 value: 7.803 - type: mrr_at_1 value: 36.25 - type: mrr_at_10 value: 46.867 - type: mrr_at_100 value: 47.654 - type: mrr_at_1000 value: 47.679 - type: mrr_at_3 value: 43.791999999999994 - type: mrr_at_5 value: 45.742 - type: ndcg_at_1 value: 26.75 - type: ndcg_at_10 value: 21.146 - type: ndcg_at_100 value: 25.113999999999997 - type: ndcg_at_1000 value: 31.873 - type: ndcg_at_3 value: 23.142 - type: ndcg_at_5 value: 22.273 - type: precision_at_1 value: 36.25 - type: precision_at_10 value: 18.25 - type: precision_at_100 value: 6.16 - type: precision_at_1000 value: 1.34 - type: precision_at_3 value: 27.250000000000004 - type: precision_at_5 value: 23.75 - type: recall_at_1 value: 4.529 - type: recall_at_10 value: 13.442000000000002 - type: recall_at_100 value: 32.534 - type: recall_at_1000 value: 55.346 - type: recall_at_3 value: 7.771999999999999 - type: recall_at_5 value: 10.061 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 37.89000000000001 - type: f1 value: 34.12692942265391 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.28 - type: map_at_10 value: 24.729 - type: map_at_100 value: 25.785999999999998 - type: map_at_1000 value: 25.855 - type: map_at_3 value: 22.083 - type: map_at_5 value: 23.534 - type: mrr_at_1 value: 17.462 - type: mrr_at_10 value: 26.358999999999998 - type: mrr_at_100 value: 27.412 - type: mrr_at_1000 value: 27.473 - type: mrr_at_3 value: 23.615 - type: mrr_at_5 value: 25.115 - type: ndcg_at_1 value: 17.462 - type: ndcg_at_10 value: 29.885 - type: ndcg_at_100 value: 35.268 - type: ndcg_at_1000 value: 37.203 - type: ndcg_at_3 value: 24.397 - type: ndcg_at_5 value: 26.995 - type: precision_at_1 value: 17.462 - type: precision_at_10 value: 4.851 - type: precision_at_100 value: 0.77 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 10.666 - type: precision_at_5 value: 7.762 - type: recall_at_1 value: 16.28 - type: recall_at_10 value: 44.554 - type: recall_at_100 value: 69.736 - type: recall_at_1000 value: 84.654 - type: recall_at_3 value: 29.529 - type: recall_at_5 value: 35.789 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.406 - type: map_at_10 value: 12.162 - type: map_at_100 value: 13.501 - type: map_at_1000 value: 13.700000000000001 - type: map_at_3 value: 10.282 - type: map_at_5 value: 11.182 - type: mrr_at_1 value: 14.969 - type: mrr_at_10 value: 21.453 - type: mrr_at_100 value: 22.579 - type: mrr_at_1000 value: 22.665 - type: mrr_at_3 value: 19.084 - type: mrr_at_5 value: 20.233999999999998 - type: ndcg_at_1 value: 14.969 - type: ndcg_at_10 value: 17.022000000000002 - type: ndcg_at_100 value: 23.415 - type: ndcg_at_1000 value: 27.811000000000003 - type: ndcg_at_3 value: 14.191999999999998 - type: ndcg_at_5 value: 15.026 - type: precision_at_1 value: 14.969 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 1.133 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 9.516 - type: precision_at_5 value: 7.191 - type: recall_at_1 value: 7.406 - type: recall_at_10 value: 22.404 - type: recall_at_100 value: 47.351 - type: recall_at_1000 value: 74.701 - type: recall_at_3 value: 13.108 - type: recall_at_5 value: 16.531000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.662 - type: map_at_10 value: 28.956 - type: map_at_100 value: 29.942999999999998 - type: map_at_1000 value: 30.052 - type: map_at_3 value: 26.767999999999997 - type: map_at_5 value: 28.011000000000003 - type: mrr_at_1 value: 41.323 - type: mrr_at_10 value: 49.242999999999995 - type: mrr_at_100 value: 49.97 - type: mrr_at_1000 value: 50.016000000000005 - type: mrr_at_3 value: 47.207 - type: mrr_at_5 value: 48.364000000000004 - type: ndcg_at_1 value: 41.323 - type: ndcg_at_10 value: 36.756 - type: ndcg_at_100 value: 41.189 - type: ndcg_at_1000 value: 43.667 - type: ndcg_at_3 value: 32.690999999999995 - type: ndcg_at_5 value: 34.703 - type: precision_at_1 value: 41.323 - type: precision_at_10 value: 8.015 - type: precision_at_100 value: 1.155 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 20.612 - type: precision_at_5 value: 13.961000000000002 - type: recall_at_1 value: 20.662 - type: recall_at_10 value: 40.074 - type: recall_at_100 value: 57.745000000000005 - type: recall_at_1000 value: 74.24 - type: recall_at_3 value: 30.918 - type: recall_at_5 value: 34.902 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 64.62239999999998 - type: ap value: 59.505106899987936 - type: f1 value: 64.39587267286105 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.507000000000001 - type: map_at_10 value: 11.542 - type: map_at_100 value: 12.542 - type: map_at_1000 value: 12.658 - type: map_at_3 value: 9.67 - type: map_at_5 value: 10.631 - type: mrr_at_1 value: 6.705 - type: mrr_at_10 value: 11.857 - type: mrr_at_100 value: 12.863 - type: mrr_at_1000 value: 12.974 - type: mrr_at_3 value: 9.957 - type: mrr_at_5 value: 10.933 - type: ndcg_at_1 value: 6.705 - type: ndcg_at_10 value: 14.764 - type: ndcg_at_100 value: 20.258000000000003 - type: ndcg_at_1000 value: 23.685000000000002 - type: ndcg_at_3 value: 10.809000000000001 - type: ndcg_at_5 value: 12.543000000000001 - type: precision_at_1 value: 6.705 - type: precision_at_10 value: 2.579 - type: precision_at_100 value: 0.543 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 4.771 - type: precision_at_5 value: 3.734 - type: recall_at_1 value: 6.507000000000001 - type: recall_at_10 value: 24.842 - type: recall_at_100 value: 51.697 - type: recall_at_1000 value: 79.081 - type: recall_at_3 value: 13.828 - type: recall_at_5 value: 18.009 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.40264477884178 - type: f1 value: 83.43871348215795 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.90196078431372 - type: f1 value: 35.66115135754105 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.371889710827176 - type: f1 value: 58.91304009131599 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.52185608607937 - type: f1 value: 66.27921261407421 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.40912967319626 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.77476593032722 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.522211560565317 - type: mrr value: 31.540554976019745 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 2.871 - type: map_at_10 value: 6.643000000000001 - type: map_at_100 value: 8.801 - type: map_at_1000 value: 9.961 - type: map_at_3 value: 4.862 - type: map_at_5 value: 5.704 - type: mrr_at_1 value: 29.102 - type: mrr_at_10 value: 38.79 - type: mrr_at_100 value: 39.616 - type: mrr_at_1000 value: 39.659 - type: mrr_at_3 value: 35.913000000000004 - type: mrr_at_5 value: 37.74 - type: ndcg_at_1 value: 27.554000000000002 - type: ndcg_at_10 value: 22.215 - type: ndcg_at_100 value: 21.386 - type: ndcg_at_1000 value: 30.615 - type: ndcg_at_3 value: 25.546000000000003 - type: ndcg_at_5 value: 24.425 - type: precision_at_1 value: 29.102 - type: precision_at_10 value: 17.121 - type: precision_at_100 value: 6.146 - type: precision_at_1000 value: 1.9029999999999998 - type: precision_at_3 value: 24.871 - type: precision_at_5 value: 22.291 - type: recall_at_1 value: 2.871 - type: recall_at_10 value: 10.184999999999999 - type: recall_at_100 value: 24.057000000000002 - type: recall_at_1000 value: 56.788000000000004 - type: recall_at_3 value: 5.606 - type: recall_at_5 value: 7.353 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.455 - type: map_at_10 value: 17.904999999999998 - type: map_at_100 value: 19.215 - type: map_at_1000 value: 19.314 - type: map_at_3 value: 15.133 - type: map_at_5 value: 16.624 - type: mrr_at_1 value: 11.906 - type: mrr_at_10 value: 19.595000000000002 - type: mrr_at_100 value: 20.765 - type: mrr_at_1000 value: 20.845 - type: mrr_at_3 value: 16.7 - type: mrr_at_5 value: 18.314 - type: ndcg_at_1 value: 11.906 - type: ndcg_at_10 value: 22.733999999999998 - type: ndcg_at_100 value: 29.179 - type: ndcg_at_1000 value: 31.848 - type: ndcg_at_3 value: 16.98 - type: ndcg_at_5 value: 19.695 - type: precision_at_1 value: 11.906 - type: precision_at_10 value: 4.234999999999999 - type: precision_at_100 value: 0.79 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.976 - type: precision_at_5 value: 6.286 - type: recall_at_1 value: 10.455 - type: recall_at_10 value: 36.114000000000004 - type: recall_at_100 value: 65.742 - type: recall_at_1000 value: 86.22800000000001 - type: recall_at_3 value: 20.826 - type: recall_at_5 value: 27.165 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.336000000000006 - type: map_at_10 value: 76.859 - type: map_at_100 value: 77.679 - type: map_at_1000 value: 77.705 - type: map_at_3 value: 73.681 - type: map_at_5 value: 75.558 - type: mrr_at_1 value: 73.13 - type: mrr_at_10 value: 80.757 - type: mrr_at_100 value: 80.99300000000001 - type: mrr_at_1000 value: 80.99499999999999 - type: mrr_at_3 value: 79.267 - type: mrr_at_5 value: 80.209 - type: ndcg_at_1 value: 73.15 - type: ndcg_at_10 value: 81.693 - type: ndcg_at_100 value: 83.733 - type: ndcg_at_1000 value: 83.943 - type: ndcg_at_3 value: 77.866 - type: ndcg_at_5 value: 79.779 - type: precision_at_1 value: 73.15 - type: precision_at_10 value: 12.603 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.123 - type: precision_at_5 value: 22.636 - type: recall_at_1 value: 63.336000000000006 - type: recall_at_10 value: 91.36999999999999 - type: recall_at_100 value: 98.831 - type: recall_at_1000 value: 99.901 - type: recall_at_3 value: 80.495 - type: recall_at_5 value: 85.799 - type: map_at_1 value: 3.5479999999999996 - type: map_at_10 value: 8.923 - type: map_at_100 value: 11.038 - type: map_at_1000 value: 11.384 - type: map_at_3 value: 6.387 - type: map_at_5 value: 7.646999999999999 - type: mrr_at_1 value: 17.5 - type: mrr_at_10 value: 27.71 - type: mrr_at_100 value: 28.898000000000003 - type: mrr_at_1000 value: 28.96 - type: mrr_at_3 value: 24.282999999999998 - type: mrr_at_5 value: 26.123 - type: ndcg_at_1 value: 17.5 - type: ndcg_at_10 value: 15.831999999999999 - type: ndcg_at_100 value: 24.478 - type: ndcg_at_1000 value: 30.548 - type: ndcg_at_3 value: 14.66 - type: ndcg_at_5 value: 12.969 - type: precision_at_1 value: 17.5 - type: precision_at_10 value: 8.38 - type: precision_at_100 value: 2.103 - type: precision_at_1000 value: 0.356 - type: precision_at_3 value: 13.866999999999999 - type: precision_at_5 value: 11.58 - type: recall_at_1 value: 3.5479999999999996 - type: recall_at_10 value: 16.958000000000002 - type: recall_at_100 value: 42.687999999999995 - type: recall_at_1000 value: 72.173 - type: recall_at_3 value: 8.437999999999999 - type: recall_at_5 value: 11.738 - type: map_at_1 value: 0.186 - type: map_at_10 value: 1.2149999999999999 - type: map_at_100 value: 6.516 - type: map_at_1000 value: 14.704999999999998 - type: map_at_3 value: 0.469 - type: map_at_5 value: 0.701 - type: mrr_at_1 value: 72.0 - type: mrr_at_10 value: 80.238 - type: mrr_at_100 value: 80.622 - type: mrr_at_1000 value: 80.622 - type: mrr_at_3 value: 79.667 - type: mrr_at_5 value: 79.667 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 57.147000000000006 - type: ndcg_at_100 value: 40.5 - type: ndcg_at_1000 value: 33.954 - type: ndcg_at_3 value: 62.754 - type: ndcg_at_5 value: 59.933 - type: precision_at_1 value: 72.0 - type: precision_at_10 value: 60.6 - type: precision_at_100 value: 42.1 - type: precision_at_1000 value: 15.512 - type: precision_at_3 value: 67.333 - type: precision_at_5 value: 64.0 - type: recall_at_1 value: 0.186 - type: recall_at_10 value: 1.385 - type: recall_at_100 value: 9.332 - type: recall_at_1000 value: 31.922 - type: recall_at_3 value: 0.503 - type: recall_at_5 value: 0.759 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.4964655583453 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 48.31404856068323 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.88215495721286 - type: cos_sim_spearman value: 66.95635868609415 - type: euclidean_pearson value: 71.95058611790435 - type: euclidean_spearman value: 66.95635868609415 - type: manhattan_pearson value: 71.73499967722593 - type: manhattan_spearman value: 66.76136105777387 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.56521014258115 - type: cos_sim_spearman value: 64.21841908004934 - type: euclidean_pearson value: 68.51846331737438 - type: euclidean_spearman value: 64.21841908004934 - type: manhattan_pearson value: 68.27567108498233 - type: manhattan_spearman value: 64.09725470920785 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 72.71775862893193 - type: cos_sim_spearman value: 73.28911820172492 - type: euclidean_pearson value: 72.83254599010056 - type: euclidean_spearman value: 73.28922176679981 - type: manhattan_pearson value: 72.56589783996398 - 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type: cos_sim_pearson value: 29.69015090602311 - type: cos_sim_spearman value: 30.124791626004075 - type: dot_pearson value: 29.69015070868056 - type: dot_spearman value: 30.09621990241238 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.0660000000000003 - type: map_at_10 value: 9.783999999999999 - type: map_at_100 value: 16.005 - type: map_at_1000 value: 17.694 - type: map_at_3 value: 4.524 - type: map_at_5 value: 6.651 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 49.26 - type: mrr_at_100 value: 49.791000000000004 - type: mrr_at_1000 value: 49.791000000000004 - type: mrr_at_3 value: 45.238 - type: mrr_at_5 value: 47.177 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.35 - type: ndcg_at_100 value: 38.078 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 28.749000000000002 - type: ndcg_at_5 value: 28.156 - type: precision_at_1 value: 32.653 - 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type: euclidean_precision value: 69.10146492945385 - type: euclidean_recall value: 78.81121034801355 - type: manhattan_accuracy value: 86.47882951061435 - type: manhattan_ap value: 80.56146544234434 - type: manhattan_f1 value: 73.43608995415659 - type: manhattan_precision value: 69.1267414203194 - type: manhattan_recall value: 78.31844779796735 - type: max_accuracy value: 86.58167423448597 - type: max_ap value: 80.70277058558774 - type: max_f1 value: 73.6376389338513 ---	[ "SUMMARIZATION" ]	[ "BIOSSES", "SCIFACT" ]
NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-gooaq-en	NickyNicky	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "es", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2025-01-16T00:58:56	2025-01-22T03:45:27	2	--- datasets: - sentence-transformers/gooaq language: - en - es library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - Language: en <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(256000, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-gooaq-en") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0007 \| 1 \| 48.9183 \| - \| \| 0.0682 \| 100 \| 24.7453 \| 3.5934 \| \| 0.1363 \| 200 \| 8.3975 \| 2.4385 \| \| 0.2045 \| 300 \| 6.3171 \| 1.9962 \| \| 0.2727 \| 400 \| 5.3817 \| 1.7536 \| \| 0.3408 \| 500 \| 4.8295 \| 1.6392 \| \| 0.4090 \| 600 \| 4.4745 \| 1.5070 \| \| 0.4772 \| 700 \| 4.1783 \| 1.4406 \| \| 0.5453 \| 800 \| 3.952 \| 1.3655 \| \| 0.6135 \| 900 \| 3.7352 \| 1.3114 \| \| 0.6817 \| 1000 \| 3.6185 \| 1.2551 \| \| 0.7498 \| 1100 \| 3.4514 \| 1.2143 \| \| 0.8180 \| 1200 \| 3.3535 \| 1.1816 \| \| 0.8862 \| 1300 \| 3.2741 \| 1.1527 \| \| 0.9543 \| 1400 \| 3.1862 \| 1.1411 \| ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## NanoBEIREvaluator > 0.8 ``` { "NanoDBPedia_cosine_accuracy@3": 0.86, "NanoDBPedia_cosine_accuracy@5": 0.92, "NanoDBPedia_cosine_accuracy@10": 0.96, "NanoFEVER_cosine_accuracy@3": 0.86, "NanoFEVER_cosine_accuracy@5": 0.92, "NanoFEVER_cosine_accuracy@10": 0.96, "NanoHotpotQA_cosine_accuracy@3": 0.82, "NanoHotpotQA_cosine_accuracy@5": 0.84, "NanoHotpotQA_cosine_accuracy@10": 0.88, "NanoQuoraRetrieval_cosine_accuracy@1": 0.88, "NanoQuoraRetrieval_cosine_accuracy@3": 0.96, "NanoQuoraRetrieval_cosine_accuracy@5": 1.0, "NanoQuoraRetrieval_cosine_accuracy@10": 1.0, "NanoSCIDOCS_cosine_accuracy@5": 0.82, "NanoSCIDOCS_cosine_accuracy@10": 0.92, "NanoArguAna_cosine_accuracy@10": 0.92, "NanoSciFact_cosine_accuracy@10": 0.88, "NanoTouche2020_cosine_accuracy@3": 0.8367346938775511, "NanoTouche2020_cosine_accuracy@5": 0.9183673469387755, "NanoTouche2020_cosine_accuracy@10": 0.9387755102040817, "NanoBEIR_mean_cosine_accuracy@10": 0.8583673469387756 } ```` ## All NanoBEIREvaluator ```bibtext {'NanoClimateFEVER_cosine_accuracy@1': 0.28, 'NanoClimateFEVER_cosine_accuracy@3': 0.44, 'NanoClimateFEVER_cosine_accuracy@5': 0.54, 'NanoClimateFEVER_cosine_accuracy@10': 0.72, 'NanoClimateFEVER_cosine_precision@1': 0.28, 'NanoClimateFEVER_cosine_precision@3': 0.15333333333333332, 'NanoClimateFEVER_cosine_precision@5': 0.124, 'NanoClimateFEVER_cosine_precision@10': 0.08999999999999998, 'NanoClimateFEVER_cosine_recall@1': 0.145, 'NanoClimateFEVER_cosine_recall@3': 0.205, 'NanoClimateFEVER_cosine_recall@5': 0.264, 'NanoClimateFEVER_cosine_recall@10': 0.36200000000000004, 'NanoClimateFEVER_cosine_ndcg@10': 0.2957527689242254, 'NanoClimateFEVER_cosine_mrr@10': 0.3996666666666668, 'NanoClimateFEVER_cosine_map@100': 0.23258384801937396, 'NanoDBPedia_cosine_accuracy@1': 0.68, 'NanoDBPedia_cosine_accuracy@3': 0.86, 'NanoDBPedia_cosine_accuracy@5': 0.92, 'NanoDBPedia_cosine_accuracy@10': 0.96, 'NanoDBPedia_cosine_precision@1': 0.68, 'NanoDBPedia_cosine_precision@3': 0.56, 'NanoDBPedia_cosine_precision@5': 0.5120000000000001, 'NanoDBPedia_cosine_precision@10': 0.43800000000000006, 'NanoDBPedia_cosine_recall@1': 0.07601531530835434, 'NanoDBPedia_cosine_recall@3': 0.1438904710839341, 'NanoDBPedia_cosine_recall@5': 0.20681359525684506, 'NanoDBPedia_cosine_recall@10': 0.319966975132044, 'NanoDBPedia_cosine_ndcg@10': 0.5501100350453579, 'NanoDBPedia_cosine_mrr@10': 0.7855000000000001, 'NanoDBPedia_cosine_map@100': 0.39476156890024533, 'NanoFEVER_cosine_accuracy@1': 0.68, 'NanoFEVER_cosine_accuracy@3': 0.86, 'NanoFEVER_cosine_accuracy@5': 0.92, 'NanoFEVER_cosine_accuracy@10': 0.96, 'NanoFEVER_cosine_precision@1': 0.68, 'NanoFEVER_cosine_precision@3': 0.29333333333333333, 'NanoFEVER_cosine_precision@5': 0.19199999999999995, 'NanoFEVER_cosine_precision@10': 0.10199999999999998, 'NanoFEVER_cosine_recall@1': 0.6266666666666666, 'NanoFEVER_cosine_recall@3': 0.8133333333333332, 'NanoFEVER_cosine_recall@5': 0.8833333333333333, 'NanoFEVER_cosine_recall@10': 0.9233333333333333, 'NanoFEVER_cosine_ndcg@10': 0.7933479848498471, 'NanoFEVER_cosine_mrr@10': 0.7780793650793651, 'NanoFEVER_cosine_map@100': 0.7406571665049926, 'NanoFiQA2018_cosine_accuracy@1': 0.46, 'NanoFiQA2018_cosine_accuracy@3': 0.64, 'NanoFiQA2018_cosine_accuracy@5': 0.7, 'NanoFiQA2018_cosine_accuracy@10': 0.72, 'NanoFiQA2018_cosine_precision@1': 0.46, 'NanoFiQA2018_cosine_precision@3': 0.2866666666666666, 'NanoFiQA2018_cosine_precision@5': 0.22399999999999998, 'NanoFiQA2018_cosine_precision@10': 0.12999999999999998, 'NanoFiQA2018_cosine_recall@1': 0.23924603174603173, 'NanoFiQA2018_cosine_recall@3': 0.4251031746031746, 'NanoFiQA2018_cosine_recall@5': 0.5099603174603174, 'NanoFiQA2018_cosine_recall@10': 0.566015873015873, 'NanoFiQA2018_cosine_ndcg@10': 0.4774545077577204, 'NanoFiQA2018_cosine_mrr@10': 0.5475555555555556, 'NanoFiQA2018_cosine_map@100': 0.4125452702654584, 'NanoHotpotQA_cosine_accuracy@1': 0.64, 'NanoHotpotQA_cosine_accuracy@3': 0.82, 'NanoHotpotQA_cosine_accuracy@5': 0.84, 'NanoHotpotQA_cosine_accuracy@10': 0.88, 'NanoHotpotQA_cosine_precision@1': 0.64, 'NanoHotpotQA_cosine_precision@3': 0.3533333333333333, 'NanoHotpotQA_cosine_precision@5': 0.23599999999999993, 'NanoHotpotQA_cosine_precision@10': 0.128, 'NanoHotpotQA_cosine_recall@1': 0.32, 'NanoHotpotQA_cosine_recall@3': 0.53, 'NanoHotpotQA_cosine_recall@5': 0.59, 'NanoHotpotQA_cosine_recall@10': 0.64, 'NanoHotpotQA_cosine_ndcg@10': 0.5959681682828366, 'NanoHotpotQA_cosine_mrr@10': 0.723888888888889, 'NanoHotpotQA_cosine_map@100': 0.5262469568756968, 'NanoMSMARCO_cosine_accuracy@1': 0.36, 'NanoMSMARCO_cosine_accuracy@3': 0.52, 'NanoMSMARCO_cosine_accuracy@5': 0.58, 'NanoMSMARCO_cosine_accuracy@10': 0.8, 'NanoMSMARCO_cosine_precision@1': 0.36, 'NanoMSMARCO_cosine_precision@3': 0.1733333333333333, 'NanoMSMARCO_cosine_precision@5': 0.11599999999999999, 'NanoMSMARCO_cosine_precision@10': 0.08, 'NanoMSMARCO_cosine_recall@1': 0.36, 'NanoMSMARCO_cosine_recall@3': 0.52, 'NanoMSMARCO_cosine_recall@5': 0.58, 'NanoMSMARCO_cosine_recall@10': 0.8, 'NanoMSMARCO_cosine_ndcg@10': 0.5539831330912274, 'NanoMSMARCO_cosine_mrr@10': 0.47960317460317464, 'NanoMSMARCO_cosine_map@100': 0.4907628900864195, 'NanoNFCorpus_cosine_accuracy@1': 0.42, 'NanoNFCorpus_cosine_accuracy@3': 0.56, 'NanoNFCorpus_cosine_accuracy@5': 0.6, 'NanoNFCorpus_cosine_accuracy@10': 0.7, 'NanoNFCorpus_cosine_precision@1': 0.42, 'NanoNFCorpus_cosine_precision@3': 0.3466666666666666, 'NanoNFCorpus_cosine_precision@5': 0.32800000000000007, 'NanoNFCorpus_cosine_precision@10': 0.286, 'NanoNFCorpus_cosine_recall@1': 0.03391318439564492, 'NanoNFCorpus_cosine_recall@3': 0.06311668492872162, 'NanoNFCorpus_cosine_recall@5': 0.08191277059586696, 'NanoNFCorpus_cosine_recall@10': 0.13476845853527392, 'NanoNFCorpus_cosine_ndcg@10': 0.3322933792371396, 'NanoNFCorpus_cosine_mrr@10': 0.4983333333333333, 'NanoNFCorpus_cosine_map@100': 0.13985354018581944, 'NanoNQ_cosine_accuracy@1': 0.44, 'NanoNQ_cosine_accuracy@3': 0.64, 'NanoNQ_cosine_accuracy@5': 0.66, 'NanoNQ_cosine_accuracy@10': 0.76, 'NanoNQ_cosine_precision@1': 0.44, 'NanoNQ_cosine_precision@3': 0.22, 'NanoNQ_cosine_precision@5': 0.14, 'NanoNQ_cosine_precision@10': 0.08199999999999999, 'NanoNQ_cosine_recall@1': 0.42, 'NanoNQ_cosine_recall@3': 0.62, 'NanoNQ_cosine_recall@5': 0.64, 'NanoNQ_cosine_recall@10': 0.75, 'NanoNQ_cosine_ndcg@10': 0.5903874296113161, 'NanoNQ_cosine_mrr@10': 0.5456349206349206, 'NanoNQ_cosine_map@100': 0.5437440035864959, 'NanoQuoraRetrieval_cosine_accuracy@1': 0.88, 'NanoQuoraRetrieval_cosine_accuracy@3': 0.96, 'NanoQuoraRetrieval_cosine_accuracy@5': 1.0, 'NanoQuoraRetrieval_cosine_accuracy@10': 1.0, 'NanoQuoraRetrieval_cosine_precision@1': 0.88, 'NanoQuoraRetrieval_cosine_precision@3': 0.3933333333333333, 'NanoQuoraRetrieval_cosine_precision@5': 0.256, 'NanoQuoraRetrieval_cosine_precision@10': 0.13599999999999998, 'NanoQuoraRetrieval_cosine_recall@1': 0.784, 'NanoQuoraRetrieval_cosine_recall@3': 0.9186666666666667, 'NanoQuoraRetrieval_cosine_recall@5': 0.976, 'NanoQuoraRetrieval_cosine_recall@10': 0.9933333333333334, 'NanoQuoraRetrieval_cosine_ndcg@10': 0.9367841595958026, 'NanoQuoraRetrieval_cosine_mrr@10': 0.9246666666666666, 'NanoQuoraRetrieval_cosine_map@100': 0.913554834054834, 'NanoSCIDOCS_cosine_accuracy@1': 0.52, 'NanoSCIDOCS_cosine_accuracy@3': 0.68, 'NanoSCIDOCS_cosine_accuracy@5': 0.82, 'NanoSCIDOCS_cosine_accuracy@10': 0.92, 'NanoSCIDOCS_cosine_precision@1': 0.52, 'NanoSCIDOCS_cosine_precision@3': 0.3933333333333333, 'NanoSCIDOCS_cosine_precision@5': 0.33599999999999997, 'NanoSCIDOCS_cosine_precision@10': 0.21600000000000003, 'NanoSCIDOCS_cosine_recall@1': 0.10966666666666666, 'NanoSCIDOCS_cosine_recall@3': 0.24466666666666664, 'NanoSCIDOCS_cosine_recall@5': 0.34566666666666657, 'NanoSCIDOCS_cosine_recall@10': 0.44266666666666665, 'NanoSCIDOCS_cosine_ndcg@10': 0.4328110226758414, 'NanoSCIDOCS_cosine_mrr@10': 0.6317222222222222, 'NanoSCIDOCS_cosine_map@100': 0.34997841607847063, 'NanoArguAna_cosine_accuracy@1': 0.2, 'NanoArguAna_cosine_accuracy@3': 0.56, 'NanoArguAna_cosine_accuracy@5': 0.76, 'NanoArguAna_cosine_accuracy@10': 0.92, 'NanoArguAna_cosine_precision@1': 0.2, 'NanoArguAna_cosine_precision@3': 0.18666666666666668, 'NanoArguAna_cosine_precision@5': 0.15200000000000002, 'NanoArguAna_cosine_precision@10': 0.092, 'NanoArguAna_cosine_recall@1': 0.2, 'NanoArguAna_cosine_recall@3': 0.56, 'NanoArguAna_cosine_recall@5': 0.76, 'NanoArguAna_cosine_recall@10': 0.92, 'NanoArguAna_cosine_ndcg@10': 0.5499071039525992, 'NanoArguAna_cosine_mrr@10': 0.43229365079365073, 'NanoArguAna_cosine_map@100': 0.43523820792684886, 'NanoSciFact_cosine_accuracy@1': 0.6, 'NanoSciFact_cosine_accuracy@3': 0.72, 'NanoSciFact_cosine_accuracy@5': 0.8, 'NanoSciFact_cosine_accuracy@10': 0.88, 'NanoSciFact_cosine_precision@1': 0.6, 'NanoSciFact_cosine_precision@3': 0.25333333333333335, 'NanoSciFact_cosine_precision@5': 0.18, 'NanoSciFact_cosine_precision@10': 0.09799999999999999, 'NanoSciFact_cosine_recall@1': 0.58, 'NanoSciFact_cosine_recall@3': 0.7, 'NanoSciFact_cosine_recall@5': 0.8, 'NanoSciFact_cosine_recall@10': 0.87, 'NanoSciFact_cosine_ndcg@10': 0.7265348054031264, 'NanoSciFact_cosine_mrr@10': 0.6841031746031746, 'NanoSciFact_cosine_map@100': 0.6810233866101422, 'NanoTouche2020_cosine_accuracy@1': 0.5102040816326531, 'NanoTouche2020_cosine_accuracy@3': 0.8367346938775511, 'NanoTouche2020_cosine_accuracy@5': 0.9183673469387755, 'NanoTouche2020_cosine_accuracy@10': 0.9387755102040817, 'NanoTouche2020_cosine_precision@1': 0.5102040816326531, 'NanoTouche2020_cosine_precision@3': 0.5374149659863945, 'NanoTouche2020_cosine_precision@5': 0.5061224489795918, 'NanoTouche2020_cosine_precision@10': 0.43265306122448977, 'NanoTouche2020_cosine_recall@1': 0.03546508562664911, 'NanoTouche2020_cosine_recall@3': 0.11189238805791148, 'NanoTouche2020_cosine_recall@5': 0.1673503566176574, 'NanoTouche2020_cosine_recall@10': 0.2818808841266296, 'NanoTouche2020_cosine_ndcg@10': 0.47479704449085264, 'NanoTouche2020_cosine_mrr@10': 0.6714285714285714, 'NanoTouche2020_cosine_map@100': 0.3438320372291555, 'NanoBEIR_mean_cosine_accuracy@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_accuracy@3': 0.6997488226059654, 'NanoBEIR_mean_cosine_accuracy@5': 0.7737205651491367, 'NanoBEIR_mean_cosine_accuracy@10': 0.8583673469387756, 'NanoBEIR_mean_cosine_precision@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_precision@3': 0.31928833071690216, 'NanoBEIR_mean_cosine_precision@5': 0.2540094191522763, 'NanoBEIR_mean_cosine_precision@10': 0.1777425431711146, 'NanoBEIR_mean_cosine_recall@1': 0.302305611570001, 'NanoBEIR_mean_cosine_recall@3': 0.4504361065646467, 'NanoBEIR_mean_cosine_recall@5': 0.5234643876869758, 'NanoBEIR_mean_cosine_recall@10': 0.6156896557033196, 'NanoBEIR_mean_cosine_ndcg@10': 0.5623178109936842, 'NanoBEIR_mean_cosine_mrr@10': 0.6232673992673993, 'NanoBEIR_mean_cosine_map@100': 0.47729093279415025} ``` ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-en-es	NickyNicky	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:4322286", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2025-01-16T04:07:42	2025-01-22T03:44:46	2	--- library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4322286 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. --- <!-- ### Nicko colab de pruebas fine tune. https://colab.research.google.com/drive/1IbcgP-KT01-5csBBB-SJ6kMiI1Udbokt#scrollTo=XgNQ1C1wWbTg&uniqifier=1 --> # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(256000, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-en-es") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,322,286 training samples english and spanish [dataset news, QA, summary,news cryptocurrency]. * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 10,005 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0005 \| 1 \| 49.8746 \| - \| \| 0.0474 \| 100 \| 35.8567 \| 7.1776 \| \| 0.0947 \| 200 \| 13.988 \| 3.2848 \| \| 0.1421 \| 300 \| 8.0009 \| 2.3610 \| \| 0.1895 \| 400 \| 6.3293 \| 2.0293 \| \| 0.2369 \| 500 \| 5.6296 \| 1.8849 \| \| 0.2842 \| 600 \| 5.238 \| 1.7495 \| \| 0.3316 \| 700 \| 4.9115 \| 1.6694 \| \| 0.3790 \| 800 \| 4.5779 \| 1.5583 \| \| 0.4263 \| 900 \| 4.2608 \| 1.4784 \| \| 0.4737 \| 1000 \| 4.0893 \| 1.4020 \| \| 0.5211 \| 1100 \| 3.8669 \| 1.3426 \| \| 0.5685 \| 1200 \| 3.7505 \| 1.3160 \| \| 0.6158 \| 1300 \| 3.6529 \| 1.2822 \| \| 0.6632 \| 1400 \| 3.5203 \| 1.2612 \| \| 0.7106 \| 1500 \| 5.1906 \| 1.4469 \| \| 0.7579 \| 1600 \| 4.0273 \| 1.6219 \| \| 0.8053 \| 1700 \| 4.8308 \| 3.1338 \| \| 0.8527 \| 1800 \| 0.5336 \| 3.2854 \| \| 0.9000 \| 1900 \| 0.3 \| 3.3757 \| \| 0.9474 \| 2000 \| 0.0886 \| 3.3620 \| \| 0.9948 \| 2100 \| 0.0817 \| 3.3510 \| \| 1.0417 \| 2200 \| 4.0692 \| 1.3638 \| ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## NanoBEIREvaluator > 0.8 ``` { "NanoDBPedia_cosine_accuracy@3": 0.86, "NanoDBPedia_cosine_accuracy@5": 0.92, "NanoDBPedia_cosine_accuracy@10": 0.96, "NanoFEVER_cosine_accuracy@3": 0.86, "NanoFEVER_cosine_accuracy@5": 0.92, "NanoFEVER_cosine_accuracy@10": 0.96, "NanoQuoraRetrieval_cosine_accuracy@1": 0.88, "NanoQuoraRetrieval_cosine_accuracy@3": 0.96, "NanoQuoraRetrieval_cosine_accuracy@5": 1.0, "NanoQuoraRetrieval_cosine_accuracy@10": 1.0, "NanoSCIDOCS_cosine_accuracy@5": 0.82, "NanoSCIDOCS_cosine_accuracy@10": 0.92, "NanoArguAna_cosine_accuracy@10": 0.92, "NanoSciFact_cosine_accuracy@10": 0.88, "NanoHotpotQA_cosine_accuracy@10": 0.88, "NanoTouche2020_cosine_accuracy@5": 0.9183673469387755, "NanoTouche2020_cosine_accuracy@10": 0.9387755102040817, "NanoBEIR_mean_cosine_accuracy@10": 0.8583673469387756 } ``` ## All NanoBEIREvaluator ``` {'NanoClimateFEVER_cosine_accuracy@1': 0.28, 'NanoClimateFEVER_cosine_accuracy@3': 0.44, 'NanoClimateFEVER_cosine_accuracy@5': 0.54, 'NanoClimateFEVER_cosine_accuracy@10': 0.72, 'NanoClimateFEVER_cosine_precision@1': 0.28, 'NanoClimateFEVER_cosine_precision@3': 0.15333333333333332, 'NanoClimateFEVER_cosine_precision@5': 0.124, 'NanoClimateFEVER_cosine_precision@10': 0.08999999999999998, 'NanoClimateFEVER_cosine_recall@1': 0.145, 'NanoClimateFEVER_cosine_recall@3': 0.205, 'NanoClimateFEVER_cosine_recall@5': 0.264, 'NanoClimateFEVER_cosine_recall@10': 0.36200000000000004, 'NanoClimateFEVER_cosine_ndcg@10': 0.2957527689242254, 'NanoClimateFEVER_cosine_mrr@10': 0.3996666666666668, 'NanoClimateFEVER_cosine_map@100': 0.23258384801937396, 'NanoDBPedia_cosine_accuracy@1': 0.68, 'NanoDBPedia_cosine_accuracy@3': 0.86, 'NanoDBPedia_cosine_accuracy@5': 0.92, 'NanoDBPedia_cosine_accuracy@10': 0.96, 'NanoDBPedia_cosine_precision@1': 0.68, 'NanoDBPedia_cosine_precision@3': 0.56, 'NanoDBPedia_cosine_precision@5': 0.5120000000000001, 'NanoDBPedia_cosine_precision@10': 0.43800000000000006, 'NanoDBPedia_cosine_recall@1': 0.07601531530835434, 'NanoDBPedia_cosine_recall@3': 0.1438904710839341, 'NanoDBPedia_cosine_recall@5': 0.20681359525684506, 'NanoDBPedia_cosine_recall@10': 0.319966975132044, 'NanoDBPedia_cosine_ndcg@10': 0.5501100350453579, 'NanoDBPedia_cosine_mrr@10': 0.7855000000000001, 'NanoDBPedia_cosine_map@100': 0.39476156890024533, 'NanoFEVER_cosine_accuracy@1': 0.68, 'NanoFEVER_cosine_accuracy@3': 0.86, 'NanoFEVER_cosine_accuracy@5': 0.92, 'NanoFEVER_cosine_accuracy@10': 0.96, 'NanoFEVER_cosine_precision@1': 0.68, 'NanoFEVER_cosine_precision@3': 0.29333333333333333, 'NanoFEVER_cosine_precision@5': 0.19199999999999995, 'NanoFEVER_cosine_precision@10': 0.10199999999999998, 'NanoFEVER_cosine_recall@1': 0.6266666666666666, 'NanoFEVER_cosine_recall@3': 0.8133333333333332, 'NanoFEVER_cosine_recall@5': 0.8833333333333333, 'NanoFEVER_cosine_recall@10': 0.9233333333333333, 'NanoFEVER_cosine_ndcg@10': 0.7933479848498471, 'NanoFEVER_cosine_mrr@10': 0.7780793650793651, 'NanoFEVER_cosine_map@100': 0.7406571665049926, 'NanoFiQA2018_cosine_accuracy@1': 0.46, 'NanoFiQA2018_cosine_accuracy@3': 0.64, 'NanoFiQA2018_cosine_accuracy@5': 0.7, 'NanoFiQA2018_cosine_accuracy@10': 0.72, 'NanoFiQA2018_cosine_precision@1': 0.46, 'NanoFiQA2018_cosine_precision@3': 0.2866666666666666, 'NanoFiQA2018_cosine_precision@5': 0.22399999999999998, 'NanoFiQA2018_cosine_precision@10': 0.12999999999999998, 'NanoFiQA2018_cosine_recall@1': 0.23924603174603173, 'NanoFiQA2018_cosine_recall@3': 0.4251031746031746, 'NanoFiQA2018_cosine_recall@5': 0.5099603174603174, 'NanoFiQA2018_cosine_recall@10': 0.566015873015873, 'NanoFiQA2018_cosine_ndcg@10': 0.4774545077577204, 'NanoFiQA2018_cosine_mrr@10': 0.5475555555555556, 'NanoFiQA2018_cosine_map@100': 0.4125452702654584, 'NanoHotpotQA_cosine_accuracy@1': 0.64, 'NanoHotpotQA_cosine_accuracy@3': 0.82, 'NanoHotpotQA_cosine_accuracy@5': 0.84, 'NanoHotpotQA_cosine_accuracy@10': 0.88, 'NanoHotpotQA_cosine_precision@1': 0.64, 'NanoHotpotQA_cosine_precision@3': 0.3533333333333333, 'NanoHotpotQA_cosine_precision@5': 0.23599999999999993, 'NanoHotpotQA_cosine_precision@10': 0.128, 'NanoHotpotQA_cosine_recall@1': 0.32, 'NanoHotpotQA_cosine_recall@3': 0.53, 'NanoHotpotQA_cosine_recall@5': 0.59, 'NanoHotpotQA_cosine_recall@10': 0.64, 'NanoHotpotQA_cosine_ndcg@10': 0.5959681682828366, 'NanoHotpotQA_cosine_mrr@10': 0.723888888888889, 'NanoHotpotQA_cosine_map@100': 0.5262469568756968, 'NanoMSMARCO_cosine_accuracy@1': 0.36, 'NanoMSMARCO_cosine_accuracy@3': 0.52, 'NanoMSMARCO_cosine_accuracy@5': 0.58, 'NanoMSMARCO_cosine_accuracy@10': 0.8, 'NanoMSMARCO_cosine_precision@1': 0.36, 'NanoMSMARCO_cosine_precision@3': 0.1733333333333333, 'NanoMSMARCO_cosine_precision@5': 0.11599999999999999, 'NanoMSMARCO_cosine_precision@10': 0.08, 'NanoMSMARCO_cosine_recall@1': 0.36, 'NanoMSMARCO_cosine_recall@3': 0.52, 'NanoMSMARCO_cosine_recall@5': 0.58, 'NanoMSMARCO_cosine_recall@10': 0.8, 'NanoMSMARCO_cosine_ndcg@10': 0.5539831330912274, 'NanoMSMARCO_cosine_mrr@10': 0.47960317460317464, 'NanoMSMARCO_cosine_map@100': 0.4907628900864195, 'NanoNFCorpus_cosine_accuracy@1': 0.42, 'NanoNFCorpus_cosine_accuracy@3': 0.56, 'NanoNFCorpus_cosine_accuracy@5': 0.6, 'NanoNFCorpus_cosine_accuracy@10': 0.7, 'NanoNFCorpus_cosine_precision@1': 0.42, 'NanoNFCorpus_cosine_precision@3': 0.3466666666666666, 'NanoNFCorpus_cosine_precision@5': 0.32800000000000007, 'NanoNFCorpus_cosine_precision@10': 0.286, 'NanoNFCorpus_cosine_recall@1': 0.03391318439564492, 'NanoNFCorpus_cosine_recall@3': 0.06311668492872162, 'NanoNFCorpus_cosine_recall@5': 0.08191277059586696, 'NanoNFCorpus_cosine_recall@10': 0.13476845853527392, 'NanoNFCorpus_cosine_ndcg@10': 0.3322933792371396, 'NanoNFCorpus_cosine_mrr@10': 0.4983333333333333, 'NanoNFCorpus_cosine_map@100': 0.13985354018581944, 'NanoNQ_cosine_accuracy@1': 0.44, 'NanoNQ_cosine_accuracy@3': 0.64, 'NanoNQ_cosine_accuracy@5': 0.66, 'NanoNQ_cosine_accuracy@10': 0.76, 'NanoNQ_cosine_precision@1': 0.44, 'NanoNQ_cosine_precision@3': 0.22, 'NanoNQ_cosine_precision@5': 0.14, 'NanoNQ_cosine_precision@10': 0.08199999999999999, 'NanoNQ_cosine_recall@1': 0.42, 'NanoNQ_cosine_recall@3': 0.62, 'NanoNQ_cosine_recall@5': 0.64, 'NanoNQ_cosine_recall@10': 0.75, 'NanoNQ_cosine_ndcg@10': 0.5903874296113161, 'NanoNQ_cosine_mrr@10': 0.5456349206349206, 'NanoNQ_cosine_map@100': 0.5437440035864959, 'NanoQuoraRetrieval_cosine_accuracy@1': 0.88, 'NanoQuoraRetrieval_cosine_accuracy@3': 0.96, 'NanoQuoraRetrieval_cosine_accuracy@5': 1.0, 'NanoQuoraRetrieval_cosine_accuracy@10': 1.0, 'NanoQuoraRetrieval_cosine_precision@1': 0.88, 'NanoQuoraRetrieval_cosine_precision@3': 0.3933333333333333, 'NanoQuoraRetrieval_cosine_precision@5': 0.256, 'NanoQuoraRetrieval_cosine_precision@10': 0.13599999999999998, 'NanoQuoraRetrieval_cosine_recall@1': 0.784, 'NanoQuoraRetrieval_cosine_recall@3': 0.9186666666666667, 'NanoQuoraRetrieval_cosine_recall@5': 0.976, 'NanoQuoraRetrieval_cosine_recall@10': 0.9933333333333334, 'NanoQuoraRetrieval_cosine_ndcg@10': 0.9367841595958026, 'NanoQuoraRetrieval_cosine_mrr@10': 0.9246666666666666, 'NanoQuoraRetrieval_cosine_map@100': 0.913554834054834, 'NanoSCIDOCS_cosine_accuracy@1': 0.52, 'NanoSCIDOCS_cosine_accuracy@3': 0.68, 'NanoSCIDOCS_cosine_accuracy@5': 0.82, 'NanoSCIDOCS_cosine_accuracy@10': 0.92, 'NanoSCIDOCS_cosine_precision@1': 0.52, 'NanoSCIDOCS_cosine_precision@3': 0.3933333333333333, 'NanoSCIDOCS_cosine_precision@5': 0.33599999999999997, 'NanoSCIDOCS_cosine_precision@10': 0.21600000000000003, 'NanoSCIDOCS_cosine_recall@1': 0.10966666666666666, 'NanoSCIDOCS_cosine_recall@3': 0.24466666666666664, 'NanoSCIDOCS_cosine_recall@5': 0.34566666666666657, 'NanoSCIDOCS_cosine_recall@10': 0.44266666666666665, 'NanoSCIDOCS_cosine_ndcg@10': 0.4328110226758414, 'NanoSCIDOCS_cosine_mrr@10': 0.6317222222222222, 'NanoSCIDOCS_cosine_map@100': 0.34997841607847063, 'NanoArguAna_cosine_accuracy@1': 0.2, 'NanoArguAna_cosine_accuracy@3': 0.56, 'NanoArguAna_cosine_accuracy@5': 0.76, 'NanoArguAna_cosine_accuracy@10': 0.92, 'NanoArguAna_cosine_precision@1': 0.2, 'NanoArguAna_cosine_precision@3': 0.18666666666666668, 'NanoArguAna_cosine_precision@5': 0.15200000000000002, 'NanoArguAna_cosine_precision@10': 0.092, 'NanoArguAna_cosine_recall@1': 0.2, 'NanoArguAna_cosine_recall@3': 0.56, 'NanoArguAna_cosine_recall@5': 0.76, 'NanoArguAna_cosine_recall@10': 0.92, 'NanoArguAna_cosine_ndcg@10': 0.5499071039525992, 'NanoArguAna_cosine_mrr@10': 0.43229365079365073, 'NanoArguAna_cosine_map@100': 0.43523820792684886, 'NanoSciFact_cosine_accuracy@1': 0.6, 'NanoSciFact_cosine_accuracy@3': 0.72, 'NanoSciFact_cosine_accuracy@5': 0.8, 'NanoSciFact_cosine_accuracy@10': 0.88, 'NanoSciFact_cosine_precision@1': 0.6, 'NanoSciFact_cosine_precision@3': 0.25333333333333335, 'NanoSciFact_cosine_precision@5': 0.18, 'NanoSciFact_cosine_precision@10': 0.09799999999999999, 'NanoSciFact_cosine_recall@1': 0.58, 'NanoSciFact_cosine_recall@3': 0.7, 'NanoSciFact_cosine_recall@5': 0.8, 'NanoSciFact_cosine_recall@10': 0.87, 'NanoSciFact_cosine_ndcg@10': 0.7265348054031264, 'NanoSciFact_cosine_mrr@10': 0.6841031746031746, 'NanoSciFact_cosine_map@100': 0.6810233866101422, 'NanoTouche2020_cosine_accuracy@1': 0.5102040816326531, 'NanoTouche2020_cosine_accuracy@3': 0.8367346938775511, 'NanoTouche2020_cosine_accuracy@5': 0.9183673469387755, 'NanoTouche2020_cosine_accuracy@10': 0.9387755102040817, 'NanoTouche2020_cosine_precision@1': 0.5102040816326531, 'NanoTouche2020_cosine_precision@3': 0.5374149659863945, 'NanoTouche2020_cosine_precision@5': 0.5061224489795918, 'NanoTouche2020_cosine_precision@10': 0.43265306122448977, 'NanoTouche2020_cosine_recall@1': 0.03546508562664911, 'NanoTouche2020_cosine_recall@3': 0.11189238805791148, 'NanoTouche2020_cosine_recall@5': 0.1673503566176574, 'NanoTouche2020_cosine_recall@10': 0.2818808841266296, 'NanoTouche2020_cosine_ndcg@10': 0.47479704449085264, 'NanoTouche2020_cosine_mrr@10': 0.6714285714285714, 'NanoTouche2020_cosine_map@100': 0.3438320372291555, 'NanoBEIR_mean_cosine_accuracy@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_accuracy@3': 0.6997488226059654, 'NanoBEIR_mean_cosine_accuracy@5': 0.7737205651491367, 'NanoBEIR_mean_cosine_accuracy@10': 0.8583673469387756, 'NanoBEIR_mean_cosine_precision@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_precision@3': 0.31928833071690216, 'NanoBEIR_mean_cosine_precision@5': 0.2540094191522763, 'NanoBEIR_mean_cosine_precision@10': 0.1777425431711146, 'NanoBEIR_mean_cosine_recall@1': 0.302305611570001, 'NanoBEIR_mean_cosine_recall@3': 0.4504361065646467, 'NanoBEIR_mean_cosine_recall@5': 0.5234643876869758, 'NanoBEIR_mean_cosine_recall@10': 0.6156896557033196, 'NanoBEIR_mean_cosine_ndcg@10': 0.5623178109936842, 'NanoBEIR_mean_cosine_mrr@10': 0.6232673992673993, 'NanoBEIR_mean_cosine_map@100': 0.47729093279415025} ``` ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
carlfeynman/reproduce-static-retrieval-mrl-en-v1	carlfeynman	sentence-similarity	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:68534726", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/s2orc", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/paq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2025-01-17T11:22:01	2025-01-17T11:22:11	0	--- datasets: - sentence-transformers/gooaq - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/s2orc - sentence-transformers/all-nli - sentence-transformers/paq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:68534726 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - source_sentence: what is upwork sentences: - Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork has nine million registered freelancers and four million registered clients. Three million jobs are posted annually, worth a total of $1 billion USD, making it the world's largest freelancer marketplace. - Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork has nine million registered freelancers and four million registered clients. Three million jobs are posted annually, worth a total of $1 billion USD, making it the world's largest freelancer marketplace. - 'That is, while fructose consumption may increase uric acid levels, to actually precipitate a gout attack, you need to deviate from the narrow band of normal blood pH range: 7.35 to 7.45. Ideally you wanna be at 7.45 or slightly above.' - source_sentence: how many km is a mile sentences: - Periodontal disease is a bacterial infection of the gums and bone that if not treated, can cause you to lose your teeth. Medical research is now showing that these bacteria in your mouth can also travel through your bloodstream into other organs in the body. - Master the formula for converting kilometers to miles. 1 kilometer is equal to 0.621371 miles (often shortened to .62).1 mile is equal to 1.609344 kilometers. Thus, to convert kilometers to miles, simply multiply the number of kilometers by 0.62137. For example, let's say you start with 5 kilometers. People are often interested in this conversion because they want to know how many miles are in a 5K run. The formula is 5 X 0.62137= 3.1 miles. - To find out how many kilometers in miles, multiply by this factor or simply use the converter below. 1 Mile = 1.609344 Kilometers. Mile is an imperial and US customary length unit and equals to 5280 feet. The abbreviation is mi. Kilometer is a metric length unit and equals to 1000 meters. - source_sentence: A group of children walking on a trail. sentences: - The man is performing. - Children are walking. - The people are adults. - source_sentence: A boy with a basketballs glowers at the camera. sentences: - The boy is smiling - The boy scowls - Surfer in red catches a wave. model-index: - name: '[REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets' results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.152 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15666666666666665 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.31633333333333336 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.44133333333333336 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.35027529831718174 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4537698412698412 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2754610667422747 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.88 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.92 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6066666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5479999999999999 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.45399999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05820050708225643 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1660478879214754 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2233296888728599 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.32642161484749216 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5611886908023029 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7551904761904763 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42159733554382045 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.54 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.54 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333334 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5066666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7566666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8033333333333332 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9033333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7223300246075101 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6857460317460319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6591296848555135 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.22 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.22 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09799999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12688888888888888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29007936507936505 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3347460317460317 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.453015873015873 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33206103177846985 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.34974603174603175 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2723064374777477 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.66 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.66 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.35999999999999993 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.264 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14799999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.33 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.54 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.66 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6507660730204244 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.746690476190476 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5743825107321581 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.16 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.44 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.66 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4069260774532657 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3269126984126984 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.34104660879940385 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.34666666666666673 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24400000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06140064224956239 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09381944627241434 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11465220470723159 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13758064454249494 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3251344168353932 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.49083333333333345 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.15346080343511273 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.44 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.55 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.67 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4284752232212853 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3555714285714285 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.35954687250943856 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.8 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.92 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.96 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.35999999999999993 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23999999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7106666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8653333333333333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9226666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9593333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.874423773707081 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8666666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8354028527028526 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.184 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1416666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18966666666666665 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2886666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2657817193581118 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4188571428571429 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20270708890067454 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15999999999999998 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.48 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4064179360568565 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.31785714285714284 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33454708384798976 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.64 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.485 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.61 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.655 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.72 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6053823991819648 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5862222222222221 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5721097562068183 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5918367346938775 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9183673469387755 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9795918367346939 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5918367346938775 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5850340136054422 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.6000000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5204081632653061 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0405610423291237 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12039267252775386 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.20296687044371778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3313283589291373 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5594653746925154 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.749514091350826 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4414984325557448 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.41937205651491377 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6475667189952904 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7168916797488225 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8030769230769231 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.41937205651491377 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2942333856619571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23784615384615387 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17172370486656197 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.23120905747819215 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.399538926035975 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4702072919822955 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5623856275385894 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4991252337717202 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5464290448780245 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.41870742571611924 name: Cosine Map@100 --- # [REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) and [paq](https://huggingface.co/datasets/sentence-transformers/paq) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer <!-- - Base model: [Unknown](https://huggingface.co/unknown) --> - Maximum Sequence Length: inf tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity - Training Datasets: - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) - [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) - [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) - [paq](https://huggingface.co/datasets/sentence-transformers/paq) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("carlfeynman/reproduce-static-retrieval-mrl-en-v1") # Run inference sentences = [ 'A boy with a basketballs glowers at the camera.', 'The boy scowls', 'The boy is smiling', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| cosine_accuracy@1 \| 0.32 \| 0.64 \| 0.54 \| 0.22 \| 0.66 \| 0.16 \| 0.4 \| 0.2 \| 0.8 \| 0.28 \| 0.12 \| 0.52 \| 0.5918 \| \| cosine_accuracy@3 \| 0.54 \| 0.88 \| 0.82 \| 0.44 \| 0.82 \| 0.44 \| 0.54 \| 0.46 \| 0.92 \| 0.52 \| 0.48 \| 0.64 \| 0.9184 \| \| cosine_accuracy@5 \| 0.64 \| 0.92 \| 0.84 \| 0.5 \| 0.86 \| 0.54 \| 0.6 \| 0.58 \| 0.96 \| 0.62 \| 0.6 \| 0.68 \| 0.9796 \| \| cosine_accuracy@10 \| 0.82 \| 0.94 \| 0.94 \| 0.64 \| 0.94 \| 0.66 \| 0.7 \| 0.68 \| 0.98 \| 0.72 \| 0.68 \| 0.74 \| 1.0 \| \| cosine_precision@1 \| 0.32 \| 0.64 \| 0.54 \| 0.22 \| 0.66 \| 0.16 \| 0.4 \| 0.2 \| 0.8 \| 0.28 \| 0.12 \| 0.52 \| 0.5918 \| \| cosine_precision@3 \| 0.2 \| 0.6067 \| 0.2733 \| 0.1867 \| 0.36 \| 0.1467 \| 0.3467 \| 0.1533 \| 0.36 \| 0.2267 \| 0.16 \| 0.22 \| 0.585 \| \| cosine_precision@5 \| 0.152 \| 0.548 \| 0.18 \| 0.132 \| 0.264 \| 0.108 \| 0.3 \| 0.12 \| 0.24 \| 0.184 \| 0.12 \| 0.144 \| 0.6 \| \| cosine_precision@10 \| 0.112 \| 0.454 \| 0.1 \| 0.098 \| 0.148 \| 0.066 \| 0.244 \| 0.074 \| 0.128 \| 0.14 \| 0.068 \| 0.08 \| 0.5204 \| \| cosine_recall@1 \| 0.1567 \| 0.0582 \| 0.5067 \| 0.1269 \| 0.33 \| 0.16 \| 0.0614 \| 0.19 \| 0.7107 \| 0.0597 \| 0.12 \| 0.485 \| 0.0406 \| \| cosine_recall@3 \| 0.25 \| 0.166 \| 0.7567 \| 0.2901 \| 0.54 \| 0.44 \| 0.0938 \| 0.44 \| 0.8653 \| 0.1417 \| 0.48 \| 0.61 \| 0.1204 \| \| cosine_recall@5 \| 0.3163 \| 0.2233 \| 0.8033 \| 0.3347 \| 0.66 \| 0.54 \| 0.1147 \| 0.55 \| 0.9227 \| 0.1897 \| 0.6 \| 0.655 \| 0.203 \| \| cosine_recall@10 \| 0.4413 \| 0.3264 \| 0.9033 \| 0.453 \| 0.74 \| 0.66 \| 0.1376 \| 0.67 \| 0.9593 \| 0.2887 \| 0.68 \| 0.72 \| 0.3313 \| \| cosine_ndcg@10 \| 0.3503 \| 0.5612 \| 0.7223 \| 0.3321 \| 0.6508 \| 0.4069 \| 0.3251 \| 0.4285 \| 0.8744 \| 0.2658 \| 0.4064 \| 0.6054 \| 0.5595 \| \| cosine_mrr@10 \| 0.4538 \| 0.7552 \| 0.6857 \| 0.3497 \| 0.7467 \| 0.3269 \| 0.4908 \| 0.3556 \| 0.8667 \| 0.4189 \| 0.3179 \| 0.5862 \| 0.7495 \| \| cosine_map@100 \| 0.2755 \| 0.4216 \| 0.6591 \| 0.2723 \| 0.5744 \| 0.341 \| 0.1535 \| 0.3595 \| 0.8354 \| 0.2027 \| 0.3345 \| 0.5721 \| 0.4415 \| #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.4194 \| \| cosine_accuracy@3 \| 0.6476 \| \| cosine_accuracy@5 \| 0.7169 \| \| cosine_accuracy@10 \| 0.8031 \| \| cosine_precision@1 \| 0.4194 \| \| cosine_precision@3 \| 0.2942 \| \| cosine_precision@5 \| 0.2378 \| \| cosine_precision@10 \| 0.1717 \| \| cosine_recall@1 \| 0.2312 \| \| cosine_recall@3 \| 0.3995 \| \| cosine_recall@5 \| 0.4702 \| \| cosine_recall@10 \| 0.5624 \| \| cosine_ndcg@10 \| 0.4991 \| \| cosine_mrr@10 \| 0.5464 \| \| cosine_map@100 \| 0.4187 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Datasets #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> \| <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>what is the difference between broilers and layers?</code> \| <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> \| \| <code>what is the difference between chronological order and spatial order?</code> \| <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> \| \| <code>is kamagra same as viagra?</code> \| <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 11 characters</li><li>mean: 33.26 characters</li><li>max: 197 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 356.24 characters</li><li>max: 1006 characters</li></ul> \| <ul><li>min: 68 characters</li><li>mean: 327.52 characters</li><li>max: 995 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:---------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>when was the sullivan acts</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \| <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> \| \| <code>can lavender grow indoors</code> \| <code>Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.</code> \| <code>Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.</code> \| \| <code>what kind of barley do you malt</code> \| <code>Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.</code> \| <code>Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### s2orc * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 90,000 training samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: \| \| title \| abstract \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 31 characters</li><li>mean: 80.02 characters</li><li>max: 185 characters</li></ul> \| <ul><li>min: 84 characters</li><li>mean: 635.31 characters</li><li>max: 1023 characters</li></ul> \| * Samples: \| title \| abstract \| \|:----------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions</code> \| <code>The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.</code> \| \| <code>Enlightening on medical administration by clinical governance in British</code> \| <code>Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.</code> \| \| <code>APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION</code> \| <code>Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### allnli * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 557,850 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 34.88 characters</li><li>max: 193 characters</li></ul> \| <ul><li>min: 15 characters</li><li>mean: 46.49 characters</li><li>max: 181 characters</li></ul> \| <ul><li>min: 16 characters</li><li>mean: 50.47 characters</li><li>max: 204 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\| \| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \| \| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \| \| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### paq * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 25 characters</li><li>mean: 50.56 characters</li><li>max: 104 characters</li></ul> \| <ul><li>min: 509 characters</li><li>mean: 620.96 characters</li><li>max: 773 characters</li></ul> \| * Samples: \| query \| answer \| \|:----------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>in veetla visheshanga ganesh is the husband of</code> \| <code>Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him</code> \| \| <code>when did simon property group became a publicly traded company</code> \| <code>of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly</code> \| \| <code>what was the nationality of antoine faivre</code> \| <code>Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Datasets #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| question \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> \| <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> \| * Samples: \| question \| answer \| \|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \| \| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \| \| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| query \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 10 characters</li><li>mean: 33.36 characters</li><li>max: 137 characters</li></ul> \| <ul><li>min: 67 characters</li><li>mean: 347.87 characters</li><li>max: 906 characters</li></ul> \| <ul><li>min: 57 characters</li><li>mean: 318.18 characters</li><li>max: 906 characters</li></ul> \| * Samples: \| query \| positive \| negative \| \|:-------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>is cabinet refacing worth the cost?</code> \| <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> \| <code>Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.</code> \| \| <code>is the fovea ethmoidalis a bone</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \| \| <code>average pitches per inning</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### s2orc * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 10,000 evaluation samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: \| \| title \| abstract \| \|:--------\|:------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 31 characters</li><li>mean: 80.04 characters</li><li>max: 198 characters</li></ul> \| <ul><li>min: 96 characters</li><li>mean: 653.93 characters</li><li>max: 1023 characters</li></ul> \| * Samples: \| title \| abstract \| \|:-------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna</code> \| <code>The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.</code> \| \| <code>Subclinical organ damage and cardiovascular risk prediction</code> \| <code>AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...</code> \| \| <code>A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan</code> \| <code>In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### allnli * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 6,584 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| negative \| \|:--------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------\| \| type \| string \| string \| string \| \| details \| <ul><li>min: 15 characters</li><li>mean: 72.82 characters</li><li>max: 300 characters</li></ul> \| <ul><li>min: 12 characters</li><li>mean: 34.11 characters</li><li>max: 126 characters</li></ul> \| <ul><li>min: 11 characters</li><li>mean: 36.38 characters</li><li>max: 121 characters</li></ul> \| * Samples: \| anchor \| positive \| negative \| \|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:--------------------------------------------------------\| \| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \| \| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \| \| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> \| <code>A man selling donuts to a customer.</code> \| <code>A woman drinks her coffee in a small cafe.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### paq * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 25 characters</li><li>mean: 51.3 characters</li><li>max: 108 characters</li></ul> \| <ul><li>min: 504 characters</li><li>mean: 623.09 characters</li><li>max: 835 characters</li></ul> \| * Samples: \| query \| answer \| \|:---------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>when did season 3 of the voice brasil start</code> \| <code>The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on</code> \| \| <code>when did the little ranger first come out</code> \| <code>Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures</code> \| \| <code>what is the name of rachel's sister in ninjaaiden</code> \| <code>her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16384 - `per_device_eval_batch_size`: 4096 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16384 - `per_device_eval_batch_size`: 4096 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| gooaq loss \| msmarco loss \| s2orc loss \| allnli loss \| paq loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \| \|:------:\|:----:\|:-------------:\|:----------:\|:------------:\|:----------:\|:-----------:\|:--------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\| \| 0.0002 \| 1 \| 43.5181 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0597 \| 250 \| 17.804 \| 2.1081 \| 12.8291 \| 10.8194 \| 14.2895 \| 5.3792 \| 0.3202 \| 0.5446 \| 0.6721 \| 0.3176 \| 0.6222 \| 0.3867 \| 0.3022 \| 0.3952 \| 0.8741 \| 0.2474 \| 0.3986 \| 0.5913 \| 0.5463 \| 0.4783 \| \| 0.1195 \| 500 \| 9.6842 \| 1.6991 \| 12.2374 \| 10.6084 \| 13.9790 \| 4.7183 \| 0.3148 \| 0.5759 \| 0.7063 \| 0.3640 \| 0.6250 \| 0.3846 \| 0.2832 \| 0.4168 \| 0.8659 \| 0.2537 \| 0.3744 \| 0.5732 \| 0.5509 \| 0.4837 \| \| 0.1792 \| 750 \| 8.7691 \| 1.6922 \| 12.0631 \| 10.3970 \| 12.4485 \| 4.4473 \| 0.3496 \| 0.5664 \| 0.7157 \| 0.3179 \| 0.6585 \| 0.3826 \| 0.2934 \| 0.4040 \| 0.8782 \| 0.2523 \| 0.3845 \| 0.5962 \| 0.5502 \| 0.4884 \| \| 0.2389 \| 1000 \| 8.606 \| 1.6685 \| 11.7765 \| 10.2828 \| 12.4139 \| 4.2823 \| 0.3509 \| 0.5636 \| 0.7026 \| 0.3249 \| 0.6562 \| 0.4049 \| 0.3123 \| 0.4174 \| 0.8673 \| 0.2657 \| 0.3969 \| 0.5582 \| 0.5514 \| 0.4902 \| \| 0.2987 \| 1250 \| 8.4178 \| 1.6072 \| 11.7581 \| 9.2590 \| 12.8865 \| 4.2231 \| 0.3341 \| 0.5587 \| 0.7103 \| 0.3354 \| 0.6534 \| 0.4033 \| 0.3116 \| 0.4294 \| 0.8663 \| 0.2718 \| 0.4048 \| 0.5891 \| 0.5466 \| 0.4934 \| \| 0.3584 \| 1500 \| 8.1084 \| 1.6751 \| 11.8237 \| 9.8291 \| 11.5805 \| 4.1559 \| 0.3345 \| 0.5668 \| 0.7094 \| 0.3287 \| 0.6535 \| 0.3948 \| 0.3311 \| 0.4098 \| 0.8632 \| 0.2649 \| 0.4171 \| 0.5913 \| 0.5514 \| 0.4936 \| \| 0.4182 \| 1750 \| 7.9489 \| 1.5858 \| 11.8367 \| 9.8385 \| 13.0328 \| 4.0980 \| 0.3543 \| 0.5464 \| 0.6984 \| 0.3158 \| 0.6582 \| 0.3862 \| 0.3233 \| 0.4201 \| 0.8665 \| 0.2743 \| 0.3924 \| 0.5909 \| 0.5577 \| 0.4911 \| \| 0.4779 \| 2000 \| 8.2594 \| 1.6123 \| 11.8052 \| 9.9075 \| 11.3651 \| 4.0788 \| 0.3491 \| 0.5551 \| 0.7208 \| 0.3235 \| 0.6570 \| 0.4058 \| 0.3220 \| 0.4215 \| 0.8801 \| 0.2629 \| 0.4143 \| 0.5998 \| 0.5514 \| 0.4972 \| \| 0.5376 \| 2250 \| 8.299 \| 1.6416 \| 11.7180 \| 9.9462 \| 10.7895 \| 4.0423 \| 0.3636 \| 0.5582 \| 0.7071 \| 0.3048 \| 0.6649 \| 0.3951 \| 0.3248 \| 0.4316 \| 0.8804 \| 0.2561 \| 0.4252 \| 0.6036 \| 0.5484 \| 0.4972 \| \| 0.5974 \| 2500 \| 7.7807 \| 1.6518 \| 11.7898 \| 9.9235 \| 11.1670 \| 4.0001 \| 0.3639 \| 0.5556 \| 0.7288 \| 0.3148 \| 0.6525 \| 0.3979 \| 0.3178 \| 0.4436 \| 0.8860 \| 0.2593 \| 0.4208 \| 0.5935 \| 0.5581 \| 0.4994 \| \| 0.6571 \| 2750 \| 7.8997 \| 1.5797 \| 11.6813 \| 9.5124 \| 11.4893 \| 3.9633 \| 0.3465 \| 0.5562 \| 0.7084 \| 0.3101 \| 0.6631 \| 0.4102 \| 0.3194 \| 0.4410 \| 0.8805 \| 0.2566 \| 0.4261 \| 0.5983 \| 0.5552 \| 0.4978 \| \| 0.7168 \| 3000 \| 8.0204 \| 1.5620 \| 11.6746 \| 9.6655 \| 10.8783 \| 3.9539 \| 0.3439 \| 0.5569 \| 0.7295 \| 0.3173 \| 0.6606 \| 0.4129 \| 0.3180 \| 0.4521 \| 0.8888 \| 0.2576 \| 0.4012 \| 0.6065 \| 0.5560 \| 0.5001 \| \| 0.7766 \| 3250 \| 8.0225 \| 1.4596 \| 11.5664 \| 9.6954 \| 10.9838 \| 3.9493 \| 0.3496 \| 0.5626 \| 0.7239 \| 0.3330 \| 0.6551 \| 0.4197 \| 0.3129 \| 0.4491 \| 0.8893 \| 0.2726 \| 0.4061 \| 0.6103 \| 0.5555 \| 0.5031 \| \| 0.8363 \| 3500 \| 7.6933 \| 1.5522 \| 11.6974 \| 9.1753 \| 11.2026 \| 3.9082 \| 0.3581 \| 0.5570 \| 0.7170 \| 0.3216 \| 0.6492 \| 0.4018 \| 0.3204 \| 0.4360 \| 0.8841 \| 0.2675 \| 0.4031 \| 0.6052 \| 0.5553 \| 0.4982 \| \| 0.8961 \| 3750 \| 7.711 \| 1.5267 \| 11.6615 \| 9.4673 \| 11.3195 \| 3.8847 \| 0.3563 \| 0.5613 \| 0.7162 \| 0.3265 \| 0.6497 \| 0.4109 \| 0.3253 \| 0.4384 \| 0.8713 \| 0.2657 \| 0.4195 \| 0.6058 \| 0.5566 \| 0.5003 \| \| 0.9558 \| 4000 \| 7.8549 \| 1.5300 \| 11.6244 \| 9.1383 \| 11.0781 \| 3.8785 \| 0.3533 \| 0.5609 \| 0.7153 \| 0.3285 \| 0.6528 \| 0.4069 \| 0.3250 \| 0.4382 \| 0.8744 \| 0.2642 \| 0.4068 \| 0.5961 \| 0.5595 \| 0.4986 \| \| 1.0 \| 4185 \| - \| - \| - \| - \| - \| - \| 0.3503 \| 0.5612 \| 0.7223 \| 0.3321 \| 0.6508 \| 0.4069 \| 0.3251 \| 0.4285 \| 0.8744 \| 0.2658 \| 0.4064 \| 0.6054 \| 0.5595 \| 0.4991 \| ### Framework Versions - Python: 3.10.15 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.4.1 - Accelerate: 1.1.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	[ "TEXT_CLASSIFICATION" ]	[ "CRAFT" ]
fdehlinger/english-4U-bge-small	fdehlinger	feature-extraction	[ "sentence-transformers", "pytorch", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2401.03462", "arxiv:2312.15503", "arxiv:2311.13534", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	2025-01-20T14:03:22	2025-01-21T11:45:54	0	--- language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-small-en-v1.5 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 37.21923821573361 - type: f1 value: 68.0914945617093 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.75377499999999 - type: ap value: 89.46766124546022 - type: f1 value: 92.73884001331487 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.986 - type: f1 value: 46.55936786727896 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.846000000000004 - type: map_at_10 value: 51.388 - type: map_at_100 value: 52.132999999999996 - type: map_at_1000 value: 52.141000000000005 - type: map_at_3 value: 47.037 - type: map_at_5 value: 49.579 - type: mrr_at_1 value: 36.558 - type: mrr_at_10 value: 51.658 - type: mrr_at_100 value: 52.402 - type: mrr_at_1000 value: 52.410000000000004 - type: mrr_at_3 value: 47.345 - type: mrr_at_5 value: 49.797999999999995 - type: ndcg_at_1 value: 35.846000000000004 - type: ndcg_at_10 value: 59.550000000000004 - type: ndcg_at_100 value: 62.596 - type: ndcg_at_1000 value: 62.759 - type: ndcg_at_3 value: 50.666999999999994 - type: ndcg_at_5 value: 55.228 - type: precision_at_1 value: 35.846000000000004 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.389 - type: precision_at_5 value: 14.438 - type: recall_at_1 value: 35.846000000000004 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.166 - type: recall_at_5 value: 72.191 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.402770198163594 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.01545436974177 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.586465273207196 - type: mrr value: 74.42169019038825 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1891186537969 - type: cos_sim_spearman value: 83.75492046087288 - type: euclidean_pearson value: 84.11766204805357 - type: euclidean_spearman value: 84.01456493126516 - type: manhattan_pearson value: 84.2132950502772 - type: manhattan_spearman value: 83.89227298813377 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.74025974025975 - type: f1 value: 85.71493566466381 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.467181385006434 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.719496037339056 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 29.587000000000003 - type: map_at_10 value: 41.114 - type: map_at_100 value: 42.532 - type: map_at_1000 value: 42.661 - type: map_at_3 value: 37.483 - type: map_at_5 value: 39.652 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.763 - type: mrr_at_100 value: 47.393 - type: mrr_at_1000 value: 47.445 - type: mrr_at_3 value: 43.538 - type: mrr_at_5 value: 45.556000000000004 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.658 - type: ndcg_at_100 value: 52.824000000000005 - type: ndcg_at_1000 value: 54.913999999999994 - type: ndcg_at_3 value: 41.989 - type: ndcg_at_5 value: 44.944 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.4789999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 29.587000000000003 - type: recall_at_10 value: 60.746 - type: recall_at_100 value: 82.157 - type: recall_at_1000 value: 95.645 - type: recall_at_3 value: 44.821 - type: recall_at_5 value: 52.819 - type: map_at_1 value: 30.239 - type: map_at_10 value: 39.989000000000004 - type: map_at_100 value: 41.196 - type: map_at_1000 value: 41.325 - type: map_at_3 value: 37.261 - type: map_at_5 value: 38.833 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 46.177 - type: mrr_at_100 value: 46.806 - type: mrr_at_1000 value: 46.849000000000004 - type: mrr_at_3 value: 44.002 - type: mrr_at_5 value: 45.34 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.586 - type: ndcg_at_100 value: 49.897000000000006 - type: ndcg_at_1000 value: 51.955 - type: ndcg_at_3 value: 41.684 - type: ndcg_at_5 value: 43.617 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.522 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.105999999999998 - type: precision_at_5 value: 14.152999999999999 - type: recall_at_1 value: 30.239 - type: recall_at_10 value: 55.03 - type: recall_at_100 value: 73.375 - type: recall_at_1000 value: 86.29599999999999 - type: recall_at_3 value: 43.269000000000005 - type: recall_at_5 value: 48.878 - type: map_at_1 value: 38.338 - type: map_at_10 value: 50.468999999999994 - type: map_at_100 value: 51.553000000000004 - type: map_at_1000 value: 51.608 - type: map_at_3 value: 47.107 - type: map_at_5 value: 49.101 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 54.057 - type: mrr_at_100 value: 54.764 - type: mrr_at_1000 value: 54.791000000000004 - type: mrr_at_3 value: 51.56699999999999 - type: mrr_at_5 value: 53.05 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 56.379000000000005 - type: ndcg_at_100 value: 60.645 - type: ndcg_at_1000 value: 61.73499999999999 - type: ndcg_at_3 value: 50.726000000000006 - type: ndcg_at_5 value: 53.58500000000001 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 9.141 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.654 - type: precision_at_5 value: 15.723999999999998 - type: recall_at_1 value: 38.338 - type: recall_at_10 value: 70.30499999999999 - type: recall_at_100 value: 88.77199999999999 - type: recall_at_1000 value: 96.49799999999999 - type: recall_at_3 value: 55.218 - type: recall_at_5 value: 62.104000000000006 - type: map_at_1 value: 25.682 - type: map_at_10 value: 33.498 - type: map_at_100 value: 34.461000000000006 - type: map_at_1000 value: 34.544000000000004 - type: map_at_3 value: 30.503999999999998 - type: map_at_5 value: 32.216 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 35.467999999999996 - type: mrr_at_100 value: 36.32 - type: mrr_at_1000 value: 36.386 - type: mrr_at_3 value: 32.618 - type: mrr_at_5 value: 34.262 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 38.378 - type: ndcg_at_100 value: 43.288 - type: ndcg_at_1000 value: 45.413 - type: ndcg_at_3 value: 32.586 - type: ndcg_at_5 value: 35.499 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.864 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 25.682 - type: recall_at_10 value: 51.712 - type: recall_at_100 value: 74.446 - type: recall_at_1000 value: 90.472 - type: recall_at_3 value: 36.236000000000004 - type: recall_at_5 value: 43.234 - type: map_at_1 value: 16.073999999999998 - type: map_at_10 value: 24.352999999999998 - type: map_at_100 value: 25.438 - type: map_at_1000 value: 25.545 - type: map_at_3 value: 21.614 - type: map_at_5 value: 23.104 - type: mrr_at_1 value: 19.776 - type: mrr_at_10 value: 28.837000000000003 - type: mrr_at_100 value: 29.755 - type: mrr_at_1000 value: 29.817 - type: mrr_at_3 value: 26.201999999999998 - type: mrr_at_5 value: 27.714 - type: ndcg_at_1 value: 19.776 - type: ndcg_at_10 value: 29.701 - type: ndcg_at_100 value: 35.307 - type: ndcg_at_1000 value: 37.942 - type: ndcg_at_3 value: 24.764 - type: ndcg_at_5 value: 27.025 - type: precision_at_1 value: 19.776 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 0.971 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.905000000000001 - type: recall_at_1 value: 16.073999999999998 - type: recall_at_10 value: 41.647 - type: recall_at_100 value: 66.884 - type: recall_at_1000 value: 85.91499999999999 - type: recall_at_3 value: 27.916 - type: recall_at_5 value: 33.729 - type: map_at_1 value: 28.444999999999997 - type: map_at_10 value: 38.218999999999994 - type: map_at_100 value: 39.595 - type: map_at_1000 value: 39.709 - type: map_at_3 value: 35.586 - type: map_at_5 value: 36.895 - type: mrr_at_1 value: 34.841 - type: mrr_at_10 value: 44.106 - type: mrr_at_100 value: 44.98 - type: mrr_at_1000 value: 45.03 - type: mrr_at_3 value: 41.979 - type: mrr_at_5 value: 43.047999999999995 - type: ndcg_at_1 value: 34.841 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.504999999999995 - type: ndcg_at_1000 value: 51.675000000000004 - type: ndcg_at_3 value: 39.858 - type: ndcg_at_5 value: 41.408 - type: precision_at_1 value: 34.841 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.2449999999999999 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 18.993 - type: precision_at_5 value: 13.032 - type: recall_at_1 value: 28.444999999999997 - type: recall_at_10 value: 54.984 - type: recall_at_100 value: 78.342 - type: recall_at_1000 value: 92.77 - type: recall_at_3 value: 42.842999999999996 - type: recall_at_5 value: 47.247 - type: map_at_1 value: 23.072 - type: map_at_10 value: 32.354 - type: map_at_100 value: 33.800000000000004 - type: map_at_1000 value: 33.908 - type: map_at_3 value: 29.232000000000003 - type: map_at_5 value: 31.049 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 38.03 - type: mrr_at_100 value: 39.032 - type: mrr_at_1000 value: 39.086999999999996 - type: mrr_at_3 value: 35.407 - type: mrr_at_5 value: 36.76 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 38.231 - type: ndcg_at_100 value: 44.425 - type: ndcg_at_1000 value: 46.771 - type: ndcg_at_3 value: 33.095 - type: ndcg_at_5 value: 35.459 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 7.215000000000001 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 50.285999999999994 - type: recall_at_100 value: 76.596 - type: recall_at_1000 value: 92.861 - type: recall_at_3 value: 35.702 - type: recall_at_5 value: 42.152 - type: map_at_1 value: 24.937916666666666 - type: map_at_10 value: 33.755250000000004 - type: map_at_100 value: 34.955999999999996 - type: map_at_1000 value: 35.070499999999996 - type: map_at_3 value: 30.98708333333333 - type: map_at_5 value: 32.51491666666666 - type: mrr_at_1 value: 29.48708333333333 - type: mrr_at_10 value: 37.92183333333334 - type: mrr_at_100 value: 38.76583333333333 - type: mrr_at_1000 value: 38.82466666666667 - type: mrr_at_3 value: 35.45125 - type: mrr_at_5 value: 36.827000000000005 - type: ndcg_at_1 value: 29.48708333333333 - type: ndcg_at_10 value: 39.05225 - type: ndcg_at_100 value: 44.25983333333334 - type: ndcg_at_1000 value: 46.568333333333335 - type: ndcg_at_3 value: 34.271583333333325 - type: ndcg_at_5 value: 36.483916666666666 - type: precision_at_1 value: 29.48708333333333 - type: precision_at_10 value: 6.865749999999999 - type: precision_at_100 value: 1.1195833333333332 - type: precision_at_1000 value: 0.15058333333333335 - type: precision_at_3 value: 15.742083333333333 - type: precision_at_5 value: 11.221916666666667 - type: recall_at_1 value: 24.937916666666666 - type: recall_at_10 value: 50.650416666666665 - type: recall_at_100 value: 73.55383333333334 - type: recall_at_1000 value: 89.61691666666667 - type: recall_at_3 value: 37.27808333333334 - type: recall_at_5 value: 42.99475 - type: map_at_1 value: 23.947 - type: map_at_10 value: 30.575000000000003 - type: map_at_100 value: 31.465 - type: map_at_1000 value: 31.558000000000003 - type: map_at_3 value: 28.814 - type: map_at_5 value: 29.738999999999997 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.415 - type: mrr_at_100 value: 34.18 - type: mrr_at_1000 value: 34.245 - type: mrr_at_3 value: 31.621 - type: mrr_at_5 value: 32.549 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.482 - type: ndcg_at_100 value: 38.915 - type: ndcg_at_1000 value: 41.355 - type: ndcg_at_3 value: 31.139 - type: ndcg_at_5 value: 32.589 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.322 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.344000000000001 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 23.947 - type: recall_at_10 value: 43.647999999999996 - type: recall_at_100 value: 63.851 - type: recall_at_1000 value: 82.0 - type: recall_at_3 value: 34.288000000000004 - type: recall_at_5 value: 38.117000000000004 - type: map_at_1 value: 16.197 - type: map_at_10 value: 22.968 - type: map_at_100 value: 24.095 - type: map_at_1000 value: 24.217 - type: map_at_3 value: 20.771 - type: map_at_5 value: 21.995 - type: mrr_at_1 value: 19.511 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.500999999999998 - type: mrr_at_1000 value: 27.578999999999997 - type: mrr_at_3 value: 24.421 - type: mrr_at_5 value: 25.604 - type: ndcg_at_1 value: 19.511 - type: ndcg_at_10 value: 27.386 - type: ndcg_at_100 value: 32.828 - type: ndcg_at_1000 value: 35.739 - type: ndcg_at_3 value: 23.405 - type: ndcg_at_5 value: 25.255 - type: precision_at_1 value: 19.511 - type: precision_at_10 value: 5.017 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.023 - type: precision_at_5 value: 8.025 - type: recall_at_1 value: 16.197 - type: recall_at_10 value: 37.09 - type: recall_at_100 value: 61.778 - type: recall_at_1000 value: 82.56599999999999 - type: recall_at_3 value: 26.034000000000002 - type: recall_at_5 value: 30.762 - type: map_at_1 value: 25.41 - type: map_at_10 value: 33.655 - type: map_at_100 value: 34.892 - type: map_at_1000 value: 34.995 - type: map_at_3 value: 30.94 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 29.477999999999998 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.383 - type: mrr_at_1000 value: 38.440000000000005 - type: mrr_at_3 value: 34.949999999999996 - type: mrr_at_5 value: 36.228 - type: ndcg_at_1 value: 29.477999999999998 - type: ndcg_at_10 value: 38.769 - type: ndcg_at_100 value: 44.245000000000005 - type: ndcg_at_1000 value: 46.593 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 35.766 - type: precision_at_1 value: 29.477999999999998 - type: precision_at_10 value: 6.455 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 14.893999999999998 - type: precision_at_5 value: 10.485 - type: recall_at_1 value: 25.41 - type: recall_at_10 value: 50.669 - type: recall_at_100 value: 74.084 - type: recall_at_1000 value: 90.435 - type: recall_at_3 value: 36.679 - type: recall_at_5 value: 41.94 - type: map_at_1 value: 23.339 - type: map_at_10 value: 31.852000000000004 - type: map_at_100 value: 33.411 - type: map_at_1000 value: 33.62 - type: map_at_3 value: 28.929 - type: map_at_5 value: 30.542 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.301 - type: mrr_at_100 value: 37.288 - type: mrr_at_1000 value: 37.349 - type: mrr_at_3 value: 33.663 - type: mrr_at_5 value: 35.165 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 37.462 - type: ndcg_at_100 value: 43.620999999999995 - type: ndcg_at_1000 value: 46.211 - type: ndcg_at_3 value: 32.68 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.1739999999999995 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.265 - type: recall_at_1 value: 23.339 - type: recall_at_10 value: 48.376999999999995 - type: recall_at_100 value: 76.053 - type: recall_at_1000 value: 92.455 - type: recall_at_3 value: 34.735 - type: recall_at_5 value: 40.71 - type: map_at_1 value: 18.925 - type: map_at_10 value: 26.017000000000003 - type: map_at_100 value: 27.034000000000002 - type: map_at_1000 value: 27.156000000000002 - type: map_at_3 value: 23.604 - type: map_at_5 value: 24.75 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 27.915 - type: mrr_at_100 value: 28.788000000000004 - type: mrr_at_1000 value: 28.877999999999997 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.648 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 30.673000000000002 - type: ndcg_at_100 value: 35.618 - type: ndcg_at_1000 value: 38.517 - type: ndcg_at_3 value: 25.71 - type: ndcg_at_5 value: 27.679 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.8740000000000006 - type: recall_at_1 value: 18.925 - type: recall_at_10 value: 43.311 - type: recall_at_100 value: 66.308 - type: recall_at_1000 value: 87.49 - type: recall_at_3 value: 29.596 - type: recall_at_5 value: 34.245 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 13.714 - type: map_at_10 value: 23.194 - type: map_at_100 value: 24.976000000000003 - type: map_at_1000 value: 25.166 - type: map_at_3 value: 19.709 - type: map_at_5 value: 21.523999999999997 - type: mrr_at_1 value: 30.619000000000003 - type: mrr_at_10 value: 42.563 - type: mrr_at_100 value: 43.386 - type: mrr_at_1000 value: 43.423 - type: mrr_at_3 value: 39.555 - type: mrr_at_5 value: 41.268 - type: ndcg_at_1 value: 30.619000000000003 - type: ndcg_at_10 value: 31.836 - type: ndcg_at_100 value: 38.652 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 26.733 - type: ndcg_at_5 value: 28.435 - type: precision_at_1 value: 30.619000000000003 - type: precision_at_10 value: 9.751999999999999 - type: precision_at_100 value: 1.71 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 19.935 - type: precision_at_5 value: 14.984 - type: recall_at_1 value: 13.714 - type: recall_at_10 value: 37.26 - type: recall_at_100 value: 60.546 - type: recall_at_1000 value: 79.899 - type: recall_at_3 value: 24.325 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.462 - type: map_at_10 value: 18.637 - type: map_at_100 value: 26.131999999999998 - type: map_at_1000 value: 27.607 - type: map_at_3 value: 13.333 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.32600000000001 - type: mrr_at_100 value: 74.60900000000001 - type: mrr_at_1000 value: 74.62 - type: mrr_at_3 value: 72.667 - type: mrr_at_5 value: 73.817 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.028999999999996 - type: ndcg_at_100 value: 44.199 - type: ndcg_at_1000 value: 51.629999999999995 - type: ndcg_at_3 value: 44.113 - type: ndcg_at_5 value: 41.731 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 31.900000000000002 - type: precision_at_100 value: 10.043000000000001 - type: precision_at_1000 value: 1.926 - type: precision_at_3 value: 47.417 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.462 - type: recall_at_10 value: 24.293 - type: recall_at_100 value: 50.146 - type: recall_at_1000 value: 74.034 - type: recall_at_3 value: 14.967 - type: recall_at_5 value: 18.682000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.84499999999999 - type: f1 value: 42.48106691979349 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.034 - type: map_at_10 value: 82.76 - type: map_at_100 value: 82.968 - type: map_at_1000 value: 82.98299999999999 - type: map_at_3 value: 81.768 - type: map_at_5 value: 82.418 - type: mrr_at_1 value: 80.048 - type: mrr_at_10 value: 87.64999999999999 - type: mrr_at_100 value: 87.712 - type: mrr_at_1000 value: 87.713 - type: mrr_at_3 value: 87.01100000000001 - type: mrr_at_5 value: 87.466 - type: ndcg_at_1 value: 80.048 - type: ndcg_at_10 value: 86.643 - type: ndcg_at_100 value: 87.361 - type: ndcg_at_1000 value: 87.606 - type: ndcg_at_3 value: 85.137 - type: ndcg_at_5 value: 86.016 - type: precision_at_1 value: 80.048 - type: precision_at_10 value: 10.372 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.638 - type: precision_at_5 value: 20.177 - type: recall_at_1 value: 74.034 - type: recall_at_10 value: 93.769 - type: recall_at_100 value: 96.569 - type: recall_at_1000 value: 98.039 - type: recall_at_3 value: 89.581 - type: recall_at_5 value: 91.906 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.5 - type: map_at_10 value: 32.857 - type: map_at_100 value: 34.589 - type: map_at_1000 value: 34.778 - type: map_at_3 value: 29.160999999999998 - type: map_at_5 value: 31.033 - type: mrr_at_1 value: 40.123 - type: mrr_at_10 value: 48.776 - type: mrr_at_100 value: 49.495 - type: mrr_at_1000 value: 49.539 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 47.654 - type: ndcg_at_1 value: 40.123 - type: ndcg_at_10 value: 40.343 - type: ndcg_at_100 value: 46.56 - type: ndcg_at_1000 value: 49.777 - type: ndcg_at_3 value: 37.322 - type: ndcg_at_5 value: 37.791000000000004 - type: precision_at_1 value: 40.123 - type: precision_at_10 value: 11.08 - type: precision_at_100 value: 1.752 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 24.897 - type: precision_at_5 value: 17.809 - type: recall_at_1 value: 20.5 - type: recall_at_10 value: 46.388 - type: recall_at_100 value: 69.552 - type: recall_at_1000 value: 89.011 - type: recall_at_3 value: 33.617999999999995 - type: recall_at_5 value: 38.211 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.135999999999996 - type: map_at_10 value: 61.673 - type: map_at_100 value: 62.562 - type: map_at_1000 value: 62.62 - type: map_at_3 value: 58.467999999999996 - type: map_at_5 value: 60.463 - type: mrr_at_1 value: 78.271 - type: mrr_at_10 value: 84.119 - type: mrr_at_100 value: 84.29299999999999 - type: mrr_at_1000 value: 84.299 - type: mrr_at_3 value: 83.18900000000001 - type: mrr_at_5 value: 83.786 - type: ndcg_at_1 value: 78.271 - type: ndcg_at_10 value: 69.935 - type: ndcg_at_100 value: 73.01299999999999 - type: ndcg_at_1000 value: 74.126 - type: ndcg_at_3 value: 65.388 - type: ndcg_at_5 value: 67.906 - type: precision_at_1 value: 78.271 - type: precision_at_10 value: 14.562 - type: precision_at_100 value: 1.6969999999999998 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.841 - type: precision_at_5 value: 27.087 - type: recall_at_1 value: 39.135999999999996 - type: recall_at_10 value: 72.809 - type: recall_at_100 value: 84.86200000000001 - type: recall_at_1000 value: 92.208 - type: recall_at_3 value: 62.76199999999999 - type: recall_at_5 value: 67.718 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.60600000000001 - type: ap value: 86.6579587804335 - type: f1 value: 90.5938853929307 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.852 - type: map_at_10 value: 33.982 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.167 - type: map_at_3 value: 30.134 - type: map_at_5 value: 32.340999999999994 - type: mrr_at_1 value: 22.479 - type: mrr_at_10 value: 34.594 - type: mrr_at_100 value: 35.672 - type: mrr_at_1000 value: 35.716 - type: mrr_at_3 value: 30.84 - type: mrr_at_5 value: 32.998 - type: ndcg_at_1 value: 22.493 - type: ndcg_at_10 value: 40.833000000000006 - type: ndcg_at_100 value: 46.357 - type: ndcg_at_1000 value: 47.637 - type: ndcg_at_3 value: 32.995999999999995 - type: ndcg_at_5 value: 36.919000000000004 - type: precision_at_1 value: 22.493 - type: precision_at_10 value: 6.465999999999999 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.030999999999999 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.852 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 87.611 - type: recall_at_1000 value: 97.441 - type: recall_at_3 value: 40.583999999999996 - type: recall_at_5 value: 49.992999999999995 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.36069311445507 - type: f1 value: 93.16456330371453 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.74692202462381 - type: f1 value: 58.17903579421599 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.80833893745796 - type: f1 value: 72.70786592684664 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69872225958305 - type: f1 value: 78.61626934504731 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.058658628717694 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.85561739360599 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.290259910144385 - type: mrr value: 32.44223046102856 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.288 - type: map_at_10 value: 12.267999999999999 - type: map_at_100 value: 15.557000000000002 - type: map_at_1000 value: 16.98 - type: map_at_3 value: 8.866 - type: map_at_5 value: 10.418 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 52.681 - type: mrr_at_100 value: 53.315999999999995 - type: mrr_at_1000 value: 53.357 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 51.903999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.305 - type: ndcg_at_100 value: 30.825999999999997 - type: ndcg_at_1000 value: 39.393 - type: ndcg_at_3 value: 39.931 - type: ndcg_at_5 value: 37.519999999999996 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.728 - type: precision_at_100 value: 7.932 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.184000000000005 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.288 - type: recall_at_10 value: 16.195 - type: recall_at_100 value: 31.135 - type: recall_at_1000 value: 61.531000000000006 - type: recall_at_3 value: 10.313 - type: recall_at_5 value: 12.754999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 28.216 - type: map_at_10 value: 42.588 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.739 - type: map_at_3 value: 38.177 - type: map_at_5 value: 40.754000000000005 - type: mrr_at_1 value: 31.866 - type: mrr_at_10 value: 45.189 - type: mrr_at_100 value: 46.056000000000004 - type: mrr_at_1000 value: 46.081 - type: mrr_at_3 value: 41.526999999999994 - type: mrr_at_5 value: 43.704 - type: ndcg_at_1 value: 31.837 - type: ndcg_at_10 value: 50.178 - type: ndcg_at_100 value: 54.98800000000001 - type: ndcg_at_1000 value: 55.812 - type: ndcg_at_3 value: 41.853 - type: ndcg_at_5 value: 46.153 - type: precision_at_1 value: 31.837 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.911000000000001 - type: recall_at_1 value: 28.216 - type: recall_at_10 value: 70.8 - type: recall_at_100 value: 91.857 - type: recall_at_1000 value: 97.941 - type: recall_at_3 value: 49.196 - type: recall_at_5 value: 59.072 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.22800000000001 - type: map_at_10 value: 85.115 - type: map_at_100 value: 85.72 - type: map_at_1000 value: 85.737 - type: map_at_3 value: 82.149 - type: map_at_5 value: 84.029 - type: mrr_at_1 value: 81.96 - type: mrr_at_10 value: 88.00200000000001 - type: mrr_at_100 value: 88.088 - type: mrr_at_1000 value: 88.089 - type: mrr_at_3 value: 87.055 - type: mrr_at_5 value: 87.715 - type: ndcg_at_1 value: 82.01 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.91 - type: ndcg_at_1000 value: 90.013 - type: ndcg_at_3 value: 85.957 - type: ndcg_at_5 value: 87.56 - type: precision_at_1 value: 82.01 - type: precision_at_10 value: 13.462 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.732000000000003 - type: recall_at_1 value: 71.22800000000001 - type: recall_at_10 value: 95.69 - type: recall_at_100 value: 99.531 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 87.632 - type: recall_at_5 value: 92.117 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 52.31768034366916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.640266772723606 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.7780000000000005 - type: map_at_10 value: 12.299 - type: map_at_100 value: 14.363000000000001 - type: map_at_1000 value: 14.71 - type: map_at_3 value: 8.738999999999999 - type: map_at_5 value: 10.397 - type: mrr_at_1 value: 23.599999999999998 - type: mrr_at_10 value: 34.845 - type: mrr_at_100 value: 35.916 - type: mrr_at_1000 value: 35.973 - type: mrr_at_3 value: 31.7 - type: mrr_at_5 value: 33.535 - type: ndcg_at_1 value: 23.599999999999998 - type: ndcg_at_10 value: 20.522000000000002 - type: ndcg_at_100 value: 28.737000000000002 - type: ndcg_at_1000 value: 34.596 - type: ndcg_at_3 value: 19.542 - type: ndcg_at_5 value: 16.958000000000002 - type: precision_at_1 value: 23.599999999999998 - type: precision_at_10 value: 10.67 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 14.879999999999999 - type: recall_at_1 value: 4.7780000000000005 - type: recall_at_10 value: 21.617 - type: recall_at_100 value: 45.905 - type: recall_at_1000 value: 74.42 - type: recall_at_3 value: 11.148 - type: recall_at_5 value: 15.082999999999998 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.22372750297885 - type: cos_sim_spearman value: 79.40972617119405 - type: euclidean_pearson value: 80.6101072020434 - type: euclidean_spearman value: 79.53844217225202 - type: manhattan_pearson value: 80.57265975286111 - type: manhattan_spearman value: 79.46335611792958 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.43713315520749 - type: cos_sim_spearman value: 77.44128693329532 - type: euclidean_pearson value: 81.63869928101123 - type: euclidean_spearman value: 77.29512977961515 - type: manhattan_pearson value: 81.63704185566183 - type: manhattan_spearman value: 77.29909412738657 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.59451537860527 - type: cos_sim_spearman value: 82.97994638856723 - type: euclidean_pearson value: 82.89478688288412 - type: euclidean_spearman value: 83.58740751053104 - type: manhattan_pearson value: 82.69140840941608 - type: manhattan_spearman value: 83.33665956040555 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.00756527711764 - type: cos_sim_spearman value: 81.83560996841379 - type: euclidean_pearson value: 82.07684151976518 - type: euclidean_spearman value: 82.00913052060511 - type: manhattan_pearson value: 82.05690778488794 - type: manhattan_spearman value: 82.02260252019525 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.13710262895447 - type: cos_sim_spearman value: 87.26412811156248 - type: euclidean_pearson value: 86.94151453230228 - type: euclidean_spearman value: 87.5363796699571 - type: manhattan_pearson value: 86.86989424083748 - type: manhattan_spearman value: 87.47315940781353 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.0230597603627 - type: cos_sim_spearman value: 84.93344499318864 - type: euclidean_pearson value: 84.23754743431141 - type: euclidean_spearman value: 85.09707376597099 - type: manhattan_pearson value: 84.04325160987763 - type: manhattan_spearman value: 84.89353071339909 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.75620824563921 - type: cos_sim_spearman value: 87.15065513706398 - type: euclidean_pearson value: 88.26281533633521 - type: euclidean_spearman value: 87.51963738643983 - type: manhattan_pearson value: 88.25599267618065 - type: manhattan_spearman value: 87.58048736047483 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.74645319195137 - type: cos_sim_spearman value: 65.29996325037214 - type: euclidean_pearson value: 67.04297794086443 - type: euclidean_spearman value: 65.43841726694343 - type: manhattan_pearson value: 67.39459955690904 - type: manhattan_spearman value: 65.92864704413651 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.31291020270801 - type: cos_sim_spearman value: 85.86473738688068 - type: euclidean_pearson value: 85.65537275064152 - type: euclidean_spearman value: 86.13087454209642 - type: manhattan_pearson value: 85.43946955047609 - type: manhattan_spearman value: 85.91568175344916 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.93798118350695 - type: mrr value: 95.93536274908824 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.594 - type: map_at_10 value: 66.81899999999999 - type: map_at_100 value: 67.368 - type: map_at_1000 value: 67.4 - type: map_at_3 value: 64.061 - type: map_at_5 value: 65.47 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.219 - type: mrr_at_100 value: 68.655 - type: mrr_at_1000 value: 68.684 - type: mrr_at_3 value: 66.22200000000001 - type: mrr_at_5 value: 67.289 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.275 - type: ndcg_at_100 value: 73.642 - type: ndcg_at_1000 value: 74.373 - type: ndcg_at_3 value: 66.521 - type: ndcg_at_5 value: 68.581 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.433 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.556 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 57.594 - type: recall_at_10 value: 83.622 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.64399999999999 - type: recall_at_5 value: 75.983 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85841584158416 - type: cos_sim_ap value: 96.66996142314342 - type: cos_sim_f1 value: 92.83208020050125 - type: cos_sim_precision value: 93.06532663316584 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.85841584158416 - type: dot_ap value: 96.6775307676576 - type: dot_f1 value: 92.69289729177312 - type: dot_precision value: 94.77533960292581 - type: dot_recall value: 90.7 - type: euclidean_accuracy value: 99.86138613861387 - type: euclidean_ap value: 96.6338454403108 - type: euclidean_f1 value: 92.92214357937311 - type: euclidean_precision value: 93.96728016359918 - type: euclidean_recall value: 91.9 - type: manhattan_accuracy value: 99.86237623762376 - type: manhattan_ap value: 96.60370449645053 - type: manhattan_f1 value: 92.91177970423253 - type: manhattan_precision value: 94.7970863683663 - type: manhattan_recall value: 91.10000000000001 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.6775307676576 - type: max_f1 value: 92.92214357937311 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.77977058695198 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.2725272535638 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.64052466362125 - type: mrr value: 54.533067014684654 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.677624219206578 - type: cos_sim_spearman value: 30.121368518123447 - type: dot_pearson value: 30.69870088041608 - type: dot_spearman value: 29.61284927093751 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.855 - type: map_at_100 value: 9.885 - type: map_at_1000 value: 23.416999999999998 - type: map_at_3 value: 0.637 - type: map_at_5 value: 1.024 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.067 - type: mrr_at_100 value: 93.067 - type: mrr_at_1000 value: 93.067 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 93.067 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 75.899 - type: ndcg_at_100 value: 55.115 - type: ndcg_at_1000 value: 48.368 - type: ndcg_at_3 value: 79.704 - type: ndcg_at_5 value: 78.39699999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 79.60000000000001 - type: precision_at_100 value: 56.06 - type: precision_at_1000 value: 21.206 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 83.2 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.078 - type: recall_at_100 value: 13.297 - type: recall_at_1000 value: 44.979 - type: recall_at_3 value: 0.6689999999999999 - type: recall_at_5 value: 1.106 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.258 - type: map_at_10 value: 10.439 - type: map_at_100 value: 16.89 - type: map_at_1000 value: 18.407999999999998 - type: map_at_3 value: 5.668 - type: map_at_5 value: 7.718 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 51.159 - type: mrr_at_100 value: 51.714000000000006 - type: mrr_at_1000 value: 51.714000000000006 - type: mrr_at_3 value: 47.959 - type: mrr_at_5 value: 50.407999999999994 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.037 - type: ndcg_at_100 value: 37.924 - type: ndcg_at_1000 value: 49.126999999999995 - type: ndcg_at_3 value: 30.631999999999998 - type: ndcg_at_5 value: 28.571 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.529 - type: precision_at_3 value: 34.014 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.258 - type: recall_at_10 value: 16.554 - type: recall_at_100 value: 48.439 - type: recall_at_1000 value: 82.80499999999999 - type: recall_at_3 value: 7.283 - type: recall_at_5 value: 10.732 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.8858 - type: ap value: 13.835684144362109 - type: f1 value: 53.803351693244586 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.50650820599886 - type: f1 value: 60.84357825979259 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.52131044852134 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59337187816654 - type: cos_sim_ap value: 73.23925826533437 - type: cos_sim_f1 value: 67.34693877551021 - type: cos_sim_precision value: 62.40432237730752 - type: cos_sim_recall value: 73.13984168865434 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.30723963807422 - type: dot_f1 value: 66.47051612112296 - type: dot_precision value: 62.0792305930845 - type: dot_recall value: 71.53034300791556 - type: euclidean_accuracy value: 85.61125350181797 - type: euclidean_ap value: 73.32843720487845 - type: euclidean_f1 value: 67.36549633745895 - type: euclidean_precision value: 64.60755813953489 - type: euclidean_recall value: 70.36939313984169 - type: manhattan_accuracy value: 85.63509566668654 - type: manhattan_ap value: 73.16658488311325 - type: manhattan_f1 value: 67.20597386434349 - type: manhattan_precision value: 63.60424028268551 - type: manhattan_recall value: 71.2401055408971 - type: max_accuracy value: 85.63509566668654 - type: max_ap value: 73.32843720487845 - type: max_f1 value: 67.36549633745895 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.33779640625606 - type: cos_sim_ap value: 84.83868375898157 - type: cos_sim_f1 value: 77.16506154017773 - type: cos_sim_precision value: 74.62064005753327 - type: cos_sim_recall value: 79.88912842623961 - type: dot_accuracy value: 88.02732176815307 - type: dot_ap value: 83.95089283763002 - type: dot_f1 value: 76.29635101196631 - type: dot_precision value: 73.31771720613288 - type: dot_recall value: 79.52725592854944 - type: euclidean_accuracy value: 88.44452206310397 - type: euclidean_ap value: 84.98384576824827 - type: euclidean_f1 value: 77.29311047696697 - type: euclidean_precision value: 74.51232583065381 - type: euclidean_recall value: 80.28949799815214 - type: manhattan_accuracy value: 88.47362906042613 - type: manhattan_ap value: 84.91421462218432 - type: manhattan_f1 value: 77.05107637204792 - type: manhattan_precision value: 74.74484256243214 - type: manhattan_recall value: 79.50415768401602 - type: max_accuracy value: 88.47362906042613 - type: max_ap value: 84.98384576824827 - type: max_f1 value: 77.29311047696697 --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> \| <a href=#frequently-asked-questions>FAQ</a> \| <a href=#usage>Usage</a> \| <a href="#evaluation">Evaluation</a> \| <a href="#train">Train</a> \| <a href="#contact">Contact</a> \| <a href="#citation">Citation</a> \| <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) \| [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - Long-Context LLM: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - Fine-tuning of LM : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - Dense Retrieval: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - Reranker Model: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - Benchmark: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - New reranker model: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - update embedding model: release `bge--v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into Langchain, you can use it like [this](#using-langchain); C-MTEB leaderboard* is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗 - 08/02/2023: Release `bge-large-`(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark!* :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (C-MTEB), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. \| Model \| Language \| \| Description \| query instruction for retrieval [1] \| \|:-------------------------------\|:--------:\| :--------:\| :--------:\|:--------:\| \| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) \| Multilingual \| [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) \| Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) \| \| \| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) \| English \| [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) \| a unified embedding model to support diverse retrieval augmentation needs for LLMs \| See [README](./FlagEmbedding/llm_embedder/README.md) \| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient [2] \| \| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient [2] \| \| \| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-en` \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \|a small-scale model but with competitive performance \| `Represent this sentence for searching relevant passages: ` \| \| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-zh` \| `为这个句子生成表示以用于检索相关文章：` \| \| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a small-scale model but with competitive performance \| `为这个句子生成表示以用于检索相关文章：` \| [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, no instruction needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary> <!-- ### How to fine-tune bge embedding model? --> Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> Suggest to use bge v1.5, which alleviates the issue of the similarity distribution. Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, what matters is the relative order of the scores, not the absolute value. If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge--v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.* In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard! For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - MTEB: \| Model Name \| Dimension \| Sequence Length \| Average (56) \| Retrieval (15) \|Clustering (11) \| Pair Classification (3) \| Reranking (4) \| STS (10) \| Summarization (1) \| Classification (12) \| \|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\| \| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| 1024 \| 512 \| 64.23 \| 54.29 \| 46.08 \| 87.12 \| 60.03 \| 83.11 \| 31.61 \| 75.97 \| \| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| 768 \| 512 \| 63.55 \| 53.25 \| 45.77 \| 86.55 \| 58.86 \| 82.4 \| 31.07 \| 75.53 \| \| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| 384 \| 512 \| 62.17 \|51.68 \| 43.82 \| 84.92 \| 58.36 \| 81.59 \| 30.12 \| 74.14 \| \| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| 1024 \| 512 \| 63.98 \| 53.9 \| 46.98 \| 85.8 \| 59.48 \| 81.56 \| 32.06 \| 76.21 \| \| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| 768 \| 512 \| 63.36 \| 53.0 \| 46.32 \| 85.86 \| 58.7 \| 81.84 \| 29.27 \| 75.27 \| \| [gte-large](https://huggingface.co/thenlper/gte-large) \| 1024 \| 512 \| 63.13 \| 52.22 \| 46.84 \| 85.00 \| 59.13 \| 83.35 \| 31.66 \| 73.33 \| \| [gte-base](https://huggingface.co/thenlper/gte-base) \| 768 \| 512 \| 62.39 \| 51.14 \| 46.2 \| 84.57 \| 58.61 \| 82.3 \| 31.17 \| 73.01 \| \| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) \| 1024\| 512 \| 62.25 \| 50.56 \| 44.49 \| 86.03 \| 56.61 \| 82.05 \| 30.19 \| 75.24 \| \| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| 384 \| 512 \| 62.11 \| 51.82 \| 44.31 \| 83.78 \| 57.97 \| 80.72 \| 30.53 \| 74.37 \| \| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) \| 768 \| 512 \| 61.79 \| 49.26 \| 44.74 \| 86.62 \| 57.29 \| 83.06 \| 32.32 \| 61.79 \| \| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) \| 768 \| 512 \| 61.5 \| 50.29 \| 43.80 \| 85.73 \| 55.91 \| 81.05 \| 30.28 \| 73.84 \| \| [gte-small](https://huggingface.co/thenlper/gte-small) \| 384 \| 512 \| 61.36 \| 49.46 \| 44.89 \| 83.54 \| 57.7 \| 82.07 \| 30.42 \| 72.31 \| \| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| 1536 \| 8192 \| 60.99 \| 49.25 \| 45.9 \| 84.89 \| 56.32 \| 80.97 \| 30.8 \| 70.93 \| \| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) \| 384 \| 512 \| 59.93 \| 49.04 \| 39.92 \| 84.67 \| 54.32 \| 80.39 \| 31.16 \| 72.94 \| \| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) \| 768 \| 512 \| 59.51 \| 42.24 \| 43.72 \| 85.06 \| 56.42 \| 82.63 \| 30.08 \| 73.42 \| \| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \| 768 \| 514 \| 57.78 \| 43.81 \| 43.69 \| 83.04 \| 59.36 \| 80.28 \| 27.49 \| 65.07 \| \| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \| 4096 \| 2048 \| 57.59 \| 48.22 \| 38.93 \| 81.9 \| 55.65 \| 77.74 \| 33.6 \| 66.19 \| - C-MTEB: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. \| Model \| Embedding dimension \| Avg \| Retrieval \| STS \| PairClassification \| Classification \| Reranking \| Clustering \| \|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| 1024 \| 64.53 \| 70.46 \| 56.25 \| 81.6 \| 69.13 \| 65.84 \| 48.99 \| \| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| 768 \| 63.13 \| 69.49 \| 53.72 \| 79.75 \| 68.07 \| 65.39 \| 47.53 \| \| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| 512 \| 57.82 \| 61.77 \| 49.11 \| 70.41 \| 63.96 \| 60.92 \| 44.18 \| \| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| 1024 \| 64.20 \| 71.53 \| 54.98 \| 78.94 \| 68.32 \| 65.11 \| 48.39 \| \| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) \| 1024 \| 63.53 \| 70.55 \| 53 \| 76.77 \| 68.58 \| 64.91 \| 50.01 \| \| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| 768 \| 62.96 \| 69.53 \| 54.12 \| 77.5 \| 67.07 \| 64.91 \| 47.63 \| \| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) \| 1024 \| 58.79 \| 63.66 \| 48.44 \| 69.89 \| 67.34 \| 56.00 \| 48.23 \| \| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| 512 \| 58.27 \| 63.07 \| 49.45 \| 70.35 \| 63.64 \| 61.48 \| 45.09 \| \| [m3e-base](https://huggingface.co/moka-ai/m3e-base) \| 768 \| 57.10 \| 56.91 \| 50.47 \| 63.99 \| 67.52 \| 59.34 \| 47.68 \| \| [m3e-large](https://huggingface.co/moka-ai/m3e-large) \| 1024 \| 57.05 \| 54.75 \| 50.42 \| 64.3 \| 68.2 \| 59.66 \| 48.88 \| \| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) \| 768 \| 55.48 \| 61.63 \| 46.49 \| 67.07 \| 65.35 \| 54.35 \| 40.68 \| \| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) \| 384 \| 55.38 \| 59.95 \| 45.27 \| 66.45 \| 65.85 \| 53.86 \| 45.26 \| \| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) \| 1536 \| 53.02 \| 52.0 \| 43.35 \| 69.56 \| 64.31 \| 54.28 \| 45.68 \| \| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) \| 1024 \| 49.37 \| 44.4 \| 42.78 \| 66.62 \| 61 \| 49.25 \| 44.39 \| \| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) \| 768 \| 47.63 \| 38.79 \| 43.41 \| 67.41 \| 62.19 \| 49.45 \| 37.66 \| \| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) \| 1024 \| 47.36 \| 41.94 \| 44.97 \| 70.86 \| 60.66 \| 49.16 \| 30.02 \| - Reranking: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. \| Model \| T2Reranking \| T2RerankingZh2En\* \| T2RerankingEn2Zh\* \| MMarcoReranking \| CMedQAv1 \| CMedQAv2 \| Avg \| \|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| text2vec-base-multilingual \| 64.66 \| 62.94 \| 62.51 \| 14.37 \| 48.46 \| 48.6 \| 50.26 \| \| multilingual-e5-small \| 65.62 \| 60.94 \| 56.41 \| 29.91 \| 67.26 \| 66.54 \| 57.78 \| \| multilingual-e5-large \| 64.55 \| 61.61 \| 54.28 \| 28.6 \| 67.42 \| 67.92 \| 57.4 \| \| multilingual-e5-base \| 64.21 \| 62.13 \| 54.68 \| 29.5 \| 66.23 \| 66.98 \| 57.29 \| \| m3e-base \| 66.03 \| 62.74 \| 56.07 \| 17.51 \| 77.05 \| 76.76 \| 59.36 \| \| m3e-large \| 66.13 \| 62.72 \| 56.1 \| 16.46 \| 77.76 \| 78.27 \| 59.57 \| \| bge-base-zh-v1.5 \| 66.49 \| 63.25 \| 57.02 \| 29.74 \| 80.47 \| 84.88 \| 63.64 \| \| bge-large-zh-v1.5 \| 65.74 \| 63.39 \| 57.03 \| 28.74 \| 83.45 \| 85.44 \| 63.97 \| \| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| 67.28 \| 63.95 \| 60.45 \| 35.46 \| 81.26 \| 84.1 \| 65.42 \| \| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| 67.6 \| 64.03 \| 61.44 \| 37.16 \| 82.15 \| 84.18 \| 66.09 \| \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune). We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.	[ "SEMANTIC_SIMILARITY", "SUMMARIZATION" ]	[ "BEAR", "BIOSSES", "SCIFACT" ]
Adjoumani/BaouleTokenizer_V1	Adjoumani	null	[ "region:us" ]	2025-02-02T19:37:05	2025-02-02T20:09:06	0	--- {} --- ```markdown --- language: - "baq" # Code ISO 639-3 pour le Baoulé - "fr" # Français tags: - "translation" - "low-resource" - "african-nlp" - "tonal-language" license: "apache-2.0" datasets: - "custom" metrics: - "bleu" - "ter" - "chrF" widget: - text: "Mɔ́kɛ́ mɩnɩn wɛ?" example_title: "Salutation basique" pipeline_tag: "translation" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 Premier tokenizer SentencePiece spécialisé pour la langue Baoulé (Côte d'Ivoire) 🇨🇮 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/Adjoumani/BaouleTokenizer_V1) ## Fonctionnalités Clés ✅ Prise en charge complète des caractères tonals Baoulé (ɛ́, ɩ̄, ɔ̀, etc.) ✅ Optimisé pour les modèles de traduction automatique (Transformer) ✅ Vocabulaire de 206 tokens avec couverture linguistique complète ✅ Intégration native avec 🤗 Transformers et Tokenizers ✅ Compatible avec Google Traduction Custom Model et Amazon Translate ## Installation et Utilisation ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Utilisation du tokenizer text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'e', '▁l', 'a', 'f', 'i', '▁s', 'u', '▁k', 'ɛ', '▁b', 'é', '▁t', 'r', 'á', 'n', '▁a', 's', 'i', 'ɛ', '’', 'n', '▁s', 'u', '▁w', 'a', '’', 'n', ',', '▁b', 'e', '▁b', 'u', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'é', '▁k', 'ɔ', '́', '▁ɲ', 'a', 'n', 'm', 'i', 'ɛ', 'n'] ``` ## Détails Techniques \| Paramètre \| Valeur \| \|--------------------\|----------------------\| \| Architecture \| SentencePiece BPE \| \| Taille du vocabulaire \| 206 \| \| Caractères couverts \| 1.0 (Unicode) \| \| Tokens spéciaux \| [BOS], [EOS], [UNK], [PAD] \| \| Langues cibles \| Français ↔ Baoulé \| \| Encodage \| UTF-8 \| ## Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : \| Caractère \| Code Unicode \| Exemple \| \|-----------\|--------------\|---------\| \| ɛ́ \| U+025B U+0301\| Mɔ́kɛ́ \| \| ɩ̄ \| U+0269 U+0304\| Ɩ̄tɩ̄ \| \| ɔ̀ \| U+0254 U+0300\| Kɔ̀lɔ̀ \| \| ɛ̂ \| U+025B U+0302\| Ɛ̂sɛ̂ \| ## Cas d'Usage Recommandés - Traduction automatique Français-Baoulé - Synthèse vocale pour systèmes d'assistance vocale - Reconnaissance de la parole Baoulé - Outils éducatifs numériques - Préservation du patrimoine linguistique ## Meilleures Pratiques ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` ## Jeu de Données d'Entraînement Données collectées grâce à : - Traductions de textes bibliques : Les données ont été extraites en grande partie depuis [Glosbe](https://www.glosbe.com/) et structurées manuellement pour assurer une qualité et une précision optimales. Le contenu a été nettoyé pour supprimer les balises HTML indésirables et formaté de manière cohérente. - Corpus oral transcrit (projet UNESCO) - Phrases quotidiennes annotées - Textes gouvernementaux bilingues Taille du corpus : 1500 phrases alignées (en cours d'expansion) ## Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2025, author = {Koffi Wilfried Adjoumani}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` ## Licence Apache 2.0 - [Voir la licence complète](LICENSE) ## Contribuer Nous encourageons les contributions notamment pour : - L'expansion du vocabulaire - L'annotation des tons - L'ajout de dialectes régionaux Contact : [[email protected]](mailto:[email protected]) --- Mots-clés SEO : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine --- ```	[ "TRANSLATION" ]	[ "CAS" ]
Adjoumani/baoule-tokenizer	Adjoumani	null	[ "region:us" ]	2025-02-04T03:26:31	2025-02-04T03:26:32	0	--- {} --- Votre fichier `README.md` est déjà bien structuré, mais je vais l'améliorer pour qu'il soit encore plus conforme aux principes de référencement (SEO) de Hugging Face et Google. Voici une version optimisée : --- ### README.md Optimisé ```markdown --- language: - baq - bci - fr tags: - african-nlp - low-resource-language - sentencepiece - tokenizer - baoule - cote-divoire - translation - tonal-language datasets: - custom license: apache-2.0 library_name: transformers pipeline_tag: text2text-generation widget: - text: "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" example_title: "Exemple de traduction Baoulé" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 Premier tokenizer spécialisé pour la langue Baoulé (Côte d'Ivoire) 🇨🇮 Ce tokenizer a été conçu spécifiquement pour la traduction automatique entre le français et le baoulé, une langue tonale africaine parlée en Côte d'Ivoire. [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/Adjoumani/BaouleTokenizer_V1) ## 📋 Fonctionnalités Clés ✅ Prise en charge complète des caractères tonaux Baoulé (ɛ́, ɩ̄, ɔ̀, etc.) ✅ Optimisé pour les modèles de traduction automatique basés sur Transformer ✅ Vocabulaire compact avec une taille de 206 tokens et une couverture linguistique complète ✅ Intégration native avec 🤗 Transformers et Tokenizers ✅ Compatible avec Google Translate Custom Model, Amazon Translate, et autres outils de NLP --- ## 🚀 Installation et Utilisation Installez les bibliothèques nécessaires : ```bash pip install transformers sentencepiece ``` Chargez et utilisez le tokenizer : ```python from transformers import AutoTokenizer # Charger le tokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Exemple d'utilisation text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', ...] ``` --- ## 📊 Détails Techniques \| Paramètre \| Valeur \| \|--------------------\|----------------------\| \| Architecture \| SentencePiece BPE \| \| Taille du vocabulaire \| 206 \| \| Caractères couverts \| 1.0 (Unicode) \| \| Tokens spéciaux \| `[BOS]`, `[EOS]`, `[UNK]`, `[PAD]` \| \| Langues cibles \| Français ↔ Baoulé \| \| Encodage \| UTF-8 \| --- ## 🎵 Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : \| Caractère \| Code Unicode \| Exemple \| \|-----------\|--------------\|--------------\| \| ɛ́ \| U+025B U+0301 \| Mɔ́kɛ́ \| \| ɩ̄ \| U+0269 U+0304 \| Ɩ̄tɩ̄ \| \| ɔ̀ \| U+0254 U+0300 \| Kɔ̀lɔ̀ \| \| ɛ̂ \| U+025B U+0302 \| Ɛ̂sɛ̂ \| --- ## 💡 Cas d'Usage Recommandés - Traduction automatique entre le français et le baoulé - Synthèse vocale pour systèmes d'assistance vocale - Reconnaissance de la parole Baoulé - Outils éducatifs numériques pour apprendre le baoulé - Préservation du patrimoine linguistique africain --- ## 🛠️ Meilleures Pratiques Gérez les phrases longues et ajoutez des tokens personnalisés si nécessaire : ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` --- ## 📚 Jeu de Données d'Entraînement Les données d'entraînement ont été collectées à partir des sources suivantes : - Traductions de textes bibliques : Les données ont été extraites depuis [Glosbe](https://fr.glosbe.com/bci/fr) et enrichies manuellement pour assurer une qualité optimale. - Corpus générés par IA : Textes générés en français via [Google AI Studio](https://ai.studio.google.com/) et traduits en baoulé via Google Translate. - Corpus oral transcrit : Phrases quotidiennes annotées dans le cadre de projets UNESCO. - Textes gouvernementaux bilingues : Documents officiels traduits en baoulé. Taille du corpus : ~1500 phrases alignées (en cours d'expansion). --- ## 📝 Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2023, author = {Adjoumani Kouakou}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` --- ## 📜 Licence Apache 2.0 - [Voir la licence complète](LICENSE) --- ## 🤝 Contribuer Nous encourageons les contributions pour améliorer ce tokenizer : - Expansion du vocabulaire - Annotation des tons manquants - Ajout de dialectes régionaux Pour toute question ou suggestion, contactez-nous à : [[email protected]](mailto:[email protected]) --- Mots-clés SEO : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine ``` --- ### Améliorations Apportées 1. Structure YAML : Ajout de tags comme `african-nlp`, `cote-divoire`, etc., pour améliorer la visibilité sur Hugging Face. 2. SEO : Inclusion de mots-clés pertinents pour le référencement Google (ex. "NLP Africain", "Langues Tonales"). 3. Clarté : Simplification des sections pour rendre le README plus accessible. 4. Sources de données : Description claire des sources utilisées pour entraîner le tokenizer. 5. Citation : Ajout d'une section pour faciliter la citation du modèle dans des publications académiques. 6. Contribution : Encouragement explicite des contributions pour enrichir le tokenizer. Ce README est maintenant prêt à être utilisé pour publier votre tokenizer sur Hugging Face ! 😊	[ "TRANSLATION" ]	[ "CAS" ]
Adjoumani/baouleTokenizer	Adjoumani	null	[ "region:us" ]	2025-02-08T15:06:48	2025-02-08T15:15:13	0	--- {} --- ```markdown --- language: - baq - bci - fr tags: - African NLP - low-resource language - sentencepiece - tokenizer - Baoulé - Côte d'Ivoire - translation - tonal language datasets: - custom license: apache-2.0 library_name: transformers pipeline_tag: text2text-generation widget: - text: "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" example_title: "Traduction de base" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 Premier tokenizer SentencePiece spécialisé pour la langue Baoulé (Côte d'Ivoire) 🇨🇮 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/votre_username/baoule-tokenizer) ## Fonctionnalités Clés ✅ Prise en charge complète des caractères tonals Baoulé (ɛ́, ɩ̄, ɔ̀, etc.) ✅ Optimisé pour les modèles de traduction automatique (Transformer) ✅ Vocabulaire de 206 tokens avec couverture linguistique complète ✅ Intégration native avec 🤗 Transformers et Tokenizers ✅ Compatible avec Google Traduction Custom Model et Amazon Translate ## Installation et Utilisation ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Utilisation du tokenizer text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'e', '▁l', 'a', 'f', 'i', '▁s', 'u', '▁k', 'ɛ', '▁b', 'é', '▁t', 'r', 'á', 'n', '▁a', 's', 'i', 'ɛ', '’', 'n', '▁s', 'u', '▁w', 'a', '’', 'n', ',', '▁b', 'e', '▁b', 'u', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'é', '▁k', 'ɔ', '́', '▁ɲ', 'a', 'n', 'm', 'i', 'ɛ', 'n'] ``` ## Détails Techniques \| Paramètre \| Valeur \| \|--------------------\|----------------------\| \| Architecture \| SentencePiece BPE \| \| Taille du vocabulaire \| 206 \| \| Caractères couverts \| 1.0 (Unicode) \| \| Tokens spéciaux \| [BOS], [EOS], [UNK], [PAD] \| \| Langues cibles \| Français ↔ Baoulé \| \| Encodage \| UTF-8 \| ## Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : \| Caractère \| Code Unicode \| Exemple \| \|-----------\|--------------\|---------\| \| ɛ́ \| U+025B U+0301\| Mɔ́kɛ́ \| \| ɩ̄ \| U+0269 U+0304\| Ɩ̄tɩ̄ \| \| ɔ̀ \| U+0254 U+0300\| Kɔ̀lɔ̀ \| \| ɛ̂ \| U+025B U+0302\| Ɛ̂sɛ̂ \| ## Cas d'Usage Recommandés - Traduction automatique Français-Baoulé - Synthèse vocale pour systèmes d'assistance vocale - Reconnaissance de la parole Baoulé - Outils éducatifs numériques - Préservation du patrimoine linguistique ## Meilleures Pratiques ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` ## Jeu de Données d'Entraînement Données collectées grâce à : - Traductions de textes bibliques : Les données ont été extraites en grande partie depuis [Glosbe](https://www.glosbe.com/) et structurées manuellement pour assurer une qualité et une précision optimales. Le contenu a été nettoyé pour supprimer les balises HTML indésirables et formaté de manière cohérente. - Corpus oral transcrit (projet UNESCO) - Phrases quotidiennes annotées - Textes gouvernementaux bilingues Taille du corpus : 1500 phrases alignées (en cours d'expansion) ## Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2023, author = {Votre Nom}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` ## Licence Apache 2.0 - [Voir la licence complète](LICENSE) ## Contribuer Nous encourageons les contributions notamment pour : - L'expansion du vocabulaire - L'annotation des tons - L'ajout de dialectes régionaux Contact : [[email protected]](mailto:[email protected]) --- Mots-clés SEO : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine ```	[ "TRANSLATION" ]	[ "CAS" ]

twadada/nmc-300-w50k-b10k

twadada

null

[ "mteb", "model-index", "region:us" ]

2024-09-16T23:06:01

2024-09-16T23:06:15

0

--- tags: - mteb model-index: - name: nomic_classification_307_w50k_b10k results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.70149253731343 - type: ap value: 33.79646861902238 - type: f1 value: 65.39377031734182 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.577125 - type: ap value: 59.69737246109206 - type: f1 value: 64.3577747072318 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.748 - type: f1 value: 33.3254582178127 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.199 - type: map_at_10 value: 34.28 - type: map_at_100 value: 35.480000000000004 - type: map_at_1000 value: 35.504999999999995 - type: map_at_3 value: 29.682 - type: map_at_5 value: 32.385000000000005 - type: mrr_at_1 value: 20.91 - type: mrr_at_10 value: 34.536 - type: mrr_at_100 value: 35.743 - type: mrr_at_1000 value: 35.768 - type: mrr_at_3 value: 29.931 - type: mrr_at_5 value: 32.623000000000005 - type: ndcg_at_1 value: 20.199 - type: ndcg_at_10 value: 42.278 - type: ndcg_at_100 value: 47.924 - type: ndcg_at_1000 value: 48.537 - type: ndcg_at_3 value: 32.815 - type: ndcg_at_5 value: 37.681 - type: precision_at_1 value: 20.199 - type: precision_at_10 value: 6.792 - type: precision_at_100 value: 0.9390000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 13.963999999999999 - type: precision_at_5 value: 10.74 - type: recall_at_1 value: 20.199 - type: recall_at_10 value: 67.923 - type: recall_at_100 value: 93.88300000000001 - type: recall_at_1000 value: 98.578 - type: recall_at_3 value: 41.892 - type: recall_at_5 value: 53.698 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 31.715994496712955 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 22.014928355542406 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 52.73401198259723 - type: mrr value: 66.18574946137272 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.32819163750328 - type: cos_sim_spearman value: 76.32884763830812 - type: euclidean_pearson value: 77.6247892757331 - type: euclidean_spearman value: 76.32884763830812 - type: manhattan_pearson value: 77.4560490620549 - type: manhattan_spearman value: 76.11679461376502 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.16883116883118 - type: f1 value: 71.34298475263475 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 29.528784676707033 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 19.565519101446977 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.581 - type: map_at_10 value: 28.322999999999997 - type: map_at_100 value: 29.392000000000003 - type: map_at_1000 value: 29.547 - type: map_at_3 value: 26.214 - type: map_at_5 value: 27.339000000000002 - type: mrr_at_1 value: 27.182000000000002 - type: mrr_at_10 value: 34.075 - type: mrr_at_100 value: 34.92 - type: mrr_at_1000 value: 34.997 - type: mrr_at_3 value: 32.26 - type: mrr_at_5 value: 33.283 - type: ndcg_at_1 value: 27.182000000000002 - type: ndcg_at_10 value: 32.903999999999996 - type: ndcg_at_100 value: 37.852999999999994 - type: ndcg_at_1000 value: 41.177 - type: ndcg_at_3 value: 29.976999999999997 - type: ndcg_at_5 value: 31.039 - type: precision_at_1 value: 27.182000000000002 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.09 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 14.354 - type: precision_at_5 value: 10.157 - type: recall_at_1 value: 21.581 - type: recall_at_10 value: 40.487 - type: recall_at_100 value: 62.832 - type: recall_at_1000 value: 85.768 - type: recall_at_3 value: 30.842000000000002 - type: recall_at_5 value: 34.497 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.495 - type: map_at_10 value: 21.625 - type: map_at_100 value: 22.506 - type: map_at_1000 value: 22.633 - type: map_at_3 value: 19.819 - type: map_at_5 value: 20.817 - type: mrr_at_1 value: 20.892 - type: mrr_at_10 value: 25.768 - type: mrr_at_100 value: 26.533 - type: mrr_at_1000 value: 26.61 - type: mrr_at_3 value: 23.96 - type: mrr_at_5 value: 24.893 - type: ndcg_at_1 value: 20.892 - type: ndcg_at_10 value: 25.144 - type: ndcg_at_100 value: 29.425 - type: ndcg_at_1000 value: 32.436 - type: ndcg_at_3 value: 22.105 - type: ndcg_at_5 value: 23.416 - type: precision_at_1 value: 20.892 - type: precision_at_10 value: 4.6240000000000006 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.403 - type: precision_at_5 value: 7.4270000000000005 - type: recall_at_1 value: 16.495 - type: recall_at_10 value: 31.627 - type: recall_at_100 value: 50.653999999999996 - type: recall_at_1000 value: 71.38 - type: recall_at_3 value: 22.987 - type: recall_at_5 value: 26.518000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.19 - type: map_at_10 value: 33.159 - type: map_at_100 value: 34.223 - type: map_at_1000 value: 34.322 - type: map_at_3 value: 30.866 - type: map_at_5 value: 32.016 - type: mrr_at_1 value: 29.091 - type: mrr_at_10 value: 36.208 - type: mrr_at_100 value: 37.059999999999995 - type: mrr_at_1000 value: 37.124 - type: mrr_at_3 value: 34.001999999999995 - type: mrr_at_5 value: 35.089999999999996 - type: ndcg_at_1 value: 29.091 - type: ndcg_at_10 value: 37.696000000000005 - type: ndcg_at_100 value: 42.774 - type: ndcg_at_1000 value: 45.064 - type: ndcg_at_3 value: 33.298 - type: ndcg_at_5 value: 35.089 - type: precision_at_1 value: 29.091 - type: precision_at_10 value: 6.132 - type: precision_at_100 value: 0.9530000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.754000000000001 - type: precision_at_5 value: 10.082 - type: recall_at_1 value: 25.19 - type: recall_at_10 value: 48.542 - type: recall_at_100 value: 71.475 - type: recall_at_1000 value: 88.157 - type: recall_at_3 value: 36.512 - type: recall_at_5 value: 40.998000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.979 - type: map_at_10 value: 15.160000000000002 - type: map_at_100 value: 15.927 - type: map_at_1000 value: 16.039 - type: map_at_3 value: 13.905000000000001 - type: map_at_5 value: 14.603 - type: mrr_at_1 value: 12.09 - type: mrr_at_10 value: 16.317999999999998 - type: mrr_at_100 value: 17.094 - type: mrr_at_1000 value: 17.198 - type: mrr_at_3 value: 15.028 - type: mrr_at_5 value: 15.712000000000002 - type: ndcg_at_1 value: 12.09 - type: ndcg_at_10 value: 17.71 - type: ndcg_at_100 value: 21.923000000000002 - type: ndcg_at_1000 value: 25.407999999999998 - type: ndcg_at_3 value: 15.139 - type: ndcg_at_5 value: 16.372 - type: precision_at_1 value: 12.09 - type: precision_at_10 value: 2.768 - type: precision_at_100 value: 0.521 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.478000000000001 - type: precision_at_5 value: 4.542 - type: recall_at_1 value: 10.979 - type: recall_at_10 value: 24.548000000000002 - type: recall_at_100 value: 44.659 - type: recall_at_1000 value: 72.15899999999999 - type: recall_at_3 value: 17.552 - type: recall_at_5 value: 20.584 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.703 - type: map_at_10 value: 9.588000000000001 - type: map_at_100 value: 10.312000000000001 - type: map_at_1000 value: 10.428999999999998 - type: map_at_3 value: 8.473 - type: map_at_5 value: 9.118 - type: mrr_at_1 value: 8.706 - type: mrr_at_10 value: 11.818 - type: mrr_at_100 value: 12.568999999999999 - type: mrr_at_1000 value: 12.664 - type: mrr_at_3 value: 10.551 - type: mrr_at_5 value: 11.235000000000001 - type: ndcg_at_1 value: 8.706 - type: ndcg_at_10 value: 11.823 - type: ndcg_at_100 value: 15.674 - type: ndcg_at_1000 value: 19.256 - type: ndcg_at_3 value: 9.637 - type: ndcg_at_5 value: 10.661 - type: precision_at_1 value: 8.706 - type: precision_at_10 value: 2.251 - type: precision_at_100 value: 0.484 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 4.601999999999999 - type: precision_at_5 value: 3.458 - type: recall_at_1 value: 6.703 - type: recall_at_10 value: 16.579 - type: recall_at_100 value: 34.054 - type: recall_at_1000 value: 60.769 - type: recall_at_3 value: 10.530000000000001 - type: recall_at_5 value: 13.126 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.688000000000002 - type: map_at_10 value: 23.169 - type: map_at_100 value: 24.275 - type: map_at_1000 value: 24.409 - type: map_at_3 value: 21.284 - type: map_at_5 value: 22.171 - type: mrr_at_1 value: 22.233 - type: mrr_at_10 value: 27.857 - type: mrr_at_100 value: 28.76 - type: mrr_at_1000 value: 28.841 - type: mrr_at_3 value: 25.857999999999997 - type: mrr_at_5 value: 26.922 - type: ndcg_at_1 value: 22.233 - type: ndcg_at_10 value: 27.203 - type: ndcg_at_100 value: 32.543 - type: ndcg_at_1000 value: 35.654 - type: ndcg_at_3 value: 23.863 - type: ndcg_at_5 value: 25.117 - type: precision_at_1 value: 22.233 - type: precision_at_10 value: 4.957000000000001 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.036 - type: precision_at_5 value: 7.8149999999999995 - type: recall_at_1 value: 17.688000000000002 - type: recall_at_10 value: 34.969 - type: recall_at_100 value: 58.370999999999995 - type: recall_at_1000 value: 80.02 - type: recall_at_3 value: 25.332 - type: recall_at_5 value: 28.703 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 11.15 - type: map_at_10 value: 16.797 - type: map_at_100 value: 17.822 - type: map_at_1000 value: 17.956 - type: map_at_3 value: 14.985999999999999 - type: map_at_5 value: 16.044 - type: mrr_at_1 value: 14.155000000000001 - type: mrr_at_10 value: 20.01 - type: mrr_at_100 value: 20.966 - type: mrr_at_1000 value: 21.049 - type: mrr_at_3 value: 18.227 - type: mrr_at_5 value: 19.231 - type: ndcg_at_1 value: 14.155000000000001 - type: ndcg_at_10 value: 20.327 - type: ndcg_at_100 value: 25.490000000000002 - type: ndcg_at_1000 value: 28.854000000000003 - type: ndcg_at_3 value: 17.046 - type: ndcg_at_5 value: 18.647 - type: precision_at_1 value: 14.155000000000001 - type: precision_at_10 value: 3.893 - type: precision_at_100 value: 0.771 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.219 - type: precision_at_5 value: 6.164 - type: recall_at_1 value: 11.15 - type: recall_at_10 value: 27.750999999999998 - type: recall_at_100 value: 50.612 - type: recall_at_1000 value: 74.617 - type: recall_at_3 value: 19.101000000000003 - type: recall_at_5 value: 22.999 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 13.562333333333335 - type: map_at_10 value: 18.514583333333334 - type: map_at_100 value: 19.362916666666667 - type: map_at_1000 value: 19.48625 - type: map_at_3 value: 16.955583333333337 - type: map_at_5 value: 17.788 - type: mrr_at_1 value: 16.54575 - type: mrr_at_10 value: 21.549249999999997 - type: mrr_at_100 value: 22.318500000000004 - type: mrr_at_1000 value: 22.405583333333333 - type: mrr_at_3 value: 19.9585 - type: mrr_at_5 value: 20.82183333333333 - type: ndcg_at_1 value: 16.54575 - type: ndcg_at_10 value: 21.80341666666667 - type: ndcg_at_100 value: 26.133833333333328 - type: ndcg_at_1000 value: 29.348666666666666 - type: ndcg_at_3 value: 18.973499999999998 - type: ndcg_at_5 value: 20.200833333333332 - type: precision_at_1 value: 16.54575 - type: precision_at_10 value: 3.895333333333334 - type: precision_at_100 value: 0.7226666666666668 - type: precision_at_1000 value: 0.11775 - type: precision_at_3 value: 8.796666666666667 - type: precision_at_5 value: 6.278083333333333 - type: recall_at_1 value: 13.562333333333335 - type: recall_at_10 value: 28.738833333333336 - type: recall_at_100 value: 48.66516666666668 - type: recall_at_1000 value: 72.21291666666666 - type: recall_at_3 value: 20.722166666666663 - type: recall_at_5 value: 23.920416666666668 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.612 - type: map_at_10 value: 14.233 - type: map_at_100 value: 14.804999999999998 - type: map_at_1000 value: 14.887 - type: map_at_3 value: 13.050999999999998 - type: map_at_5 value: 13.642999999999999 - type: mrr_at_1 value: 12.577 - type: mrr_at_10 value: 16.256999999999998 - type: mrr_at_100 value: 16.830000000000002 - type: mrr_at_1000 value: 16.909 - type: mrr_at_3 value: 15.031 - type: mrr_at_5 value: 15.613 - type: ndcg_at_1 value: 12.577 - type: ndcg_at_10 value: 16.81 - type: ndcg_at_100 value: 20.085 - type: ndcg_at_1000 value: 22.684 - type: ndcg_at_3 value: 14.471 - type: ndcg_at_5 value: 15.384 - type: precision_at_1 value: 12.577 - type: precision_at_10 value: 2.8529999999999998 - type: precision_at_100 value: 0.49699999999999994 - type: precision_at_1000 value: 0.079 - type: precision_at_3 value: 6.544 - type: precision_at_5 value: 4.601 - type: recall_at_1 value: 10.612 - type: recall_at_10 value: 22.983999999999998 - type: recall_at_100 value: 38.745000000000005 - type: recall_at_1000 value: 58.886 - type: recall_at_3 value: 15.982 - type: recall_at_5 value: 18.433 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.04 - type: map_at_10 value: 10.277 - type: map_at_100 value: 10.873 - type: map_at_1000 value: 10.989 - type: map_at_3 value: 9.243 - type: map_at_5 value: 9.843 - type: mrr_at_1 value: 8.774999999999999 - type: mrr_at_10 value: 12.468 - type: mrr_at_100 value: 13.084999999999999 - type: mrr_at_1000 value: 13.184000000000001 - type: mrr_at_3 value: 11.293000000000001 - type: mrr_at_5 value: 12.034 - type: ndcg_at_1 value: 8.774999999999999 - type: ndcg_at_10 value: 12.527 - type: ndcg_at_100 value: 15.939 - type: ndcg_at_1000 value: 19.383 - type: ndcg_at_3 value: 10.565 - type: ndcg_at_5 value: 11.555 - type: precision_at_1 value: 8.774999999999999 - type: precision_at_10 value: 2.3640000000000003 - type: precision_at_100 value: 0.49 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 5.047 - type: precision_at_5 value: 3.8129999999999997 - type: recall_at_1 value: 7.04 - type: recall_at_10 value: 17.193 - type: recall_at_100 value: 33.33 - type: recall_at_1000 value: 59.134 - type: recall_at_3 value: 11.859 - type: recall_at_5 value: 14.243 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.381 - type: map_at_10 value: 15.676000000000002 - type: map_at_100 value: 16.448999999999998 - type: map_at_1000 value: 16.563 - type: map_at_3 value: 14.313 - type: map_at_5 value: 15.010000000000002 - type: mrr_at_1 value: 14.086000000000002 - type: mrr_at_10 value: 18.621 - type: mrr_at_100 value: 19.41 - type: mrr_at_1000 value: 19.506999999999998 - type: mrr_at_3 value: 17.149 - type: mrr_at_5 value: 17.918 - type: ndcg_at_1 value: 14.086000000000002 - type: ndcg_at_10 value: 18.647 - type: ndcg_at_100 value: 22.823 - type: ndcg_at_1000 value: 26.207 - type: ndcg_at_3 value: 15.986 - type: ndcg_at_5 value: 17.108 - type: precision_at_1 value: 14.086000000000002 - type: precision_at_10 value: 3.218 - type: precision_at_100 value: 0.585 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 7.369000000000001 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 11.381 - type: recall_at_10 value: 25.008999999999997 - type: recall_at_100 value: 44.368 - type: recall_at_1000 value: 69.587 - type: recall_at_3 value: 17.612 - type: recall_at_5 value: 20.506 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 12.641 - type: map_at_10 value: 19.067 - type: map_at_100 value: 20.046 - type: map_at_1000 value: 20.221 - type: map_at_3 value: 17.699 - type: map_at_5 value: 18.458 - type: mrr_at_1 value: 16.008 - type: mrr_at_10 value: 22.526 - type: mrr_at_100 value: 23.307 - type: mrr_at_1000 value: 23.391000000000002 - type: mrr_at_3 value: 21.047 - type: mrr_at_5 value: 21.956999999999997 - type: ndcg_at_1 value: 16.008 - type: ndcg_at_10 value: 23.029 - type: ndcg_at_100 value: 27.533 - type: ndcg_at_1000 value: 31.096 - type: ndcg_at_3 value: 20.806 - type: ndcg_at_5 value: 21.859 - type: precision_at_1 value: 16.008 - type: precision_at_10 value: 4.605 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.470000000000001 - type: recall_at_1 value: 12.641 - type: recall_at_10 value: 30.236 - type: recall_at_100 value: 51.543000000000006 - type: recall_at_1000 value: 76.265 - type: recall_at_3 value: 23.677999999999997 - type: recall_at_5 value: 26.456000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.288 - type: map_at_10 value: 15.101 - type: map_at_100 value: 15.725 - type: map_at_1000 value: 15.840000000000002 - type: map_at_3 value: 13.614 - type: map_at_5 value: 14.394000000000002 - type: mrr_at_1 value: 12.753999999999998 - type: mrr_at_10 value: 16.665 - type: mrr_at_100 value: 17.288 - type: mrr_at_1000 value: 17.393 - type: mrr_at_3 value: 15.096000000000002 - type: mrr_at_5 value: 15.974 - type: ndcg_at_1 value: 12.753999999999998 - type: ndcg_at_10 value: 17.821 - type: ndcg_at_100 value: 21.544 - type: ndcg_at_1000 value: 24.965 - type: ndcg_at_3 value: 14.789 - type: ndcg_at_5 value: 16.163 - type: precision_at_1 value: 12.753999999999998 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.518 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.621 - type: recall_at_1 value: 11.288 - type: recall_at_10 value: 24.941 - type: recall_at_100 value: 43.339 - type: recall_at_1000 value: 69.813 - type: recall_at_3 value: 16.679 - type: recall_at_5 value: 19.982 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.319 - type: map_at_10 value: 9.538 - type: map_at_100 value: 10.786 - type: map_at_1000 value: 10.979 - type: map_at_3 value: 7.693999999999999 - type: map_at_5 value: 8.623 - type: mrr_at_1 value: 11.922 - type: mrr_at_10 value: 19.683 - type: mrr_at_100 value: 20.881 - type: mrr_at_1000 value: 20.961 - type: mrr_at_3 value: 17.014000000000003 - type: mrr_at_5 value: 18.47 - type: ndcg_at_1 value: 11.922 - type: ndcg_at_10 value: 14.517 - type: ndcg_at_100 value: 20.541999999999998 - type: ndcg_at_1000 value: 24.648999999999997 - type: ndcg_at_3 value: 10.975 - type: ndcg_at_5 value: 12.276 - type: precision_at_1 value: 11.922 - type: precision_at_10 value: 4.893 - type: precision_at_100 value: 1.129 - type: precision_at_1000 value: 0.187 - type: precision_at_3 value: 8.382000000000001 - type: precision_at_5 value: 6.801 - type: recall_at_1 value: 5.319 - type: recall_at_10 value: 18.593 - type: recall_at_100 value: 39.957 - type: recall_at_1000 value: 63.748000000000005 - type: recall_at_3 value: 10.314 - type: recall_at_5 value: 13.564000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 3.819 - type: map_at_10 value: 7.771999999999999 - type: map_at_100 value: 10.424999999999999 - type: map_at_1000 value: 11.165 - type: map_at_3 value: 5.586 - type: map_at_5 value: 6.524000000000001 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 43.289 - type: mrr_at_100 value: 44.184 - type: mrr_at_1000 value: 44.239 - type: mrr_at_3 value: 40.75 - type: mrr_at_5 value: 42.175000000000004 - type: ndcg_at_1 value: 25.5 - type: ndcg_at_10 value: 19.994 - type: ndcg_at_100 value: 21.802 - type: ndcg_at_1000 value: 28.086 - type: ndcg_at_3 value: 22.279 - type: ndcg_at_5 value: 20.986 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 17.65 - type: precision_at_100 value: 5.317 - type: precision_at_1000 value: 1.146 - type: precision_at_3 value: 25.75 - type: precision_at_5 value: 22.400000000000002 - type: recall_at_1 value: 3.819 - type: recall_at_10 value: 11.533 - type: recall_at_100 value: 26.484999999999996 - type: recall_at_1000 value: 47.63 - type: recall_at_3 value: 6.268999999999999 - type: recall_at_5 value: 8.218 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.69500000000001 - type: f1 value: 39.81935458907266 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 10.544 - type: map_at_10 value: 16.105 - type: map_at_100 value: 16.91 - type: map_at_1000 value: 16.993 - type: map_at_3 value: 14.273 - type: map_at_5 value: 15.259 - type: mrr_at_1 value: 11.206000000000001 - type: mrr_at_10 value: 17.129 - type: mrr_at_100 value: 17.955 - type: mrr_at_1000 value: 18.032999999999998 - type: mrr_at_3 value: 15.214 - type: mrr_at_5 value: 16.249 - type: ndcg_at_1 value: 11.206000000000001 - type: ndcg_at_10 value: 19.546 - type: ndcg_at_100 value: 23.934 - type: ndcg_at_1000 value: 26.356 - type: ndcg_at_3 value: 15.706999999999999 - type: ndcg_at_5 value: 17.488999999999997 - type: precision_at_1 value: 11.206000000000001 - type: precision_at_10 value: 3.195 - type: precision_at_100 value: 0.557 - type: precision_at_1000 value: 0.078 - type: precision_at_3 value: 6.7860000000000005 - type: precision_at_5 value: 4.997999999999999 - type: recall_at_1 value: 10.544 - type: recall_at_10 value: 29.421999999999997 - type: recall_at_100 value: 50.54 - type: recall_at_1000 value: 69.53200000000001 - type: recall_at_3 value: 18.901 - type: recall_at_5 value: 23.183999999999997 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 5.688 - type: map_at_10 value: 9.454 - type: map_at_100 value: 10.459 - type: map_at_1000 value: 10.645 - type: map_at_3 value: 7.914000000000001 - type: map_at_5 value: 8.622 - type: mrr_at_1 value: 11.42 - type: mrr_at_10 value: 16.608 - type: mrr_at_100 value: 17.566000000000003 - type: mrr_at_1000 value: 17.675 - type: mrr_at_3 value: 14.712 - type: mrr_at_5 value: 15.638 - type: ndcg_at_1 value: 11.42 - type: ndcg_at_10 value: 13.293 - type: ndcg_at_100 value: 18.289 - type: ndcg_at_1000 value: 22.781000000000002 - type: ndcg_at_3 value: 10.835 - type: ndcg_at_5 value: 11.576 - type: precision_at_1 value: 11.42 - type: precision_at_10 value: 3.997 - type: precision_at_100 value: 0.897 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 7.356 - type: precision_at_5 value: 5.772 - type: recall_at_1 value: 5.688 - type: recall_at_10 value: 17.544 - type: recall_at_100 value: 37.358999999999995 - type: recall_at_1000 value: 65.735 - type: recall_at_3 value: 9.987 - type: recall_at_5 value: 12.337 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 13.248 - type: map_at_10 value: 18.584 - type: map_at_100 value: 19.348000000000003 - type: map_at_1000 value: 19.457 - type: map_at_3 value: 16.962 - type: map_at_5 value: 17.862000000000002 - type: mrr_at_1 value: 26.496 - type: mrr_at_10 value: 32.580999999999996 - type: mrr_at_100 value: 33.314 - type: mrr_at_1000 value: 33.387 - type: mrr_at_3 value: 30.808000000000003 - type: mrr_at_5 value: 31.805 - type: ndcg_at_1 value: 26.496 - type: ndcg_at_10 value: 24.198 - type: ndcg_at_100 value: 28.017999999999997 - type: ndcg_at_1000 value: 30.839 - type: ndcg_at_3 value: 21.002000000000002 - type: ndcg_at_5 value: 22.547 - type: precision_at_1 value: 26.496 - type: precision_at_10 value: 5.415 - type: precision_at_100 value: 0.8500000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 13.234000000000002 - type: precision_at_5 value: 9.164 - type: recall_at_1 value: 13.248 - type: recall_at_10 value: 27.076 - type: recall_at_100 value: 42.512 - type: recall_at_1000 value: 61.41799999999999 - type: recall_at_3 value: 19.851 - type: recall_at_5 value: 22.91 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.98560000000001 - type: ap value: 59.217561950701445 - type: f1 value: 63.818409911217046 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 5.179 - type: map_at_10 value: 9.055 - type: map_at_100 value: 9.814 - type: map_at_1000 value: 9.911 - type: map_at_3 value: 7.631 - type: map_at_5 value: 8.415000000000001 - type: mrr_at_1 value: 5.3580000000000005 - type: mrr_at_10 value: 9.302000000000001 - type: mrr_at_100 value: 10.075000000000001 - type: mrr_at_1000 value: 10.169 - type: mrr_at_3 value: 7.856000000000001 - type: mrr_at_5 value: 8.654 - type: ndcg_at_1 value: 5.33 - type: ndcg_at_10 value: 11.491 - type: ndcg_at_100 value: 15.735 - type: ndcg_at_1000 value: 18.721 - type: ndcg_at_3 value: 8.522 - type: ndcg_at_5 value: 9.943 - type: precision_at_1 value: 5.33 - type: precision_at_10 value: 1.983 - type: precision_at_100 value: 0.42 - type: precision_at_1000 value: 0.068 - type: precision_at_3 value: 3.763 - type: precision_at_5 value: 2.9770000000000003 - type: recall_at_1 value: 5.179 - type: recall_at_10 value: 19.069 - type: recall_at_100 value: 39.946 - type: recall_at_1000 value: 64.031 - type: recall_at_3 value: 10.91 - type: recall_at_5 value: 14.334 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.25444596443229 - type: f1 value: 88.34114464691379 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 59.3251253989968 - type: f1 value: 39.879870396124964 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.90316072629455 - type: f1 value: 59.6419867903448 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.5474108944183 - type: f1 value: 67.13260105586494 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.08360278577924 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.539985814012603 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 28.52217790319968 - type: mrr value: 29.375037759331086 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.202 - type: map_at_10 value: 6.216 - type: map_at_100 value: 7.902000000000001 - type: map_at_1000 value: 9.114 - type: map_at_3 value: 4.752 - type: map_at_5 value: 5.414 - type: mrr_at_1 value: 31.269000000000002 - type: mrr_at_10 value: 39.649 - type: mrr_at_100 value: 40.261 - type: mrr_at_1000 value: 40.338 - type: mrr_at_3 value: 37.049 - type: mrr_at_5 value: 38.643 - type: ndcg_at_1 value: 29.412 - type: ndcg_at_10 value: 21.224 - type: ndcg_at_100 value: 19.897000000000002 - type: ndcg_at_1000 value: 29.53 - type: ndcg_at_3 value: 24.635 - type: ndcg_at_5 value: 23.114 - type: precision_at_1 value: 31.269000000000002 - type: precision_at_10 value: 15.697 - type: precision_at_100 value: 5.842 - type: precision_at_1000 value: 1.8880000000000001 - type: precision_at_3 value: 23.013 - type: precision_at_5 value: 19.628 - type: recall_at_1 value: 3.202 - type: recall_at_10 value: 9.889000000000001 - type: recall_at_100 value: 21.366 - type: recall_at_1000 value: 56.267999999999994 - type: recall_at_3 value: 5.7459999999999996 - type: recall_at_5 value: 7.473000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 7.892 - type: map_at_10 value: 13.358999999999998 - type: map_at_100 value: 14.396 - type: map_at_1000 value: 14.499 - type: map_at_3 value: 11.335 - type: map_at_5 value: 12.375 - type: mrr_at_1 value: 8.98 - type: mrr_at_10 value: 14.762 - type: mrr_at_100 value: 15.787 - type: mrr_at_1000 value: 15.873000000000001 - type: mrr_at_3 value: 12.65 - type: mrr_at_5 value: 13.761000000000001 - type: ndcg_at_1 value: 8.98 - type: ndcg_at_10 value: 17.013 - type: ndcg_at_100 value: 22.582 - type: ndcg_at_1000 value: 25.546000000000003 - type: ndcg_at_3 value: 12.765 - type: ndcg_at_5 value: 14.662 - type: precision_at_1 value: 8.98 - type: precision_at_10 value: 3.152 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 5.997 - type: precision_at_5 value: 4.652 - type: recall_at_1 value: 7.892 - type: recall_at_10 value: 27.081 - type: recall_at_100 value: 53.36300000000001 - type: recall_at_1000 value: 76.419 - type: recall_at_3 value: 15.623999999999999 - type: recall_at_5 value: 20.104 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 61.224999999999994 - type: map_at_10 value: 73.768 - type: map_at_100 value: 74.54899999999999 - type: map_at_1000 value: 74.588 - type: map_at_3 value: 70.845 - type: map_at_5 value: 72.61 - type: mrr_at_1 value: 70.63000000000001 - type: mrr_at_10 value: 78.204 - type: mrr_at_100 value: 78.469 - type: mrr_at_1000 value: 78.477 - type: mrr_at_3 value: 76.67500000000001 - type: mrr_at_5 value: 77.644 - type: ndcg_at_1 value: 70.61 - type: ndcg_at_10 value: 78.586 - type: ndcg_at_100 value: 80.852 - type: ndcg_at_1000 value: 81.32000000000001 - type: ndcg_at_3 value: 74.902 - type: ndcg_at_5 value: 76.787 - type: precision_at_1 value: 70.61 - type: precision_at_10 value: 11.904 - type: precision_at_100 value: 1.438 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 32.503 - type: precision_at_5 value: 21.526 - type: recall_at_1 value: 61.224999999999994 - type: recall_at_10 value: 87.908 - type: recall_at_100 value: 96.63000000000001 - type: recall_at_1000 value: 99.367 - type: recall_at_3 value: 77.358 - type: recall_at_5 value: 82.584 - type: map_at_1 value: 2.585 - type: map_at_10 value: 5.988 - type: map_at_100 value: 7.21 - type: map_at_1000 value: 7.449999999999999 - type: map_at_3 value: 4.372 - type: map_at_5 value: 5.194 - type: mrr_at_1 value: 12.8 - type: mrr_at_10 value: 19.963 - type: mrr_at_100 value: 21.195 - type: mrr_at_1000 value: 21.29 - type: mrr_at_3 value: 17.533 - type: mrr_at_5 value: 18.853 - type: ndcg_at_1 value: 12.8 - type: ndcg_at_10 value: 10.874 - type: ndcg_at_100 value: 16.695 - type: ndcg_at_1000 value: 21.762999999999998 - type: ndcg_at_3 value: 10.209 - type: ndcg_at_5 value: 8.999 - type: precision_at_1 value: 12.8 - type: precision_at_10 value: 5.65 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.264 - type: precision_at_3 value: 9.433 - type: precision_at_5 value: 7.88 - type: recall_at_1 value: 2.585 - type: recall_at_10 value: 11.455 - type: recall_at_100 value: 28.665000000000003 - type: recall_at_1000 value: 53.547999999999995 - type: recall_at_3 value: 5.748 - type: recall_at_5 value: 7.983 - type: map_at_1 value: 0.133 - type: map_at_10 value: 0.707 - type: map_at_100 value: 3.759 - type: map_at_1000 value: 9.02 - type: map_at_3 value: 0.27399999999999997 - type: map_at_5 value: 0.4 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 61.147 - type: mrr_at_100 value: 62.076 - type: mrr_at_1000 value: 62.076 - type: mrr_at_3 value: 57.99999999999999 - type: mrr_at_5 value: 59.3 - type: ndcg_at_1 value: 44.0 - type: ndcg_at_10 value: 36.039 - type: ndcg_at_100 value: 28.122999999999998 - type: ndcg_at_1000 value: 25.650000000000002 - type: ndcg_at_3 value: 38.173 - type: ndcg_at_5 value: 37.35 - type: precision_at_1 value: 52.0 - type: precision_at_10 value: 39.4 - type: precision_at_100 value: 29.82 - type: precision_at_1000 value: 12.690000000000001 - type: precision_at_3 value: 42.0 - type: precision_at_5 value: 40.400000000000006 - type: recall_at_1 value: 0.133 - type: recall_at_10 value: 0.897 - type: recall_at_100 value: 6.336 - type: recall_at_1000 value: 24.990000000000002 - type: recall_at_3 value: 0.301 - type: recall_at_5 value: 0.462 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 33.05951893381823 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 42.691497046210955 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 76.47089854443493 - type: cos_sim_spearman value: 67.65881641628117 - type: euclidean_pearson value: 72.75220596907191 - type: euclidean_spearman value: 67.65881507675402 - type: manhattan_pearson value: 71.2932268352905 - type: manhattan_spearman value: 66.28937203768146 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 73.59904123111602 - type: cos_sim_spearman value: 66.64191118455778 - type: euclidean_pearson value: 70.031991407929 - type: euclidean_spearman value: 66.64312867708462 - type: manhattan_pearson value: 70.87113974670322 - type: manhattan_spearman value: 67.87998624470126 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.1868454480634 - type: cos_sim_spearman value: 78.5663631376088 - type: euclidean_pearson value: 78.1441330499307 - type: euclidean_spearman value: 78.5663753212518 - type: manhattan_pearson value: 78.7258747377543 - type: manhattan_spearman value: 79.24251325682667 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.39709143417873 - type: cos_sim_spearman value: 74.33024682805708 - type: euclidean_pearson value: 76.65457389990631 - type: euclidean_spearman value: 74.33023713728515 - type: manhattan_pearson value: 76.73342787471654 - type: manhattan_spearman value: 74.74461118652161 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.9395037638594 - type: cos_sim_spearman value: 81.01819776486752 - type: euclidean_pearson value: 81.03043241994847 - type: euclidean_spearman value: 81.01819627953365 - type: manhattan_pearson value: 81.68968136619384 - type: manhattan_spearman value: 81.82363999592259 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.243504336461 - type: cos_sim_spearman value: 76.61917655422197 - type: euclidean_pearson value: 76.26910712210864 - type: euclidean_spearman value: 76.62000560376505 - type: manhattan_pearson value: 76.91613259757325 - type: manhattan_spearman value: 77.4215820608173 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.99178286054092 - type: cos_sim_spearman value: 84.2361483019332 - type: euclidean_pearson value: 84.30885968598922 - type: euclidean_spearman value: 84.23702233300253 - type: manhattan_pearson value: 84.64734537899606 - type: manhattan_spearman value: 84.71355882886535 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.18741235485141 - type: cos_sim_spearman value: 60.873579764468225 - type: euclidean_pearson value: 63.18427359110471 - type: euclidean_spearman value: 60.873579764468225 - type: manhattan_pearson value: 63.443408253414354 - type: manhattan_spearman value: 61.5997912341628 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 77.15144919426055 - type: cos_sim_spearman value: 75.76050778643061 - type: euclidean_pearson value: 77.30073366013343 - type: euclidean_spearman value: 75.76052625455534 - type: manhattan_pearson value: 77.41746598074477 - type: manhattan_spearman value: 75.98770131791319 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 70.66070662385174 - type: mrr value: 90.05894523051387 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 31.139 - type: map_at_10 value: 38.127 - type: map_at_100 value: 39.216 - type: map_at_1000 value: 39.290000000000006 - type: map_at_3 value: 35.667 - type: map_at_5 value: 37.317 - type: mrr_at_1 value: 33.333 - type: mrr_at_10 value: 39.972 - type: mrr_at_100 value: 40.892 - type: mrr_at_1000 value: 40.955000000000005 - type: mrr_at_3 value: 37.889 - type: mrr_at_5 value: 39.222 - type: ndcg_at_1 value: 33.333 - type: ndcg_at_10 value: 42.177 - type: ndcg_at_100 value: 47.772999999999996 - type: ndcg_at_1000 value: 49.738 - type: ndcg_at_3 value: 37.568 - type: ndcg_at_5 value: 40.294999999999995 - type: precision_at_1 value: 33.333 - type: precision_at_10 value: 5.867 - type: precision_at_100 value: 0.903 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 14.777999999999999 - type: precision_at_5 value: 10.4 - type: recall_at_1 value: 31.139 - type: recall_at_10 value: 53.056000000000004 - type: recall_at_100 value: 79.60000000000001 - type: recall_at_1000 value: 95.133 - type: recall_at_3 value: 40.75 - type: recall_at_5 value: 47.417 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.63663366336634 - type: cos_sim_ap value: 87.41569381811651 - type: cos_sim_f1 value: 80.8154730789336 - type: cos_sim_precision value: 84.66593647316539 - type: cos_sim_recall value: 77.3 - type: dot_accuracy value: 99.63663366336634 - type: dot_ap value: 87.41569381811651 - type: dot_f1 value: 80.8154730789336 - type: dot_precision value: 84.66593647316539 - type: dot_recall value: 77.3 - type: euclidean_accuracy value: 99.63663366336634 - type: euclidean_ap value: 87.41569381811651 - type: euclidean_f1 value: 80.8154730789336 - type: euclidean_precision value: 84.66593647316539 - type: euclidean_recall value: 77.3 - type: manhattan_accuracy value: 99.6930693069307 - type: manhattan_ap value: 90.67306262109962 - type: manhattan_f1 value: 84.03707518022657 - type: manhattan_precision value: 86.62420382165605 - type: manhattan_recall value: 81.6 - type: max_accuracy value: 99.6930693069307 - type: max_ap value: 90.67306262109962 - type: max_f1 value: 84.03707518022657 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 36.46819467809413 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.142679626551587 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.08118718504021 - type: mrr value: 43.547356442577026 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.26989671913281 - type: cos_sim_spearman value: 30.01993799277349 - type: dot_pearson value: 30.26989672303903 - type: dot_spearman value: 30.03106981258351 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.664 - type: map_at_10 value: 8.95 - type: map_at_100 value: 14.699000000000002 - type: map_at_1000 value: 16.275000000000002 - type: map_at_3 value: 4.963 - type: map_at_5 value: 6.707000000000001 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 48.016 - type: mrr_at_100 value: 48.826 - type: mrr_at_1000 value: 48.826 - type: mrr_at_3 value: 44.558 - type: mrr_at_5 value: 46.394999999999996 - type: ndcg_at_1 value: 33.672999999999995 - type: ndcg_at_10 value: 21.981 - type: ndcg_at_100 value: 35.227000000000004 - type: ndcg_at_1000 value: 46.428999999999995 - type: ndcg_at_3 value: 27.496 - type: ndcg_at_5 value: 24.886 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 19.184 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.486 - type: precision_at_3 value: 27.891 - type: precision_at_5 value: 24.898 - type: recall_at_1 value: 2.664 - type: recall_at_10 value: 13.309999999999999 - type: recall_at_100 value: 46.727000000000004 - type: recall_at_1000 value: 81.158 - type: recall_at_3 value: 5.872 - type: recall_at_5 value: 8.694 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.86019999999999 - type: ap value: 13.439585186117995 - type: f1 value: 53.53111224664294 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.539898132427844 - type: f1 value: 53.736121370681076 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.790329189415395 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.59063002920665 - type: cos_sim_ap value: 65.1646758019036 - type: cos_sim_f1 value: 61.95799041886746 - type: cos_sim_precision value: 57.96368650884855 - type: cos_sim_recall value: 66.54353562005278 - type: dot_accuracy value: 83.59063002920665 - type: dot_ap value: 65.1646758019036 - type: dot_f1 value: 61.95799041886746 - type: dot_precision value: 57.96368650884855 - type: dot_recall value: 66.54353562005278 - type: euclidean_accuracy value: 83.59063002920665 - type: euclidean_ap value: 65.1646758019036 - type: euclidean_f1 value: 61.95799041886746 - type: euclidean_precision value: 57.96368650884855 - type: euclidean_recall value: 66.54353562005278 - type: manhattan_accuracy value: 83.29856350956668 - type: manhattan_ap value: 63.803561536283404 - type: manhattan_f1 value: 60.45279383429673 - type: manhattan_precision value: 55.60478511298184 - type: manhattan_recall value: 66.2269129287599 - type: max_accuracy value: 83.59063002920665 - type: max_ap value: 65.1646758019036 - type: max_f1 value: 61.95799041886746 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.46264602010324 - type: cos_sim_ap value: 82.64331601180713 - type: cos_sim_f1 value: 74.66489420627008 - type: cos_sim_precision value: 71.73774214148868 - type: cos_sim_recall value: 77.8410840776101 - type: dot_accuracy value: 87.46264602010324 - type: dot_ap value: 82.64331811121104 - type: dot_f1 value: 74.66489420627008 - type: dot_precision value: 71.73774214148868 - type: dot_recall value: 77.8410840776101 - type: euclidean_accuracy value: 87.46264602010324 - type: euclidean_ap value: 82.64331792274162 - type: euclidean_f1 value: 74.66489420627008 - type: euclidean_precision value: 71.73774214148868 - type: euclidean_recall value: 77.8410840776101 - type: manhattan_accuracy value: 87.35203943027904 - type: manhattan_ap value: 82.69548093072707 - type: manhattan_f1 value: 74.90158915293776 - type: manhattan_precision value: 71.1171096345515 - type: manhattan_recall value: 79.11148752694795 - type: max_accuracy value: 87.46264602010324 - type: max_ap value: 82.69548093072707 - type: max_f1 value: 74.90158915293776 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/nmc-300-w75k-b10k

twadada

null

[ "mteb", "model-index", "region:us" ]

2024-09-16T23:06:27

2024-09-16T23:06:35

0

--- tags: - mteb model-index: - name: nomic_classification_307_w75k_b10k results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.71641791044776 - type: ap value: 33.83144952728368 - type: f1 value: 65.42257056593405 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.614075 - type: ap value: 59.729707124101274 - type: f1 value: 64.38848477168871 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.842 - type: f1 value: 33.39663457981403 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 19.986 - type: map_at_10 value: 34.064 - type: map_at_100 value: 35.275 - type: map_at_1000 value: 35.303000000000004 - type: map_at_3 value: 29.610999999999997 - type: map_at_5 value: 32.104 - type: mrr_at_1 value: 20.413 - type: mrr_at_10 value: 34.205000000000005 - type: mrr_at_100 value: 35.429 - type: mrr_at_1000 value: 35.457 - type: mrr_at_3 value: 29.813000000000002 - type: mrr_at_5 value: 32.245000000000005 - type: ndcg_at_1 value: 19.986 - type: ndcg_at_10 value: 42.03 - type: ndcg_at_100 value: 47.709 - type: ndcg_at_1000 value: 48.372 - type: ndcg_at_3 value: 32.815 - type: ndcg_at_5 value: 37.287 - type: precision_at_1 value: 19.986 - type: precision_at_10 value: 6.757000000000001 - type: precision_at_100 value: 0.936 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.035 - type: precision_at_5 value: 10.583 - type: recall_at_1 value: 19.986 - type: recall_at_10 value: 67.568 - type: recall_at_100 value: 93.599 - type: recall_at_1000 value: 98.649 - type: recall_at_3 value: 42.105 - type: recall_at_5 value: 52.916 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 32.3683689352825 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 22.612199003814784 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 52.783575323019186 - type: mrr value: 66.44813788858109 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 78.91542000311537 - type: cos_sim_spearman value: 76.96982609885886 - type: euclidean_pearson value: 78.2167413496553 - type: euclidean_spearman value: 76.96982609885886 - type: manhattan_pearson value: 78.37464609362752 - type: manhattan_spearman value: 77.44875267417638 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.1948051948052 - type: f1 value: 71.36111601799189 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.50999941209323 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 21.012511459928128 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.223 - type: map_at_10 value: 28.166000000000004 - type: map_at_100 value: 29.25 - type: map_at_1000 value: 29.408 - type: map_at_3 value: 26.027 - type: map_at_5 value: 27.211999999999996 - type: mrr_at_1 value: 26.753 - type: mrr_at_10 value: 33.852 - type: mrr_at_100 value: 34.726 - type: mrr_at_1000 value: 34.806 - type: mrr_at_3 value: 32.046 - type: mrr_at_5 value: 33.112 - type: ndcg_at_1 value: 26.753 - type: ndcg_at_10 value: 32.75 - type: ndcg_at_100 value: 37.772 - type: ndcg_at_1000 value: 41.116 - type: ndcg_at_3 value: 29.865000000000002 - type: ndcg_at_5 value: 31.023 - type: precision_at_1 value: 26.753 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.089 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 14.449000000000002 - type: precision_at_5 value: 10.272 - type: recall_at_1 value: 21.223 - type: recall_at_10 value: 40.269 - type: recall_at_100 value: 62.9 - type: recall_at_1000 value: 85.92699999999999 - type: recall_at_3 value: 30.89 - type: recall_at_5 value: 34.756 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.796 - type: map_at_10 value: 21.91 - type: map_at_100 value: 22.802 - type: map_at_1000 value: 22.925 - type: map_at_3 value: 20.143 - type: map_at_5 value: 21.066 - type: mrr_at_1 value: 21.274 - type: mrr_at_10 value: 26.076 - type: mrr_at_100 value: 26.857999999999997 - type: mrr_at_1000 value: 26.932000000000002 - type: mrr_at_3 value: 24.257 - type: mrr_at_5 value: 25.145 - type: ndcg_at_1 value: 21.274 - type: ndcg_at_10 value: 25.428 - type: ndcg_at_100 value: 29.804000000000002 - type: ndcg_at_1000 value: 32.766 - type: ndcg_at_3 value: 22.434 - type: ndcg_at_5 value: 23.639 - type: precision_at_1 value: 21.274 - type: precision_at_10 value: 4.662 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 10.552 - type: precision_at_5 value: 7.465 - type: recall_at_1 value: 16.796 - type: recall_at_10 value: 31.897 - type: recall_at_100 value: 51.413 - type: recall_at_1000 value: 71.885 - type: recall_at_3 value: 23.318 - type: recall_at_5 value: 26.636 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 24.834999999999997 - type: map_at_10 value: 32.919 - type: map_at_100 value: 34.035 - type: map_at_1000 value: 34.132 - type: map_at_3 value: 30.495 - type: map_at_5 value: 31.805 - type: mrr_at_1 value: 28.715000000000003 - type: mrr_at_10 value: 36.014 - type: mrr_at_100 value: 36.902 - type: mrr_at_1000 value: 36.964999999999996 - type: mrr_at_3 value: 33.71 - type: mrr_at_5 value: 34.935 - type: ndcg_at_1 value: 28.715000000000003 - type: ndcg_at_10 value: 37.524 - type: ndcg_at_100 value: 42.74 - type: ndcg_at_1000 value: 45.011 - type: ndcg_at_3 value: 32.927 - type: ndcg_at_5 value: 35.010000000000005 - type: precision_at_1 value: 28.715000000000003 - type: precision_at_10 value: 6.132 - type: precision_at_100 value: 0.9570000000000001 - type: precision_at_1000 value: 0.123 - type: precision_at_3 value: 14.587 - type: precision_at_5 value: 10.169 - type: recall_at_1 value: 24.834999999999997 - type: recall_at_10 value: 48.5 - type: recall_at_100 value: 71.89 - type: recall_at_1000 value: 88.402 - type: recall_at_3 value: 36.052 - type: recall_at_5 value: 41.22 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.527000000000001 - type: map_at_10 value: 14.774000000000001 - type: map_at_100 value: 15.579 - type: map_at_1000 value: 15.689 - type: map_at_3 value: 13.481000000000002 - type: map_at_5 value: 14.212 - type: mrr_at_1 value: 11.638 - type: mrr_at_10 value: 15.895999999999999 - type: mrr_at_100 value: 16.717000000000002 - type: mrr_at_1000 value: 16.819 - type: mrr_at_3 value: 14.576 - type: mrr_at_5 value: 15.305 - type: ndcg_at_1 value: 11.638 - type: ndcg_at_10 value: 17.331 - type: ndcg_at_100 value: 21.675 - type: ndcg_at_1000 value: 25.127 - type: ndcg_at_3 value: 14.688 - type: ndcg_at_5 value: 15.963 - type: precision_at_1 value: 11.638 - type: precision_at_10 value: 2.7119999999999997 - type: precision_at_100 value: 0.52 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.29 - type: precision_at_5 value: 4.452 - type: recall_at_1 value: 10.527000000000001 - type: recall_at_10 value: 24.269 - type: recall_at_100 value: 44.942 - type: recall_at_1000 value: 72.14 - type: recall_at_3 value: 17.043 - type: recall_at_5 value: 20.113 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.859 - type: map_at_10 value: 9.765 - type: map_at_100 value: 10.458 - type: map_at_1000 value: 10.58 - type: map_at_3 value: 8.417 - type: map_at_5 value: 9.248000000000001 - type: mrr_at_1 value: 8.831 - type: mrr_at_10 value: 11.966000000000001 - type: mrr_at_100 value: 12.681999999999999 - type: mrr_at_1000 value: 12.783 - type: mrr_at_3 value: 10.406 - type: mrr_at_5 value: 11.302 - type: ndcg_at_1 value: 8.831 - type: ndcg_at_10 value: 12.094000000000001 - type: ndcg_at_100 value: 15.875 - type: ndcg_at_1000 value: 19.563 - type: ndcg_at_3 value: 9.442 - type: ndcg_at_5 value: 10.8 - type: precision_at_1 value: 8.831 - type: precision_at_10 value: 2.313 - type: precision_at_100 value: 0.49 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 4.353 - type: precision_at_5 value: 3.483 - type: recall_at_1 value: 6.859 - type: recall_at_10 value: 17.201 - type: recall_at_100 value: 34.441 - type: recall_at_1000 value: 62.007 - type: recall_at_3 value: 10.012 - type: recall_at_5 value: 13.370000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.488 - type: map_at_10 value: 23.080000000000002 - type: map_at_100 value: 24.230999999999998 - type: map_at_1000 value: 24.371000000000002 - type: map_at_3 value: 21.288 - type: map_at_5 value: 22.192999999999998 - type: mrr_at_1 value: 21.848 - type: mrr_at_10 value: 27.642 - type: mrr_at_100 value: 28.609 - type: mrr_at_1000 value: 28.692 - type: mrr_at_3 value: 25.746000000000002 - type: mrr_at_5 value: 26.848 - type: ndcg_at_1 value: 21.848 - type: ndcg_at_10 value: 27.032 - type: ndcg_at_100 value: 32.489000000000004 - type: ndcg_at_1000 value: 35.692 - type: ndcg_at_3 value: 23.874000000000002 - type: ndcg_at_5 value: 25.192999999999998 - type: precision_at_1 value: 21.848 - type: precision_at_10 value: 4.936999999999999 - type: precision_at_100 value: 0.915 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.1 - type: precision_at_5 value: 7.854 - type: recall_at_1 value: 17.488 - type: recall_at_10 value: 34.572 - type: recall_at_100 value: 58.347 - type: recall_at_1000 value: 80.541 - type: recall_at_3 value: 25.417 - type: recall_at_5 value: 28.998 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 11.131 - type: map_at_10 value: 16.721 - type: map_at_100 value: 17.743000000000002 - type: map_at_1000 value: 17.878 - type: map_at_3 value: 14.92 - type: map_at_5 value: 15.963 - type: mrr_at_1 value: 14.155000000000001 - type: mrr_at_10 value: 19.974 - type: mrr_at_100 value: 20.905 - type: mrr_at_1000 value: 20.988 - type: mrr_at_3 value: 18.132 - type: mrr_at_5 value: 19.176000000000002 - type: ndcg_at_1 value: 14.155000000000001 - type: ndcg_at_10 value: 20.302999999999997 - type: ndcg_at_100 value: 25.421 - type: ndcg_at_1000 value: 28.832 - type: ndcg_at_3 value: 16.973 - type: ndcg_at_5 value: 18.590999999999998 - type: precision_at_1 value: 14.155000000000001 - type: precision_at_10 value: 3.904 - type: precision_at_100 value: 0.768 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.219 - type: precision_at_5 value: 6.187 - type: recall_at_1 value: 11.131 - type: recall_at_10 value: 27.894000000000002 - type: recall_at_100 value: 50.52799999999999 - type: recall_at_1000 value: 74.969 - type: recall_at_3 value: 19.043 - type: recall_at_5 value: 22.994 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 13.556083333333332 - type: map_at_10 value: 18.54741666666667 - type: map_at_100 value: 19.411 - type: map_at_1000 value: 19.53608333333333 - type: map_at_3 value: 16.97208333333333 - type: map_at_5 value: 17.831583333333327 - type: mrr_at_1 value: 16.519666666666666 - type: mrr_at_10 value: 21.57283333333333 - type: mrr_at_100 value: 22.353166666666667 - type: mrr_at_1000 value: 22.441999999999997 - type: mrr_at_3 value: 19.954500000000003 - type: mrr_at_5 value: 20.84958333333333 - type: ndcg_at_1 value: 16.519666666666666 - type: ndcg_at_10 value: 21.846416666666666 - type: ndcg_at_100 value: 26.199750000000005 - type: ndcg_at_1000 value: 29.44333333333333 - type: ndcg_at_3 value: 18.98058333333334 - type: ndcg_at_5 value: 20.266666666666666 - type: precision_at_1 value: 16.519666666666666 - type: precision_at_10 value: 3.907999999999999 - type: precision_at_100 value: 0.7232500000000001 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.805166666666668 - type: precision_at_5 value: 6.317166666666665 - type: recall_at_1 value: 13.556083333333332 - type: recall_at_10 value: 28.798916666666663 - type: recall_at_100 value: 48.75083333333334 - type: recall_at_1000 value: 72.47908333333334 - type: recall_at_3 value: 20.702583333333333 - type: recall_at_5 value: 24.039250000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.536 - type: map_at_10 value: 14.155000000000001 - type: map_at_100 value: 14.77 - type: map_at_1000 value: 14.860999999999999 - type: map_at_3 value: 13.055 - type: map_at_5 value: 13.608999999999998 - type: mrr_at_1 value: 12.577 - type: mrr_at_10 value: 16.247 - type: mrr_at_100 value: 16.857 - type: mrr_at_1000 value: 16.942 - type: mrr_at_3 value: 15.056 - type: mrr_at_5 value: 15.647 - type: ndcg_at_1 value: 12.577 - type: ndcg_at_10 value: 16.636 - type: ndcg_at_100 value: 19.947 - type: ndcg_at_1000 value: 22.643 - type: ndcg_at_3 value: 14.472 - type: ndcg_at_5 value: 15.339 - type: precision_at_1 value: 12.577 - type: precision_at_10 value: 2.807 - type: precision_at_100 value: 0.48900000000000005 - type: precision_at_1000 value: 0.079 - type: precision_at_3 value: 6.544 - type: precision_at_5 value: 4.601 - type: recall_at_1 value: 10.536 - type: recall_at_10 value: 22.345000000000002 - type: recall_at_100 value: 37.877 - type: recall_at_1000 value: 58.648 - type: recall_at_3 value: 16.048000000000002 - type: recall_at_5 value: 18.267 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.292 - type: map_at_10 value: 10.58 - type: map_at_100 value: 11.173 - type: map_at_1000 value: 11.29 - type: map_at_3 value: 9.633 - type: map_at_5 value: 10.163 - type: mrr_at_1 value: 9.016 - type: mrr_at_10 value: 12.757 - type: mrr_at_100 value: 13.370000000000001 - type: mrr_at_1000 value: 13.472000000000001 - type: mrr_at_3 value: 11.688 - type: mrr_at_5 value: 12.328999999999999 - type: ndcg_at_1 value: 9.016 - type: ndcg_at_10 value: 12.806000000000001 - type: ndcg_at_100 value: 16.174 - type: ndcg_at_1000 value: 19.644000000000002 - type: ndcg_at_3 value: 11.021 - type: ndcg_at_5 value: 11.871 - type: precision_at_1 value: 9.016 - type: precision_at_10 value: 2.385 - type: precision_at_100 value: 0.49100000000000005 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 5.322 - type: precision_at_5 value: 3.8890000000000002 - type: recall_at_1 value: 7.292 - type: recall_at_10 value: 17.39 - type: recall_at_100 value: 33.239999999999995 - type: recall_at_1000 value: 59.223000000000006 - type: recall_at_3 value: 12.44 - type: recall_at_5 value: 14.544 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.661000000000001 - type: map_at_10 value: 15.949 - type: map_at_100 value: 16.689999999999998 - type: map_at_1000 value: 16.805999999999997 - type: map_at_3 value: 14.398 - type: map_at_5 value: 15.204 - type: mrr_at_1 value: 14.272000000000002 - type: mrr_at_10 value: 18.85 - type: mrr_at_100 value: 19.603 - type: mrr_at_1000 value: 19.698999999999998 - type: mrr_at_3 value: 17.18 - type: mrr_at_5 value: 18.005 - type: ndcg_at_1 value: 14.272000000000002 - type: ndcg_at_10 value: 19.082 - type: ndcg_at_100 value: 23.078000000000003 - type: ndcg_at_1000 value: 26.444000000000003 - type: ndcg_at_3 value: 16.006 - type: ndcg_at_5 value: 17.281 - type: precision_at_1 value: 14.272000000000002 - type: precision_at_10 value: 3.321 - type: precision_at_100 value: 0.585 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 7.3069999999999995 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 11.661000000000001 - type: recall_at_10 value: 26.07 - type: recall_at_100 value: 44.537 - type: recall_at_1000 value: 69.446 - type: recall_at_3 value: 17.471999999999998 - type: recall_at_5 value: 20.744 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.037 - type: map_at_10 value: 19.348000000000003 - type: map_at_100 value: 20.357 - type: map_at_1000 value: 20.53 - type: map_at_3 value: 17.979 - type: map_at_5 value: 18.773 - type: mrr_at_1 value: 16.403000000000002 - type: mrr_at_10 value: 22.846 - type: mrr_at_100 value: 23.622 - type: mrr_at_1000 value: 23.708000000000002 - type: mrr_at_3 value: 21.377 - type: mrr_at_5 value: 22.325 - type: ndcg_at_1 value: 16.403000000000002 - type: ndcg_at_10 value: 23.275000000000002 - type: ndcg_at_100 value: 27.79 - type: ndcg_at_1000 value: 31.371 - type: ndcg_at_3 value: 21.035 - type: ndcg_at_5 value: 22.209 - type: precision_at_1 value: 16.403000000000002 - type: precision_at_10 value: 4.644 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.5889999999999995 - type: recall_at_1 value: 13.037 - type: recall_at_10 value: 30.239 - type: recall_at_100 value: 51.649 - type: recall_at_1000 value: 76.57000000000001 - type: recall_at_3 value: 23.677999999999997 - type: recall_at_5 value: 26.755000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.288 - type: map_at_10 value: 15.201999999999998 - type: map_at_100 value: 15.844 - type: map_at_1000 value: 15.963 - type: map_at_3 value: 13.828999999999999 - type: map_at_5 value: 14.530999999999999 - type: mrr_at_1 value: 12.753999999999998 - type: mrr_at_10 value: 16.753999999999998 - type: mrr_at_100 value: 17.387 - type: mrr_at_1000 value: 17.498 - type: mrr_at_3 value: 15.28 - type: mrr_at_5 value: 16.066 - type: ndcg_at_1 value: 12.753999999999998 - type: ndcg_at_10 value: 17.896 - type: ndcg_at_100 value: 21.632 - type: ndcg_at_1000 value: 25.111 - type: ndcg_at_3 value: 15.03 - type: ndcg_at_5 value: 16.281000000000002 - type: precision_at_1 value: 12.753999999999998 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.516 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 6.531000000000001 - type: precision_at_5 value: 4.658 - type: recall_at_1 value: 11.288 - type: recall_at_10 value: 24.941 - type: recall_at_100 value: 43.246 - type: recall_at_1000 value: 69.991 - type: recall_at_3 value: 17.018 - type: recall_at_5 value: 20.074 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.522 - type: map_at_10 value: 9.718 - type: map_at_100 value: 10.987 - type: map_at_1000 value: 11.185 - type: map_at_3 value: 7.89 - type: map_at_5 value: 8.794 - type: mrr_at_1 value: 12.508 - type: mrr_at_10 value: 20.039 - type: mrr_at_100 value: 21.271 - type: mrr_at_1000 value: 21.353 - type: mrr_at_3 value: 17.394000000000002 - type: mrr_at_5 value: 18.889 - type: ndcg_at_1 value: 12.508 - type: ndcg_at_10 value: 14.698 - type: ndcg_at_100 value: 20.794 - type: ndcg_at_1000 value: 24.978 - type: ndcg_at_3 value: 11.229 - type: ndcg_at_5 value: 12.483 - type: precision_at_1 value: 12.508 - type: precision_at_10 value: 4.925 - type: precision_at_100 value: 1.1360000000000001 - type: precision_at_1000 value: 0.189 - type: precision_at_3 value: 8.512 - type: precision_at_5 value: 6.893000000000001 - type: recall_at_1 value: 5.522 - type: recall_at_10 value: 18.645999999999997 - type: recall_at_100 value: 40.231 - type: recall_at_1000 value: 64.398 - type: recall_at_3 value: 10.520999999999999 - type: recall_at_5 value: 13.686000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 3.2840000000000003 - type: map_at_10 value: 7.598000000000001 - type: map_at_100 value: 10.347000000000001 - type: map_at_1000 value: 11.106 - type: map_at_3 value: 5.428999999999999 - type: map_at_5 value: 6.365 - type: mrr_at_1 value: 34.75 - type: mrr_at_10 value: 43.762 - type: mrr_at_100 value: 44.546 - type: mrr_at_1000 value: 44.592 - type: mrr_at_3 value: 41.125 - type: mrr_at_5 value: 42.699999999999996 - type: ndcg_at_1 value: 25.25 - type: ndcg_at_10 value: 20.081 - type: ndcg_at_100 value: 21.996 - type: ndcg_at_1000 value: 28.327999999999996 - type: ndcg_at_3 value: 22.421 - type: ndcg_at_5 value: 21.227999999999998 - type: precision_at_1 value: 34.75 - type: precision_at_10 value: 17.925 - type: precision_at_100 value: 5.437 - type: precision_at_1000 value: 1.157 - type: precision_at_3 value: 26.333000000000002 - type: precision_at_5 value: 23.0 - type: recall_at_1 value: 3.2840000000000003 - type: recall_at_10 value: 11.799999999999999 - type: recall_at_100 value: 27.08 - type: recall_at_1000 value: 48.348 - type: recall_at_3 value: 6.701 - type: recall_at_5 value: 8.515 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.57 - type: f1 value: 39.68887710167085 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 11.396 - type: map_at_10 value: 17.108999999999998 - type: map_at_100 value: 17.953 - type: map_at_1000 value: 18.035999999999998 - type: map_at_3 value: 15.177 - type: map_at_5 value: 16.232 - type: mrr_at_1 value: 12.166 - type: mrr_at_10 value: 18.236 - type: mrr_at_100 value: 19.1 - type: mrr_at_1000 value: 19.176000000000002 - type: mrr_at_3 value: 16.212 - type: mrr_at_5 value: 17.317 - type: ndcg_at_1 value: 12.166 - type: ndcg_at_10 value: 20.671999999999997 - type: ndcg_at_100 value: 25.227 - type: ndcg_at_1000 value: 27.613 - type: ndcg_at_3 value: 16.656000000000002 - type: ndcg_at_5 value: 18.549 - type: precision_at_1 value: 12.166 - type: precision_at_10 value: 3.3529999999999998 - type: precision_at_100 value: 0.582 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 7.141 - type: precision_at_5 value: 5.272 - type: recall_at_1 value: 11.396 - type: recall_at_10 value: 30.825999999999997 - type: recall_at_100 value: 52.641000000000005 - type: recall_at_1000 value: 71.34899999999999 - type: recall_at_3 value: 19.886 - type: recall_at_5 value: 24.417 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 5.534 - type: map_at_10 value: 9.422 - type: map_at_100 value: 10.471 - type: map_at_1000 value: 10.655000000000001 - type: map_at_3 value: 7.938000000000001 - type: map_at_5 value: 8.569 - type: mrr_at_1 value: 11.265 - type: mrr_at_10 value: 16.473 - type: mrr_at_100 value: 17.491 - type: mrr_at_1000 value: 17.592 - type: mrr_at_3 value: 14.738000000000001 - type: mrr_at_5 value: 15.470999999999998 - type: ndcg_at_1 value: 11.265 - type: ndcg_at_10 value: 13.194 - type: ndcg_at_100 value: 18.403 - type: ndcg_at_1000 value: 22.804 - type: ndcg_at_3 value: 10.92 - type: ndcg_at_5 value: 11.445 - type: precision_at_1 value: 11.265 - type: precision_at_10 value: 3.9510000000000005 - type: precision_at_100 value: 0.909 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 7.4590000000000005 - type: precision_at_5 value: 5.648000000000001 - type: recall_at_1 value: 5.534 - type: recall_at_10 value: 17.299999999999997 - type: recall_at_100 value: 37.901 - type: recall_at_1000 value: 65.745 - type: recall_at_3 value: 10.138 - type: recall_at_5 value: 12.078999999999999 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 13.633000000000001 - type: map_at_10 value: 19.076 - type: map_at_100 value: 19.851 - type: map_at_1000 value: 19.961000000000002 - type: map_at_3 value: 17.451 - type: map_at_5 value: 18.33 - type: mrr_at_1 value: 27.265 - type: mrr_at_10 value: 33.426 - type: mrr_at_100 value: 34.154 - type: mrr_at_1000 value: 34.226 - type: mrr_at_3 value: 31.648 - type: mrr_at_5 value: 32.613 - type: ndcg_at_1 value: 27.265 - type: ndcg_at_10 value: 24.81 - type: ndcg_at_100 value: 28.668 - type: ndcg_at_1000 value: 31.474000000000004 - type: ndcg_at_3 value: 21.586 - type: ndcg_at_5 value: 23.091 - type: precision_at_1 value: 27.265 - type: precision_at_10 value: 5.5440000000000005 - type: precision_at_100 value: 0.8659999999999999 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 13.594000000000001 - type: precision_at_5 value: 9.347999999999999 - type: recall_at_1 value: 13.633000000000001 - type: recall_at_10 value: 27.717999999999996 - type: recall_at_100 value: 43.322 - type: recall_at_1000 value: 62.107 - type: recall_at_3 value: 20.392 - type: recall_at_5 value: 23.369 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.9896 - type: ap value: 59.21073897376523 - type: f1 value: 63.83200686849996 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 5.203 - type: map_at_10 value: 9.144 - type: map_at_100 value: 9.93 - type: map_at_1000 value: 10.03 - type: map_at_3 value: 7.703 - type: map_at_5 value: 8.488 - type: mrr_at_1 value: 5.3580000000000005 - type: mrr_at_10 value: 9.392 - type: mrr_at_100 value: 10.188 - type: mrr_at_1000 value: 10.286 - type: mrr_at_3 value: 7.927 - type: mrr_at_5 value: 8.725 - type: ndcg_at_1 value: 5.344 - type: ndcg_at_10 value: 11.625 - type: ndcg_at_100 value: 15.953999999999999 - type: ndcg_at_1000 value: 19.003999999999998 - type: ndcg_at_3 value: 8.615 - type: ndcg_at_5 value: 10.038 - type: precision_at_1 value: 5.344 - type: precision_at_10 value: 2.013 - type: precision_at_100 value: 0.426 - type: precision_at_1000 value: 0.06899999999999999 - type: precision_at_3 value: 3.816 - type: precision_at_5 value: 3.0140000000000002 - type: recall_at_1 value: 5.203 - type: recall_at_10 value: 19.336000000000002 - type: recall_at_100 value: 40.534 - type: recall_at_1000 value: 65.063 - type: recall_at_3 value: 11.06 - type: recall_at_5 value: 14.488999999999999 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.34336525307798 - type: f1 value: 88.44434240756266 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 59.546283629730944 - type: f1 value: 40.1240023106233 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.956960322797585 - type: f1 value: 59.679846427125426 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.69535978480161 - type: f1 value: 67.29552687986417 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.401510844328 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 24.30315769309524 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 28.572900233549426 - type: mrr value: 29.419117722742016 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.627 - type: map_at_10 value: 6.662 - type: map_at_100 value: 8.429 - type: map_at_1000 value: 9.669 - type: map_at_3 value: 5.106 - type: map_at_5 value: 5.7410000000000005 - type: mrr_at_1 value: 31.889 - type: mrr_at_10 value: 40.531 - type: mrr_at_100 value: 41.237 - type: mrr_at_1000 value: 41.304 - type: mrr_at_3 value: 37.926 - type: mrr_at_5 value: 39.474 - type: ndcg_at_1 value: 29.256999999999998 - type: ndcg_at_10 value: 21.928 - type: ndcg_at_100 value: 20.74 - type: ndcg_at_1000 value: 30.375000000000004 - type: ndcg_at_3 value: 25.296000000000003 - type: ndcg_at_5 value: 23.602999999999998 - type: precision_at_1 value: 31.579 - type: precision_at_10 value: 16.067999999999998 - type: precision_at_100 value: 5.972 - type: precision_at_1000 value: 1.918 - type: precision_at_3 value: 23.735999999999997 - type: precision_at_5 value: 20.124 - type: recall_at_1 value: 3.627 - type: recall_at_10 value: 10.487 - type: recall_at_100 value: 22.634 - type: recall_at_1000 value: 57.052 - type: recall_at_3 value: 5.975 - type: recall_at_5 value: 7.495 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 7.954 - type: map_at_10 value: 13.513 - type: map_at_100 value: 14.582 - type: map_at_1000 value: 14.687 - type: map_at_3 value: 11.437999999999999 - type: map_at_5 value: 12.531999999999998 - type: mrr_at_1 value: 9.038 - type: mrr_at_10 value: 14.932 - type: mrr_at_100 value: 15.978 - type: mrr_at_1000 value: 16.066 - type: mrr_at_3 value: 12.775 - type: mrr_at_5 value: 13.937 - type: ndcg_at_1 value: 9.038 - type: ndcg_at_10 value: 17.224 - type: ndcg_at_100 value: 22.869 - type: ndcg_at_1000 value: 25.816 - type: ndcg_at_3 value: 12.878 - type: ndcg_at_5 value: 14.874 - type: precision_at_1 value: 9.038 - type: precision_at_10 value: 3.2009999999999996 - type: precision_at_100 value: 0.643 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 6.053999999999999 - type: precision_at_5 value: 4.728000000000001 - type: recall_at_1 value: 7.954 - type: recall_at_10 value: 27.448 - type: recall_at_100 value: 53.966 - type: recall_at_1000 value: 76.69 - type: recall_at_3 value: 15.740000000000002 - type: recall_at_5 value: 20.466 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 61.474 - type: map_at_10 value: 74.008 - type: map_at_100 value: 74.79599999999999 - type: map_at_1000 value: 74.833 - type: map_at_3 value: 71.127 - type: map_at_5 value: 72.858 - type: mrr_at_1 value: 70.86 - type: mrr_at_10 value: 78.428 - type: mrr_at_100 value: 78.693 - type: mrr_at_1000 value: 78.7 - type: mrr_at_3 value: 76.942 - type: mrr_at_5 value: 77.88199999999999 - type: ndcg_at_1 value: 70.89 - type: ndcg_at_10 value: 78.804 - type: ndcg_at_100 value: 81.082 - type: ndcg_at_1000 value: 81.527 - type: ndcg_at_3 value: 75.202 - type: ndcg_at_5 value: 77.031 - type: precision_at_1 value: 70.89 - type: precision_at_10 value: 11.923 - type: precision_at_100 value: 1.4409999999999998 - type: precision_at_1000 value: 0.154 - type: precision_at_3 value: 32.65 - type: precision_at_5 value: 21.58 - type: recall_at_1 value: 61.474 - type: recall_at_10 value: 88.056 - type: recall_at_100 value: 96.814 - type: recall_at_1000 value: 99.395 - type: recall_at_3 value: 77.656 - type: recall_at_5 value: 82.796 - type: map_at_1 value: 2.675 - type: map_at_10 value: 6.066 - type: map_at_100 value: 7.263999999999999 - type: map_at_1000 value: 7.51 - type: map_at_3 value: 4.404 - type: map_at_5 value: 5.181 - type: mrr_at_1 value: 13.200000000000001 - type: mrr_at_10 value: 20.189 - type: mrr_at_100 value: 21.445 - type: mrr_at_1000 value: 21.536 - type: mrr_at_3 value: 17.666999999999998 - type: mrr_at_5 value: 19.002 - type: ndcg_at_1 value: 13.200000000000001 - type: ndcg_at_10 value: 11.025 - type: ndcg_at_100 value: 16.828000000000003 - type: ndcg_at_1000 value: 21.982 - type: ndcg_at_3 value: 10.231 - type: ndcg_at_5 value: 8.963000000000001 - type: precision_at_1 value: 13.200000000000001 - type: precision_at_10 value: 5.76 - type: precision_at_100 value: 1.422 - type: precision_at_1000 value: 0.267 - type: precision_at_3 value: 9.367 - type: precision_at_5 value: 7.76 - type: recall_at_1 value: 2.675 - type: recall_at_10 value: 11.675 - type: recall_at_100 value: 28.875 - type: recall_at_1000 value: 54.173 - type: recall_at_3 value: 5.708 - type: recall_at_5 value: 7.863 - type: map_at_1 value: 0.11100000000000002 - type: map_at_10 value: 0.679 - type: map_at_100 value: 3.8249999999999997 - type: map_at_1000 value: 9.285 - type: map_at_3 value: 0.27299999999999996 - type: map_at_5 value: 0.396 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 58.839 - type: mrr_at_100 value: 59.419999999999995 - type: mrr_at_1000 value: 59.419999999999995 - type: mrr_at_3 value: 55.00000000000001 - type: mrr_at_5 value: 56.89999999999999 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 35.836 - type: ndcg_at_100 value: 28.149 - type: ndcg_at_1000 value: 25.856 - type: ndcg_at_3 value: 39.184000000000005 - type: ndcg_at_5 value: 37.309 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 39.0 - type: precision_at_100 value: 29.7 - type: precision_at_1000 value: 12.864 - type: precision_at_3 value: 43.333 - type: precision_at_5 value: 40.400000000000006 - type: recall_at_1 value: 0.11100000000000002 - type: recall_at_10 value: 0.894 - type: recall_at_100 value: 6.444 - type: recall_at_1000 value: 25.243 - type: recall_at_3 value: 0.311 - type: recall_at_5 value: 0.471 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 34.90409674230351 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 43.15515899635581 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 76.73075802333739 - type: cos_sim_spearman value: 67.84289404493155 - type: euclidean_pearson value: 72.99765907409036 - type: euclidean_spearman value: 67.84297651880989 - type: manhattan_pearson value: 71.56824568898308 - type: manhattan_spearman value: 66.49154112140326 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.15762265059362 - type: cos_sim_spearman value: 67.06089989163219 - type: euclidean_pearson value: 70.47335445930443 - type: euclidean_spearman value: 67.06203229762373 - type: manhattan_pearson value: 71.4377325677922 - type: manhattan_spearman value: 68.40504987244773 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.36180280086563 - type: cos_sim_spearman value: 78.77519784149617 - type: euclidean_pearson value: 78.35593578221615 - type: euclidean_spearman value: 78.77523564781683 - type: manhattan_pearson value: 78.86787717837672 - type: manhattan_spearman value: 79.39186388148488 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.58231513495421 - type: cos_sim_spearman value: 74.49349421503516 - type: euclidean_pearson value: 76.83261970721901 - type: euclidean_spearman value: 74.4934845240682 - type: manhattan_pearson value: 76.93964525607548 - type: manhattan_spearman value: 74.92015195647967 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 80.1326777711964 - type: cos_sim_spearman value: 81.22730751796163 - type: euclidean_pearson value: 81.25069343254962 - type: euclidean_spearman value: 81.22730800357442 - type: manhattan_pearson value: 81.89815505669003 - type: manhattan_spearman value: 82.02113941562317 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.45738534759084 - type: cos_sim_spearman value: 76.91650991022456 - type: euclidean_pearson value: 76.63155234592621 - type: euclidean_spearman value: 76.9170601938253 - type: manhattan_pearson value: 77.33411034896274 - type: manhattan_spearman value: 77.75936963273696 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 82.88607666461446 - type: cos_sim_spearman value: 84.10579933135438 - type: euclidean_pearson value: 84.22914136437043 - type: euclidean_spearman value: 84.10667343750598 - type: manhattan_pearson value: 84.54468730779816 - type: manhattan_spearman value: 84.61630648277088 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.229353870984085 - type: cos_sim_spearman value: 60.68370513051131 - type: euclidean_pearson value: 63.22117293489078 - type: euclidean_spearman value: 60.68370513051131 - type: manhattan_pearson value: 63.49146703165777 - type: manhattan_spearman value: 61.32348416931768 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 77.1763291511081 - type: cos_sim_spearman value: 75.83877626537955 - type: euclidean_pearson value: 77.36233283234974 - type: euclidean_spearman value: 75.83879473341474 - type: manhattan_pearson value: 77.45006581598014 - type: manhattan_spearman value: 75.95747982186211 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 71.10229760427933 - type: mrr value: 90.24918799428605 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 32.639 - type: map_at_10 value: 39.007999999999996 - type: map_at_100 value: 40.101 - type: map_at_1000 value: 40.177 - type: map_at_3 value: 36.889 - type: map_at_5 value: 38.013999999999996 - type: mrr_at_1 value: 35.0 - type: mrr_at_10 value: 40.883 - type: mrr_at_100 value: 41.818 - type: mrr_at_1000 value: 41.88 - type: mrr_at_3 value: 39.056000000000004 - type: mrr_at_5 value: 40.022000000000006 - type: ndcg_at_1 value: 35.0 - type: ndcg_at_10 value: 42.94 - type: ndcg_at_100 value: 48.592999999999996 - type: ndcg_at_1000 value: 50.588 - type: ndcg_at_3 value: 38.799 - type: ndcg_at_5 value: 40.644999999999996 - type: precision_at_1 value: 35.0 - type: precision_at_10 value: 5.933 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 15.333 - type: precision_at_5 value: 10.267 - type: recall_at_1 value: 32.639 - type: recall_at_10 value: 53.556000000000004 - type: recall_at_100 value: 80.51700000000001 - type: recall_at_1000 value: 96.133 - type: recall_at_3 value: 42.0 - type: recall_at_5 value: 46.583000000000006 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.63465346534653 - type: cos_sim_ap value: 87.35445585086691 - type: cos_sim_f1 value: 80.60956384655807 - type: cos_sim_precision value: 84.9390919158361 - type: cos_sim_recall value: 76.7 - type: dot_accuracy value: 99.63465346534653 - type: dot_ap value: 87.35445585086691 - type: dot_f1 value: 80.60956384655807 - type: dot_precision value: 84.9390919158361 - type: dot_recall value: 76.7 - type: euclidean_accuracy value: 99.63465346534653 - type: euclidean_ap value: 87.35445585086691 - type: euclidean_f1 value: 80.60956384655807 - type: euclidean_precision value: 84.9390919158361 - type: euclidean_recall value: 76.7 - type: manhattan_accuracy value: 99.6970297029703 - type: manhattan_ap value: 90.68997415814273 - type: manhattan_f1 value: 84.33981576253838 - type: manhattan_precision value: 86.37316561844864 - type: manhattan_recall value: 82.39999999999999 - type: max_accuracy value: 99.6970297029703 - type: max_ap value: 90.68997415814273 - type: max_f1 value: 84.33981576253838 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 37.79012296970403 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.17355447643775 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.12070306689919 - type: mrr value: 43.573166008827776 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.133865143991105 - type: cos_sim_spearman value: 29.440326561177667 - type: dot_pearson value: 30.133865125920966 - type: dot_spearman value: 29.435121388222697 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.664 - type: map_at_10 value: 8.955 - type: map_at_100 value: 14.658 - type: map_at_1000 value: 16.243 - type: map_at_3 value: 5.002 - type: map_at_5 value: 6.609 - type: mrr_at_1 value: 36.735 - type: mrr_at_10 value: 47.676 - type: mrr_at_100 value: 48.504999999999995 - type: mrr_at_1000 value: 48.504999999999995 - type: mrr_at_3 value: 43.197 - type: mrr_at_5 value: 46.054 - type: ndcg_at_1 value: 33.672999999999995 - type: ndcg_at_10 value: 21.986 - type: ndcg_at_100 value: 35.127 - type: ndcg_at_1000 value: 46.345 - type: ndcg_at_3 value: 27.017999999999997 - type: ndcg_at_5 value: 24.57 - type: precision_at_1 value: 36.735 - type: precision_at_10 value: 19.184 - type: precision_at_100 value: 7.714 - type: precision_at_1000 value: 1.484 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 2.664 - type: recall_at_10 value: 13.305 - type: recall_at_100 value: 46.623 - type: recall_at_1000 value: 81.056 - type: recall_at_3 value: 5.844 - type: recall_at_5 value: 8.605 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 70.0554 - type: ap value: 13.578604741323947 - type: f1 value: 53.720604206564516 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 53.5851726089417 - type: f1 value: 53.78179835467554 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.95662139054496 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.62043273529235 - type: cos_sim_ap value: 65.34515253479756 - type: cos_sim_f1 value: 62.530532486565704 - type: cos_sim_precision value: 58.20827648931333 - type: cos_sim_recall value: 67.54617414248021 - type: dot_accuracy value: 83.62043273529235 - type: dot_ap value: 65.34515253479756 - type: dot_f1 value: 62.530532486565704 - type: dot_precision value: 58.20827648931333 - type: dot_recall value: 67.54617414248021 - type: euclidean_accuracy value: 83.62043273529235 - type: euclidean_ap value: 65.34515253479756 - type: euclidean_f1 value: 62.530532486565704 - type: euclidean_precision value: 58.20827648931333 - type: euclidean_recall value: 67.54617414248021 - type: manhattan_accuracy value: 83.40585325147524 - type: manhattan_ap value: 64.01718862141554 - type: manhattan_f1 value: 61.092923696801805 - type: manhattan_precision value: 58.429672447013495 - type: manhattan_recall value: 64.01055408970976 - type: max_accuracy value: 83.62043273529235 - type: max_ap value: 65.34515253479756 - type: max_f1 value: 62.530532486565704 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.48981255093724 - type: cos_sim_ap value: 82.67603725088865 - type: cos_sim_f1 value: 74.82442124038498 - type: cos_sim_precision value: 72.31200172376643 - type: cos_sim_recall value: 77.51770865414228 - type: dot_accuracy value: 87.48981255093724 - type: dot_ap value: 82.6760377923082 - type: dot_f1 value: 74.82442124038498 - type: dot_precision value: 72.31200172376643 - type: dot_recall value: 77.51770865414228 - type: euclidean_accuracy value: 87.48981255093724 - type: euclidean_ap value: 82.6760372072607 - type: euclidean_f1 value: 74.82442124038498 - type: euclidean_precision value: 72.31200172376643 - type: euclidean_recall value: 77.51770865414228 - type: manhattan_accuracy value: 87.40055109248263 - type: manhattan_ap value: 82.74988489269892 - type: manhattan_f1 value: 75.01184359170584 - type: manhattan_precision value: 71.21012938490279 - type: manhattan_recall value: 79.24237757930396 - type: max_accuracy value: 87.48981255093724 - type: max_ap value: 82.74988489269892 - type: max_f1 value: 75.01184359170584 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/nmc-nignore15

twadada

null

[ "mteb", "model-index", "region:us" ]

2024-09-17T00:36:10

2024-09-17T00:36:20

0

--- tags: - mteb model-index: - name: nomic_classification_nignore15 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.01492537313432 - type: ap value: 35.50250124630455 - type: f1 value: 66.89959317702703 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 64.84830000000001 - type: ap value: 59.73270245283254 - type: f1 value: 64.76353235413379 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 34.105999999999995 - type: f1 value: 33.54422658625557 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.622 - type: map_at_10 value: 35.826 - type: map_at_100 value: 37.053000000000004 - type: map_at_1000 value: 37.074 - type: map_at_3 value: 31.211 - type: map_at_5 value: 33.921 - type: mrr_at_1 value: 22.262 - type: mrr_at_10 value: 36.036 - type: mrr_at_100 value: 37.263000000000005 - type: mrr_at_1000 value: 37.284 - type: mrr_at_3 value: 31.484 - type: mrr_at_5 value: 34.144000000000005 - type: ndcg_at_1 value: 21.622 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.506 - type: ndcg_at_1000 value: 50.009 - type: ndcg_at_3 value: 34.372 - type: ndcg_at_5 value: 39.275 - type: precision_at_1 value: 21.622 - type: precision_at_10 value: 6.991 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.509 - type: precision_at_5 value: 11.094999999999999 - type: recall_at_1 value: 21.622 - type: recall_at_10 value: 69.915 - type: recall_at_100 value: 95.235 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 43.528 - type: recall_at_5 value: 55.477 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 34.911008985215766 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.405265668502622 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.19334227044753 - type: mrr value: 69.16600712307083 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.93657061818256 - type: cos_sim_spearman value: 80.1584529707527 - type: euclidean_pearson value: 81.85076602737361 - type: euclidean_spearman value: 80.1584529707527 - type: manhattan_pearson value: 81.32446368945303 - type: manhattan_spearman value: 80.35183087097523 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.75974025974025 - type: f1 value: 72.01384314530861 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.205913552227386 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.518894282858902 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 23.200000000000003 - type: map_at_10 value: 30.767 - type: map_at_100 value: 31.909 - type: map_at_1000 value: 32.054 - type: map_at_3 value: 28.048000000000002 - type: map_at_5 value: 29.668 - type: mrr_at_1 value: 29.185 - type: mrr_at_10 value: 36.361 - type: mrr_at_100 value: 37.212 - type: mrr_at_1000 value: 37.275999999999996 - type: mrr_at_3 value: 34.168 - type: mrr_at_5 value: 35.463 - type: ndcg_at_1 value: 29.185 - type: ndcg_at_10 value: 35.735 - type: ndcg_at_100 value: 40.884 - type: ndcg_at_1000 value: 43.887 - type: ndcg_at_3 value: 31.839000000000002 - type: ndcg_at_5 value: 33.759 - type: precision_at_1 value: 29.185 - type: precision_at_10 value: 6.723999999999999 - type: precision_at_100 value: 1.163 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 15.165000000000001 - type: precision_at_5 value: 11.101999999999999 - type: recall_at_1 value: 23.200000000000003 - type: recall_at_10 value: 44.68 - type: recall_at_100 value: 67.47999999999999 - type: recall_at_1000 value: 88.152 - type: recall_at_3 value: 33.055 - type: recall_at_5 value: 38.481 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 17.410999999999998 - type: map_at_10 value: 23.605999999999998 - type: map_at_100 value: 24.532 - type: map_at_1000 value: 24.645 - type: map_at_3 value: 21.628 - type: map_at_5 value: 22.849 - type: mrr_at_1 value: 22.038 - type: mrr_at_10 value: 27.947 - type: mrr_at_100 value: 28.701 - type: mrr_at_1000 value: 28.772 - type: mrr_at_3 value: 26.008 - type: mrr_at_5 value: 27.187 - type: ndcg_at_1 value: 22.038 - type: ndcg_at_10 value: 27.632 - type: ndcg_at_100 value: 31.852000000000004 - type: ndcg_at_1000 value: 34.587 - type: ndcg_at_3 value: 24.274 - type: ndcg_at_5 value: 26.005 - type: precision_at_1 value: 22.038 - type: precision_at_10 value: 5.108 - type: precision_at_100 value: 0.911 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 11.591999999999999 - type: precision_at_5 value: 8.42 - type: recall_at_1 value: 17.410999999999998 - type: recall_at_10 value: 35.346 - type: recall_at_100 value: 53.849000000000004 - type: recall_at_1000 value: 72.56700000000001 - type: recall_at_3 value: 25.647 - type: recall_at_5 value: 30.288999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.1 - type: map_at_10 value: 35.973 - type: map_at_100 value: 37.1 - type: map_at_1000 value: 37.191 - type: map_at_3 value: 33.409 - type: map_at_5 value: 34.867 - type: mrr_at_1 value: 31.473000000000003 - type: mrr_at_10 value: 39.35 - type: mrr_at_100 value: 40.258 - type: mrr_at_1000 value: 40.314 - type: mrr_at_3 value: 37.106 - type: mrr_at_5 value: 38.316 - type: ndcg_at_1 value: 31.473000000000003 - type: ndcg_at_10 value: 40.831 - type: ndcg_at_100 value: 46.094 - type: ndcg_at_1000 value: 48.147 - type: ndcg_at_3 value: 36.187000000000005 - type: ndcg_at_5 value: 38.389 - type: precision_at_1 value: 31.473000000000003 - type: precision_at_10 value: 6.627 - type: precision_at_100 value: 1.013 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 16.092000000000002 - type: precision_at_5 value: 11.197 - type: recall_at_1 value: 27.1 - type: recall_at_10 value: 52.208 - type: recall_at_100 value: 75.913 - type: recall_at_1000 value: 90.623 - type: recall_at_3 value: 39.696999999999996 - type: recall_at_5 value: 45.068999999999996 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.164000000000001 - type: map_at_10 value: 17.209 - type: map_at_100 value: 18.078 - type: map_at_1000 value: 18.196 - type: map_at_3 value: 15.723999999999998 - type: map_at_5 value: 16.53 - type: mrr_at_1 value: 14.35 - type: mrr_at_10 value: 18.549 - type: mrr_at_100 value: 19.378999999999998 - type: mrr_at_1000 value: 19.485 - type: mrr_at_3 value: 17.024 - type: mrr_at_5 value: 17.832 - type: ndcg_at_1 value: 14.35 - type: ndcg_at_10 value: 19.949 - type: ndcg_at_100 value: 24.59 - type: ndcg_at_1000 value: 28.102 - type: ndcg_at_3 value: 16.894000000000002 - type: ndcg_at_5 value: 18.322 - type: precision_at_1 value: 14.35 - type: precision_at_10 value: 3.0620000000000003 - type: precision_at_100 value: 0.573 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.005999999999999 - type: precision_at_5 value: 5.017 - type: recall_at_1 value: 13.164000000000001 - type: recall_at_10 value: 27.282 - type: recall_at_100 value: 49.352000000000004 - type: recall_at_1000 value: 76.7 - type: recall_at_3 value: 18.93 - type: recall_at_5 value: 22.395 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.343 - type: map_at_10 value: 10.732999999999999 - type: map_at_100 value: 11.524 - type: map_at_1000 value: 11.658 - type: map_at_3 value: 9.501999999999999 - type: map_at_5 value: 10.118 - type: mrr_at_1 value: 9.577 - type: mrr_at_10 value: 13.293 - type: mrr_at_100 value: 14.126 - type: mrr_at_1000 value: 14.234 - type: mrr_at_3 value: 11.816 - type: mrr_at_5 value: 12.519 - type: ndcg_at_1 value: 9.577 - type: ndcg_at_10 value: 13.303999999999998 - type: ndcg_at_100 value: 17.596999999999998 - type: ndcg_at_1000 value: 21.406 - type: ndcg_at_3 value: 10.788 - type: ndcg_at_5 value: 11.815000000000001 - type: precision_at_1 value: 9.577 - type: precision_at_10 value: 2.512 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 5.1 - type: precision_at_5 value: 3.781 - type: recall_at_1 value: 7.343 - type: recall_at_10 value: 18.912000000000003 - type: recall_at_100 value: 38.389 - type: recall_at_1000 value: 66.424 - type: recall_at_3 value: 11.851 - type: recall_at_5 value: 14.424000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.055 - type: map_at_10 value: 24.271 - type: map_at_100 value: 25.418000000000003 - type: map_at_1000 value: 25.554 - type: map_at_3 value: 22.067 - type: map_at_5 value: 23.274 - type: mrr_at_1 value: 22.137 - type: mrr_at_10 value: 28.671000000000003 - type: mrr_at_100 value: 29.604000000000003 - type: mrr_at_1000 value: 29.68 - type: mrr_at_3 value: 26.372 - type: mrr_at_5 value: 27.766999999999996 - type: ndcg_at_1 value: 22.137 - type: ndcg_at_10 value: 28.687 - type: ndcg_at_100 value: 34.309 - type: ndcg_at_1000 value: 37.316 - type: ndcg_at_3 value: 24.761 - type: ndcg_at_5 value: 26.598 - type: precision_at_1 value: 22.137 - type: precision_at_10 value: 5.3420000000000005 - type: precision_at_100 value: 0.9740000000000001 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.677999999999999 - type: precision_at_5 value: 8.527 - type: recall_at_1 value: 18.055 - type: recall_at_10 value: 37.701 - type: recall_at_100 value: 62.661 - type: recall_at_1000 value: 83.37299999999999 - type: recall_at_3 value: 26.491999999999997 - type: recall_at_5 value: 31.366 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.889999999999999 - type: map_at_10 value: 19.337 - type: map_at_100 value: 20.389 - type: map_at_1000 value: 20.53 - type: map_at_3 value: 17.404 - type: map_at_5 value: 18.356 - type: mrr_at_1 value: 17.122999999999998 - type: mrr_at_10 value: 22.951 - type: mrr_at_100 value: 23.919999999999998 - type: mrr_at_1000 value: 24.01 - type: mrr_at_3 value: 21.081 - type: mrr_at_5 value: 22.102 - type: ndcg_at_1 value: 17.122999999999998 - type: ndcg_at_10 value: 23.16 - type: ndcg_at_100 value: 28.337 - type: ndcg_at_1000 value: 31.808999999999997 - type: ndcg_at_3 value: 19.649 - type: ndcg_at_5 value: 21.047 - type: precision_at_1 value: 17.122999999999998 - type: precision_at_10 value: 4.406000000000001 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 9.361 - type: precision_at_5 value: 6.804 - type: recall_at_1 value: 13.889999999999999 - type: recall_at_10 value: 31.162 - type: recall_at_100 value: 53.862 - type: recall_at_1000 value: 78.668 - type: recall_at_3 value: 21.276999999999997 - type: recall_at_5 value: 24.945999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.876916666666668 - type: map_at_10 value: 20.335916666666666 - type: map_at_100 value: 21.264833333333335 - type: map_at_1000 value: 21.392083333333332 - type: map_at_3 value: 18.571416666666668 - type: map_at_5 value: 19.552416666666666 - type: mrr_at_1 value: 17.9935 - type: mrr_at_10 value: 23.497000000000003 - type: mrr_at_100 value: 24.318666666666665 - type: mrr_at_1000 value: 24.404583333333328 - type: mrr_at_3 value: 21.74641666666667 - type: mrr_at_5 value: 22.727416666666667 - type: ndcg_at_1 value: 17.9935 - type: ndcg_at_10 value: 23.92941666666667 - type: ndcg_at_100 value: 28.531999999999996 - type: ndcg_at_1000 value: 31.72616666666667 - type: ndcg_at_3 value: 20.738083333333332 - type: ndcg_at_5 value: 22.215416666666666 - type: precision_at_1 value: 17.9935 - type: precision_at_10 value: 4.256916666666667 - type: precision_at_100 value: 0.7820833333333335 - type: precision_at_1000 value: 0.12375000000000003 - type: precision_at_3 value: 9.594916666666666 - type: precision_at_5 value: 6.911333333333333 - type: recall_at_1 value: 14.876916666666668 - type: recall_at_10 value: 31.664250000000006 - type: recall_at_100 value: 52.60891666666667 - type: recall_at_1000 value: 75.82383333333334 - type: recall_at_3 value: 22.649833333333333 - type: recall_at_5 value: 26.4515 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.74 - type: map_at_10 value: 15.364 - type: map_at_100 value: 16.066 - type: map_at_1000 value: 16.147 - type: map_at_3 value: 13.871 - type: map_at_5 value: 14.724 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.657 - type: mrr_at_100 value: 18.299000000000003 - type: mrr_at_1000 value: 18.369 - type: mrr_at_3 value: 16.104 - type: mrr_at_5 value: 16.986 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.429000000000002 - type: ndcg_at_100 value: 22.144 - type: ndcg_at_1000 value: 24.647 - type: ndcg_at_3 value: 15.542 - type: ndcg_at_5 value: 16.929 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.19 - type: precision_at_100 value: 0.5579999999999999 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 7.156999999999999 - type: precision_at_5 value: 5.215 - type: recall_at_1 value: 10.74 - type: recall_at_10 value: 25.762 - type: recall_at_100 value: 43.132999999999996 - type: recall_at_1000 value: 62.26199999999999 - type: recall_at_3 value: 17.629 - type: recall_at_5 value: 21.125 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.707 - type: map_at_10 value: 11.26 - type: map_at_100 value: 11.918 - type: map_at_1000 value: 12.043 - type: map_at_3 value: 10.169 - type: map_at_5 value: 10.817 - type: mrr_at_1 value: 9.429 - type: mrr_at_10 value: 13.5 - type: mrr_at_100 value: 14.177999999999999 - type: mrr_at_1000 value: 14.280000000000001 - type: mrr_at_3 value: 12.273 - type: mrr_at_5 value: 13.048000000000002 - type: ndcg_at_1 value: 9.429 - type: ndcg_at_10 value: 13.697999999999999 - type: ndcg_at_100 value: 17.427 - type: ndcg_at_1000 value: 21.013 - type: ndcg_at_3 value: 11.639 - type: ndcg_at_5 value: 12.705 - type: precision_at_1 value: 9.429 - type: precision_at_10 value: 2.546 - type: precision_at_100 value: 0.534 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 5.666 - type: precision_at_5 value: 4.212 - type: recall_at_1 value: 7.707 - type: recall_at_10 value: 18.901 - type: recall_at_100 value: 36.38 - type: recall_at_1000 value: 63.017999999999994 - type: recall_at_3 value: 13.123999999999999 - type: recall_at_5 value: 15.834000000000001 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.034 - type: map_at_10 value: 17.943 - type: map_at_100 value: 18.787000000000003 - type: map_at_1000 value: 18.907 - type: map_at_3 value: 16.508 - type: map_at_5 value: 17.267 - type: mrr_at_1 value: 15.485 - type: mrr_at_10 value: 20.801 - type: mrr_at_100 value: 21.632 - type: mrr_at_1000 value: 21.731 - type: mrr_at_3 value: 19.279 - type: mrr_at_5 value: 20.09 - type: ndcg_at_1 value: 15.485 - type: ndcg_at_10 value: 21.301000000000002 - type: ndcg_at_100 value: 25.606 - type: ndcg_at_1000 value: 29.109 - type: ndcg_at_3 value: 18.451999999999998 - type: ndcg_at_5 value: 19.685 - type: precision_at_1 value: 15.485 - type: precision_at_10 value: 3.6470000000000002 - type: precision_at_100 value: 0.639 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.551 - type: precision_at_5 value: 5.989 - type: recall_at_1 value: 13.034 - type: recall_at_10 value: 28.909000000000002 - type: recall_at_100 value: 48.28 - type: recall_at_1000 value: 74.375 - type: recall_at_3 value: 20.871000000000002 - type: recall_at_5 value: 24.066000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.667 - type: map_at_10 value: 21.229 - type: map_at_100 value: 22.303 - type: map_at_1000 value: 22.512 - type: map_at_3 value: 19.527 - type: map_at_5 value: 20.415 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.829 - type: mrr_at_100 value: 25.623 - type: mrr_at_1000 value: 25.712000000000003 - type: mrr_at_3 value: 23.09 - type: mrr_at_5 value: 23.979 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 25.462 - type: ndcg_at_100 value: 30.255 - type: ndcg_at_1000 value: 34.019 - type: ndcg_at_3 value: 22.567999999999998 - type: ndcg_at_5 value: 23.79 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.9799999999999995 - type: precision_at_100 value: 1.099 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 10.870000000000001 - type: precision_at_5 value: 7.866 - type: recall_at_1 value: 14.667 - type: recall_at_10 value: 33.550000000000004 - type: recall_at_100 value: 56.123999999999995 - type: recall_at_1000 value: 81.883 - type: recall_at_3 value: 24.944 - type: recall_at_5 value: 28.055000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.212 - type: map_at_10 value: 16.339000000000002 - type: map_at_100 value: 17.154 - type: map_at_1000 value: 17.268 - type: map_at_3 value: 15.0 - type: map_at_5 value: 15.744 - type: mrr_at_1 value: 13.863 - type: mrr_at_10 value: 18.055 - type: mrr_at_100 value: 18.892 - type: mrr_at_1000 value: 18.992 - type: mrr_at_3 value: 16.636 - type: mrr_at_5 value: 17.44 - type: ndcg_at_1 value: 13.863 - type: ndcg_at_10 value: 18.965 - type: ndcg_at_100 value: 23.289 - type: ndcg_at_1000 value: 26.672 - type: ndcg_at_3 value: 16.264 - type: ndcg_at_5 value: 17.541 - type: precision_at_1 value: 13.863 - type: precision_at_10 value: 2.939 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 6.901 - type: precision_at_5 value: 4.806 - type: recall_at_1 value: 12.212 - type: recall_at_10 value: 25.557999999999996 - type: recall_at_100 value: 45.884 - type: recall_at_1000 value: 71.841 - type: recall_at_3 value: 18.281 - type: recall_at_5 value: 21.368000000000002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 5.991 - type: map_at_10 value: 11.031 - type: map_at_100 value: 12.415 - type: map_at_1000 value: 12.601999999999999 - type: map_at_3 value: 8.752 - type: map_at_5 value: 9.873999999999999 - type: mrr_at_1 value: 13.355 - type: mrr_at_10 value: 22.002 - type: mrr_at_100 value: 23.146 - type: mrr_at_1000 value: 23.218 - type: mrr_at_3 value: 18.719 - type: mrr_at_5 value: 20.543 - type: ndcg_at_1 value: 13.355 - type: ndcg_at_10 value: 16.711000000000002 - type: ndcg_at_100 value: 23.073 - type: ndcg_at_1000 value: 27.108999999999998 - type: ndcg_at_3 value: 12.289 - type: ndcg_at_5 value: 13.943 - type: precision_at_1 value: 13.355 - type: precision_at_10 value: 5.648000000000001 - type: precision_at_100 value: 1.248 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 9.359 - type: precision_at_5 value: 7.739 - type: recall_at_1 value: 5.991 - type: recall_at_10 value: 21.898 - type: recall_at_100 value: 44.324000000000005 - type: recall_at_1000 value: 67.777 - type: recall_at_3 value: 11.527 - type: recall_at_5 value: 15.61 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.125 - type: map_at_10 value: 9.629999999999999 - type: map_at_100 value: 13.306000000000001 - type: map_at_1000 value: 14.26 - type: map_at_3 value: 6.894 - type: map_at_5 value: 8.19 - type: mrr_at_1 value: 39.25 - type: mrr_at_10 value: 49.495 - type: mrr_at_100 value: 50.139 - type: mrr_at_1000 value: 50.169 - type: mrr_at_3 value: 46.333 - type: mrr_at_5 value: 48.008 - type: ndcg_at_1 value: 28.499999999999996 - type: ndcg_at_10 value: 23.794 - type: ndcg_at_100 value: 26.632 - type: ndcg_at_1000 value: 33.382 - type: ndcg_at_3 value: 26.282 - type: ndcg_at_5 value: 25.113000000000003 - type: precision_at_1 value: 39.25 - type: precision_at_10 value: 21.075 - type: precision_at_100 value: 6.607 - type: precision_at_1000 value: 1.366 - type: precision_at_3 value: 31.667 - type: precision_at_5 value: 27.150000000000002 - type: recall_at_1 value: 4.125 - type: recall_at_10 value: 14.603 - type: recall_at_100 value: 32.888 - type: recall_at_1000 value: 55.901 - type: recall_at_3 value: 8.396 - type: recall_at_5 value: 10.902000000000001 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.39999999999999 - type: f1 value: 41.19798638036962 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 15.998999999999999 - type: map_at_10 value: 24.037 - type: map_at_100 value: 24.979000000000003 - type: map_at_1000 value: 25.052000000000003 - type: map_at_3 value: 21.537 - type: map_at_5 value: 22.926 - type: mrr_at_1 value: 17.072000000000003 - type: mrr_at_10 value: 25.526 - type: mrr_at_100 value: 26.464 - type: mrr_at_1000 value: 26.528000000000002 - type: mrr_at_3 value: 22.922 - type: mrr_at_5 value: 24.391 - type: ndcg_at_1 value: 17.072000000000003 - type: ndcg_at_10 value: 28.933999999999997 - type: ndcg_at_100 value: 33.812999999999995 - type: ndcg_at_1000 value: 35.874 - type: ndcg_at_3 value: 23.746000000000002 - type: ndcg_at_5 value: 26.264 - type: precision_at_1 value: 17.072000000000003 - type: precision_at_10 value: 4.6739999999999995 - type: precision_at_100 value: 0.732 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 10.326 - type: precision_at_5 value: 7.531000000000001 - type: recall_at_1 value: 15.998999999999999 - type: recall_at_10 value: 42.888999999999996 - type: recall_at_100 value: 65.864 - type: recall_at_1000 value: 81.872 - type: recall_at_3 value: 28.735 - type: recall_at_5 value: 34.817 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.0889999999999995 - type: map_at_10 value: 11.533 - type: map_at_100 value: 12.626999999999999 - type: map_at_1000 value: 12.831000000000001 - type: map_at_3 value: 9.524000000000001 - type: map_at_5 value: 10.484 - type: mrr_at_1 value: 13.889000000000001 - type: mrr_at_10 value: 20.035 - type: mrr_at_100 value: 21.041999999999998 - type: mrr_at_1000 value: 21.142 - type: mrr_at_3 value: 17.695 - type: mrr_at_5 value: 18.83 - type: ndcg_at_1 value: 13.889000000000001 - type: ndcg_at_10 value: 16.122 - type: ndcg_at_100 value: 21.485000000000003 - type: ndcg_at_1000 value: 26.101999999999997 - type: ndcg_at_3 value: 12.967999999999998 - type: ndcg_at_5 value: 13.975000000000001 - type: precision_at_1 value: 13.889000000000001 - type: precision_at_10 value: 4.769 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 8.693 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 7.0889999999999995 - type: recall_at_10 value: 21.163999999999998 - type: recall_at_100 value: 42.247 - type: recall_at_1000 value: 71.395 - type: recall_at_3 value: 11.694 - type: recall_at_5 value: 15.051 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 17.752000000000002 - type: map_at_10 value: 25.064999999999998 - type: map_at_100 value: 26.003999999999998 - type: map_at_1000 value: 26.116 - type: map_at_3 value: 23.064 - type: map_at_5 value: 24.149 - type: mrr_at_1 value: 35.503 - type: mrr_at_10 value: 42.649 - type: mrr_at_100 value: 43.389 - type: mrr_at_1000 value: 43.445 - type: mrr_at_3 value: 40.699999999999996 - type: mrr_at_5 value: 41.817 - type: ndcg_at_1 value: 35.503 - type: ndcg_at_10 value: 31.968000000000004 - type: ndcg_at_100 value: 36.257 - type: ndcg_at_1000 value: 38.928000000000004 - type: ndcg_at_3 value: 28.176000000000002 - type: ndcg_at_5 value: 29.994 - type: precision_at_1 value: 35.503 - type: precision_at_10 value: 7.0440000000000005 - type: precision_at_100 value: 1.046 - type: precision_at_1000 value: 0.13999999999999999 - type: precision_at_3 value: 17.763 - type: precision_at_5 value: 12.1 - type: recall_at_1 value: 17.752000000000002 - type: recall_at_10 value: 35.219 - type: recall_at_100 value: 52.309000000000005 - type: recall_at_1000 value: 70.162 - type: recall_at_3 value: 26.644000000000002 - type: recall_at_5 value: 30.25 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 63.9016 - type: ap value: 59.11956601013746 - type: f1 value: 63.68974251662037 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.561999999999999 - type: map_at_10 value: 11.378 - type: map_at_100 value: 12.258 - type: map_at_1000 value: 12.361 - type: map_at_3 value: 9.577 - type: map_at_5 value: 10.525 - type: mrr_at_1 value: 6.762 - type: mrr_at_10 value: 11.674 - type: mrr_at_100 value: 12.554000000000002 - type: mrr_at_1000 value: 12.654000000000002 - type: mrr_at_3 value: 9.833 - type: mrr_at_5 value: 10.806000000000001 - type: ndcg_at_1 value: 6.734 - type: ndcg_at_10 value: 14.459 - type: ndcg_at_100 value: 19.317999999999998 - type: ndcg_at_1000 value: 22.407 - type: ndcg_at_3 value: 10.666 - type: ndcg_at_5 value: 12.393 - type: precision_at_1 value: 6.734 - type: precision_at_10 value: 2.5069999999999997 - type: precision_at_100 value: 0.505 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.6850000000000005 - type: precision_at_5 value: 3.682 - type: recall_at_1 value: 6.561999999999999 - type: recall_at_10 value: 24.07 - type: recall_at_100 value: 47.856 - type: recall_at_1000 value: 72.654 - type: recall_at_3 value: 13.584 - type: recall_at_5 value: 17.76 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.86000911992703 - type: f1 value: 87.98975696911226 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 58.38349293205655 - type: f1 value: 40.01779036138312 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.852723604572965 - type: f1 value: 60.13917532573332 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.14727639542704 - type: f1 value: 66.7653952309667 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.504423289782554 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 25.331311574764182 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.79405962065405 - type: mrr value: 30.80981570313803 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.8309999999999995 - type: map_at_10 value: 8.15 - type: map_at_100 value: 10.295 - type: map_at_1000 value: 11.649 - type: map_at_3 value: 6.088 - type: map_at_5 value: 7.138 - type: mrr_at_1 value: 33.745999999999995 - type: mrr_at_10 value: 43.422 - type: mrr_at_100 value: 44.193 - type: mrr_at_1000 value: 44.261 - type: mrr_at_3 value: 40.506 - type: mrr_at_5 value: 42.812 - type: ndcg_at_1 value: 31.579 - type: ndcg_at_10 value: 25.357000000000003 - type: ndcg_at_100 value: 23.597 - type: ndcg_at_1000 value: 33.143 - type: ndcg_at_3 value: 28.778 - type: ndcg_at_5 value: 27.92 - type: precision_at_1 value: 33.745999999999995 - type: precision_at_10 value: 18.854000000000003 - type: precision_at_100 value: 6.464 - type: precision_at_1000 value: 1.9900000000000002 - type: precision_at_3 value: 27.450999999999997 - type: precision_at_5 value: 24.52 - type: recall_at_1 value: 3.8309999999999995 - type: recall_at_10 value: 12.18 - type: recall_at_100 value: 25.258999999999997 - type: recall_at_1000 value: 59.059 - type: recall_at_3 value: 7.353 - type: recall_at_5 value: 9.777 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.132 - type: map_at_10 value: 15.934999999999999 - type: map_at_100 value: 17.168 - type: map_at_1000 value: 17.266000000000002 - type: map_at_3 value: 13.296 - type: map_at_5 value: 14.713000000000001 - type: mrr_at_1 value: 10.342 - type: mrr_at_10 value: 17.535 - type: mrr_at_100 value: 18.689 - type: mrr_at_1000 value: 18.77 - type: mrr_at_3 value: 14.846 - type: mrr_at_5 value: 16.337 - type: ndcg_at_1 value: 10.342 - type: ndcg_at_10 value: 20.409 - type: ndcg_at_100 value: 26.672 - type: ndcg_at_1000 value: 29.321 - type: ndcg_at_3 value: 14.982000000000001 - type: ndcg_at_5 value: 17.522 - type: precision_at_1 value: 10.342 - type: precision_at_10 value: 3.8440000000000003 - type: precision_at_100 value: 0.741 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 7.0489999999999995 - type: precision_at_5 value: 5.632000000000001 - type: recall_at_1 value: 9.132 - type: recall_at_10 value: 32.800000000000004 - type: recall_at_100 value: 61.895999999999994 - type: recall_at_1000 value: 82.146 - type: recall_at_3 value: 18.342 - type: recall_at_5 value: 24.242 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.005 - type: map_at_10 value: 76.709 - type: map_at_100 value: 77.464 - type: map_at_1000 value: 77.498 - type: map_at_3 value: 73.75699999999999 - type: map_at_5 value: 75.553 - type: mrr_at_1 value: 73.81 - type: mrr_at_10 value: 81.006 - type: mrr_at_100 value: 81.23599999999999 - type: mrr_at_1000 value: 81.241 - type: mrr_at_3 value: 79.56800000000001 - type: mrr_at_5 value: 80.50399999999999 - type: ndcg_at_1 value: 73.85000000000001 - type: ndcg_at_10 value: 81.33399999999999 - type: ndcg_at_100 value: 83.378 - type: ndcg_at_1000 value: 83.726 - type: ndcg_at_3 value: 77.791 - type: ndcg_at_5 value: 79.636 - type: precision_at_1 value: 73.85000000000001 - type: precision_at_10 value: 12.262 - type: precision_at_100 value: 1.461 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.672999999999995 - type: precision_at_5 value: 22.253999999999998 - type: recall_at_1 value: 64.005 - type: recall_at_10 value: 90.137 - type: recall_at_100 value: 97.77799999999999 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 79.972 - type: recall_at_5 value: 85.10799999999999 - type: map_at_1 value: 3.008 - type: map_at_10 value: 7.086 - type: map_at_100 value: 8.498 - type: map_at_1000 value: 8.744 - type: map_at_3 value: 5.2330000000000005 - type: map_at_5 value: 6.188 - type: mrr_at_1 value: 14.799999999999999 - type: mrr_at_10 value: 22.731 - type: mrr_at_100 value: 23.963 - type: mrr_at_1000 value: 24.046 - type: mrr_at_3 value: 19.950000000000003 - type: mrr_at_5 value: 21.38 - type: ndcg_at_1 value: 14.799999999999999 - type: ndcg_at_10 value: 12.581999999999999 - type: ndcg_at_100 value: 19.024 - type: ndcg_at_1000 value: 24.075 - type: ndcg_at_3 value: 11.937000000000001 - type: ndcg_at_5 value: 10.427 - type: precision_at_1 value: 14.799999999999999 - type: precision_at_10 value: 6.5 - type: precision_at_100 value: 1.591 - type: precision_at_1000 value: 0.281 - type: precision_at_3 value: 11.1 - type: precision_at_5 value: 9.120000000000001 - type: recall_at_1 value: 3.008 - type: recall_at_10 value: 13.197000000000001 - type: recall_at_100 value: 32.323 - type: recall_at_1000 value: 57.172999999999995 - type: recall_at_3 value: 6.753000000000001 - type: recall_at_5 value: 9.248000000000001 - type: map_at_1 value: 0.129 - type: map_at_10 value: 0.717 - type: map_at_100 value: 3.6580000000000004 - type: map_at_1000 value: 9.374 - type: map_at_3 value: 0.302 - type: map_at_5 value: 0.422 - type: mrr_at_1 value: 56.00000000000001 - type: mrr_at_10 value: 67.475 - type: mrr_at_100 value: 68.018 - type: mrr_at_1000 value: 68.035 - type: mrr_at_3 value: 65.667 - type: mrr_at_5 value: 66.567 - type: ndcg_at_1 value: 50.0 - type: ndcg_at_10 value: 40.910999999999994 - type: ndcg_at_100 value: 30.386999999999997 - type: ndcg_at_1000 value: 27.009 - type: ndcg_at_3 value: 46.776 - type: ndcg_at_5 value: 42.504 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 43.6 - type: precision_at_100 value: 31.64 - type: precision_at_1000 value: 13.214 - type: precision_at_3 value: 50.0 - type: precision_at_5 value: 44.800000000000004 - type: recall_at_1 value: 0.129 - type: recall_at_10 value: 0.95 - type: recall_at_100 value: 6.526999999999999 - type: recall_at_1000 value: 25.894000000000002 - type: recall_at_3 value: 0.34299999999999997 - type: recall_at_5 value: 0.49899999999999994 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.423003368001304 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.05364643191673 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.82474380833001 - type: cos_sim_spearman value: 66.06084939474535 - type: euclidean_pearson value: 71.1524270169037 - type: euclidean_spearman value: 66.06095698159474 - type: manhattan_pearson value: 68.79401530108056 - type: manhattan_spearman value: 64.55149376982865 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.40557358335994 - type: cos_sim_spearman value: 67.61862451971042 - type: euclidean_pearson value: 70.41404072782692 - type: euclidean_spearman value: 67.6198686410611 - type: manhattan_pearson value: 68.97879579551457 - type: manhattan_spearman value: 67.2295683767691 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.0503994579303 - type: cos_sim_spearman value: 79.15391592614903 - type: euclidean_pearson value: 78.84085468610613 - type: euclidean_spearman value: 79.15395372943995 - type: manhattan_pearson value: 78.64185478146945 - type: manhattan_spearman value: 79.14714263528944 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 78.3615928485999 - type: cos_sim_spearman value: 74.97382110132605 - type: euclidean_pearson value: 77.23854527060215 - type: euclidean_spearman value: 74.97381140978526 - type: manhattan_pearson value: 76.07931709400336 - type: manhattan_spearman value: 74.24120638811475 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.20257664300357 - type: cos_sim_spearman value: 82.00047913551732 - type: euclidean_pearson value: 81.9647778954467 - type: euclidean_spearman value: 82.0004776230638 - type: manhattan_pearson value: 81.71577106207948 - type: manhattan_spearman value: 81.99682550355493 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 76.25724348036597 - type: cos_sim_spearman value: 77.44730180182509 - type: euclidean_pearson value: 77.07257885830403 - type: euclidean_spearman value: 77.44757329765216 - type: manhattan_pearson value: 77.56516524638705 - type: manhattan_spearman value: 77.96306993156203 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.12218819562516 - type: cos_sim_spearman value: 85.1670421446071 - type: euclidean_pearson value: 84.780712654921 - type: euclidean_spearman value: 85.16791563947264 - type: manhattan_pearson value: 84.55044493571614 - type: manhattan_spearman value: 85.27489322017652 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.36228072576271 - type: cos_sim_spearman value: 60.8804162279283 - type: euclidean_pearson value: 63.45076147869696 - type: euclidean_spearman value: 60.8804162279283 - type: manhattan_pearson value: 62.75925080903245 - type: manhattan_spearman value: 60.42286149492114 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.42867098481462 - type: cos_sim_spearman value: 77.27498110374914 - type: euclidean_pearson value: 78.31189930366395 - type: euclidean_spearman value: 77.27499957014132 - type: manhattan_pearson value: 77.5492342797482 - type: manhattan_spearman value: 76.70759132287284 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.88088283802969 - type: mrr value: 91.2407327603406 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 34.75 - type: map_at_10 value: 43.795 - type: map_at_100 value: 44.897999999999996 - type: map_at_1000 value: 44.964999999999996 - type: map_at_3 value: 41.235 - type: map_at_5 value: 42.785000000000004 - type: mrr_at_1 value: 37.0 - type: mrr_at_10 value: 45.608 - type: mrr_at_100 value: 46.556999999999995 - type: mrr_at_1000 value: 46.62 - type: mrr_at_3 value: 43.333 - type: mrr_at_5 value: 44.733000000000004 - type: ndcg_at_1 value: 37.0 - type: ndcg_at_10 value: 48.620000000000005 - type: ndcg_at_100 value: 53.772 - type: ndcg_at_1000 value: 55.403999999999996 - type: ndcg_at_3 value: 43.741 - type: ndcg_at_5 value: 46.358 - type: precision_at_1 value: 37.0 - type: precision_at_10 value: 6.800000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 17.666999999999998 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 34.75 - type: recall_at_10 value: 61.87200000000001 - type: recall_at_100 value: 85.317 - type: recall_at_1000 value: 97.8 - type: recall_at_3 value: 48.567 - type: recall_at_5 value: 55.233 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71485148514851 - type: cos_sim_ap value: 91.89197853928673 - type: cos_sim_f1 value: 85.48387096774194 - type: cos_sim_precision value: 86.1788617886179 - type: cos_sim_recall value: 84.8 - type: dot_accuracy value: 99.71485148514851 - type: dot_ap value: 91.89197853928673 - type: dot_f1 value: 85.48387096774194 - type: dot_precision value: 86.1788617886179 - type: dot_recall value: 84.8 - type: euclidean_accuracy value: 99.71485148514851 - type: euclidean_ap value: 91.89197853928673 - type: euclidean_f1 value: 85.48387096774194 - type: euclidean_precision value: 86.1788617886179 - type: euclidean_recall value: 84.8 - type: manhattan_accuracy value: 99.76633663366337 - type: manhattan_ap value: 93.70793412033116 - type: manhattan_f1 value: 87.77050830397583 - type: manhattan_precision value: 88.34853090172238 - type: manhattan_recall value: 87.2 - type: max_accuracy value: 99.76633663366337 - type: max_ap value: 93.70793412033116 - type: max_f1 value: 87.77050830397583 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 42.040101504017464 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.736735784987 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.30708664346592 - type: mrr value: 43.91143578643579 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.57246866942087 - type: cos_sim_spearman value: 30.69719029010722 - type: dot_pearson value: 30.572468627823802 - type: dot_spearman value: 30.675070232612644 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.8429999999999997 - type: map_at_10 value: 8.476 - type: map_at_100 value: 14.779 - type: map_at_1000 value: 16.381 - type: map_at_3 value: 4.361000000000001 - type: map_at_5 value: 6.064 - type: mrr_at_1 value: 28.571 - type: mrr_at_10 value: 44.039 - type: mrr_at_100 value: 45.024 - type: mrr_at_1000 value: 45.024 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 42.075 - type: ndcg_at_1 value: 27.551 - type: ndcg_at_10 value: 22.672 - type: ndcg_at_100 value: 35.961999999999996 - type: ndcg_at_1000 value: 47.365 - type: ndcg_at_3 value: 26.016000000000002 - type: ndcg_at_5 value: 23.794999999999998 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 20.816000000000003 - type: precision_at_100 value: 8.245 - type: precision_at_1000 value: 1.555 - type: precision_at_3 value: 27.891 - type: precision_at_5 value: 24.082 - type: recall_at_1 value: 1.8429999999999997 - type: recall_at_10 value: 14.37 - type: recall_at_100 value: 49.120999999999995 - type: recall_at_1000 value: 83.98400000000001 - type: recall_at_3 value: 5.641 - type: recall_at_5 value: 8.321000000000002 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.1968 - type: ap value: 14.44161573930493 - type: f1 value: 54.83573235336061 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 50.94510469722694 - type: f1 value: 51.12605321866063 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 37.53693681679847 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.60851165285807 - type: cos_sim_ap value: 65.18156857786212 - type: cos_sim_f1 value: 62.61223512510475 - type: cos_sim_precision value: 57.30878807801885 - type: cos_sim_recall value: 68.99736147757255 - type: dot_accuracy value: 83.60851165285807 - type: dot_ap value: 65.18156857786212 - type: dot_f1 value: 62.61223512510475 - type: dot_precision value: 57.30878807801885 - type: dot_recall value: 68.99736147757255 - type: euclidean_accuracy value: 83.60851165285807 - type: euclidean_ap value: 65.18156857786212 - type: euclidean_f1 value: 62.61223512510475 - type: euclidean_precision value: 57.30878807801885 - type: euclidean_recall value: 68.99736147757255 - type: manhattan_accuracy value: 82.49389044525243 - type: manhattan_ap value: 62.03288965473206 - type: manhattan_f1 value: 59.25074695472304 - type: manhattan_precision value: 52.483713355048856 - type: manhattan_recall value: 68.02110817941951 - type: max_accuracy value: 83.60851165285807 - type: max_ap value: 65.18156857786212 - type: max_f1 value: 62.61223512510475 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.60235960724958 - type: cos_sim_ap value: 83.31122009321201 - type: cos_sim_f1 value: 75.5953714072415 - type: cos_sim_precision value: 73.36617881466454 - type: cos_sim_recall value: 77.96427471512165 - type: dot_accuracy value: 87.60235960724958 - type: dot_ap value: 83.31122039383348 - type: dot_f1 value: 75.5953714072415 - type: dot_precision value: 73.36617881466454 - type: dot_recall value: 77.96427471512165 - type: euclidean_accuracy value: 87.60235960724958 - type: euclidean_ap value: 83.31123735759617 - type: euclidean_f1 value: 75.5953714072415 - type: euclidean_precision value: 73.36617881466454 - type: euclidean_recall value: 77.96427471512165 - type: manhattan_accuracy value: 87.58295494236815 - type: manhattan_ap value: 83.15022211312501 - type: manhattan_f1 value: 75.28497215681878 - type: manhattan_precision value: 73.14982932674849 - type: manhattan_recall value: 77.54850631352016 - type: max_accuracy value: 87.60235960724958 - type: max_ap value: 83.31123735759617 - type: max_f1 value: 75.5953714072415 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/nmc-nignore30

twadada

null

[ "mteb", "model-index", "region:us" ]

2024-09-17T00:36:30

2024-09-17T00:36:40

0

--- tags: - mteb model-index: - name: nomic_classification_nignore30 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.02985074626865 - type: ap value: 36.54755879675939 - type: f1 value: 67.84911428462374 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 60.98745000000001 - type: ap value: 56.79972495487593 - type: f1 value: 60.79607311981127 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.606000000000005 - type: f1 value: 31.20575804283948 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.266 - type: map_at_10 value: 35.579 - type: map_at_100 value: 36.867 - type: map_at_1000 value: 36.887 - type: map_at_3 value: 31.105 - type: map_at_5 value: 33.512 - type: mrr_at_1 value: 21.764 - type: mrr_at_10 value: 35.768 - type: mrr_at_100 value: 37.049 - type: mrr_at_1000 value: 37.069 - type: mrr_at_3 value: 31.354 - type: mrr_at_5 value: 33.694 - type: ndcg_at_1 value: 21.266 - type: ndcg_at_10 value: 43.697 - type: ndcg_at_100 value: 49.444 - type: ndcg_at_1000 value: 49.918 - type: ndcg_at_3 value: 34.415 - type: ndcg_at_5 value: 38.751999999999995 - type: precision_at_1 value: 21.266 - type: precision_at_10 value: 6.97 - type: precision_at_100 value: 0.954 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.674999999999999 - type: precision_at_5 value: 10.91 - type: recall_at_1 value: 21.266 - type: recall_at_10 value: 69.70100000000001 - type: recall_at_100 value: 95.448 - type: recall_at_1000 value: 99.075 - type: recall_at_3 value: 44.025999999999996 - type: recall_at_5 value: 54.552 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.45486521675564 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.270159650279354 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 53.62399843388994 - type: mrr value: 68.1675680429143 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.62266587849676 - type: cos_sim_spearman value: 80.48918339823612 - type: euclidean_pearson value: 82.46661732971302 - type: euclidean_spearman value: 80.48918339823612 - type: manhattan_pearson value: 81.55398066885756 - type: manhattan_spearman value: 80.27411825686711 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.07142857142857 - type: f1 value: 72.39723822054579 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.426645848653045 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.54829160604571 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 24.001 - type: map_at_10 value: 31.195 - type: map_at_100 value: 32.342999999999996 - type: map_at_1000 value: 32.489000000000004 - type: map_at_3 value: 28.814 - type: map_at_5 value: 30.014000000000003 - type: mrr_at_1 value: 30.186 - type: mrr_at_10 value: 37.034 - type: mrr_at_100 value: 37.881 - type: mrr_at_1000 value: 37.946000000000005 - type: mrr_at_3 value: 35.241 - type: mrr_at_5 value: 36.120999999999995 - type: ndcg_at_1 value: 30.186 - type: ndcg_at_10 value: 35.972 - type: ndcg_at_100 value: 41.25 - type: ndcg_at_1000 value: 44.171 - type: ndcg_at_3 value: 32.674 - type: ndcg_at_5 value: 33.833 - type: precision_at_1 value: 30.186 - type: precision_at_10 value: 6.723999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.172 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 10.815 - type: recall_at_1 value: 24.001 - type: recall_at_10 value: 44.057 - type: recall_at_100 value: 67.72500000000001 - type: recall_at_1000 value: 87.464 - type: recall_at_3 value: 33.817 - type: recall_at_5 value: 37.684 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.766000000000002 - type: map_at_10 value: 23.07 - type: map_at_100 value: 24.062 - type: map_at_1000 value: 24.178 - type: map_at_3 value: 21.364 - type: map_at_5 value: 22.3 - type: mrr_at_1 value: 21.146 - type: mrr_at_10 value: 27.24 - type: mrr_at_100 value: 28.092 - type: mrr_at_1000 value: 28.163 - type: mrr_at_3 value: 25.605 - type: mrr_at_5 value: 26.567 - type: ndcg_at_1 value: 21.146 - type: ndcg_at_10 value: 27.031 - type: ndcg_at_100 value: 31.430999999999997 - type: ndcg_at_1000 value: 34.086 - type: ndcg_at_3 value: 24.136 - type: ndcg_at_5 value: 25.462 - type: precision_at_1 value: 21.146 - type: precision_at_10 value: 5.006 - type: precision_at_100 value: 0.901 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 11.762 - type: precision_at_5 value: 8.229000000000001 - type: recall_at_1 value: 16.766000000000002 - type: recall_at_10 value: 34.55 - type: recall_at_100 value: 53.542 - type: recall_at_1000 value: 71.66900000000001 - type: recall_at_3 value: 26.205000000000002 - type: recall_at_5 value: 29.854000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 26.579000000000004 - type: map_at_10 value: 35.482 - type: map_at_100 value: 36.564 - type: map_at_1000 value: 36.656 - type: map_at_3 value: 32.940999999999995 - type: map_at_5 value: 34.331 - type: mrr_at_1 value: 30.784 - type: mrr_at_10 value: 38.721 - type: mrr_at_100 value: 39.592 - type: mrr_at_1000 value: 39.653 - type: mrr_at_3 value: 36.468 - type: mrr_at_5 value: 37.688 - type: ndcg_at_1 value: 30.784 - type: ndcg_at_10 value: 40.351 - type: ndcg_at_100 value: 45.499 - type: ndcg_at_1000 value: 47.641 - type: ndcg_at_3 value: 35.605 - type: ndcg_at_5 value: 37.798 - type: precision_at_1 value: 30.784 - type: precision_at_10 value: 6.564 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.862000000000002 - type: precision_at_5 value: 11.008999999999999 - type: recall_at_1 value: 26.579000000000004 - type: recall_at_10 value: 51.978 - type: recall_at_100 value: 75.331 - type: recall_at_1000 value: 90.774 - type: recall_at_3 value: 39.149 - type: recall_at_5 value: 44.516 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.013 - type: map_at_10 value: 17.393 - type: map_at_100 value: 18.256 - type: map_at_1000 value: 18.364 - type: map_at_3 value: 15.812000000000001 - type: map_at_5 value: 16.601 - type: mrr_at_1 value: 14.237 - type: mrr_at_10 value: 18.706999999999997 - type: mrr_at_100 value: 19.553 - type: mrr_at_1000 value: 19.651 - type: mrr_at_3 value: 17.081 - type: mrr_at_5 value: 17.895 - type: ndcg_at_1 value: 14.237 - type: ndcg_at_10 value: 20.315 - type: ndcg_at_100 value: 24.914 - type: ndcg_at_1000 value: 28.244999999999997 - type: ndcg_at_3 value: 16.994 - type: ndcg_at_5 value: 18.396 - type: precision_at_1 value: 14.237 - type: precision_at_10 value: 3.198 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.0809999999999995 - type: precision_at_5 value: 4.994 - type: recall_at_1 value: 13.013 - type: recall_at_10 value: 28.297 - type: recall_at_100 value: 50.113 - type: recall_at_1000 value: 76.19500000000001 - type: recall_at_3 value: 19.062 - type: recall_at_5 value: 22.527 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.783 - type: map_at_10 value: 10.439 - type: map_at_100 value: 11.26 - type: map_at_1000 value: 11.394 - type: map_at_3 value: 9.314 - type: map_at_5 value: 9.832 - type: mrr_at_1 value: 8.831 - type: mrr_at_10 value: 12.902 - type: mrr_at_100 value: 13.799 - type: mrr_at_1000 value: 13.901 - type: mrr_at_3 value: 11.692 - type: mrr_at_5 value: 12.200999999999999 - type: ndcg_at_1 value: 8.831 - type: ndcg_at_10 value: 12.973 - type: ndcg_at_100 value: 17.465 - type: ndcg_at_1000 value: 21.203 - type: ndcg_at_3 value: 10.778 - type: ndcg_at_5 value: 11.601 - type: precision_at_1 value: 8.831 - type: precision_at_10 value: 2.475 - type: precision_at_100 value: 0.553 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.265000000000001 - type: precision_at_5 value: 3.781 - type: recall_at_1 value: 6.783 - type: recall_at_10 value: 18.386 - type: recall_at_100 value: 38.885999999999996 - type: recall_at_1000 value: 66.621 - type: recall_at_3 value: 12.235 - type: recall_at_5 value: 14.374999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.946 - type: map_at_10 value: 24.188000000000002 - type: map_at_100 value: 25.402 - type: map_at_1000 value: 25.544 - type: map_at_3 value: 22.157 - type: map_at_5 value: 23.315 - type: mrr_at_1 value: 22.233 - type: mrr_at_10 value: 28.703 - type: mrr_at_100 value: 29.669 - type: mrr_at_1000 value: 29.748 - type: mrr_at_3 value: 26.676 - type: mrr_at_5 value: 27.894000000000002 - type: ndcg_at_1 value: 22.233 - type: ndcg_at_10 value: 28.483999999999998 - type: ndcg_at_100 value: 34.239999999999995 - type: ndcg_at_1000 value: 37.351 - type: ndcg_at_3 value: 25.018 - type: ndcg_at_5 value: 26.679000000000002 - type: precision_at_1 value: 22.233 - type: precision_at_10 value: 5.236 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.806 - type: precision_at_5 value: 8.566 - type: recall_at_1 value: 17.946 - type: recall_at_10 value: 37.049 - type: recall_at_100 value: 62.473 - type: recall_at_1000 value: 83.829 - type: recall_at_3 value: 27.022000000000002 - type: recall_at_5 value: 31.435000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.966000000000001 - type: map_at_10 value: 19.797 - type: map_at_100 value: 20.764 - type: map_at_1000 value: 20.913 - type: map_at_3 value: 17.688000000000002 - type: map_at_5 value: 18.796 - type: mrr_at_1 value: 17.122999999999998 - type: mrr_at_10 value: 23.277 - type: mrr_at_100 value: 24.095 - type: mrr_at_1000 value: 24.197 - type: mrr_at_3 value: 21.176000000000002 - type: mrr_at_5 value: 22.323 - type: ndcg_at_1 value: 17.122999999999998 - type: ndcg_at_10 value: 23.860999999999997 - type: ndcg_at_100 value: 28.669 - type: ndcg_at_1000 value: 32.375 - type: ndcg_at_3 value: 19.983999999999998 - type: ndcg_at_5 value: 21.647 - type: precision_at_1 value: 17.122999999999998 - type: precision_at_10 value: 4.623 - type: precision_at_100 value: 0.839 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 9.551 - type: precision_at_5 value: 7.1 - type: recall_at_1 value: 13.966000000000001 - type: recall_at_10 value: 32.629999999999995 - type: recall_at_100 value: 53.842 - type: recall_at_1000 value: 80.583 - type: recall_at_3 value: 21.804000000000002 - type: recall_at_5 value: 26.101999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.974750000000004 - type: map_at_10 value: 20.34575 - type: map_at_100 value: 21.290416666666665 - type: map_at_1000 value: 21.41825 - type: map_at_3 value: 18.576500000000003 - type: map_at_5 value: 19.546166666666668 - type: mrr_at_1 value: 18.049249999999997 - type: mrr_at_10 value: 23.45216666666667 - type: mrr_at_100 value: 24.29241666666667 - type: mrr_at_1000 value: 24.37841666666667 - type: mrr_at_3 value: 21.728749999999998 - type: mrr_at_5 value: 22.680916666666665 - type: ndcg_at_1 value: 18.049249999999997 - type: ndcg_at_10 value: 23.90125 - type: ndcg_at_100 value: 28.57325 - type: ndcg_at_1000 value: 31.747583333333335 - type: ndcg_at_3 value: 20.71783333333333 - type: ndcg_at_5 value: 22.17008333333333 - type: precision_at_1 value: 18.049249999999997 - type: precision_at_10 value: 4.257666666666667 - type: precision_at_100 value: 0.7843333333333332 - type: precision_at_1000 value: 0.12375000000000003 - type: precision_at_3 value: 9.573750000000002 - type: precision_at_5 value: 6.871666666666666 - type: recall_at_1 value: 14.974750000000004 - type: recall_at_10 value: 31.535416666666666 - type: recall_at_100 value: 52.869583333333324 - type: recall_at_1000 value: 75.93208333333334 - type: recall_at_3 value: 22.561833333333333 - type: recall_at_5 value: 26.351583333333334 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.584 - type: map_at_10 value: 15.47 - type: map_at_100 value: 16.276 - type: map_at_1000 value: 16.361 - type: map_at_3 value: 14.022000000000002 - type: map_at_5 value: 14.884 - type: mrr_at_1 value: 13.65 - type: mrr_at_10 value: 17.566000000000003 - type: mrr_at_100 value: 18.335 - type: mrr_at_1000 value: 18.411 - type: mrr_at_3 value: 16.053 - type: mrr_at_5 value: 16.843 - type: ndcg_at_1 value: 13.65 - type: ndcg_at_10 value: 18.208 - type: ndcg_at_100 value: 22.352 - type: ndcg_at_1000 value: 24.969 - type: ndcg_at_3 value: 15.459 - type: ndcg_at_5 value: 16.817 - type: precision_at_1 value: 13.65 - type: precision_at_10 value: 3.083 - type: precision_at_100 value: 0.561 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.902 - type: precision_at_5 value: 4.968999999999999 - type: recall_at_1 value: 11.584 - type: recall_at_10 value: 24.629 - type: recall_at_100 value: 43.963 - type: recall_at_1000 value: 63.944 - type: recall_at_3 value: 17.155 - type: recall_at_5 value: 20.598 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.792000000000001 - type: map_at_10 value: 11.246 - type: map_at_100 value: 11.955 - type: map_at_1000 value: 12.076 - type: map_at_3 value: 10.176 - type: map_at_5 value: 10.802 - type: mrr_at_1 value: 9.67 - type: mrr_at_10 value: 13.591000000000001 - type: mrr_at_100 value: 14.285999999999998 - type: mrr_at_1000 value: 14.385 - type: mrr_at_3 value: 12.394 - type: mrr_at_5 value: 13.104 - type: ndcg_at_1 value: 9.67 - type: ndcg_at_10 value: 13.645 - type: ndcg_at_100 value: 17.562 - type: ndcg_at_1000 value: 21.101 - type: ndcg_at_3 value: 11.635 - type: ndcg_at_5 value: 12.638 - type: precision_at_1 value: 9.67 - type: precision_at_10 value: 2.54 - type: precision_at_100 value: 0.538 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.632000000000001 - type: precision_at_5 value: 4.136 - type: recall_at_1 value: 7.792000000000001 - type: recall_at_10 value: 18.63 - type: recall_at_100 value: 37.047999999999995 - type: recall_at_1000 value: 63.391 - type: recall_at_3 value: 12.956999999999999 - type: recall_at_5 value: 15.581 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.358999999999998 - type: map_at_10 value: 18.154999999999998 - type: map_at_100 value: 19.008 - type: map_at_1000 value: 19.125 - type: map_at_3 value: 16.645 - type: map_at_5 value: 17.544999999999998 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.973 - type: mrr_at_100 value: 21.782 - type: mrr_at_1000 value: 21.88 - type: mrr_at_3 value: 19.356 - type: mrr_at_5 value: 20.28 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 21.391 - type: ndcg_at_100 value: 25.71 - type: ndcg_at_1000 value: 29.016 - type: ndcg_at_3 value: 18.489 - type: ndcg_at_5 value: 19.916 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.573 - type: precision_at_100 value: 0.636 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.488999999999999 - type: precision_at_5 value: 5.989 - type: recall_at_1 value: 13.358999999999998 - type: recall_at_10 value: 28.695999999999998 - type: recall_at_100 value: 48.165 - type: recall_at_1000 value: 72.64500000000001 - type: recall_at_3 value: 20.573 - type: recall_at_5 value: 24.284 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.881 - type: map_at_10 value: 21.754 - type: map_at_100 value: 22.88 - type: map_at_1000 value: 23.087 - type: map_at_3 value: 19.827 - type: map_at_5 value: 20.964 - type: mrr_at_1 value: 19.564999999999998 - type: mrr_at_10 value: 25.246000000000002 - type: mrr_at_100 value: 26.163999999999998 - type: mrr_at_1000 value: 26.240999999999996 - type: mrr_at_3 value: 23.352999999999998 - type: mrr_at_5 value: 24.587999999999997 - type: ndcg_at_1 value: 19.564999999999998 - type: ndcg_at_10 value: 25.740000000000002 - type: ndcg_at_100 value: 30.977 - type: ndcg_at_1000 value: 34.486 - type: ndcg_at_3 value: 22.625 - type: ndcg_at_5 value: 24.294 - type: precision_at_1 value: 19.564999999999998 - type: precision_at_10 value: 5.0200000000000005 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 10.738 - type: precision_at_5 value: 8.103 - type: recall_at_1 value: 15.881 - type: recall_at_10 value: 32.918 - type: recall_at_100 value: 58.184000000000005 - type: recall_at_1000 value: 81.76299999999999 - type: recall_at_3 value: 23.992 - type: recall_at_5 value: 28.265 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.027000000000001 - type: map_at_10 value: 15.959999999999999 - type: map_at_100 value: 16.715 - type: map_at_1000 value: 16.832 - type: map_at_3 value: 14.158000000000001 - type: map_at_5 value: 15.17 - type: mrr_at_1 value: 13.494 - type: mrr_at_10 value: 17.466 - type: mrr_at_100 value: 18.261 - type: mrr_at_1000 value: 18.365000000000002 - type: mrr_at_3 value: 15.65 - type: mrr_at_5 value: 16.667 - type: ndcg_at_1 value: 13.494 - type: ndcg_at_10 value: 18.844 - type: ndcg_at_100 value: 22.81 - type: ndcg_at_1000 value: 26.327 - type: ndcg_at_3 value: 15.217 - type: ndcg_at_5 value: 16.96 - type: precision_at_1 value: 13.494 - type: precision_at_10 value: 3.05 - type: precision_at_100 value: 0.532 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.769 - type: recall_at_1 value: 12.027000000000001 - type: recall_at_10 value: 26.605 - type: recall_at_100 value: 45.163 - type: recall_at_1000 value: 72.307 - type: recall_at_3 value: 16.771 - type: recall_at_5 value: 20.998 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.479 - type: map_at_10 value: 11.559 - type: map_at_100 value: 12.936 - type: map_at_1000 value: 13.120000000000001 - type: map_at_3 value: 9.377 - type: map_at_5 value: 10.494 - type: mrr_at_1 value: 14.396999999999998 - type: mrr_at_10 value: 23.039 - type: mrr_at_100 value: 24.141000000000002 - type: mrr_at_1000 value: 24.215999999999998 - type: mrr_at_3 value: 19.814999999999998 - type: mrr_at_5 value: 21.656 - type: ndcg_at_1 value: 14.396999999999998 - type: ndcg_at_10 value: 17.258000000000003 - type: ndcg_at_100 value: 23.615 - type: ndcg_at_1000 value: 27.605 - type: ndcg_at_3 value: 13.114999999999998 - type: ndcg_at_5 value: 14.698 - type: precision_at_1 value: 14.396999999999998 - type: precision_at_10 value: 5.713 - type: precision_at_100 value: 1.25 - type: precision_at_1000 value: 0.198 - type: precision_at_3 value: 9.924 - type: precision_at_5 value: 8.104 - type: recall_at_1 value: 6.479 - type: recall_at_10 value: 22.088 - type: recall_at_100 value: 44.681 - type: recall_at_1000 value: 67.869 - type: recall_at_3 value: 12.203 - type: recall_at_5 value: 16.275000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.618 - type: map_at_10 value: 10.217 - type: map_at_100 value: 14.038999999999998 - type: map_at_1000 value: 15.03 - type: map_at_3 value: 7.523000000000001 - type: map_at_5 value: 8.688 - type: mrr_at_1 value: 41.75 - type: mrr_at_10 value: 51.991 - type: mrr_at_100 value: 52.711 - type: mrr_at_1000 value: 52.746 - type: mrr_at_3 value: 49.5 - type: mrr_at_5 value: 50.961999999999996 - type: ndcg_at_1 value: 30.875000000000004 - type: ndcg_at_10 value: 24.709999999999997 - type: ndcg_at_100 value: 27.584999999999997 - type: ndcg_at_1000 value: 34.508 - type: ndcg_at_3 value: 27.88 - type: ndcg_at_5 value: 26.168999999999997 - type: precision_at_1 value: 41.75 - type: precision_at_10 value: 21.45 - type: precision_at_100 value: 6.795 - type: precision_at_1000 value: 1.43 - type: precision_at_3 value: 33.083 - type: precision_at_5 value: 27.750000000000004 - type: recall_at_1 value: 4.618 - type: recall_at_10 value: 14.898 - type: recall_at_100 value: 33.027 - type: recall_at_1000 value: 57.036 - type: recall_at_3 value: 8.995000000000001 - type: recall_at_5 value: 11.23 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.19499999999999 - type: f1 value: 40.60048839070268 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.11 - type: map_at_10 value: 25.478 - type: map_at_100 value: 26.436 - type: map_at_1000 value: 26.51 - type: map_at_3 value: 22.996 - type: map_at_5 value: 24.329 - type: mrr_at_1 value: 18.317 - type: mrr_at_10 value: 27.090999999999998 - type: mrr_at_100 value: 28.037 - type: mrr_at_1000 value: 28.102 - type: mrr_at_3 value: 24.532 - type: mrr_at_5 value: 25.918999999999997 - type: ndcg_at_1 value: 18.317 - type: ndcg_at_10 value: 30.448999999999998 - type: ndcg_at_100 value: 35.302 - type: ndcg_at_1000 value: 37.325 - type: ndcg_at_3 value: 25.326999999999998 - type: ndcg_at_5 value: 27.716 - type: precision_at_1 value: 18.317 - type: precision_at_10 value: 4.8469999999999995 - type: precision_at_100 value: 0.747 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 10.975999999999999 - type: precision_at_5 value: 7.846 - type: recall_at_1 value: 17.11 - type: recall_at_10 value: 44.466 - type: recall_at_100 value: 67.06299999999999 - type: recall_at_1000 value: 82.64200000000001 - type: recall_at_3 value: 30.509999999999998 - type: recall_at_5 value: 36.27 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.446999999999999 - type: map_at_10 value: 12.188 - type: map_at_100 value: 13.241 - type: map_at_1000 value: 13.450000000000001 - type: map_at_3 value: 10.184999999999999 - type: map_at_5 value: 11.266 - type: mrr_at_1 value: 15.123000000000001 - type: mrr_at_10 value: 21.397 - type: mrr_at_100 value: 22.303 - type: mrr_at_1000 value: 22.398 - type: mrr_at_3 value: 19.187 - type: mrr_at_5 value: 20.383000000000003 - type: ndcg_at_1 value: 15.123000000000001 - type: ndcg_at_10 value: 16.957 - type: ndcg_at_100 value: 22.147 - type: ndcg_at_1000 value: 26.759 - type: ndcg_at_3 value: 14.091000000000001 - type: ndcg_at_5 value: 15.135000000000002 - type: precision_at_1 value: 15.123000000000001 - type: precision_at_10 value: 4.938 - type: precision_at_100 value: 1.019 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 9.568 - type: precision_at_5 value: 7.438000000000001 - type: recall_at_1 value: 7.446999999999999 - type: recall_at_10 value: 22.094 - type: recall_at_100 value: 42.397 - type: recall_at_1000 value: 71.15700000000001 - type: recall_at_3 value: 12.879 - type: recall_at_5 value: 16.49 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.285 - type: map_at_10 value: 25.703 - type: map_at_100 value: 26.634 - type: map_at_1000 value: 26.741999999999997 - type: map_at_3 value: 23.642 - type: map_at_5 value: 24.826 - type: mrr_at_1 value: 36.57 - type: mrr_at_10 value: 43.772 - type: mrr_at_100 value: 44.51 - type: mrr_at_1000 value: 44.561 - type: mrr_at_3 value: 41.787 - type: mrr_at_5 value: 42.964 - type: ndcg_at_1 value: 36.57 - type: ndcg_at_10 value: 32.763999999999996 - type: ndcg_at_100 value: 37.077 - type: ndcg_at_1000 value: 39.666000000000004 - type: ndcg_at_3 value: 28.906 - type: ndcg_at_5 value: 30.86 - type: precision_at_1 value: 36.57 - type: precision_at_10 value: 7.202 - type: precision_at_100 value: 1.065 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 18.231 - type: precision_at_5 value: 12.483 - type: recall_at_1 value: 18.285 - type: recall_at_10 value: 36.009 - type: recall_at_100 value: 53.27499999999999 - type: recall_at_1000 value: 70.635 - type: recall_at_3 value: 27.345999999999997 - type: recall_at_5 value: 31.208999999999996 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 61.361999999999995 - type: ap value: 57.09674595597791 - type: f1 value: 60.94720401382382 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.686 - type: map_at_10 value: 11.454 - type: map_at_100 value: 12.342 - type: map_at_1000 value: 12.447 - type: map_at_3 value: 9.722 - type: map_at_5 value: 10.632 - type: mrr_at_1 value: 6.891 - type: mrr_at_10 value: 11.768 - type: mrr_at_100 value: 12.651000000000002 - type: mrr_at_1000 value: 12.753 - type: mrr_at_3 value: 10.001999999999999 - type: mrr_at_5 value: 10.918999999999999 - type: ndcg_at_1 value: 6.848 - type: ndcg_at_10 value: 14.466000000000001 - type: ndcg_at_100 value: 19.301 - type: ndcg_at_1000 value: 22.458 - type: ndcg_at_3 value: 10.836 - type: ndcg_at_5 value: 12.475 - type: precision_at_1 value: 6.848 - type: precision_at_10 value: 2.48 - type: precision_at_100 value: 0.49899999999999994 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.766 - type: precision_at_5 value: 3.682 - type: recall_at_1 value: 6.686 - type: recall_at_10 value: 23.82 - type: recall_at_100 value: 47.349999999999994 - type: recall_at_1000 value: 72.66 - type: recall_at_3 value: 13.811000000000002 - type: recall_at_5 value: 17.76 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 87.67213862289103 - type: f1 value: 86.45841301738238 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.82170542635659 - type: f1 value: 39.12615117855274 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 60.18829858776058 - type: f1 value: 58.617914607265064 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.45595158036315 - type: f1 value: 64.9778374481982 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.531989286141012 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.070324322784792 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.516858081965257 - type: mrr value: 30.51047930520146 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.131 - type: map_at_10 value: 8.826 - type: map_at_100 value: 11.094999999999999 - type: map_at_1000 value: 12.484 - type: map_at_3 value: 6.723 - type: map_at_5 value: 7.683 - type: mrr_at_1 value: 34.985 - type: mrr_at_10 value: 44.921 - type: mrr_at_100 value: 45.62 - type: mrr_at_1000 value: 45.676 - type: mrr_at_3 value: 42.931000000000004 - type: mrr_at_5 value: 44.385999999999996 - type: ndcg_at_1 value: 32.507999999999996 - type: ndcg_at_10 value: 26.773000000000003 - type: ndcg_at_100 value: 24.751 - type: ndcg_at_1000 value: 34.19 - type: ndcg_at_3 value: 31.213 - type: ndcg_at_5 value: 29.249000000000002 - type: precision_at_1 value: 34.985 - type: precision_at_10 value: 20.247999999999998 - type: precision_at_100 value: 6.907000000000001 - type: precision_at_1000 value: 2.031 - type: precision_at_3 value: 30.341 - type: precision_at_5 value: 25.759 - type: recall_at_1 value: 4.131 - type: recall_at_10 value: 12.465 - type: recall_at_100 value: 25.776 - type: recall_at_1000 value: 59.876 - type: recall_at_3 value: 7.968 - type: recall_at_5 value: 9.968 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.277000000000001 - type: map_at_10 value: 15.709999999999999 - type: map_at_100 value: 16.980999999999998 - type: map_at_1000 value: 17.074 - type: map_at_3 value: 13.157 - type: map_at_5 value: 14.571000000000002 - type: mrr_at_1 value: 10.574 - type: mrr_at_10 value: 17.344 - type: mrr_at_100 value: 18.506 - type: mrr_at_1000 value: 18.584999999999997 - type: mrr_at_3 value: 14.677000000000001 - type: mrr_at_5 value: 16.213 - type: ndcg_at_1 value: 10.574 - type: ndcg_at_10 value: 20.044 - type: ndcg_at_100 value: 26.447 - type: ndcg_at_1000 value: 29.084 - type: ndcg_at_3 value: 14.787 - type: ndcg_at_5 value: 17.362 - type: precision_at_1 value: 10.574 - type: precision_at_10 value: 3.7600000000000002 - type: precision_at_100 value: 0.738 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 6.933 - type: precision_at_5 value: 5.608 - type: recall_at_1 value: 9.277000000000001 - type: recall_at_10 value: 31.948 - type: recall_at_100 value: 61.708 - type: recall_at_1000 value: 82.07799999999999 - type: recall_at_3 value: 18.045 - type: recall_at_5 value: 24.038999999999998 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.268 - type: map_at_10 value: 77.19500000000001 - type: map_at_100 value: 77.95299999999999 - type: map_at_1000 value: 77.986 - type: map_at_3 value: 74.30499999999999 - type: map_at_5 value: 76.054 - type: mrr_at_1 value: 74.09 - type: mrr_at_10 value: 81.384 - type: mrr_at_100 value: 81.592 - type: mrr_at_1000 value: 81.597 - type: mrr_at_3 value: 80.00500000000001 - type: mrr_at_5 value: 80.876 - type: ndcg_at_1 value: 74.16 - type: ndcg_at_10 value: 81.813 - type: ndcg_at_100 value: 83.787 - type: ndcg_at_1000 value: 84.11800000000001 - type: ndcg_at_3 value: 78.389 - type: ndcg_at_5 value: 80.123 - type: precision_at_1 value: 74.16 - type: precision_at_10 value: 12.35 - type: precision_at_100 value: 1.466 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.067 - type: precision_at_5 value: 22.442 - type: recall_at_1 value: 64.268 - type: recall_at_10 value: 90.67 - type: recall_at_100 value: 97.935 - type: recall_at_1000 value: 99.703 - type: recall_at_3 value: 80.752 - type: recall_at_5 value: 85.63300000000001 - type: map_at_1 value: 2.948 - type: map_at_10 value: 7.126 - type: map_at_100 value: 8.462 - type: map_at_1000 value: 8.713 - type: map_at_3 value: 5.143 - type: map_at_5 value: 6.117 - type: mrr_at_1 value: 14.499999999999998 - type: mrr_at_10 value: 22.455 - type: mrr_at_100 value: 23.666 - type: mrr_at_1000 value: 23.745 - type: mrr_at_3 value: 19.417 - type: mrr_at_5 value: 21.117 - type: ndcg_at_1 value: 14.499999999999998 - type: ndcg_at_10 value: 12.666 - type: ndcg_at_100 value: 18.993 - type: ndcg_at_1000 value: 24.09 - type: ndcg_at_3 value: 11.655999999999999 - type: ndcg_at_5 value: 10.342 - type: precision_at_1 value: 14.499999999999998 - type: precision_at_10 value: 6.65 - type: precision_at_100 value: 1.598 - type: precision_at_1000 value: 0.28300000000000003 - type: precision_at_3 value: 10.8 - type: precision_at_5 value: 9.1 - type: recall_at_1 value: 2.948 - type: recall_at_10 value: 13.492 - type: recall_at_100 value: 32.448 - type: recall_at_1000 value: 57.553 - type: recall_at_3 value: 6.578 - type: recall_at_5 value: 9.242 - type: map_at_1 value: 0.129 - type: map_at_10 value: 0.6890000000000001 - type: map_at_100 value: 3.511 - type: map_at_1000 value: 8.943 - type: map_at_3 value: 0.304 - type: map_at_5 value: 0.42700000000000005 - type: mrr_at_1 value: 56.00000000000001 - type: mrr_at_10 value: 65.908 - type: mrr_at_100 value: 66.60199999999999 - type: mrr_at_1000 value: 66.60199999999999 - type: mrr_at_3 value: 63.333 - type: mrr_at_5 value: 64.23299999999999 - type: ndcg_at_1 value: 51.0 - type: ndcg_at_10 value: 39.304 - type: ndcg_at_100 value: 29.392000000000003 - type: ndcg_at_1000 value: 26.044 - type: ndcg_at_3 value: 45.408 - type: ndcg_at_5 value: 41.997 - type: precision_at_1 value: 56.00000000000001 - type: precision_at_10 value: 40.8 - type: precision_at_100 value: 30.48 - type: precision_at_1000 value: 12.692 - type: precision_at_3 value: 48.0 - type: precision_at_5 value: 43.6 - type: recall_at_1 value: 0.129 - type: recall_at_10 value: 0.893 - type: recall_at_100 value: 6.324000000000001 - type: recall_at_1000 value: 24.964 - type: recall_at_3 value: 0.33999999999999997 - type: recall_at_5 value: 0.505 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.191626251430044 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.00784930616429 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.38146213095916 - type: cos_sim_spearman value: 65.36914729991646 - type: euclidean_pearson value: 70.34893420889419 - type: euclidean_spearman value: 65.36925972117625 - type: manhattan_pearson value: 68.16816720045782 - type: manhattan_spearman value: 64.0884396246228 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.45813185900207 - type: cos_sim_spearman value: 68.03206487736479 - type: euclidean_pearson value: 70.55331228911669 - type: euclidean_spearman value: 68.03330456319067 - type: manhattan_pearson value: 68.32513309931606 - type: manhattan_spearman value: 66.90519361570585 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.32203252223916 - type: cos_sim_spearman value: 78.44952447167366 - type: euclidean_pearson value: 78.18870184193474 - type: euclidean_spearman value: 78.44956228059971 - type: manhattan_pearson value: 77.82417744157945 - type: manhattan_spearman value: 78.17317129725184 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.98515479114604 - type: cos_sim_spearman value: 74.70914230860409 - type: euclidean_pearson value: 76.81874418213698 - type: euclidean_spearman value: 74.70913261737951 - type: manhattan_pearson value: 75.54410520012546 - type: manhattan_spearman value: 73.74596322038998 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.34912352314105 - type: cos_sim_spearman value: 82.13479378308254 - type: euclidean_pearson value: 82.07291865315551 - type: euclidean_spearman value: 82.13479226815167 - type: manhattan_pearson value: 81.51909627091456 - type: manhattan_spearman value: 81.70075499671213 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 76.1116492955691 - type: cos_sim_spearman value: 77.19800116078945 - type: euclidean_pearson value: 76.8231316467101 - type: euclidean_spearman value: 77.19883015620502 - type: manhattan_pearson value: 77.10588536013977 - type: manhattan_spearman value: 77.50215416532438 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.10770264372314 - type: cos_sim_spearman value: 84.97403648808209 - type: euclidean_pearson value: 84.41825024902698 - type: euclidean_spearman value: 84.97491009412074 - type: manhattan_pearson value: 84.16827578787243 - type: manhattan_spearman value: 84.92739867128569 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 62.91807215204278 - type: cos_sim_spearman value: 59.61282196137074 - type: euclidean_pearson value: 62.702286829442436 - type: euclidean_spearman value: 59.61282196137074 - type: manhattan_pearson value: 62.26491120673072 - type: manhattan_spearman value: 59.7161013914999 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.43114398442724 - type: cos_sim_spearman value: 77.35423527756463 - type: euclidean_pearson value: 78.2269102978861 - type: euclidean_spearman value: 77.35428366374488 - type: manhattan_pearson value: 77.26973789544932 - type: manhattan_spearman value: 76.58307796792111 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.61932743075317 - type: mrr value: 91.38920810489437 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 36.861 - type: map_at_10 value: 45.93 - type: map_at_100 value: 46.861000000000004 - type: map_at_1000 value: 46.924 - type: map_at_3 value: 43.283 - type: map_at_5 value: 44.675 - type: mrr_at_1 value: 39.333 - type: mrr_at_10 value: 47.906 - type: mrr_at_100 value: 48.665000000000006 - type: mrr_at_1000 value: 48.722 - type: mrr_at_3 value: 45.611000000000004 - type: mrr_at_5 value: 46.778 - type: ndcg_at_1 value: 39.333 - type: ndcg_at_10 value: 50.970000000000006 - type: ndcg_at_100 value: 55.491 - type: ndcg_at_1000 value: 57.099 - type: ndcg_at_3 value: 45.837 - type: ndcg_at_5 value: 48.081 - type: precision_at_1 value: 39.333 - type: precision_at_10 value: 7.199999999999999 - type: precision_at_100 value: 0.9730000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.4 - type: recall_at_1 value: 36.861 - type: recall_at_10 value: 64.839 - type: recall_at_100 value: 85.983 - type: recall_at_1000 value: 98.467 - type: recall_at_3 value: 50.678 - type: recall_at_5 value: 56.24400000000001 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.75742574257426 - type: cos_sim_ap value: 93.4938177052363 - type: cos_sim_f1 value: 87.45019920318725 - type: cos_sim_precision value: 87.10317460317461 - type: cos_sim_recall value: 87.8 - type: dot_accuracy value: 99.75742574257426 - type: dot_ap value: 93.4938177052363 - type: dot_f1 value: 87.45019920318725 - type: dot_precision value: 87.10317460317461 - type: dot_recall value: 87.8 - type: euclidean_accuracy value: 99.75742574257426 - type: euclidean_ap value: 93.4938177052363 - type: euclidean_f1 value: 87.45019920318725 - type: euclidean_precision value: 87.10317460317461 - type: euclidean_recall value: 87.8 - type: manhattan_accuracy value: 99.77425742574258 - type: manhattan_ap value: 94.11582049960462 - type: manhattan_f1 value: 88.3367139959432 - type: manhattan_precision value: 89.60905349794238 - type: manhattan_recall value: 87.1 - type: max_accuracy value: 99.77425742574258 - type: max_ap value: 94.11582049960462 - type: max_f1 value: 88.3367139959432 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 40.69098529569445 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.68544212745689 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.065922067847836 - type: mrr value: 43.64432136490961 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.750957237960492 - type: cos_sim_spearman value: 30.099771071145582 - type: dot_pearson value: 29.75095720371408 - type: dot_spearman value: 30.128683537072114 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.6179999999999999 - type: map_at_10 value: 8.232000000000001 - type: map_at_100 value: 14.643999999999998 - type: map_at_1000 value: 16.259 - type: map_at_3 value: 4.2090000000000005 - type: map_at_5 value: 5.401 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 43.963 - type: mrr_at_100 value: 45.022 - type: mrr_at_1000 value: 45.039 - type: mrr_at_3 value: 42.177 - type: mrr_at_5 value: 42.687000000000005 - type: ndcg_at_1 value: 23.469 - type: ndcg_at_10 value: 22.526 - type: ndcg_at_100 value: 36.411 - type: ndcg_at_1000 value: 47.461 - type: ndcg_at_3 value: 27.176000000000002 - type: ndcg_at_5 value: 23.787 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 20.0 - type: precision_at_100 value: 8.286 - type: precision_at_1000 value: 1.543 - type: precision_at_3 value: 29.252 - type: precision_at_5 value: 23.265 - type: recall_at_1 value: 1.6179999999999999 - type: recall_at_10 value: 14.443 - type: recall_at_100 value: 50.073 - type: recall_at_1000 value: 83.56700000000001 - type: recall_at_3 value: 5.831 - type: recall_at_5 value: 7.797 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.5932 - type: ap value: 13.748287764670659 - type: f1 value: 53.6121537777008 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 48.60498019241653 - type: f1 value: 48.8190614849162 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 37.40279692338929 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.11378673183525 - type: cos_sim_ap value: 63.412004464549696 - type: cos_sim_f1 value: 60.880921286952386 - type: cos_sim_precision value: 55.34211094323332 - type: cos_sim_recall value: 67.65171503957784 - type: dot_accuracy value: 83.11378673183525 - type: dot_ap value: 63.412004464549696 - type: dot_f1 value: 60.880921286952386 - type: dot_precision value: 55.34211094323332 - type: dot_recall value: 67.65171503957784 - type: euclidean_accuracy value: 83.11378673183525 - type: euclidean_ap value: 63.412004464549696 - type: euclidean_f1 value: 60.880921286952386 - type: euclidean_precision value: 55.34211094323332 - type: euclidean_recall value: 67.65171503957784 - type: manhattan_accuracy value: 82.13625797222389 - type: manhattan_ap value: 60.704142220415335 - type: manhattan_f1 value: 58.10686319668357 - type: manhattan_precision value: 51.55292194523907 - type: manhattan_recall value: 66.56992084432719 - type: max_accuracy value: 83.11378673183525 - type: max_ap value: 63.412004464549696 - type: max_f1 value: 60.880921286952386 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.48593161796096 - type: cos_sim_ap value: 83.09276630048417 - type: cos_sim_f1 value: 75.22376690154258 - type: cos_sim_precision value: 74.4328031958996 - type: cos_sim_recall value: 76.03172158915923 - type: dot_accuracy value: 87.48593161796096 - type: dot_ap value: 83.09276630048417 - type: dot_f1 value: 75.22376690154258 - type: dot_precision value: 74.4328031958996 - type: dot_recall value: 76.03172158915923 - type: euclidean_accuracy value: 87.48593161796096 - type: euclidean_ap value: 83.09276683624702 - type: euclidean_f1 value: 75.22376690154258 - type: euclidean_precision value: 74.4328031958996 - type: euclidean_recall value: 76.03172158915923 - type: manhattan_accuracy value: 87.49369348391353 - type: manhattan_ap value: 82.94869347657408 - type: manhattan_f1 value: 74.95875695376942 - type: manhattan_precision value: 74.70367821365757 - type: manhattan_recall value: 75.21558361564522 - type: max_accuracy value: 87.49369348391353 - type: max_ap value: 83.09276683624702 - type: max_f1 value: 75.22376690154258 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/nmc-nignore50

twadada

null

[ "mteb", "model-index", "region:us" ]

2024-09-17T00:36:52

2024-09-17T00:37:01

0

--- tags: - mteb model-index: - name: nomic_classification_nignore50 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.34328358208954 - type: ap value: 35.68453876944336 - type: f1 value: 67.06992889373645 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 59.672925 - type: ap value: 55.848120844301974 - type: f1 value: 59.45636290076794 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 30.610000000000003 - type: f1 value: 30.300316542261875 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.697 - type: map_at_10 value: 35.189 - type: map_at_100 value: 36.4 - type: map_at_1000 value: 36.425000000000004 - type: map_at_3 value: 30.714000000000002 - type: map_at_5 value: 32.918 - type: mrr_at_1 value: 21.337 - type: mrr_at_10 value: 35.424 - type: mrr_at_100 value: 36.634 - type: mrr_at_1000 value: 36.659000000000006 - type: mrr_at_3 value: 30.927 - type: mrr_at_5 value: 33.132 - type: ndcg_at_1 value: 20.697 - type: ndcg_at_10 value: 43.525999999999996 - type: ndcg_at_100 value: 48.978 - type: ndcg_at_1000 value: 49.527 - type: ndcg_at_3 value: 34.087 - type: ndcg_at_5 value: 38.080999999999996 - type: precision_at_1 value: 20.697 - type: precision_at_10 value: 7.034 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.628 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 20.697 - type: recall_at_10 value: 70.341 - type: recall_at_100 value: 94.879 - type: recall_at_1000 value: 99.004 - type: recall_at_3 value: 43.883 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.49094114007186 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 24.389506327986794 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.325008936884444 - type: mrr value: 69.04992745020445 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 83.44469574569193 - type: cos_sim_spearman value: 80.2397801250519 - type: euclidean_pearson value: 82.22256751323195 - type: euclidean_spearman value: 80.2397801250519 - type: manhattan_pearson value: 81.55829597082581 - type: manhattan_spearman value: 79.90603758074258 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 71.56818181818181 - type: f1 value: 70.79234518018967 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 32.120028817225204 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 23.27141125132152 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 22.482 - type: map_at_10 value: 29.986 - type: map_at_100 value: 31.19 - type: map_at_1000 value: 31.339 - type: map_at_3 value: 27.869 - type: map_at_5 value: 28.83 - type: mrr_at_1 value: 28.326 - type: mrr_at_10 value: 35.675000000000004 - type: mrr_at_100 value: 36.559000000000005 - type: mrr_at_1000 value: 36.632999999999996 - type: mrr_at_3 value: 34.049 - type: mrr_at_5 value: 34.8 - type: ndcg_at_1 value: 28.326 - type: ndcg_at_10 value: 34.788000000000004 - type: ndcg_at_100 value: 40.162 - type: ndcg_at_1000 value: 43.145 - type: ndcg_at_3 value: 31.903 - type: ndcg_at_5 value: 32.74 - type: precision_at_1 value: 28.326 - type: precision_at_10 value: 6.552 - type: precision_at_100 value: 1.146 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 10.644 - type: recall_at_1 value: 22.482 - type: recall_at_10 value: 42.995 - type: recall_at_100 value: 66.79899999999999 - type: recall_at_1000 value: 87.02600000000001 - type: recall_at_3 value: 33.495999999999995 - type: recall_at_5 value: 36.7 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 17.147000000000002 - type: map_at_10 value: 23.28 - type: map_at_100 value: 24.191 - type: map_at_1000 value: 24.309 - type: map_at_3 value: 21.542 - type: map_at_5 value: 22.486 - type: mrr_at_1 value: 21.529 - type: mrr_at_10 value: 27.366 - type: mrr_at_100 value: 28.138 - type: mrr_at_1000 value: 28.206999999999997 - type: mrr_at_3 value: 25.668999999999997 - type: mrr_at_5 value: 26.653 - type: ndcg_at_1 value: 21.529 - type: ndcg_at_10 value: 27.107999999999997 - type: ndcg_at_100 value: 31.381999999999998 - type: ndcg_at_1000 value: 34.050000000000004 - type: ndcg_at_3 value: 24.101 - type: ndcg_at_5 value: 25.465 - type: precision_at_1 value: 21.529 - type: precision_at_10 value: 4.9430000000000005 - type: precision_at_100 value: 0.8829999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.591999999999999 - type: precision_at_5 value: 8.153 - type: recall_at_1 value: 17.147000000000002 - type: recall_at_10 value: 34.510000000000005 - type: recall_at_100 value: 53.337999999999994 - type: recall_at_1000 value: 71.25200000000001 - type: recall_at_3 value: 25.711000000000002 - type: recall_at_5 value: 29.448999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.628 - type: map_at_10 value: 34.53 - type: map_at_100 value: 35.648 - type: map_at_1000 value: 35.746 - type: map_at_3 value: 32.001000000000005 - type: map_at_5 value: 33.516 - type: mrr_at_1 value: 29.842999999999996 - type: mrr_at_10 value: 37.679 - type: mrr_at_100 value: 38.607 - type: mrr_at_1000 value: 38.671 - type: mrr_at_3 value: 35.528 - type: mrr_at_5 value: 36.747 - type: ndcg_at_1 value: 29.842999999999996 - type: ndcg_at_10 value: 39.271 - type: ndcg_at_100 value: 44.507000000000005 - type: ndcg_at_1000 value: 46.79 - type: ndcg_at_3 value: 34.699999999999996 - type: ndcg_at_5 value: 37.032 - type: precision_at_1 value: 29.842999999999996 - type: precision_at_10 value: 6.3759999999999994 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.126 - type: precision_at_3 value: 15.507000000000001 - type: precision_at_5 value: 10.871 - type: recall_at_1 value: 25.628 - type: recall_at_10 value: 50.552 - type: recall_at_100 value: 74.008 - type: recall_at_1000 value: 90.518 - type: recall_at_3 value: 38.397 - type: recall_at_5 value: 44.052 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.07 - type: map_at_10 value: 17.630000000000003 - type: map_at_100 value: 18.502 - type: map_at_1000 value: 18.614 - type: map_at_3 value: 16.213 - type: map_at_5 value: 17.029 - type: mrr_at_1 value: 14.124 - type: mrr_at_10 value: 18.706999999999997 - type: mrr_at_100 value: 19.604 - type: mrr_at_1000 value: 19.705000000000002 - type: mrr_at_3 value: 17.326 - type: mrr_at_5 value: 18.099999999999998 - type: ndcg_at_1 value: 14.124 - type: ndcg_at_10 value: 20.368 - type: ndcg_at_100 value: 25.020999999999997 - type: ndcg_at_1000 value: 28.360000000000003 - type: ndcg_at_3 value: 17.518 - type: ndcg_at_5 value: 18.914 - type: precision_at_1 value: 14.124 - type: precision_at_10 value: 3.164 - type: precision_at_100 value: 0.577 - type: precision_at_1000 value: 0.092 - type: precision_at_3 value: 7.457999999999999 - type: precision_at_5 value: 5.266 - type: recall_at_1 value: 13.07 - type: recall_at_10 value: 27.811000000000003 - type: recall_at_100 value: 49.874 - type: recall_at_1000 value: 75.786 - type: recall_at_3 value: 20.06 - type: recall_at_5 value: 23.397000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.646000000000001 - type: map_at_10 value: 10.141 - type: map_at_100 value: 10.9 - type: map_at_1000 value: 11.036999999999999 - type: map_at_3 value: 8.781 - type: map_at_5 value: 9.581000000000001 - type: mrr_at_1 value: 8.333 - type: mrr_at_10 value: 12.504000000000001 - type: mrr_at_100 value: 13.331000000000001 - type: mrr_at_1000 value: 13.435 - type: mrr_at_3 value: 11.007 - type: mrr_at_5 value: 11.909 - type: ndcg_at_1 value: 8.333 - type: ndcg_at_10 value: 12.796 - type: ndcg_at_100 value: 17.115 - type: ndcg_at_1000 value: 20.84 - type: ndcg_at_3 value: 10.171 - type: ndcg_at_5 value: 11.496 - type: precision_at_1 value: 8.333 - type: precision_at_10 value: 2.5 - type: precision_at_100 value: 0.5519999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 5.017 - type: precision_at_5 value: 3.955 - type: recall_at_1 value: 6.646000000000001 - type: recall_at_10 value: 18.725 - type: recall_at_100 value: 38.590999999999994 - type: recall_at_1000 value: 65.955 - type: recall_at_3 value: 11.376999999999999 - type: recall_at_5 value: 14.673 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 17.865000000000002 - type: map_at_10 value: 23.877000000000002 - type: map_at_100 value: 25.058000000000003 - type: map_at_1000 value: 25.202999999999996 - type: map_at_3 value: 21.861 - type: map_at_5 value: 22.958000000000002 - type: mrr_at_1 value: 22.137 - type: mrr_at_10 value: 28.391 - type: mrr_at_100 value: 29.364 - type: mrr_at_1000 value: 29.454 - type: mrr_at_3 value: 26.372 - type: mrr_at_5 value: 27.551 - type: ndcg_at_1 value: 22.137 - type: ndcg_at_10 value: 28.183999999999997 - type: ndcg_at_100 value: 33.806000000000004 - type: ndcg_at_1000 value: 37.099 - type: ndcg_at_3 value: 24.621000000000002 - type: ndcg_at_5 value: 26.235000000000003 - type: precision_at_1 value: 22.137 - type: precision_at_10 value: 5.178 - type: precision_at_100 value: 0.9440000000000001 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 11.613999999999999 - type: precision_at_5 value: 8.373 - type: recall_at_1 value: 17.865000000000002 - type: recall_at_10 value: 36.885 - type: recall_at_100 value: 61.49400000000001 - type: recall_at_1000 value: 84.309 - type: recall_at_3 value: 26.51 - type: recall_at_5 value: 30.788 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 13.691999999999998 - type: map_at_10 value: 19.463 - type: map_at_100 value: 20.558 - type: map_at_1000 value: 20.703 - type: map_at_3 value: 17.277 - type: map_at_5 value: 18.607000000000003 - type: mrr_at_1 value: 16.895 - type: mrr_at_10 value: 22.939999999999998 - type: mrr_at_100 value: 23.902 - type: mrr_at_1000 value: 23.993000000000002 - type: mrr_at_3 value: 20.89 - type: mrr_at_5 value: 22.158 - type: ndcg_at_1 value: 16.895 - type: ndcg_at_10 value: 23.308 - type: ndcg_at_100 value: 28.675 - type: ndcg_at_1000 value: 32.156 - type: ndcg_at_3 value: 19.53 - type: ndcg_at_5 value: 21.519 - type: precision_at_1 value: 16.895 - type: precision_at_10 value: 4.417999999999999 - type: precision_at_100 value: 0.844 - type: precision_at_1000 value: 0.134 - type: precision_at_3 value: 9.399000000000001 - type: precision_at_5 value: 7.146 - type: recall_at_1 value: 13.691999999999998 - type: recall_at_10 value: 31.424000000000003 - type: recall_at_100 value: 55.223 - type: recall_at_1000 value: 79.842 - type: recall_at_3 value: 20.949 - type: recall_at_5 value: 26.166 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.61475 - type: map_at_10 value: 19.97075 - type: map_at_100 value: 20.916083333333333 - type: map_at_1000 value: 21.046 - type: map_at_3 value: 18.229250000000004 - type: map_at_5 value: 19.20266666666667 - type: mrr_at_1 value: 17.608083333333333 - type: mrr_at_10 value: 23.027083333333334 - type: mrr_at_100 value: 23.87941666666667 - type: mrr_at_1000 value: 23.96783333333333 - type: mrr_at_3 value: 21.377083333333335 - type: mrr_at_5 value: 22.300833333333333 - type: ndcg_at_1 value: 17.608083333333333 - type: ndcg_at_10 value: 23.473083333333335 - type: ndcg_at_100 value: 28.176750000000002 - type: ndcg_at_1000 value: 31.36625 - type: ndcg_at_3 value: 20.38383333333334 - type: ndcg_at_5 value: 21.82283333333333 - type: precision_at_1 value: 17.608083333333333 - type: precision_at_10 value: 4.162666666666666 - type: precision_at_100 value: 0.7761666666666668 - type: precision_at_1000 value: 0.12325000000000001 - type: precision_at_3 value: 9.466000000000001 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 14.61475 - type: recall_at_10 value: 31.009500000000003 - type: recall_at_100 value: 52.470000000000006 - type: recall_at_1000 value: 75.52175 - type: recall_at_3 value: 22.22875 - type: recall_at_5 value: 26.018083333333337 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.016 - type: map_at_10 value: 15.293999999999999 - type: map_at_100 value: 16.08 - type: map_at_1000 value: 16.177 - type: map_at_3 value: 13.898 - type: map_at_5 value: 14.637 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.279 - type: mrr_at_100 value: 18.082 - type: mrr_at_1000 value: 18.169 - type: mrr_at_3 value: 15.9 - type: mrr_at_5 value: 16.667 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.073 - type: ndcg_at_100 value: 22.142 - type: ndcg_at_1000 value: 24.887999999999998 - type: ndcg_at_3 value: 15.405 - type: ndcg_at_5 value: 16.622999999999998 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.052 - type: precision_at_100 value: 0.5519999999999999 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 7.0040000000000004 - type: precision_at_5 value: 4.939 - type: recall_at_1 value: 11.016 - type: recall_at_10 value: 24.72 - type: recall_at_100 value: 43.553999999999995 - type: recall_at_1000 value: 64.189 - type: recall_at_3 value: 17.308 - type: recall_at_5 value: 20.455000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.148000000000001 - type: map_at_10 value: 10.878 - type: map_at_100 value: 11.547 - type: map_at_1000 value: 11.67 - type: map_at_3 value: 9.679 - type: map_at_5 value: 10.41 - type: mrr_at_1 value: 8.878 - type: mrr_at_10 value: 13.142999999999999 - type: mrr_at_100 value: 13.825999999999999 - type: mrr_at_1000 value: 13.927999999999999 - type: mrr_at_3 value: 11.786000000000001 - type: mrr_at_5 value: 12.623999999999999 - type: ndcg_at_1 value: 8.878 - type: ndcg_at_10 value: 13.414000000000001 - type: ndcg_at_100 value: 17.129 - type: ndcg_at_1000 value: 20.754 - type: ndcg_at_3 value: 11.135 - type: ndcg_at_5 value: 12.333 - type: precision_at_1 value: 8.878 - type: precision_at_10 value: 2.564 - type: precision_at_100 value: 0.525 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 5.448 - type: precision_at_5 value: 4.136 - type: recall_at_1 value: 7.148000000000001 - type: recall_at_10 value: 18.875 - type: recall_at_100 value: 36.321999999999996 - type: recall_at_1000 value: 63.273999999999994 - type: recall_at_3 value: 12.590000000000002 - type: recall_at_5 value: 15.6 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.178 - type: map_at_10 value: 17.538 - type: map_at_100 value: 18.343999999999998 - type: map_at_1000 value: 18.462999999999997 - type: map_at_3 value: 15.984000000000002 - type: map_at_5 value: 16.78 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.385 - type: mrr_at_100 value: 21.160999999999998 - type: mrr_at_1000 value: 21.252 - type: mrr_at_3 value: 18.766 - type: mrr_at_5 value: 19.554 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 20.785 - type: ndcg_at_100 value: 25.089 - type: ndcg_at_1000 value: 28.396 - type: ndcg_at_3 value: 17.701 - type: ndcg_at_5 value: 18.968 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.5069999999999997 - type: precision_at_100 value: 0.633 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 8.022 - type: precision_at_5 value: 5.634 - type: recall_at_1 value: 13.178 - type: recall_at_10 value: 28.227999999999998 - type: recall_at_100 value: 48.022999999999996 - type: recall_at_1000 value: 72.475 - type: recall_at_3 value: 19.425 - type: recall_at_5 value: 22.783 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.662999999999998 - type: map_at_10 value: 21.191 - type: map_at_100 value: 22.436 - type: map_at_1000 value: 22.634999999999998 - type: map_at_3 value: 19.493 - type: map_at_5 value: 20.543 - type: mrr_at_1 value: 19.368 - type: mrr_at_10 value: 24.866 - type: mrr_at_100 value: 25.875999999999998 - type: mrr_at_1000 value: 25.958 - type: mrr_at_3 value: 23.551 - type: mrr_at_5 value: 24.233 - type: ndcg_at_1 value: 19.368 - type: ndcg_at_10 value: 24.869 - type: ndcg_at_100 value: 30.54 - type: ndcg_at_1000 value: 33.994 - type: ndcg_at_3 value: 22.522000000000002 - type: ndcg_at_5 value: 23.674 - type: precision_at_1 value: 19.368 - type: precision_at_10 value: 4.704 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 10.671999999999999 - type: precision_at_5 value: 7.668 - type: recall_at_1 value: 15.662999999999998 - type: recall_at_10 value: 31.03 - type: recall_at_100 value: 57.861 - type: recall_at_1000 value: 81.179 - type: recall_at_3 value: 23.843 - type: recall_at_5 value: 27.223999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.842 - type: map_at_10 value: 15.841 - type: map_at_100 value: 16.539 - type: map_at_1000 value: 16.656000000000002 - type: map_at_3 value: 14.152999999999999 - type: map_at_5 value: 15.055 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.39 - type: mrr_at_100 value: 18.102999999999998 - type: mrr_at_1000 value: 18.209 - type: mrr_at_3 value: 15.681000000000001 - type: mrr_at_5 value: 16.614 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 18.712999999999997 - type: ndcg_at_100 value: 22.553 - type: ndcg_at_1000 value: 25.923000000000002 - type: ndcg_at_3 value: 15.299 - type: ndcg_at_5 value: 16.875 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.994 - type: precision_at_100 value: 0.529 - type: precision_at_1000 value: 0.089 - type: precision_at_3 value: 6.4079999999999995 - type: precision_at_5 value: 4.695 - type: recall_at_1 value: 11.842 - type: recall_at_10 value: 26.358999999999998 - type: recall_at_100 value: 44.553 - type: recall_at_1000 value: 70.456 - type: recall_at_3 value: 17.079 - type: recall_at_5 value: 20.93 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.841 - type: map_at_10 value: 11.955 - type: map_at_100 value: 13.322000000000001 - type: map_at_1000 value: 13.517000000000001 - type: map_at_3 value: 9.818 - type: map_at_5 value: 10.875 - type: mrr_at_1 value: 15.504999999999999 - type: mrr_at_10 value: 24.087 - type: mrr_at_100 value: 25.149 - type: mrr_at_1000 value: 25.221 - type: mrr_at_3 value: 20.923 - type: mrr_at_5 value: 22.64 - type: ndcg_at_1 value: 15.504999999999999 - type: ndcg_at_10 value: 17.787 - type: ndcg_at_100 value: 24.032 - type: ndcg_at_1000 value: 28.058 - type: ndcg_at_3 value: 13.729 - type: ndcg_at_5 value: 15.165999999999999 - type: precision_at_1 value: 15.504999999999999 - type: precision_at_10 value: 5.811 - type: precision_at_100 value: 1.246 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 10.228 - type: precision_at_5 value: 8.155999999999999 - type: recall_at_1 value: 6.841 - type: recall_at_10 value: 22.451999999999998 - type: recall_at_100 value: 44.588 - type: recall_at_1000 value: 67.806 - type: recall_at_3 value: 12.824 - type: recall_at_5 value: 16.59 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.601999999999999 - type: map_at_10 value: 10.209 - type: map_at_100 value: 14.172 - type: map_at_1000 value: 15.121 - type: map_at_3 value: 7.59 - type: map_at_5 value: 8.853 - type: mrr_at_1 value: 41.75 - type: mrr_at_10 value: 51.949 - type: mrr_at_100 value: 52.678000000000004 - type: mrr_at_1000 value: 52.708 - type: mrr_at_3 value: 49.542 - type: mrr_at_5 value: 51.054 - type: ndcg_at_1 value: 31.125000000000004 - type: ndcg_at_10 value: 24.581 - type: ndcg_at_100 value: 27.894999999999996 - type: ndcg_at_1000 value: 34.438 - type: ndcg_at_3 value: 27.877999999999997 - type: ndcg_at_5 value: 26.273000000000003 - type: precision_at_1 value: 41.75 - type: precision_at_10 value: 21.15 - type: precision_at_100 value: 6.787999999999999 - type: precision_at_1000 value: 1.394 - type: precision_at_3 value: 32.917 - type: precision_at_5 value: 28.000000000000004 - type: recall_at_1 value: 4.601999999999999 - type: recall_at_10 value: 14.434 - type: recall_at_100 value: 33.838 - type: recall_at_1000 value: 56.438 - type: recall_at_3 value: 9.073 - type: recall_at_5 value: 11.395 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 43.364999999999995 - type: f1 value: 41.05024359810464 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.078 - type: map_at_10 value: 25.590000000000003 - type: map_at_100 value: 26.581 - type: map_at_1000 value: 26.651000000000003 - type: map_at_3 value: 23.113 - type: map_at_5 value: 24.457 - type: mrr_at_1 value: 18.257 - type: mrr_at_10 value: 27.171 - type: mrr_at_100 value: 28.147 - type: mrr_at_1000 value: 28.206999999999997 - type: mrr_at_3 value: 24.607 - type: mrr_at_5 value: 26.005 - type: ndcg_at_1 value: 18.257 - type: ndcg_at_10 value: 30.617 - type: ndcg_at_100 value: 35.625 - type: ndcg_at_1000 value: 37.566 - type: ndcg_at_3 value: 25.488 - type: ndcg_at_5 value: 27.897 - type: precision_at_1 value: 18.257 - type: precision_at_10 value: 4.877 - type: precision_at_100 value: 0.76 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 11.081000000000001 - type: precision_at_5 value: 7.933 - type: recall_at_1 value: 17.078 - type: recall_at_10 value: 44.866 - type: recall_at_100 value: 68.148 - type: recall_at_1000 value: 83.134 - type: recall_at_3 value: 30.830999999999996 - type: recall_at_5 value: 36.629 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.515 - type: map_at_10 value: 11.788 - type: map_at_100 value: 12.974 - type: map_at_1000 value: 13.177 - type: map_at_3 value: 9.844 - type: map_at_5 value: 10.8 - type: mrr_at_1 value: 14.352 - type: mrr_at_10 value: 20.134 - type: mrr_at_100 value: 21.288 - type: mrr_at_1000 value: 21.384 - type: mrr_at_3 value: 18.133 - type: mrr_at_5 value: 19.097 - type: ndcg_at_1 value: 14.352 - type: ndcg_at_10 value: 16.203 - type: ndcg_at_100 value: 21.891 - type: ndcg_at_1000 value: 26.653 - type: ndcg_at_3 value: 13.369 - type: ndcg_at_5 value: 14.327000000000002 - type: precision_at_1 value: 14.352 - type: precision_at_10 value: 4.645 - type: precision_at_100 value: 1.028 - type: precision_at_1000 value: 0.185 - type: precision_at_3 value: 8.796 - type: precision_at_5 value: 6.728000000000001 - type: recall_at_1 value: 7.515 - type: recall_at_10 value: 21.018 - type: recall_at_100 value: 42.887 - type: recall_at_1000 value: 72.898 - type: recall_at_3 value: 12.215 - type: recall_at_5 value: 15.612 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.332 - type: map_at_10 value: 25.791999999999998 - type: map_at_100 value: 26.706999999999997 - type: map_at_1000 value: 26.810000000000002 - type: map_at_3 value: 23.742 - type: map_at_5 value: 24.903 - type: mrr_at_1 value: 36.664 - type: mrr_at_10 value: 44.067 - type: mrr_at_100 value: 44.756 - type: mrr_at_1000 value: 44.806000000000004 - type: mrr_at_3 value: 42.091 - type: mrr_at_5 value: 43.206 - type: ndcg_at_1 value: 36.664 - type: ndcg_at_10 value: 32.938 - type: ndcg_at_100 value: 37.184 - type: ndcg_at_1000 value: 39.723000000000006 - type: ndcg_at_3 value: 29.087000000000003 - type: ndcg_at_5 value: 30.986000000000004 - type: precision_at_1 value: 36.664 - type: precision_at_10 value: 7.245 - type: precision_at_100 value: 1.0670000000000002 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 18.37 - type: precision_at_5 value: 12.527 - type: recall_at_1 value: 18.332 - type: recall_at_10 value: 36.226 - type: recall_at_100 value: 53.342 - type: recall_at_1000 value: 70.42500000000001 - type: recall_at_3 value: 27.556000000000004 - type: recall_at_5 value: 31.317 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 58.64719999999999 - type: ap value: 55.19966477229678 - type: f1 value: 58.24961396114563 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.459 - type: map_at_10 value: 11.254999999999999 - type: map_at_100 value: 12.132 - type: map_at_1000 value: 12.237 - type: map_at_3 value: 9.522 - type: map_at_5 value: 10.456999999999999 - type: mrr_at_1 value: 6.633 - type: mrr_at_10 value: 11.518 - type: mrr_at_100 value: 12.395 - type: mrr_at_1000 value: 12.497 - type: mrr_at_3 value: 9.751999999999999 - type: mrr_at_5 value: 10.708 - type: ndcg_at_1 value: 6.59 - type: ndcg_at_10 value: 14.264 - type: ndcg_at_100 value: 19.070999999999998 - type: ndcg_at_1000 value: 22.235 - type: ndcg_at_3 value: 10.631 - type: ndcg_at_5 value: 12.339 - type: precision_at_1 value: 6.59 - type: precision_at_10 value: 2.461 - type: precision_at_100 value: 0.496 - type: precision_at_1000 value: 0.077 - type: precision_at_3 value: 4.6850000000000005 - type: precision_at_5 value: 3.6790000000000003 - type: recall_at_1 value: 6.459 - type: recall_at_10 value: 23.649 - type: recall_at_100 value: 47.13 - type: recall_at_1000 value: 72.51100000000001 - type: recall_at_3 value: 13.641 - type: recall_at_5 value: 17.787 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.75695394436845 - type: f1 value: 84.39448911327825 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.072047423620596 - type: f1 value: 36.95943939246465 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 58.78614660390047 - type: f1 value: 57.44347017992026 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 64.28379287155346 - type: f1 value: 62.76816678345901 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 29.671378352049466 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 25.86783118767072 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.346696390536565 - type: mrr value: 30.303238754879626 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.9190000000000005 - type: map_at_10 value: 8.742999999999999 - type: map_at_100 value: 11.073 - type: map_at_1000 value: 12.447999999999999 - type: map_at_3 value: 6.2379999999999995 - type: map_at_5 value: 7.443 - type: mrr_at_1 value: 34.365 - type: mrr_at_10 value: 44.238 - type: mrr_at_100 value: 44.857 - type: mrr_at_1000 value: 44.91 - type: mrr_at_3 value: 41.744 - type: mrr_at_5 value: 43.478 - type: ndcg_at_1 value: 32.353 - type: ndcg_at_10 value: 26.695 - type: ndcg_at_100 value: 24.728 - type: ndcg_at_1000 value: 34.013 - type: ndcg_at_3 value: 30.268 - type: ndcg_at_5 value: 28.95 - type: precision_at_1 value: 34.365 - type: precision_at_10 value: 20.31 - type: precision_at_100 value: 6.984999999999999 - type: precision_at_1000 value: 2.019 - type: precision_at_3 value: 28.896 - type: precision_at_5 value: 25.697 - type: recall_at_1 value: 3.9190000000000005 - type: recall_at_10 value: 13.046 - type: recall_at_100 value: 26.08 - type: recall_at_1000 value: 60.207 - type: recall_at_3 value: 7.255000000000001 - type: recall_at_5 value: 9.853000000000002 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 8.408 - type: map_at_10 value: 15.064 - type: map_at_100 value: 16.282 - type: map_at_1000 value: 16.384999999999998 - type: map_at_3 value: 12.605 - type: map_at_5 value: 13.866 - type: mrr_at_1 value: 9.589 - type: mrr_at_10 value: 16.598 - type: mrr_at_100 value: 17.716 - type: mrr_at_1000 value: 17.8 - type: mrr_at_3 value: 14.021 - type: mrr_at_5 value: 15.39 - type: ndcg_at_1 value: 9.589 - type: ndcg_at_10 value: 19.408 - type: ndcg_at_100 value: 25.549 - type: ndcg_at_1000 value: 28.344 - type: ndcg_at_3 value: 14.258000000000001 - type: ndcg_at_5 value: 16.549 - type: precision_at_1 value: 9.589 - type: precision_at_10 value: 3.711 - type: precision_at_100 value: 0.717 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 6.769 - type: precision_at_5 value: 5.359 - type: recall_at_1 value: 8.408 - type: recall_at_10 value: 31.442999999999998 - type: recall_at_100 value: 59.931 - type: recall_at_1000 value: 81.40899999999999 - type: recall_at_3 value: 17.654 - type: recall_at_5 value: 22.982 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.35600000000001 - type: map_at_10 value: 77.12400000000001 - type: map_at_100 value: 77.873 - type: map_at_1000 value: 77.905 - type: map_at_3 value: 74.18599999999999 - type: map_at_5 value: 76.00099999999999 - type: mrr_at_1 value: 74.22 - type: mrr_at_10 value: 81.326 - type: mrr_at_100 value: 81.54 - type: mrr_at_1000 value: 81.545 - type: mrr_at_3 value: 79.912 - type: mrr_at_5 value: 80.833 - type: ndcg_at_1 value: 74.26 - type: ndcg_at_10 value: 81.709 - type: ndcg_at_100 value: 83.688 - type: ndcg_at_1000 value: 84.029 - type: ndcg_at_3 value: 78.214 - type: ndcg_at_5 value: 80.07 - type: precision_at_1 value: 74.26 - type: precision_at_10 value: 12.334 - type: precision_at_100 value: 1.463 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.977000000000004 - type: precision_at_5 value: 22.442 - type: recall_at_1 value: 64.35600000000001 - type: recall_at_10 value: 90.50200000000001 - type: recall_at_100 value: 97.833 - type: recall_at_1000 value: 99.681 - type: recall_at_3 value: 80.426 - type: recall_at_5 value: 85.627 - type: map_at_1 value: 2.988 - type: map_at_10 value: 7.183000000000001 - type: map_at_100 value: 8.522 - type: map_at_1000 value: 8.769 - type: map_at_3 value: 5.29 - type: map_at_5 value: 6.249 - type: mrr_at_1 value: 14.7 - type: mrr_at_10 value: 22.526 - type: mrr_at_100 value: 23.749000000000002 - type: mrr_at_1000 value: 23.827 - type: mrr_at_3 value: 19.617 - type: mrr_at_5 value: 21.322 - type: ndcg_at_1 value: 14.7 - type: ndcg_at_10 value: 12.666 - type: ndcg_at_100 value: 18.999 - type: ndcg_at_1000 value: 24.060000000000002 - type: ndcg_at_3 value: 11.908000000000001 - type: ndcg_at_5 value: 10.517999999999999 - type: precision_at_1 value: 14.7 - type: precision_at_10 value: 6.59 - type: precision_at_100 value: 1.592 - type: precision_at_1000 value: 0.28200000000000003 - type: precision_at_3 value: 11.1 - type: precision_at_5 value: 9.28 - type: recall_at_1 value: 2.988 - type: recall_at_10 value: 13.361999999999998 - type: recall_at_100 value: 32.312999999999995 - type: recall_at_1000 value: 57.293000000000006 - type: recall_at_3 value: 6.768000000000001 - type: recall_at_5 value: 9.408 - type: map_at_1 value: 0.15 - type: map_at_10 value: 0.732 - type: map_at_100 value: 3.402 - type: map_at_1000 value: 8.562 - type: map_at_3 value: 0.311 - type: map_at_5 value: 0.45799999999999996 - type: mrr_at_1 value: 60.0 - type: mrr_at_10 value: 66.297 - type: mrr_at_100 value: 67.486 - type: mrr_at_1000 value: 67.495 - type: mrr_at_3 value: 64.667 - type: mrr_at_5 value: 65.567 - type: ndcg_at_1 value: 53.0 - type: ndcg_at_10 value: 39.839999999999996 - type: ndcg_at_100 value: 28.401 - type: ndcg_at_1000 value: 24.781 - type: ndcg_at_3 value: 45.173 - type: ndcg_at_5 value: 42.543 - type: precision_at_1 value: 57.99999999999999 - type: precision_at_10 value: 41.199999999999996 - type: precision_at_100 value: 29.299999999999997 - type: precision_at_1000 value: 12.06 - type: precision_at_3 value: 46.666999999999994 - type: precision_at_5 value: 44.0 - type: recall_at_1 value: 0.15 - type: recall_at_10 value: 0.928 - type: recall_at_100 value: 6.021 - type: recall_at_1000 value: 23.61 - type: recall_at_3 value: 0.332 - type: recall_at_5 value: 0.521 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 36.774788977863864 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 46.77683019813603 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 74.41889076801608 - type: cos_sim_spearman value: 64.0943040174807 - type: euclidean_pearson value: 68.82034211304835 - type: euclidean_spearman value: 64.09442214998937 - type: manhattan_pearson value: 67.28965034113492 - type: manhattan_spearman value: 63.44420264246327 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 74.19404151109595 - type: cos_sim_spearman value: 67.74306846452986 - type: euclidean_pearson value: 70.18395120349778 - type: euclidean_spearman value: 67.74435929212751 - type: manhattan_pearson value: 68.00233535704764 - type: manhattan_spearman value: 66.49484254678912 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 76.87296571665527 - type: cos_sim_spearman value: 77.89561218206916 - type: euclidean_pearson value: 77.69387561554153 - type: euclidean_spearman value: 77.89564999542652 - type: manhattan_pearson value: 77.2962286612409 - type: manhattan_spearman value: 77.62183070015116 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 77.57868393517904 - type: cos_sim_spearman value: 74.3463323181872 - type: euclidean_pearson value: 76.34645562235912 - type: euclidean_spearman value: 74.34632262739605 - type: manhattan_pearson value: 75.18605845598414 - type: manhattan_spearman value: 73.37658913779082 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.3634602887491 - type: cos_sim_spearman value: 82.08782263146122 - type: euclidean_pearson value: 81.98650592402285 - type: euclidean_spearman value: 82.08782111739671 - type: manhattan_pearson value: 81.3116524025808 - type: manhattan_spearman value: 81.48861424628144 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.87657339764795 - type: cos_sim_spearman value: 76.85063393951995 - type: euclidean_pearson value: 76.4294473355708 - type: euclidean_spearman value: 76.85118422928095 - type: manhattan_pearson value: 76.64930860894692 - type: manhattan_spearman value: 77.10013496496948 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 83.34326345648941 - type: cos_sim_spearman value: 83.88446362725742 - type: euclidean_pearson value: 83.46533085232362 - type: euclidean_spearman value: 83.8853378609005 - type: manhattan_pearson value: 83.1484584186202 - type: manhattan_spearman value: 83.95137720105474 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 63.83321542257857 - type: cos_sim_spearman value: 60.82336431262449 - type: euclidean_pearson value: 63.11807243264151 - type: euclidean_spearman value: 60.82336431262449 - type: manhattan_pearson value: 62.08662855750554 - type: manhattan_spearman value: 59.96327210016991 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.52565754458857 - type: cos_sim_spearman value: 77.31638441143197 - type: euclidean_pearson value: 78.14871856195552 - type: euclidean_spearman value: 77.31637278095369 - type: manhattan_pearson value: 77.18966645743699 - type: manhattan_spearman value: 76.60273070312653 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.10656484834338 - type: mrr value: 90.98610811846106 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.75 - type: map_at_10 value: 47.549 - type: map_at_100 value: 48.579 - type: map_at_1000 value: 48.653 - type: map_at_3 value: 45.135 - type: map_at_5 value: 46.444 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 49.418 - type: mrr_at_100 value: 50.253 - type: mrr_at_1000 value: 50.319 - type: mrr_at_3 value: 47.221999999999994 - type: mrr_at_5 value: 48.439 - type: ndcg_at_1 value: 42.0 - type: ndcg_at_10 value: 51.922999999999995 - type: ndcg_at_100 value: 56.607 - type: ndcg_at_1000 value: 58.3 - type: ndcg_at_3 value: 47.316 - type: ndcg_at_5 value: 49.446 - type: precision_at_1 value: 42.0 - type: precision_at_10 value: 7.066999999999999 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 18.556 - type: precision_at_5 value: 12.4 - type: recall_at_1 value: 39.75 - type: recall_at_10 value: 63.727999999999994 - type: recall_at_100 value: 84.983 - type: recall_at_1000 value: 97.8 - type: recall_at_3 value: 51.233 - type: recall_at_5 value: 56.494 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.77623762376237 - type: cos_sim_ap value: 94.03474205524832 - type: cos_sim_f1 value: 88.17533129459734 - type: cos_sim_precision value: 89.91683991683992 - type: cos_sim_recall value: 86.5 - type: dot_accuracy value: 99.77623762376237 - type: dot_ap value: 94.03474205524832 - type: dot_f1 value: 88.17533129459734 - type: dot_precision value: 89.91683991683992 - type: dot_recall value: 86.5 - type: euclidean_accuracy value: 99.77623762376237 - type: euclidean_ap value: 94.03474205524832 - type: euclidean_f1 value: 88.17533129459734 - type: euclidean_precision value: 89.91683991683992 - type: euclidean_recall value: 86.5 - type: manhattan_accuracy value: 99.77722772277228 - type: manhattan_ap value: 94.18601558420747 - type: manhattan_f1 value: 88.37209302325581 - type: manhattan_precision value: 91.44385026737967 - type: manhattan_recall value: 85.5 - type: max_accuracy value: 99.77722772277228 - type: max_ap value: 94.18601558420747 - type: max_f1 value: 88.37209302325581 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 39.59223629185949 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.72427682478714 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 42.58781744864796 - type: mrr value: 43.04660311094135 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.162875776593765 - type: cos_sim_spearman value: 29.469578163520644 - type: dot_pearson value: 29.16287568844527 - type: dot_spearman value: 29.491226084003042 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.6969999999999998 - type: map_at_10 value: 7.6579999999999995 - type: map_at_100 value: 14.119000000000002 - type: map_at_1000 value: 15.695999999999998 - type: map_at_3 value: 3.599 - type: map_at_5 value: 5.076 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 43.033 - type: mrr_at_100 value: 43.957 - type: mrr_at_1000 value: 43.976 - type: mrr_at_3 value: 38.435 - type: mrr_at_5 value: 42.211 - type: ndcg_at_1 value: 24.490000000000002 - type: ndcg_at_10 value: 22.114 - type: ndcg_at_100 value: 35.583999999999996 - type: ndcg_at_1000 value: 46.697 - type: ndcg_at_3 value: 23.521 - type: ndcg_at_5 value: 23.363 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 20.408 - type: precision_at_100 value: 8.265 - type: precision_at_1000 value: 1.551 - type: precision_at_3 value: 25.169999999999998 - type: precision_at_5 value: 24.490000000000002 - type: recall_at_1 value: 1.6969999999999998 - type: recall_at_10 value: 14.554 - type: recall_at_100 value: 49.858000000000004 - type: recall_at_1000 value: 83.635 - type: recall_at_3 value: 4.822 - type: recall_at_5 value: 7.933 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 68.9474 - type: ap value: 13.286323018191338 - type: f1 value: 52.98797630385014 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 47.993774759479344 - type: f1 value: 48.15808612169315 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 34.53065734021412 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 82.61906181081243 - type: cos_sim_ap value: 61.91869955453384 - type: cos_sim_f1 value: 59.794798377475544 - type: cos_sim_precision value: 54.57317073170732 - type: cos_sim_recall value: 66.12137203166228 - type: dot_accuracy value: 82.61906181081243 - type: dot_ap value: 61.91869955453384 - type: dot_f1 value: 59.794798377475544 - type: dot_precision value: 54.57317073170732 - type: dot_recall value: 66.12137203166228 - type: euclidean_accuracy value: 82.61906181081243 - type: euclidean_ap value: 61.91869955453384 - type: euclidean_f1 value: 59.794798377475544 - type: euclidean_precision value: 54.57317073170732 - type: euclidean_recall value: 66.12137203166228 - type: manhattan_accuracy value: 81.89187578232104 - type: manhattan_ap value: 59.62564983212156 - type: manhattan_f1 value: 57.711442786069654 - type: manhattan_precision value: 52.364574376612204 - type: manhattan_recall value: 64.27440633245382 - type: max_accuracy value: 82.61906181081243 - type: max_ap value: 61.91869955453384 - type: max_f1 value: 59.794798377475544 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.49563395040167 - type: cos_sim_ap value: 82.90539955967866 - type: cos_sim_f1 value: 74.9081462237892 - type: cos_sim_precision value: 72.30780253636169 - type: cos_sim_recall value: 77.70249461040962 - type: dot_accuracy value: 87.49563395040167 - type: dot_ap value: 82.90539958269324 - type: dot_f1 value: 74.9081462237892 - type: dot_precision value: 72.30780253636169 - type: dot_recall value: 77.70249461040962 - type: euclidean_accuracy value: 87.49563395040167 - type: euclidean_ap value: 82.9054181491746 - type: euclidean_f1 value: 74.9081462237892 - type: euclidean_precision value: 72.30780253636169 - type: euclidean_recall value: 77.70249461040962 - type: manhattan_accuracy value: 87.37920596111304 - type: manhattan_ap value: 82.78475210950901 - type: manhattan_f1 value: 74.73338499575631 - type: manhattan_precision value: 71.7596201544894 - type: manhattan_recall value: 77.96427471512165 - type: max_accuracy value: 87.49563395040167 - type: max_ap value: 82.9054181491746 - type: max_f1 value: 74.9081462237892 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

Baiming123/Calcu_Disease_Similarity

Baiming123

sentence-similarity

[ "sentence-transformers", "pytorch", "bert", "sentence-similarity", "dataset:Baiming123/MeSHDS", "base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1", "base_model:finetune:sentence-transformers/multi-qa-MiniLM-L6-cos-v1", "doi:10.57967/hf/3108", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

2024-09-20T15:58:13

2024-12-14T10:10:29

0

3

--- base_model: - sentence-transformers/multi-qa-MiniLM-L6-cos-v1 datasets: - Baiming123/MeSHDS pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity --- # Model Description This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.The 'Calcu_Disease_Similarity' model is designed to encode two disease terms and compute their **semantic similarity**. The model has been fine-tuned on disease-related datasets 'MeSHDS' and achieves a high F1 score in distinguishing experimentally validated miRNA-target interactions (MTIs) and predicted MTIs by considering disease similarity. If you use this model in your research, please cite the following paper: ``` @article {Chen2024.05.17.594604, author = {Chen, Baiming}, title = {miRTarDS: High-Accuracy Refining Protein-level MicroRNA Target Interactions from Prediction Databases Using Sentence-BERT}, elocation-id = {2024.05.17.594604}, year = {2024}, doi = {10.1101/2024.05.17.594604}, publisher = {Cold Spring Harbor Laboratory}, abstract = {MicroRNAs (miRNAs) regulate gene expression by binding to mRNAs, inhibiting translation, or promoting mRNA degradation. miRNAs are of great importance in the development of various diseases. Currently, numerous sequence-based miRNA target prediction tools are available, however, only 1\% of their predictions have been experimentally validated. In this study, we propose a novel approach that leverages disease similarity between miRNAs and genes as a key feature to further refine and screen human sequence-based predicted miRNA target interactions (MTIs). To quantify the semantic similarity of diseases, we fine-tuned the Sentence-BERT model. Our method achieved an F1 score of 0.88 in accurately distinguishing human protein-level experimentally validated MTIs (functional MTIs, validated through western blot or reporter assay) and predicted MTIs. Moreover, this method exhibits exceptional generalizability across different databases. We applied the proposed method to analyze 1,220,904 human MTIs sourced from miRTarbase, miRDB, and miRWalk, encompassing 6,085 genes and 1,261 pre-miRNAs. Notably, we accurately identified 3,883 out of 3,962 MTIs with strong experimental evidence from miRTarbase. This study has the potential to provide valuable insights into the understanding of miRNA-gene regulatory networks and to promote advancements in disease diagnosis, treatment, and drug development.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2024/12/08/2024.05.17.594604}, eprint = {https://www.biorxiv.org/content/early/2024/12/08/2024.05.17.594604.full.pdf}, journal = {bioRxiv} } ``` ## Key Features: - Fine-tuned to compute semantic similarity between disease names. - Achieves an F1 score of 0.88 in distinguishing protein-level experimentally (western blot, reporter assay) validated MTIs and predicted MTIs. - Built for applications in understanding miRNA-gene regulatory networks, disease diagnosis, treatment, and drug discovery. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` # Usage (Sentence-Transformers) ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python # Load the pre-trained SBERT model from sentence_transformers import SentenceTransformer, util # Replace 'your/path/to/Calcu_Disease_Similarity' with the actual path to the model model = SentenceTransformer("Baiming123/Calcu_Disease_Similarity") # Example usage disease1 = "lung cancer" disease2 = "pulmonary fibrosis" def sts(sentence_a, sentence_b) -> float: query_emb = model.encode(sentence_a) doc_emb = model.encode(sentence_b) [score] = util.dot_score(query_emb, doc_emb)[0].tolist() return score similarity = sts(disease1, disease2) print(similarity) ``` # Additional Information ## License This model is licensed under CC-BY-NC 4.0 International license. If you use this model, please adhere to the license requirements. ## Questions or Issues If you encounter any issues or have any questions while using the model, feel free to reach out to the author for assistance. Thank you for your support and for using this model!

[ "SEMANTIC_SIMILARITY", "TRANSLATION" ]

[ "MIRNA" ]

tomaarsen/static-bert-uncased-gooaq

tomaarsen

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-09-26T11:47:56

2024-10-18T10:35:51

0

4

--- datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 6.448001991119035 energy_consumed: 0.0165885485310573 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.109 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 1024 dev type: gooaq-1024-dev metrics: - type: cosine_accuracy@1 value: 0.6309 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8409 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8986 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9444 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6309 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28029999999999994 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17972000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09444000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6309 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8409 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8986 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9444 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7932643237589305 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7440336111111036 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7465739001132767 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 512 dev type: gooaq-512-dev metrics: - type: cosine_accuracy@1 value: 0.6271 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8366 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8946 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9431 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6271 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27886666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17892000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09431000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6271 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8366 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8946 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9431 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7904860196985286 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7408453174603101 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7434337897783787 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 256 dev type: gooaq-256-dev metrics: - type: cosine_accuracy@1 value: 0.6192 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8235 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8866 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9364 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6192 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27449999999999997 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17732000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09364000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6192 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8235 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9364 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7821476540310974 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7321259126984055 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7348893313013708 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 128 dev type: gooaq-128-dev metrics: - type: cosine_accuracy@1 value: 0.5942 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.804 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8721 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9249 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5942 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.268 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17442000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09249 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5942 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.804 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8721 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9249 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7627845665665897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7103426587301529 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7133975871277517 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 64 dev type: gooaq-64-dev metrics: - type: cosine_accuracy@1 value: 0.556 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7553 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8267 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8945 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.556 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25176666666666664 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16534000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08945 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.556 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7553 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8267 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8945 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7246435400765202 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6701957142857087 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6743443703166442 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: gooaq 32 dev type: gooaq-32-dev metrics: - type: cosine_accuracy@1 value: 0.4628 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6619 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7415 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8241 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4628 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2206333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1483 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08241 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4628 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6619 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7415 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8241 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6387155548290799 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5797731349206319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5857231820662888 name: Cosine Map@100 --- # Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using the [train_script.py](train_script.py) code. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-bert-uncased-gooaq") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Dataset: `gooaq-1024-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6309 | | cosine_accuracy@3 | 0.8409 | | cosine_accuracy@5 | 0.8986 | | cosine_accuracy@10 | 0.9444 | | cosine_precision@1 | 0.6309 | | cosine_precision@3 | 0.2803 | | cosine_precision@5 | 0.1797 | | cosine_precision@10 | 0.0944 | | cosine_recall@1 | 0.6309 | | cosine_recall@3 | 0.8409 | | cosine_recall@5 | 0.8986 | | cosine_recall@10 | 0.9444 | | cosine_ndcg@10 | 0.7933 | | cosine_mrr@10 | 0.744 | | **cosine_map@100** | **0.7466** | #### Information Retrieval * Dataset: `gooaq-512-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6271 | | cosine_accuracy@3 | 0.8366 | | cosine_accuracy@5 | 0.8946 | | cosine_accuracy@10 | 0.9431 | | cosine_precision@1 | 0.6271 | | cosine_precision@3 | 0.2789 | | cosine_precision@5 | 0.1789 | | cosine_precision@10 | 0.0943 | | cosine_recall@1 | 0.6271 | | cosine_recall@3 | 0.8366 | | cosine_recall@5 | 0.8946 | | cosine_recall@10 | 0.9431 | | cosine_ndcg@10 | 0.7905 | | cosine_mrr@10 | 0.7408 | | **cosine_map@100** | **0.7434** | #### Information Retrieval * Dataset: `gooaq-256-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6192 | | cosine_accuracy@3 | 0.8235 | | cosine_accuracy@5 | 0.8866 | | cosine_accuracy@10 | 0.9364 | | cosine_precision@1 | 0.6192 | | cosine_precision@3 | 0.2745 | | cosine_precision@5 | 0.1773 | | cosine_precision@10 | 0.0936 | | cosine_recall@1 | 0.6192 | | cosine_recall@3 | 0.8235 | | cosine_recall@5 | 0.8866 | | cosine_recall@10 | 0.9364 | | cosine_ndcg@10 | 0.7821 | | cosine_mrr@10 | 0.7321 | | **cosine_map@100** | **0.7349** | #### Information Retrieval * Dataset: `gooaq-128-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5942 | | cosine_accuracy@3 | 0.804 | | cosine_accuracy@5 | 0.8721 | | cosine_accuracy@10 | 0.9249 | | cosine_precision@1 | 0.5942 | | cosine_precision@3 | 0.268 | | cosine_precision@5 | 0.1744 | | cosine_precision@10 | 0.0925 | | cosine_recall@1 | 0.5942 | | cosine_recall@3 | 0.804 | | cosine_recall@5 | 0.8721 | | cosine_recall@10 | 0.9249 | | cosine_ndcg@10 | 0.7628 | | cosine_mrr@10 | 0.7103 | | **cosine_map@100** | **0.7134** | #### Information Retrieval * Dataset: `gooaq-64-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.556 | | cosine_accuracy@3 | 0.7553 | | cosine_accuracy@5 | 0.8267 | | cosine_accuracy@10 | 0.8945 | | cosine_precision@1 | 0.556 | | cosine_precision@3 | 0.2518 | | cosine_precision@5 | 0.1653 | | cosine_precision@10 | 0.0895 | | cosine_recall@1 | 0.556 | | cosine_recall@3 | 0.7553 | | cosine_recall@5 | 0.8267 | | cosine_recall@10 | 0.8945 | | cosine_ndcg@10 | 0.7246 | | cosine_mrr@10 | 0.6702 | | **cosine_map@100** | **0.6743** | #### Information Retrieval * Dataset: `gooaq-32-dev` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4628 | | cosine_accuracy@3 | 0.6619 | | cosine_accuracy@5 | 0.7415 | | cosine_accuracy@10 | 0.8241 | | cosine_precision@1 | 0.4628 | | cosine_precision@3 | 0.2206 | | cosine_precision@5 | 0.1483 | | cosine_precision@10 | 0.0824 | | cosine_recall@1 | 0.4628 | | cosine_recall@3 | 0.6619 | | cosine_recall@5 | 0.7415 | | cosine_recall@10 | 0.8241 | | cosine_ndcg@10 | 0.6387 | | cosine_mrr@10 | 0.5798 | | **cosine_map@100** | **0.5857** |   ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | gooaq-1024-dev_cosine_map@100 | gooaq-512-dev_cosine_map@100 | gooaq-256-dev_cosine_map@100 | gooaq-128-dev_cosine_map@100 | gooaq-64-dev_cosine_map@100 | gooaq-32-dev_cosine_map@100 | |:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:---------------------------:|:---------------------------:| | 0 | 0 | - | - | 0.2095 | 0.2010 | 0.1735 | 0.1381 | 0.0750 | 0.0331 | | 0.0007 | 1 | 34.953 | - | - | - | - | - | - | - | | 0.0682 | 100 | 16.2504 | - | - | - | - | - | - | - | | 0.1363 | 200 | 5.9502 | - | - | - | - | - | - | - | | 0.1704 | 250 | - | 1.6781 | 0.6791 | 0.6729 | 0.6619 | 0.6409 | 0.5904 | 0.4934 | | 0.2045 | 300 | 4.8411 | - | - | - | - | - | - | - | | 0.2727 | 400 | 4.336 | - | - | - | - | - | - | - | | 0.3408 | 500 | 4.0484 | 1.3935 | 0.7104 | 0.7055 | 0.6968 | 0.6756 | 0.6322 | 0.5358 | | 0.4090 | 600 | 3.8378 | - | - | - | - | - | - | - | | 0.4772 | 700 | 3.6765 | - | - | - | - | - | - | - | | 0.5112 | 750 | - | 1.2549 | 0.7246 | 0.7216 | 0.7133 | 0.6943 | 0.6482 | 0.5582 | | 0.5453 | 800 | 3.5439 | - | - | - | - | - | - | - | | 0.6135 | 900 | 3.4284 | - | - | - | - | - | - | - | | 0.6817 | 1000 | 3.3576 | 1.1656 | 0.7359 | 0.7338 | 0.7252 | 0.7040 | 0.6604 | 0.5715 | | 0.7498 | 1100 | 3.2456 | - | - | - | - | - | - | - | | 0.8180 | 1200 | 3.2014 | - | - | - | - | - | - | - | | 0.8521 | 1250 | - | 1.1133 | 0.7438 | 0.7398 | 0.7310 | 0.7099 | 0.6704 | 0.5796 | | 0.8862 | 1300 | 3.1536 | - | - | - | - | - | - | - | | 0.9543 | 1400 | 3.0696 | - | - | - | - | - | - | - | | 1.0 | 1467 | - | - | 0.7466 | 0.7434 | 0.7349 | 0.7134 | 0.6743 | 0.5857 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.017 kWh - **Carbon Emitted**: 0.006 kg of CO2 - **Hours Used**: 0.109 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.2.0.dev0 - Transformers: 4.43.4 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 0.31.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

benayad7/concat-e5-small-bge-small-01

benayad7

null

[ "mteb", "model-index", "region:us" ]

2024-10-10T09:07:21

2024-10-14T09:37:01

0

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type: main_score value: 86.05361584367344 - type: manhattan_accuracy value: 89.02472154305894 - type: manhattan_accuracy_threshold value: 1732.803726196289 - type: manhattan_ap value: 86.05361584367344 - type: manhattan_f1 value: 78.20484500404977 - type: manhattan_f1_threshold value: 1832.084083557129 - type: manhattan_precision value: 74.93297587131367 - type: manhattan_recall value: 81.7754850631352 - type: max_accuracy value: 89.02472154305894 - type: max_ap value: 86.05361584367344 - type: max_f1 value: 78.21080602302922 - type: max_precision value: 75.10632265381344 - type: max_recall value: 81.7754850631352 - type: similarity_accuracy value: 88.97038848139093 - type: similarity_accuracy_threshold value: 83.80404710769653 - type: similarity_ap value: 85.91804313814289 - type: similarity_f1 value: 78.21080602302922 - type: similarity_f1_threshold value: 82.21379518508911 - type: similarity_precision value: 75.10632265381344 - type: similarity_recall value: 81.5829996920234 --- Add stuff later!

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

zhw-e8/LAMAR

zhw-e8

null

[ "safetensors", "license:apache-2.0", "region:us" ]

2024-10-15T02:55:31

2024-10-15T05:43:35

0

--- {} --- # LAMAR A Foundation **La**nguage **M**odel for RN**A** **R**egulation This repository contains pre-trained and fine-tuned weights for RNA foundation language model **LAMAR**. LAMAR outperformed benchmark models in various RNA regulation tasks, helping to decipher the regulation rules of RNA. LAMAR was developed by Rnasys Lab and Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health (SINH), Chinese Academy of Sciences (CAS). ## Model weights SpliceSitePred: Weight of fine-tuned LAMAR predict splice site of pre-mRNA UTR5TEPred: Weight of fine-tuned LAMAR predict translation efficiency of mRNA based on 5' UTR UTR3DegPred: Weight of fine-tuned LAMAR predict degradation rate of mRNA based on 3' UTR IRESPred: Weight of fine-tuned LAMAR predicting internal ribosome entry site (IRES) mammalian80D_2048len1mer1sw_80M: Pretrained weights of LAMAR-2k mammalian80D_4096len1mer1sw_80M: Pretrained weights of LAMAR-4k ## Citation https://www.biorxiv.org/content/10.1101/2024.10.12.617732v2 Github link: https://github.com/zhw-e8/LAMAR

[ "TRANSLATION" ]

[ "CAS" ]

bobox/DeBERTa3-s-CustomPoolin-v3-step1

bobox

sentence-similarity

[ "sentence-transformers", "safetensors", "deberta-v2", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:225247", "loss:CachedGISTEmbedLoss", "en", "dataset:tals/vitaminc", "arxiv:1908.10084", "base_model:microsoft/deberta-v3-small", "base_model:finetune:microsoft/deberta-v3-small", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-10-20T22:06:27

2024-10-24T21:36:59

0

--- base_model: microsoft/deberta-v3-small datasets: - tals/vitaminc language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:225247 - loss:CachedGISTEmbedLoss widget: - source_sentence: what is exfo toolbox sentences: - Eye dilation from eye drops used for examination of the eye usually lasts from 4 to 24 hours, depending upon the strength of the drop and upon the individual patient. - Garden Grove is a city in northern Orange County in the U.S. state of California, 34 miles (55 km) south of Los Angeles. The population was 170,883 at the 2010 United States Census. State Route 22, also known as the Garden Grove Freeway, passes through the city in an east-west direction. - EXFO ToolBox Office is a product that offers you a collection of viewers and analyzers. It enables you to manage and analyze results acquired from fiber optic test modules and instruments. - source_sentence: More than 273 people have died from the 2019-20 coronavirus outside mainland China . sentences: - 'More than 3,700 people have died : around 3,100 in mainland China and around 550 in all other countries combined .' - 'More than 3,200 people have died : almost 3,000 in mainland China and around 275 in other countries .' - more than 4,900 deaths have been attributed to COVID-19 . - source_sentence: Ultrasound, a diagnostic technology, uses high-frequency vibrations transmitted into any tissue in contact with the transducer. sentences: - What diagnostic technology uses high-frequency vibrations transmitted into any tissue in contact with the transducer? - The abnormal cells cannot carry oxygen properly and can get stuck where? - What type of organism is a bacteria? - source_sentence: When you add moles of gas to a baloon by blowing it up, the volume increases. sentences: - What shape is the lens of the eye? - What happens to the volume of a balloon when you add moles of gas to it by blowing up? - Most turtle bodies are covered by a special bony or cartilaginous shell developed from their what? - source_sentence: What was the name of eleven rulers of the 19th and 20th Egyptian dynasties? sentences: - 'Airlines Yugoslavia 1968 - 1968 Renamed ^ Comments : Aviogenex was formed on 21May1968 as Genex Airlines. Restarted under current name on 30Apr1969 & liquidated in Feb2015 ^ Genealogy : Genex Airlines >Aviogenex 1968 - 1986 Renamed ^ Comments : Adria Airways was formed on 14Mar1961 & operations started on 30Jun1961 as Adria Airways, renamed to Inex in 1968 and back to Adria again in 1986. National airline of Slovenia ^ Genealogy : Adria Airways >Inex Adria Airways >Adria Airways JAT (Jugoslovenski Aerotransport) 1947 - 2003 Renamed ^ Comments : Air Serbia was founded as Aeroput on 17Jun1927, renamed to JAT on 01Apr1947. Started ops on 15Apr1947, Renamed again on 08Aug2003 to JAT Airways & reformed as Air Serbia on 26Oct2013 ^ Genealogy : Aeroput >JAT (Jugoslovenski Aerotransport) >JAT Airways >Air Serbia Jugoslovenski Aerotransport' - List of Rulers of Ancient Egypt and Nubia | Lists of Rulers | Heilbrunn Timeline of Art History | The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use. - 'What is the "Jack Sprat" nursery rhyme? | Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose''s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king''s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.' model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7673854808079448 name: Pearson Cosine - type: spearman_cosine value: 0.7776198286738142 name: Spearman Cosine - type: pearson_manhattan value: 0.782368447545155 name: Pearson Manhattan - type: spearman_manhattan value: 0.7720687033298573 name: Spearman Manhattan - type: pearson_euclidean value: 0.7882638792170585 name: Pearson Euclidean - type: spearman_euclidean value: 0.7775073687564514 name: Spearman Euclidean - type: pearson_dot value: 0.7669147371310585 name: Pearson Dot - type: spearman_dot value: 0.7762894632049069 name: Spearman Dot - type: pearson_max value: 0.7882638792170585 name: Pearson Max - type: spearman_max value: 0.7776198286738142 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.708984375 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.8714957237243652 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.5913043478260869 name: Cosine F1 - type: cosine_f1_threshold value: 0.7768557071685791 name: Cosine F1 Threshold - type: cosine_precision value: 0.4738675958188153 name: Cosine Precision - type: cosine_recall value: 0.7861271676300579 name: Cosine Recall - type: cosine_ap value: 0.5644305887001508 name: Cosine Ap - type: dot_accuracy value: 0.7109375 name: Dot Accuracy - type: dot_accuracy_threshold value: 674.426025390625 name: Dot Accuracy Threshold - type: dot_f1 value: 0.5913043478260869 name: Dot F1 - type: dot_f1_threshold value: 603.435302734375 name: Dot F1 Threshold - type: dot_precision value: 0.4738675958188153 name: Dot Precision - type: dot_recall value: 0.7861271676300579 name: Dot Recall - type: dot_ap value: 0.5664868031504724 name: Dot Ap - type: manhattan_accuracy value: 0.7109375 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 294.4728088378906 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.5935483870967742 name: Manhattan F1 - type: manhattan_f1_threshold value: 401.1482849121094 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.4726027397260274 name: Manhattan Precision - type: manhattan_recall value: 0.7976878612716763 name: Manhattan Recall - type: manhattan_ap value: 0.5642688421649988 name: Manhattan Ap - type: euclidean_accuracy value: 0.7109375 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 14.565500259399414 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.5913043478260869 name: Euclidean F1 - type: euclidean_f1_threshold value: 18.60409164428711 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4738675958188153 name: Euclidean Precision - type: euclidean_recall value: 0.7861271676300579 name: Euclidean Recall - type: euclidean_ap value: 0.5645557227019772 name: Euclidean Ap - type: max_accuracy value: 0.7109375 name: Max Accuracy - type: max_accuracy_threshold value: 674.426025390625 name: Max Accuracy Threshold - type: max_f1 value: 0.5935483870967742 name: Max F1 - type: max_f1_threshold value: 603.435302734375 name: Max F1 Threshold - type: max_precision value: 0.4738675958188153 name: Max Precision - type: max_recall value: 0.7976878612716763 name: Max Recall - type: max_ap value: 0.5664868031504724 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.6796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7726649045944214 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6925675675675677 name: Cosine F1 - type: cosine_f1_threshold value: 0.7317887544631958 name: Cosine F1 Threshold - type: cosine_precision value: 0.5758426966292135 name: Cosine Precision - type: cosine_recall value: 0.8686440677966102 name: Cosine Recall - type: cosine_ap value: 0.7302564198016936 name: Cosine Ap - type: dot_accuracy value: 0.67578125 name: Dot Accuracy - type: dot_accuracy_threshold value: 598.0419921875 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6912751677852348 name: Dot F1 - type: dot_f1_threshold value: 565.4718017578125 name: Dot F1 Threshold - type: dot_precision value: 0.5722222222222222 name: Dot Precision - type: dot_recall value: 0.8728813559322034 name: Dot Recall - type: dot_ap value: 0.7300462025003271 name: Dot Ap - type: manhattan_accuracy value: 0.6796875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 404.8309020996094 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6933333333333332 name: Manhattan F1 - type: manhattan_f1_threshold value: 444.99224853515625 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5714285714285714 name: Manhattan Precision - type: manhattan_recall value: 0.8813559322033898 name: Manhattan Recall - type: manhattan_ap value: 0.7369214156436785 name: Manhattan Ap - type: euclidean_accuracy value: 0.6796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 18.790739059448242 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6934306569343065 name: Euclidean F1 - type: euclidean_f1_threshold value: 19.35132598876953 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.6089743589743589 name: Euclidean Precision - type: euclidean_recall value: 0.8050847457627118 name: Euclidean Recall - type: euclidean_ap value: 0.7307381840067684 name: Euclidean Ap - type: max_accuracy value: 0.6796875 name: Max Accuracy - type: max_accuracy_threshold value: 598.0419921875 name: Max Accuracy Threshold - type: max_f1 value: 0.6934306569343065 name: Max F1 - type: max_f1_threshold value: 565.4718017578125 name: Max F1 Threshold - type: max_precision value: 0.6089743589743589 name: Max Precision - type: max_recall value: 0.8813559322033898 name: Max Recall - type: max_ap value: 0.7369214156436785 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity  - **Language:** en  ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): AdvancedWeightedPooling( (linear_cls): Linear(in_features=768, out_features=768, bias=True) (linear_mean): Linear(in_features=768, out_features=768, bias=True) (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True) ) (layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_cls): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layernorm_mean): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa3-s-CustomPoolin-v3-step1") # Run inference sentences = [ 'What was the name of eleven rulers of the 19th and 20th Egyptian dynasties?', 'List of Rulers of Ancient Egypt and Nubia | Lists of Rulers | Heilbrunn Timeline of Art History | The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use.', 'What is the "Jack Sprat" nursery rhyme? | Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose\'s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king\'s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7674 | | **spearman_cosine** | **0.7776** | | pearson_manhattan | 0.7824 | | spearman_manhattan | 0.7721 | | pearson_euclidean | 0.7883 | | spearman_euclidean | 0.7775 | | pearson_dot | 0.7669 | | spearman_dot | 0.7763 | | pearson_max | 0.7883 | | spearman_max | 0.7776 | #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.709 | | cosine_accuracy_threshold | 0.8715 | | cosine_f1 | 0.5913 | | cosine_f1_threshold | 0.7769 | | cosine_precision | 0.4739 | | cosine_recall | 0.7861 | | cosine_ap | 0.5644 | | dot_accuracy | 0.7109 | | dot_accuracy_threshold | 674.426 | | dot_f1 | 0.5913 | | dot_f1_threshold | 603.4353 | | dot_precision | 0.4739 | | dot_recall | 0.7861 | | dot_ap | 0.5665 | | manhattan_accuracy | 0.7109 | | manhattan_accuracy_threshold | 294.4728 | | manhattan_f1 | 0.5935 | | manhattan_f1_threshold | 401.1483 | | manhattan_precision | 0.4726 | | manhattan_recall | 0.7977 | | manhattan_ap | 0.5643 | | euclidean_accuracy | 0.7109 | | euclidean_accuracy_threshold | 14.5655 | | euclidean_f1 | 0.5913 | | euclidean_f1_threshold | 18.6041 | | euclidean_precision | 0.4739 | | euclidean_recall | 0.7861 | | euclidean_ap | 0.5646 | | max_accuracy | 0.7109 | | max_accuracy_threshold | 674.426 | | max_f1 | 0.5935 | | max_f1_threshold | 603.4353 | | max_precision | 0.4739 | | max_recall | 0.7977 | | **max_ap** | **0.5665** | #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6797 | | cosine_accuracy_threshold | 0.7727 | | cosine_f1 | 0.6926 | | cosine_f1_threshold | 0.7318 | | cosine_precision | 0.5758 | | cosine_recall | 0.8686 | | cosine_ap | 0.7303 | | dot_accuracy | 0.6758 | | dot_accuracy_threshold | 598.042 | | dot_f1 | 0.6913 | | dot_f1_threshold | 565.4718 | | dot_precision | 0.5722 | | dot_recall | 0.8729 | | dot_ap | 0.73 | | manhattan_accuracy | 0.6797 | | manhattan_accuracy_threshold | 404.8309 | | manhattan_f1 | 0.6933 | | manhattan_f1_threshold | 444.9922 | | manhattan_precision | 0.5714 | | manhattan_recall | 0.8814 | | manhattan_ap | 0.7369 | | euclidean_accuracy | 0.6797 | | euclidean_accuracy_threshold | 18.7907 | | euclidean_f1 | 0.6934 | | euclidean_f1_threshold | 19.3513 | | euclidean_precision | 0.609 | | euclidean_recall | 0.8051 | | euclidean_ap | 0.7307 | | max_accuracy | 0.6797 | | max_accuracy_threshold | 598.042 | | max_f1 | 0.6934 | | max_f1_threshold | 565.4718 | | max_precision | 0.609 | | max_recall | 0.8814 | | **max_ap** | **0.7369** |   ## Training Details ### Evaluation Dataset #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 128 evaluation samples * Columns: <code>claim</code> and <code>evidence</code> * Approximate statistics based on the first 128 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 21.42 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 35.55 tokens</li><li>max: 79 tokens</li></ul> | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Dragon Con had over 5000 guests .</code> | <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> | | <code>COVID-19 has reached more than 185 countries .</code> | <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> | | <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> | <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> | * Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters: ```json {'guide': SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ), 'temperature': 0.025} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 256 - `gradient_accumulation_steps`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05} - `warmup_ratio`: 0.33 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 100 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05} - `warmup_ratio`: 0.33 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | vitaminc-pairs loss | negation-triplets loss | scitail-pairs-pos loss | scitail-pairs-qa loss | xsum-pairs loss | sciq pairs loss | qasc pairs loss | openbookqa pairs loss | msmarco pairs loss | nq pairs loss | trivia pairs loss | gooaq pairs loss | paws-pos loss | global dataset loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap | |:------:|:----:|:-------------:|:-------------------:|:----------------------:|:----------------------:|:---------------------:|:---------------:|:---------------:|:---------------:|:---------------------:|:------------------:|:-------------:|:-----------------:|:----------------:|:-------------:|:-------------------:|:------------------------:|:-----------------:|:---------------:| | 0.0168 | 8 | 10.2928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0336 | 16 | 9.2166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0504 | 24 | 9.4858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0672 | 32 | 10.6143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0840 | 40 | 8.7553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1008 | 48 | 10.9939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1176 | 56 | 7.6039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1345 | 64 | 5.9498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1513 | 72 | 7.3051 | 3.2988 | 3.9604 | 1.9818 | 2.1997 | 6.0515 | 0.6095 | 6.3199 | 4.8391 | 6.4886 | 6.6406 | 6.4894 | 6.1527 | 2.0082 | 4.9577 | 0.3066 | 0.3444 | 0.5627 | | 0.1681 | 80 | 8.3034 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1849 | 88 | 7.6669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2017 | 96 | 6.6415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2185 | 104 | 5.7797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2353 | 112 | 5.8361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2521 | 120 | 5.3339 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2689 | 128 | 5.5908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2857 | 136 | 5.3209 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3025 | 144 | 5.5359 | 3.3310 | 3.8580 | 1.4769 | 1.6994 | 5.4819 | 0.5385 | 5.2021 | 4.4410 | 5.3419 | 5.5506 | 5.6972 | 5.3376 | 1.4170 | 3.9169 | 0.2954 | 0.3795 | 0.6317 | | 0.3193 | 152 | 5.4713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3361 | 160 | 4.9368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3529 | 168 | 4.6594 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3697 | 176 | 4.8392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3866 | 184 | 4.414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4034 | 192 | 4.891 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4202 | 200 | 4.4553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4370 | 208 | 3.9729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4538 | 216 | 3.7705 | 3.2468 | 3.6435 | 0.7890 | 0.7356 | 3.9327 | 0.4082 | 3.7175 | 3.5404 | 3.5351 | 4.0506 | 3.9953 | 3.6074 | 0.4195 | 2.4726 | 0.3791 | 0.4133 | 0.6779 | | 0.4706 | 224 | 3.8409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4874 | 232 | 3.7894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5042 | 240 | 3.3523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5210 | 248 | 3.2407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5378 | 256 | 3.3203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5546 | 264 | 2.8457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5714 | 272 | 2.4181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5882 | 280 | 3.4589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6050 | 288 | 2.8203 | 3.1119 | 3.1485 | 0.4531 | 0.2652 | 2.6895 | 0.2656 | 2.5542 | 2.7523 | 2.6600 | 3.1773 | 3.2099 | 2.7316 | 0.2006 | 1.6342 | 0.5257 | 0.4717 | 0.7078 | | 0.6218 | 296 | 2.4697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6387 | 304 | 2.4654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6555 | 312 | 2.4236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6723 | 320 | 2.2879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6891 | 328 | 2.2145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7059 | 336 | 1.8464 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7227 | 344 | 2.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7395 | 352 | 2.0635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7563 | 360 | 1.8584 | 3.3202 | 2.5793 | 0.3434 | 0.1618 | 1.6759 | 0.1834 | 1.6454 | 2.1257 | 2.1938 | 2.5316 | 2.4558 | 2.0596 | 0.0984 | 1.2206 | 0.6610 | 0.5199 | 0.7119 | | 0.7731 | 368 | 2.0286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7899 | 376 | 1.9389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8067 | 384 | 1.7453 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8235 | 392 | 1.6629 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 400 | 1.2724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8571 | 408 | 1.7824 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8739 | 416 | 1.5826 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8908 | 424 | 1.1971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9076 | 432 | 1.5228 | 3.3624 | 2.1952 | 0.3006 | 0.1223 | 1.1091 | 0.1582 | 1.2383 | 1.8664 | 1.7434 | 2.3959 | 2.0697 | 1.7563 | 0.0766 | 1.0193 | 0.7292 | 0.5194 | 0.7126 | | 0.9244 | 440 | 1.3323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9412 | 448 | 1.5124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9580 | 456 | 1.5565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9748 | 464 | 1.3672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9916 | 472 | 1.0382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0084 | 480 | 1.0626 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0252 | 488 | 1.3539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0420 | 496 | 1.1723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0588 | 504 | 1.4235 | 3.4031 | 1.9759 | 0.2554 | 0.0814 | 0.9034 | 0.1378 | 1.1603 | 1.7589 | 1.5608 | 2.1230 | 1.7719 | 1.6633 | 0.0720 | 0.9380 | 0.7523 | 0.5297 | 0.7129 | | 1.0756 | 512 | 1.2283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0924 | 520 | 1.2455 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1092 | 528 | 1.4265 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1261 | 536 | 1.296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1429 | 544 | 0.8763 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1597 | 552 | 1.5678 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1765 | 560 | 1.2548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1933 | 568 | 1.3731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2101 | 576 | 1.3023 | 3.3815 | 1.8740 | 0.2373 | 0.0769 | 0.7711 | 0.1237 | 0.9432 | 1.6871 | 1.5070 | 1.9947 | 1.6041 | 1.5579 | 0.0721 | 0.8661 | 0.7642 | 0.5412 | 0.7159 | | 1.2269 | 584 | 0.8135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2437 | 592 | 1.0259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2605 | 600 | 1.1896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2773 | 608 | 1.0532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2941 | 616 | 1.3221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3109 | 624 | 1.3136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3277 | 632 | 1.2238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3445 | 640 | 1.2407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3613 | 648 | 1.2245 | 3.4717 | 1.7962 | 0.2242 | 0.0488 | 0.7472 | 0.1108 | 0.9272 | 1.6692 | 1.3845 | 1.9117 | 1.3410 | 1.4387 | 0.0701 | 0.8505 | 0.7680 | 0.5471 | 0.7227 | | 1.3782 | 656 | 1.0428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3950 | 664 | 1.1391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4118 | 672 | 1.2632 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4286 | 680 | 0.9403 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4454 | 688 | 0.7571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4622 | 696 | 0.9436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4790 | 704 | 1.1239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4958 | 712 | 0.9499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5126 | 720 | 1.0945 | 3.6495 | 1.6693 | 0.2157 | 0.0492 | 0.6830 | 0.1049 | 0.9140 | 1.5967 | 1.4397 | 1.7394 | 1.3303 | 1.4334 | 0.0603 | 0.8185 | 0.7815 | 0.5606 | 0.7098 | | 1.5294 | 728 | 1.1161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5462 | 736 | 1.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5630 | 744 | 1.1743 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5798 | 752 | 0.9153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5966 | 760 | 1.1589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6134 | 768 | 0.9187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6303 | 776 | 0.6937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6471 | 784 | 0.9704 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6639 | 792 | 0.7343 | 3.5442 | 1.6493 | 0.2208 | 0.0249 | 0.6152 | 0.0969 | 0.7111 | 1.5369 | 1.4058 | 1.7066 | 1.2784 | 1.3419 | 0.0585 | 0.7827 | 0.7749 | 0.5627 | 0.7284 | | 1.6807 | 800 | 1.2878 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6975 | 808 | 0.9898 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7143 | 816 | 0.7613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7311 | 824 | 0.9612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7479 | 832 | 1.1524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7647 | 840 | 0.827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7815 | 848 | 1.1898 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7983 | 856 | 1.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8151 | 864 | 0.7019 | 3.4544 | 1.6149 | 0.2035 | 0.0181 | 0.5525 | 0.0999 | 0.6641 | 1.5456 | 1.3911 | 1.7188 | 1.2547 | 1.3517 | 0.0562 | 0.7473 | 0.7684 | 0.5697 | 0.7329 | | 1.8319 | 872 | 0.8352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8487 | 880 | 0.7836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8655 | 888 | 1.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8824 | 896 | 0.74 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8992 | 904 | 0.7263 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9160 | 912 | 0.8073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9328 | 920 | 0.8185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9496 | 928 | 1.0992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9664 | 936 | 0.9973 | 3.5110 | 1.5776 | 0.2035 | 0.0250 | 0.5881 | 0.0934 | 0.6719 | 1.5059 | 1.2970 | 1.6186 | 1.1815 | 1.2714 | 0.0564 | 0.7213 | 0.7799 | 0.5544 | 0.7341 | | 1.9832 | 944 | 0.6662 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0 | 952 | 0.533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0168 | 960 | 0.7712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0336 | 968 | 0.6879 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0504 | 976 | 0.7975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0672 | 984 | 0.873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0840 | 992 | 0.7995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1008 | 1000 | 1.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1176 | 1008 | 0.6317 | 3.6778 | 1.5845 | 0.2102 | 0.0228 | 0.5851 | 0.0977 | 0.6411 | 1.4752 | 1.2992 | 1.6314 | 1.1260 | 1.2683 | 0.0556 | 0.7329 | 0.7693 | 0.5614 | 0.7274 | | 2.1345 | 1016 | 0.72 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1513 | 1024 | 0.9418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1681 | 1032 | 0.7848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1849 | 1040 | 0.6965 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2017 | 1048 | 1.0447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2185 | 1056 | 0.6361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2353 | 1064 | 0.6837 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2521 | 1072 | 0.5713 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2689 | 1080 | 0.8193 | 3.6399 | 1.5565 | 0.2069 | 0.0213 | 0.5440 | 0.0904 | 0.6057 | 1.4815 | 1.2856 | 1.6441 | 1.1469 | 1.2540 | 0.0543 | 0.7216 | 0.7765 | 0.5599 | 0.7322 | | 2.2857 | 1088 | 0.9754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3025 | 1096 | 0.8932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3193 | 1104 | 0.8716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3361 | 1112 | 0.8787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3529 | 1120 | 0.9529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3697 | 1128 | 0.775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3866 | 1136 | 0.6178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4034 | 1144 | 0.8384 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4202 | 1152 | 0.9425 | 3.5672 | 1.5244 | 0.2111 | 0.0162 | 0.5593 | 0.0893 | 0.5759 | 1.4933 | 1.2703 | 1.5815 | 1.1202 | 1.2132 | 0.0531 | 0.7058 | 0.7730 | 0.5635 | 0.7350 | | 2.4370 | 1160 | 0.4551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4538 | 1168 | 0.6392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4706 | 1176 | 0.8341 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4874 | 1184 | 0.7392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5042 | 1192 | 0.7646 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5210 | 1200 | 0.8613 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5378 | 1208 | 0.7585 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5546 | 1216 | 1.0611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5714 | 1224 | 0.6506 | 3.6439 | 1.5040 | 0.2125 | 0.0162 | 0.5282 | 0.0863 | 0.5858 | 1.5073 | 1.2444 | 1.5493 | 1.1014 | 1.2073 | 0.0532 | 0.7022 | 0.7774 | 0.5647 | 0.7328 | | 2.5882 | 1232 | 0.8525 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6050 | 1240 | 0.6304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6218 | 1248 | 0.6354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6387 | 1256 | 0.6583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6555 | 1264 | 0.5964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6723 | 1272 | 0.818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6891 | 1280 | 0.8635 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7059 | 1288 | 0.6389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7227 | 1296 | 0.6819 | 3.6131 | 1.5104 | 0.2084 | 0.0148 | 0.5229 | 0.0854 | 0.5588 | 1.4963 | 1.2766 | 1.5679 | 1.0982 | 1.2203 | 0.0529 | 0.7059 | 0.7762 | 0.5659 | 0.7355 | | 2.7395 | 1304 | 0.7878 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7563 | 1312 | 0.7638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7731 | 1320 | 0.8885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7899 | 1328 | 0.8184 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8067 | 1336 | 0.7472 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8235 | 1344 | 0.7012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8403 | 1352 | 0.4622 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8571 | 1360 | 0.846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8739 | 1368 | 0.8308 | 3.6224 | 1.5088 | 0.2084 | 0.0148 | 0.5118 | 0.0858 | 0.5523 | 1.4941 | 1.2756 | 1.5808 | 1.0925 | 1.2114 | 0.0521 | 0.7022 | 0.7765 | 0.5662 | 0.7366 | | 2.8908 | 1376 | 0.5334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9076 | 1384 | 0.7893 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9244 | 1392 | 0.6897 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9412 | 1400 | 0.7803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9580 | 1408 | 0.841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9748 | 1416 | 0.787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9916 | 1424 | 0.5861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 3.0 | 1428 | - | 3.6139 | 1.5071 | 0.2084 | 0.0150 | 0.5124 | 0.0862 | 0.5532 | 1.4924 | 1.2700 | 1.5806 | 1.0905 | 1.2081 | 0.0519 | 0.6997 | 0.7776 | 0.5665 | 0.7369 | </details> ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.2.0 - Transformers: 4.44.2 - PyTorch: 2.4.1+cu121 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```

[ "TEXT_CLASSIFICATION", "SEMANTIC_SIMILARITY" ]

[ "SCIQ", "SCITAIL" ]

tomaarsen/static-bert-uncased-gooaq-beir

tomaarsen

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-10-23T11:12:32

2024-10-23T11:12:40

0

--- datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 6.483463467240631 energy_consumed: 0.01667977902671103 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.112 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.148 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10566666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22233333333333336 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.30566666666666664 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.40399999999999997 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3021857757296797 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.35745238095238085 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23166090256020686 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5133333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.48 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.43800000000000006 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04048260039152364 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.10679067052991392 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.16517406885695451 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.29331552217012935 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5008496215473859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6488571428571429 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3752676117852694 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.68 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08599999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.3966666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6466666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7766666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5890710274148659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.546047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5325906780111076 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.106 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1734126984126984 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.32126984126984126 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3737936507936508 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.47868253968253976 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.384612736899094 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.44405555555555554 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.32183898737919203 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.58 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.74 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.78 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.58 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3133333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.21600000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.126 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.29 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.63 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5630232180814766 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.675079365079365 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.48992202928149226 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.52 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06000000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.52 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.41343867686046815 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3524603174603175 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3712333972436779 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.36 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3440000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.264 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.019665573227317924 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07420738619382097 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.09536630802985016 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.11619353053313819 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3204704228749859 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.48633333333333334 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.12237170785886863 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.36 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.34 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.45 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.62 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3932776776815765 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.33038888888888884 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33440957968177043 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.9 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.98 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.98 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.38666666666666655 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23599999999999993 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.8106666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.922 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9259999999999999 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.99 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9424143419536263 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9395238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9189180735930736 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.21333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16799999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.13166666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.17266666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.26666666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.24548416934230666 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3969603174603174 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1751060490177909 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06400000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.64 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3703136948358056 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2856587301587301 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2982488157827007 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.46 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.076 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.425 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.47 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.58 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.685 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5455895863246394 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5181349206349206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5100938735556383 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.6326530612244898 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9591836734693877 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6326530612244898 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6326530612244897 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5877551020408164 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5163265306122449 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04221140303473122 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12126049151597706 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18889590300402684 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3304256352667907 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.563482462405376 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7857142857142857 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42991471736271825 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4040502354788069 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5922448979591837 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6692307692307693 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7692307692307693 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4040502354788069 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2794348508634223 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.2233657770800628 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.1671020408163265 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22103376474868755 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3635534658597092 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.41878691774496013 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5254577354604563 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.47186257015009897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5205128205128206 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.39319818639334664 name: Cosine Map@100 --- # Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-bert-uncased-gooaq-beir-4") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.2 | 0.52 | 0.42 | 0.36 | 0.58 | 0.2 | 0.38 | 0.2 | 0.9 | 0.28 | 0.12 | 0.46 | 0.6327 | | cosine_accuracy@3 | 0.42 | 0.7 | 0.68 | 0.48 | 0.74 | 0.5 | 0.54 | 0.36 | 0.98 | 0.44 | 0.4 | 0.5 | 0.9592 | | cosine_accuracy@5 | 0.58 | 0.82 | 0.68 | 0.54 | 0.78 | 0.52 | 0.68 | 0.48 | 0.98 | 0.56 | 0.48 | 0.6 | 1.0 | | cosine_accuracy@10 | 0.76 | 0.9 | 0.82 | 0.64 | 0.86 | 0.6 | 0.68 | 0.64 | 1.0 | 0.76 | 0.64 | 0.7 | 1.0 | | cosine_precision@1 | 0.2 | 0.52 | 0.42 | 0.36 | 0.58 | 0.2 | 0.38 | 0.2 | 0.9 | 0.28 | 0.12 | 0.46 | 0.6327 | | cosine_precision@3 | 0.1667 | 0.5133 | 0.2333 | 0.22 | 0.3133 | 0.1667 | 0.36 | 0.12 | 0.3867 | 0.2133 | 0.1333 | 0.1733 | 0.6327 | | cosine_precision@5 | 0.148 | 0.48 | 0.14 | 0.16 | 0.216 | 0.104 | 0.344 | 0.096 | 0.236 | 0.168 | 0.096 | 0.128 | 0.5878 | | cosine_precision@10 | 0.104 | 0.438 | 0.086 | 0.106 | 0.126 | 0.06 | 0.264 | 0.068 | 0.134 | 0.13 | 0.064 | 0.076 | 0.5163 | | cosine_recall@1 | 0.1057 | 0.0405 | 0.3967 | 0.1734 | 0.29 | 0.2 | 0.0197 | 0.19 | 0.8107 | 0.0597 | 0.12 | 0.425 | 0.0422 | | cosine_recall@3 | 0.2223 | 0.1068 | 0.6467 | 0.3213 | 0.47 | 0.5 | 0.0742 | 0.34 | 0.922 | 0.1317 | 0.4 | 0.47 | 0.1213 | | cosine_recall@5 | 0.3057 | 0.1652 | 0.6467 | 0.3738 | 0.54 | 0.52 | 0.0954 | 0.45 | 0.926 | 0.1727 | 0.48 | 0.58 | 0.1889 | | cosine_recall@10 | 0.404 | 0.2933 | 0.7767 | 0.4787 | 0.63 | 0.6 | 0.1162 | 0.62 | 0.99 | 0.2667 | 0.64 | 0.685 | 0.3304 | | **cosine_ndcg@10** | **0.3022** | **0.5008** | **0.5891** | **0.3846** | **0.563** | **0.4134** | **0.3205** | **0.3933** | **0.9424** | **0.2455** | **0.3703** | **0.5456** | **0.5635** | | cosine_mrr@10 | 0.3575 | 0.6489 | 0.546 | 0.4441 | 0.6751 | 0.3525 | 0.4863 | 0.3304 | 0.9395 | 0.397 | 0.2857 | 0.5181 | 0.7857 | | cosine_map@100 | 0.2317 | 0.3753 | 0.5326 | 0.3218 | 0.4899 | 0.3712 | 0.1224 | 0.3344 | 0.9189 | 0.1751 | 0.2982 | 0.5101 | 0.4299 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4041 | | cosine_accuracy@3 | 0.5922 | | cosine_accuracy@5 | 0.6692 | | cosine_accuracy@10 | 0.7692 | | cosine_precision@1 | 0.4041 | | cosine_precision@3 | 0.2794 | | cosine_precision@5 | 0.2234 | | cosine_precision@10 | 0.1671 | | cosine_recall@1 | 0.221 | | cosine_recall@3 | 0.3636 | | cosine_recall@5 | 0.4188 | | cosine_recall@10 | 0.5255 | | **cosine_ndcg@10** | **0.4719** | | cosine_mrr@10 | 0.5205 | | cosine_map@100 | 0.3932 |   ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.0726 | 0.3715 | 0.2100 | 0.1058 | 0.3196 | 0.3109 | 0.2221 | 0.1401 | 0.6737 | 0.1618 | 0.1183 | 0.4337 | 0.1331 | 0.2518 | | 0.0007 | 1 | 35.3437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0682 | 100 | 16.3878 | 2.4139 | 0.2927 | 0.4729 | 0.5725 | 0.3235 | 0.5905 | 0.3674 | 0.2994 | 0.3324 | 0.9123 | 0.2326 | 0.3407 | 0.5618 | 0.5352 | 0.4488 | | 0.1363 | 200 | 5.94 | 1.8298 | 0.2897 | 0.4880 | 0.5624 | 0.3447 | 0.5683 | 0.4311 | 0.3066 | 0.3502 | 0.9129 | 0.2533 | 0.3335 | 0.5696 | 0.5365 | 0.4575 | | 0.2045 | 300 | 4.8307 | 1.5955 | 0.2780 | 0.4896 | 0.5746 | 0.3513 | 0.5815 | 0.4040 | 0.3125 | 0.3897 | 0.9190 | 0.2578 | 0.3556 | 0.5461 | 0.5401 | 0.4615 | | 0.2727 | 400 | 4.33 | 1.4696 | 0.3113 | 0.4909 | 0.5920 | 0.3795 | 0.5836 | 0.3919 | 0.3201 | 0.4023 | 0.9355 | 0.2535 | 0.3419 | 0.5236 | 0.5524 | 0.4676 | | 0.3408 | 500 | 4.0423 | 1.3887 | 0.3085 | 0.4966 | 0.5986 | 0.3794 | 0.5914 | 0.3914 | 0.3174 | 0.3590 | 0.9309 | 0.2441 | 0.3537 | 0.5311 | 0.5534 | 0.4658 | | 0.4090 | 600 | 3.8422 | 1.3120 | 0.3034 | 0.5052 | 0.6075 | 0.3680 | 0.5834 | 0.4136 | 0.3122 | 0.3725 | 0.9257 | 0.2477 | 0.3583 | 0.5309 | 0.5646 | 0.4687 | | 0.4772 | 700 | 3.6795 | 1.2693 | 0.2975 | 0.4988 | 0.5954 | 0.3785 | 0.5811 | 0.4160 | 0.3142 | 0.3908 | 0.9362 | 0.2471 | 0.3479 | 0.5520 | 0.5601 | 0.4704 | | 0.5453 | 800 | 3.5367 | 1.2285 | 0.3011 | 0.4947 | 0.5829 | 0.3463 | 0.5689 | 0.4369 | 0.3224 | 0.3791 | 0.9310 | 0.2430 | 0.3663 | 0.5577 | 0.5585 | 0.4684 | | 0.6135 | 900 | 3.4279 | 1.1963 | 0.3059 | 0.5027 | 0.5894 | 0.3674 | 0.5758 | 0.4126 | 0.3186 | 0.4066 | 0.9349 | 0.2456 | 0.3672 | 0.5560 | 0.5624 | 0.4727 | | 0.6817 | 1000 | 3.3637 | 1.1652 | 0.3056 | 0.5022 | 0.5849 | 0.3702 | 0.5714 | 0.4238 | 0.3161 | 0.4007 | 0.9373 | 0.2430 | 0.3699 | 0.5618 | 0.5657 | 0.4733 | | 0.7498 | 1100 | 3.2336 | 1.1312 | 0.3006 | 0.5038 | 0.5920 | 0.3884 | 0.5733 | 0.4241 | 0.3247 | 0.3974 | 0.9369 | 0.2431 | 0.3670 | 0.5644 | 0.5608 | 0.4751 | | 0.8180 | 1200 | 3.1952 | 1.1132 | 0.3044 | 0.4987 | 0.5770 | 0.3630 | 0.5735 | 0.4259 | 0.3279 | 0.3955 | 0.9428 | 0.2416 | 0.3798 | 0.5659 | 0.5641 | 0.4739 | | 0.8862 | 1300 | 3.1535 | 1.0926 | 0.2983 | 0.4968 | 0.5753 | 0.3812 | 0.5684 | 0.4108 | 0.3203 | 0.3965 | 0.9421 | 0.2428 | 0.3685 | 0.5608 | 0.5628 | 0.4711 | | 0.9543 | 1400 | 3.0691 | 1.0862 | 0.3109 | 0.5008 | 0.5870 | 0.3761 | 0.5612 | 0.4121 | 0.3204 | 0.3947 | 0.9426 | 0.2414 | 0.3708 | 0.5456 | 0.5588 | 0.4709 | | 1.0 | 1467 | - | - | 0.3022 | 0.5008 | 0.5891 | 0.3846 | 0.5630 | 0.4134 | 0.3205 | 0.3933 | 0.9424 | 0.2455 | 0.3703 | 0.5456 | 0.5635 | 0.4719 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.017 kWh - **Carbon Emitted**: 0.006 kg of CO2 - **Hours Used**: 0.112 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.45.2 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.1-dev.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

sentence-transformers/static-retrieval-mrl-en-v1

sentence-transformers

sentence-similarity

[ "sentence-transformers", "onnx", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:80543469", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/squad", "dataset:sentence-transformers/s2orc", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/paq", "dataset:sentence-transformers/trivia-qa", "dataset:bclavie/msmarco-10m-triplets", "dataset:nthakur/swim-ir-monolingual", "dataset:sentence-transformers/pubmedqa", "dataset:sentence-transformers/miracl", "dataset:sentence-transformers/mldr", "dataset:sentence-transformers/mr-tydi", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-10-24T07:17:18

2025-01-17T10:54:17

0

32

--- datasets: - sentence-transformers/gooaq - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/squad - sentence-transformers/s2orc - sentence-transformers/all-nli - sentence-transformers/paq - sentence-transformers/trivia-qa - bclavie/msmarco-10m-triplets - nthakur/swim-ir-monolingual - sentence-transformers/pubmedqa - sentence-transformers/miracl - sentence-transformers/mldr - sentence-transformers/mr-tydi language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:80543469 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: What was the Office of Foods in charge of? sentences: - This area, stretching northward from the centrally located Great Hall of State, is believed to have been the site of the Office of Foods. This office stocked foods other than the rice that was paid as tax, and was in charge of providing meals for state banquets and rituals held in the palace. - In 2002, Barclay Records, then as part of Universal Music France, released a digitally remastered version of the original vinyl in CD and in 10" (25 cm) vinyl record (LP), under the same name, as part of a compilation containing re-releases of all of Dalida's studio albums recorded under the Barclay label. The album was again re-released in 2005. - Kevin Jon Davies is a British television and video director primarily associated with documentaries and spin-off videos associated with "Doctor Who", "The Hitchhiker's Guide to the Galaxy" and "Blake's 7". He also worked on the BAFTA award-winning animation sequences of the 1981 "Hitchhiker's Guide" television adaptation. - source_sentence: jak wysłać sf do krs? sentences: - '[''Kliknij na pole „Przygotowanie i składanie zgłoszeń”'', ''Następnie kliknij niebieski przycisk Dodaj zgłoszenie i uzupełnij o numer KRS Twojej jednostki na stronie „Rejestracja nowego zgłoszenia – Krok 1”'']' - When you experience a trigger, the insides of your airways swell even more. This narrows the space for air to move in and out of the lungs. The muscles that wrap around your airways also can tighten, making breathing even harder. When that happens, it's called an asthma flare-up, asthma episode or asthma "attack." - Lie (L) – The Lie scale is intended to identify individuals who are deliberately trying to avoid answering the MMPI honestly and in a frank manner. The scale measures attitudes and practices that are culturally laudable, but rarely found in most people. - source_sentence: 'Air Pollution in Eastern Asia: An Integrated Perspective: Chapter 14: Observation of Air Pollution over China Using the IASI Thermal Infrared Space Sensor' sentences: - In this chapter we describe what is achievable in terms of pollutant tracking from space using observations provided by thermal infrared remote sensors. After a general introduction on infrared remote sensing, we exploit the data provided by the Infrared Atmospheric Sounding Interferometer (IASI) missions onboard the Metop series of satellite to illustrate pollution detection at various spatial and temporal scales. Then, we focus on air pollution over China and discuss three case studies involving different pollutants. The first example discusses the geophysical conditions for detection of ammonia (NH3) and sulfur dioxide (SO2), both precursors of particulate matter (PM). The second case illustrates the seasonal variation of ozone (O3), in particular during the monsoon period. The third case shows the local accumulation of enhanced levels of carbon monoxide (CO) when pollution episodes occur. - This article explores the political aspects of Islamic parties in Jombang, East Java. The issue came about during the controversy arising out of the defection of the leader of the Tarekat Qadiriyah Wa Naqsyabandiyah, an Islamic order, prior to the 1977 general election. It raised questions as to the political orientation of Islamic groups in Indonesia during the 1970s and 1980s. - Abstract Unexpected findings on bone scintigraphy such as asymmetrical uptake in extremities may cause confusion for the diagnosis. The authors describe three cases of accidental intraarterial injection of Tc- 99m methylene diphosphonate ( 99m Tc-MDP) on the antecubital region and discuss the findings and differential diagnosis. - source_sentence: What type of stimuli do nociceptors response to? sentences: - 'After independence, Dutch was dropped as an official language and replaced by Malay. Yet the Indonesian language inherited many words from Dutch: words for everyday life as well as scientific and technological terms. One scholar argues that 20% of Indonesian words can be traced back to Dutch words, many of which are transliterated to reflect phonetic pronunciation e.g. kantoor (Dutch for "office") in Indonesian is kantor, while bus ("bus") becomes bis. In addition, many Indonesian words are calques on Dutch, for example, rumah sakit (Indonesian for "hospital") is calqued on the Dutch ziekenhuis (literally "house of the sick"), kebun binatang ("zoo") on dierentuin (literally "animal garden"), undang-undang dasar ("constitution") from grondwet (literally "ground law"). These account for some of the differences in vocabulary between Indonesian and Malay.' - Wilhelm Erb's (1874) "intensive" theory, that a pain signal can be generated by intense enough stimulation of any sensory receptor, has been soundly disproved. Some sensory fibers do not differentiate between noxious and non-noxious stimuli, while others, nociceptors, respond only to noxious, high intensity stimuli. At the peripheral end of the nociceptor, noxious stimuli generate currents that, above a given threshold, begin to send signals along the nerve fiber to the spinal cord. The "specificity" (whether it responds to thermal, chemical or mechanical features of its environment) of a nociceptor is determined by which ion channels it expresses at its peripheral end. Dozens of different types of nociceptor ion channels have so far been identified, and their exact functions are still being determined. - The first attempt to establish a proper governing body and adopted the current set of Rugby rules was the Foot Ball Association of Canada, organized on March 24, 1873 followed by the Canadian Rugby Football Union (CRFU) founded June 12, 1880, which included teams from Ontario and Quebec. Later both the Ontario and Quebec Rugby Football Union (ORFU and QRFU) were formed (January 1883), and then the Interprovincial (1907) and Western Interprovincial Football Union (1936) (IRFU and WIFU). The CRFU reorganized into an umbrella organization forming the Canadian Rugby Union (CRU) in 1891. The original forerunners to the current Canadian Football League, was established in 1956 when the IRFU and WIFU formed an umbrella organization, The Canadian Football Council (CFC). And then in 1958 the CFC left The CRFU to become The CFL. - source_sentence: 'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?' sentences: - Prior studies have demonstrated improved clinical outcomes for surgeons with a high-volume experience with certain open vascular operations. A high-volume experience with carotid artery stenting (CAS) improves clinical outcomes. Moreover, it is not known whether experience with other endovascular procedures, including percutaneous coronary interventions (PCIs), is an adequate substitute for experience with CAS. The goal of this study was to quantify the effect of increasing clinician volume of CAS, endovascular aneurysm repair (EVAR), and thoracic endovascular aortic aneurysm repair (TEVAR), and PCI on the outcomes for CAS. - While sensitive to internal carotid artery (ICA) occlusion, carotid ultrasound can produce false-positive results. CT angiography (CTA) has a high specificity for ICA occlusion and is safer and cheaper than catheter angiography, although less accurate. We determined the cost-effectiveness of CTA versus catheter angiography for confirming an ICA occlusion first suggested by carotid ultrasound. - Brachial artery FMD and GMD and carotid intima media thickness (cIMT) were studied using ultrasound in 20 patients diagnosed with early RA in whom symptoms had been present for less than 12 months, and in 20 control subjects matched for age, sex and established cardiovascular risk factors. FMD and GMD were re-assessed after 12 months in RA patients and the change in each parameter was calculated. Data were analysed by univariate regression. - Compared with preoperative clinical and conventional MR data, (1)H MRS improved the accuracy of MR imaging from 60.9% to 83%. We found (1)H MRS reliably distinguished between abscess and high-grade tumour, and between high-grade glioma and low-grade glioma, but was not able to reliably distinguish between recurrent glioma and radiation necrosis. In 12/23 cases (52%) the (1)H MRS findings positively altered our clinical management. Two representative cases are presented. - Different in-plane resolutions have been used for carotid 3T MRI. We compared the reproducibility, as well as the within- and between reader variability of high and routinely used spatial resolution in scans of patients with atherosclerotic carotid artery disease. Since no consensus exists about the optimal segmentation method, we analysed all imaging data using two different segmentation methods. - In the population-based Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) study (1016 subjects all aged 70), the prevalence of overt plaques and echogenectity (grey scale median, GSM) of carotid artery plaques were recorded by ultrasound in both of the carotid arteries. The thickness (IMT) and echogenicity (IM-GSM) of the intima-media complex were also measured. Bisphenol A (BPA) and 10 phthalate metabolites were analyzed in serum by a API 4000 liquid chromatograph/tandem mass spectrometer. - In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices. - Development of an improved MR sequence for examining the lung. - Thirty-seven carotid artery disease patients participated in this study, of whom 24 underwent magnetic resonance imaging before and after CEA. Seventeen control subjects spanning 5 decades underwent magnetic resonance imaging to assess age-related changes. Hemodynamic metrics (that is, relative time to peak and amplitude) were calculated with a γ-variate model. Linear regression was used to relate carotid artery disease burden to downstream hemodynamics in the circle of Willis. - Carotid artery stenting (CAS) is associated with a higher risk of both hemodynamic depression and new ischemic brain lesions on diffusion-weighted imaging than carotid endarterectomy (CEA). We assessed whether the occurrence of hemodynamic depression is associated with these lesions in patients with symptomatic carotid stenosis treated by CAS or CEA in the randomized International Carotid Stenting Study (ICSS)-MRI substudy. - Magnetic resonance imaging (MRI) guidance may improve the accuracy of Gleason score (GS) determination by directing the biopsy to regions of interest (ROI) that are likely to harbor high-grade prostate cancer (CaP). The aim of this study was to determine the frequency and predictors of GS upgrading when a subsequent MRI-guided biopsy is performed on patients with a diagnosis of GS 6 disease on the basis of conventional, transrectal ultrasound-guided biopsy. - Measures of carotid-femoral pulse wave velocity (cf-PWV) and carotid augmentation index (cAI) may be affected by the presence of an abdominal aortic aneurysm (AAA). We, therefore, investigated series of various measures of arterial stiffness and wave reflections in patients with AAA, before and 4 weeks after endovascular aneurysm repair (EVAR). - High spatial resolution of dynamic contrast-enhanced (DCE) MR imaging allows characterization of heterogenous tumor microenvironment. Our purpose was to determine which is the best advanced MR imaging protocol, focused on additional MR perfusion method, for predicting recurrent metastatic brain tumor following gamma-knife radiosurgery (GKRS). - To determine whether acromegalic patients have increased thyroidal vascularity and blood flow on colour flow Doppler sonography (CFDS). - To investigate whether an existing method for correction of phase offset errors in phase-contrast velocity quantification is applicable for assessment of main pulmonary artery flow with an MR scanner equipped with a high-power gradient system. - Consecutive patients (n = 292) undergoing carotid endarterectomy for symptomatic and asymptomatic carotid stenosis were included in the study. Mortality and cardiovascular ischemic events were recorded during a median follow-up of 5.2 years. Baseline plasma concentrations of adiponectin were measured. Cox regression models stratified for gender were used for estimation of risk of events. - To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms. - Phase-contrast Cardiovascular Magnetic Resonance Imaging (CMR) generally requires the analysis of stationary tissue adjacent to a blood vessel to serve as a baseline reference for zero velocity. However, for the heart and great vessels, there is often no stationary tissue immediately adjacent to the vessel. Consequently, uncorrected velocity offsets may introduce substantial errors in flow quantification. The purpose of this study was to assess the magnitude of these flow errors and to validate a clinically applicable method for their correction. - This study was a post hoc analysis of a prospective cohort comprising 485 consecutive patients undergoing carotid endarterectomy for high-grade ICAS. Patients were classified by their clinical presentation, ie, asymptomatic (n = 213) or symptomatic (within 6 months of surgery; n = 272, comprising both transient ischemic attack [TIA; n = 163] and stroke [n = 109]). We investigated the association of cl-ICAS with the primary outcome in adjusted regression models. - Several studies reported on the moderate diagnostic yield of elective invasive coronary angiography (ICA) regarding the presence of coronary artery disease (CAD), but limited data are available on how prior testing for ischaemia may contribute to improve the diagnostic yield in an every-day clinical setting. This study aimed to assess the value and use of cardiac myocardial perfusion single photon emission computed tomography (MPS) in patient selection prior to elective ICA. - The feasibility of carotid stenting (CS) is no longer questionable, although its indications remain debatable. Until the results of randomized trials are available, personal series and registries should help in the comparison of long-term results of CS with those of endarterectomy. We report here the long-term results of a large series of CS in our department with a long follow-up. This retrospective study reviews a single surgeon's 11-year experience with CS. Our results are compared with those of conventional surgery emanating from our own series and the North American Symptomatic Carotid Endarterectomy Trial (NASCET), European Carotid Surgery Trial (ECST), and Asymptomatic Carotid Atherosclerosis Study (ACAS). co2_eq_emissions: emissions: 1014.8766030829654 energy_consumed: 2.610937435575236 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 17.883 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Static Embeddings with BERT uncased tokenizer finetuned on various datasets results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.14666666666666664 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.239 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.27899999999999997 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4196666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33085031011968163 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.44530158730158725 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.259819611075427 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.84 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5866666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.544 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.45199999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0804732343549837 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.16047472236902457 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.21798474210348842 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.31433571884014205 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5681388031303078 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7853888888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4334843491187922 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.46 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4366666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8033333333333332 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9033333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6921500788245725 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.639690476190476 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.62054338159709 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10399999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15188888888888888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29826984126984124 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3792936507936508 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4837936507936508 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3651145030243953 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3914603174603174 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3023673541934707 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.96 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.26 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14799999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.56 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.65 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6547177705459605 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7485238095238096 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5797919554359183 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.42 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.66 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4040678769319761 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3244682539682539 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33886403445504565 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3733333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.32 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.244 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04278202363094378 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09842444348194118 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11962677523904507 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.1389182072247147 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3241949561078219 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5040793650793652 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1448579573714899 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.124 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.24 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.43 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.58 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.69 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4533881733265689 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3764047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3890107375543526 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.8 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.38666666666666655 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.24799999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7106666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9253333333333333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9626666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9793333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.895097527564125 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.88 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8594406482406483 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.188 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.14166666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19466666666666668 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2886666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.26425784158945775 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39979365079365076 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20502449880105952 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11200000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.46 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.56 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4077879341218404 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3033888888888889 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.31510434322531095 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.485 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.57 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.595 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.75 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6111476167014296 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5836904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5683309026222819 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8979591836734694 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9795918367346939 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6054421768707482 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.6204081632653061 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5306122448979592 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03980518443040866 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12364050983083796 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.20953289383493803 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.33697859476017505 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5702638593808323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.744047619047619 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4469881140237455 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4239560439560439 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6336891679748822 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7030455259026686 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8107692307692307 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4239560439560439 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2963160648874934 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.24064678178963897 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17327786499215073 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2302781536901455 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3979597064321778 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4654695945105992 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5726943208937448 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.503167480874536 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5481721611721612 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.420279068285741 name: Cosine Map@100 - task: type: Retrieval dataset: name: MTEB AILACasedocs (default) type: mteb/AILA_casedocs config: default split: test revision: 4106e6bcc72e0698d714ea8b101355e3e238431a metrics: - type: main_score value: 25.342 - type: map_at_1 value: 7.2059999999999995 - type: map_at_10 value: 17.343 - type: map_at_100 value: 21.356 - type: map_at_1000 value: 21.719 - type: map_at_20 value: 18.765 - type: map_at_3 value: 12.395 - type: map_at_5 value: 14.796000000000001 - type: mrr_at_1 value: 20.0 - type: mrr_at_10 value: 29.019047619047615 - type: mrr_at_100 value: 30.079478514482233 - type: mrr_at_1000 value: 30.11575302428615 - type: mrr_at_20 value: 29.311976911976913 - type: mrr_at_3 value: 25.0 - type: mrr_at_5 value: 27.399999999999995 - type: ndcg_at_1 value: 20.0 - type: ndcg_at_10 value: 25.342 - type: ndcg_at_100 value: 39.728 - type: ndcg_at_1000 value: 42.605 - type: ndcg_at_20 value: 28.157 - type: ndcg_at_3 value: 21.041999999999998 - type: ndcg_at_5 value: 22.147 - type: precision_at_1 value: 20.0 - type: precision_at_10 value: 12.2 - type: precision_at_100 value: 3.38 - type: precision_at_1000 value: 0.38999999999999996 - type: precision_at_20 value: 8.3 - type: precision_at_3 value: 18.0 - type: precision_at_5 value: 16.0 - type: recall_at_1 value: 7.2059999999999995 - type: recall_at_10 value: 32.214 - type: recall_at_100 value: 85.658 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 39.265 - type: recall_at_3 value: 16.335 - type: recall_at_5 value: 21.897 - task: type: Retrieval dataset: name: MTEB AILAStatutes (default) type: mteb/AILA_statutes config: default split: test revision: ebfcd844eadd3d667efa3c57fc5c8c87f5c2867e metrics: - type: main_score value: 22.184 - type: map_at_1 value: 5.167 - type: map_at_10 value: 12.325 - type: map_at_100 value: 19.326999999999998 - type: map_at_1000 value: 19.326999999999998 - type: map_at_20 value: 14.405999999999999 - type: map_at_3 value: 9.4 - type: map_at_5 value: 10.05 - type: mrr_at_1 value: 22.0 - type: mrr_at_10 value: 36.64682539682539 - type: mrr_at_100 value: 37.85209121304697 - type: mrr_at_1000 value: 37.85209121304697 - type: mrr_at_20 value: 37.4718241682638 - type: mrr_at_3 value: 32.666666666666664 - type: mrr_at_5 value: 33.46666666666667 - type: ndcg_at_1 value: 22.0 - type: ndcg_at_10 value: 22.184 - type: ndcg_at_100 value: 45.896 - type: ndcg_at_1000 value: 45.896 - type: ndcg_at_20 value: 27.881 - type: ndcg_at_3 value: 18.976000000000003 - type: ndcg_at_5 value: 16.728 - type: precision_at_1 value: 22.0 - type: precision_at_10 value: 10.8 - type: precision_at_100 value: 4.34 - type: precision_at_1000 value: 0.434 - type: precision_at_20 value: 8.6 - type: precision_at_3 value: 17.333000000000002 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 5.167 - type: recall_at_10 value: 26.133 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 40.5 - type: recall_at_3 value: 13.633000000000001 - type: recall_at_5 value: 15.533 - task: type: Retrieval dataset: name: MTEB ARCChallenge (default) type: RAR-b/ARC-Challenge config: default split: test revision: c481e0da3dcbbad8bce7721dea9085b74320a0a3 metrics: - type: main_score value: 6.042 - type: map_at_1 value: 1.451 - type: map_at_10 value: 4.253 - type: map_at_100 value: 4.8340000000000005 - type: map_at_1000 value: 4.9430000000000005 - type: map_at_20 value: 4.475 - type: map_at_3 value: 3.2140000000000004 - type: map_at_5 value: 3.7600000000000002 - type: mrr_at_1 value: 1.4505119453924915 - type: mrr_at_10 value: 4.250196381169083 - type: mrr_at_100 value: 4.831355631222712 - type: mrr_at_1000 value: 4.940667606986945 - type: mrr_at_20 value: 4.472323277417513 - type: mrr_at_3 value: 3.213879408418658 - type: mrr_at_5 value: 3.742889647326509 - type: ndcg_at_1 value: 1.451 - type: ndcg_at_10 value: 6.042 - type: ndcg_at_100 value: 9.765 - type: ndcg_at_1000 value: 13.655000000000001 - type: ndcg_at_20 value: 6.914 - type: ndcg_at_3 value: 3.852 - type: ndcg_at_5 value: 4.836 - type: precision_at_1 value: 1.451 - type: precision_at_10 value: 1.1860000000000002 - type: precision_at_100 value: 0.313 - type: precision_at_1000 value: 0.064 - type: precision_at_20 value: 0.772 - type: precision_at_3 value: 1.9060000000000001 - type: precision_at_5 value: 1.6209999999999998 - type: recall_at_1 value: 1.451 - type: recall_at_10 value: 11.86 - type: recall_at_100 value: 31.313999999999997 - type: recall_at_1000 value: 64.078 - type: recall_at_20 value: 15.443999999999999 - type: recall_at_3 value: 5.717 - type: recall_at_5 value: 8.106 - task: type: Retrieval dataset: name: MTEB AlphaNLI (default) type: RAR-b/alphanli config: default split: test revision: 303f40ef3d50918d3dc43577d33f2f7344ad72c1 metrics: - type: main_score value: 20.586 - type: map_at_1 value: 13.184999999999999 - type: map_at_10 value: 17.898 - type: map_at_100 value: 18.593 - type: map_at_1000 value: 18.679000000000002 - type: map_at_20 value: 18.238 - type: map_at_3 value: 16.362 - type: map_at_5 value: 17.217 - type: mrr_at_1 value: 13.185378590078328 - type: mrr_at_10 value: 17.89819822620082 - type: mrr_at_100 value: 18.5933695842888 - type: mrr_at_1000 value: 18.679218327500653 - type: mrr_at_20 value: 18.237717139340596 - type: mrr_at_3 value: 16.362053959965195 - type: mrr_at_5 value: 17.21714534377719 - type: ndcg_at_1 value: 13.184999999999999 - type: ndcg_at_10 value: 20.586 - type: ndcg_at_100 value: 24.571 - type: ndcg_at_1000 value: 27.161 - type: ndcg_at_20 value: 21.834 - type: ndcg_at_3 value: 17.375 - type: ndcg_at_5 value: 18.926000000000002 - type: precision_at_1 value: 13.184999999999999 - type: precision_at_10 value: 2.924 - type: precision_at_100 value: 0.49300000000000005 - type: precision_at_1000 value: 0.06999999999999999 - type: precision_at_20 value: 1.71 - type: precision_at_3 value: 6.7669999999999995 - type: precision_at_5 value: 4.817 - type: recall_at_1 value: 13.184999999999999 - type: recall_at_10 value: 29.243000000000002 - type: recall_at_100 value: 49.282 - type: recall_at_1000 value: 70.366 - type: recall_at_20 value: 34.204 - type: recall_at_3 value: 20.3 - type: recall_at_5 value: 24.086 - task: type: Retrieval dataset: name: MTEB AppsRetrieval (default) type: CoIR-Retrieval/apps config: default split: test revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5 metrics: - type: main_score value: 4.557 - type: map_at_1 value: 2.895 - type: map_at_10 value: 3.91 - type: map_at_100 value: 4.294 - type: map_at_1000 value: 4.391 - type: map_at_20 value: 4.089 - type: map_at_3 value: 3.4750000000000005 - type: map_at_5 value: 3.7130000000000005 - type: mrr_at_1 value: 2.895086321381142 - type: mrr_at_10 value: 3.909515377642868 - type: mrr_at_100 value: 4.293672586421636 - type: mrr_at_1000 value: 4.390523922890202 - type: mrr_at_20 value: 4.08917821169434 - type: mrr_at_3 value: 3.474988933156263 - type: mrr_at_5 value: 3.712704736609119 - type: ndcg_at_1 value: 2.895 - type: ndcg_at_10 value: 4.557 - type: ndcg_at_100 value: 6.868 - type: ndcg_at_1000 value: 10.407 - type: ndcg_at_20 value: 5.219 - type: ndcg_at_3 value: 3.6609999999999996 - type: ndcg_at_5 value: 4.088 - type: precision_at_1 value: 2.895 - type: precision_at_10 value: 0.6669999999999999 - type: precision_at_100 value: 0.185 - type: precision_at_1000 value: 0.049 - type: precision_at_20 value: 0.46499999999999997 - type: precision_at_3 value: 1.399 - type: precision_at_5 value: 1.046 - type: recall_at_1 value: 2.895 - type: recall_at_10 value: 6.666999999999999 - type: recall_at_100 value: 18.539 - type: recall_at_1000 value: 48.579 - type: recall_at_20 value: 9.296 - type: recall_at_3 value: 4.197 - type: recall_at_5 value: 5.232 - task: type: Retrieval dataset: name: MTEB ArguAna (default) type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: main_score value: 44.41 - type: map_at_1 value: 21.479 - type: map_at_10 value: 35.995 - type: map_at_100 value: 37.258 - type: map_at_1000 value: 37.273 - type: map_at_20 value: 36.908 - type: map_at_3 value: 31.247000000000003 - type: map_at_5 value: 33.751 - type: mrr_at_1 value: 21.763869132290186 - type: mrr_at_10 value: 36.12420691368057 - type: mrr_at_100 value: 37.378777397870884 - type: mrr_at_1000 value: 37.39392969162474 - type: mrr_at_20 value: 37.03325723357897 - type: mrr_at_3 value: 31.33001422475101 - type: mrr_at_5 value: 33.91180654338541 - type: ndcg_at_1 value: 21.479 - type: ndcg_at_10 value: 44.41 - type: ndcg_at_100 value: 50.032 - type: ndcg_at_1000 value: 50.388 - type: ndcg_at_20 value: 47.642 - type: ndcg_at_3 value: 34.505 - type: ndcg_at_5 value: 39.031 - type: precision_at_1 value: 21.479 - type: precision_at_10 value: 7.148000000000001 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 4.202999999999999 - type: precision_at_3 value: 14.651 - type: precision_at_5 value: 10.996 - type: recall_at_1 value: 21.479 - type: recall_at_10 value: 71.479 - type: recall_at_100 value: 96.65700000000001 - type: recall_at_1000 value: 99.36 - type: recall_at_20 value: 84.068 - type: recall_at_3 value: 43.954 - type: recall_at_5 value: 54.979 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval (default) type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: main_score value: 31.177 - type: map_at_1 value: 19.758 - type: map_at_10 value: 26.619999999999997 - type: map_at_100 value: 27.784 - type: map_at_1000 value: 27.937 - type: map_at_20 value: 27.206999999999997 - type: map_at_3 value: 24.245 - type: map_at_5 value: 25.713 - type: mrr_at_1 value: 24.892703862660944 - type: mrr_at_10 value: 31.704702863501144 - type: mrr_at_100 value: 32.608301500063966 - type: mrr_at_1000 value: 32.68725795268983 - type: mrr_at_20 value: 32.177359559981575 - type: mrr_at_3 value: 29.685264663805444 - type: mrr_at_5 value: 30.958512160228906 - type: ndcg_at_1 value: 24.893 - type: ndcg_at_10 value: 31.177 - type: ndcg_at_100 value: 36.546 - type: ndcg_at_1000 value: 39.706 - type: ndcg_at_20 value: 32.926 - type: ndcg_at_3 value: 27.58 - type: ndcg_at_5 value: 29.465000000000003 - type: precision_at_1 value: 24.893 - type: precision_at_10 value: 5.966 - type: precision_at_100 value: 1.079 - type: precision_at_1000 value: 0.166 - type: precision_at_20 value: 3.5909999999999997 - type: precision_at_3 value: 13.209000000000001 - type: precision_at_5 value: 9.728 - type: recall_at_1 value: 19.758 - type: recall_at_10 value: 39.397 - type: recall_at_100 value: 63.446999999999996 - type: recall_at_1000 value: 85.083 - type: recall_at_20 value: 45.846 - type: recall_at_3 value: 28.855999999999998 - type: recall_at_5 value: 34.165 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval (default) type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: main_score value: 25.901999999999997 - type: map_at_1 value: 16.730999999999998 - type: map_at_10 value: 22.24 - type: map_at_100 value: 23.168 - type: map_at_1000 value: 23.289 - type: map_at_20 value: 22.720000000000002 - type: map_at_3 value: 20.335 - type: map_at_5 value: 21.371000000000002 - type: mrr_at_1 value: 20.31847133757962 - type: mrr_at_10 value: 25.743908603781197 - type: mrr_at_100 value: 26.511937121538402 - type: mrr_at_1000 value: 26.58619222690668 - type: mrr_at_20 value: 26.15910380102564 - type: mrr_at_3 value: 23.77919320594478 - type: mrr_at_5 value: 24.846072186836484 - type: ndcg_at_1 value: 20.318 - type: ndcg_at_10 value: 25.901999999999997 - type: ndcg_at_100 value: 30.259999999999998 - type: ndcg_at_1000 value: 32.984 - type: ndcg_at_20 value: 27.47 - type: ndcg_at_3 value: 22.432 - type: ndcg_at_5 value: 23.999000000000002 - type: precision_at_1 value: 20.318 - type: precision_at_10 value: 4.707 - type: precision_at_100 value: 0.8580000000000001 - type: precision_at_1000 value: 0.134 - type: precision_at_20 value: 2.869 - type: precision_at_3 value: 10.552 - type: precision_at_5 value: 7.567 - type: recall_at_1 value: 16.730999999999998 - type: recall_at_10 value: 33.48 - type: recall_at_100 value: 52.245 - type: recall_at_1000 value: 70.634 - type: recall_at_20 value: 39.189 - type: recall_at_3 value: 23.805 - type: recall_at_5 value: 27.898 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval (default) type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: main_score value: 36.312 - type: map_at_1 value: 23.072 - type: map_at_10 value: 31.64 - type: map_at_100 value: 32.761 - type: map_at_1000 value: 32.862 - type: map_at_20 value: 32.24 - type: map_at_3 value: 28.921999999999997 - type: map_at_5 value: 30.603 - type: mrr_at_1 value: 26.70846394984326 - type: mrr_at_10 value: 34.47342886998059 - type: mrr_at_100 value: 35.406961114894706 - type: mrr_at_1000 value: 35.47747613970401 - type: mrr_at_20 value: 34.984787094473404 - type: mrr_at_3 value: 32.15256008359454 - type: mrr_at_5 value: 33.544409613375095 - type: ndcg_at_1 value: 26.708 - type: ndcg_at_10 value: 36.312 - type: ndcg_at_100 value: 41.748000000000005 - type: ndcg_at_1000 value: 44.206 - type: ndcg_at_20 value: 38.257000000000005 - type: ndcg_at_3 value: 31.439 - type: ndcg_at_5 value: 34.036 - type: precision_at_1 value: 26.708 - type: precision_at_10 value: 6.0440000000000005 - type: precision_at_100 value: 0.966 - type: precision_at_1000 value: 0.125 - type: precision_at_20 value: 3.549 - type: precision_at_3 value: 14.086000000000002 - type: precision_at_5 value: 10.169 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 47.687000000000005 - type: recall_at_100 value: 72.469 - type: recall_at_1000 value: 90.568 - type: recall_at_20 value: 54.861000000000004 - type: recall_at_3 value: 34.758 - type: recall_at_5 value: 41.052 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval (default) type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: main_score value: 19.756999999999998 - type: map_at_1 value: 12.546 - type: map_at_10 value: 17.009 - type: map_at_100 value: 17.758 - type: map_at_1000 value: 17.866 - type: map_at_20 value: 17.399 - type: map_at_3 value: 15.532000000000002 - type: map_at_5 value: 16.305 - type: mrr_at_1 value: 13.559322033898304 - type: mrr_at_10 value: 18.144067796610162 - type: mrr_at_100 value: 18.89867843656649 - type: mrr_at_1000 value: 18.995819754371045 - type: mrr_at_20 value: 18.54987150762981 - type: mrr_at_3 value: 16.666666666666664 - type: mrr_at_5 value: 17.43502824858757 - type: ndcg_at_1 value: 13.559 - type: ndcg_at_10 value: 19.756999999999998 - type: ndcg_at_100 value: 23.931 - type: ndcg_at_1000 value: 27.203 - type: ndcg_at_20 value: 21.173000000000002 - type: ndcg_at_3 value: 16.778000000000002 - type: ndcg_at_5 value: 18.104 - type: precision_at_1 value: 13.559 - type: precision_at_10 value: 3.141 - type: precision_at_100 value: 0.5539999999999999 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.8929999999999998 - type: precision_at_3 value: 7.156 - type: precision_at_5 value: 5.04 - type: recall_at_1 value: 12.546 - type: recall_at_10 value: 27.093 - type: recall_at_100 value: 47.325 - type: recall_at_1000 value: 72.965 - type: recall_at_20 value: 32.491 - type: recall_at_3 value: 19.122 - type: recall_at_5 value: 22.264999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval (default) type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: main_score value: 12.806000000000001 - type: map_at_1 value: 5.881 - type: map_at_10 value: 9.803 - type: map_at_100 value: 10.717 - type: map_at_1000 value: 10.816 - type: map_at_20 value: 10.244 - type: map_at_3 value: 8.163 - type: map_at_5 value: 8.956 - type: mrr_at_1 value: 7.462686567164178 - type: mrr_at_10 value: 12.077805417357661 - type: mrr_at_100 value: 12.966483256051529 - type: mrr_at_1000 value: 13.047246067329347 - 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type: main_score value: 28.765 - type: map_at_1 value: 17.519000000000002 - type: map_at_10 value: 24.136 - type: map_at_100 value: 25.352999999999998 - type: map_at_1000 value: 25.499 - type: map_at_20 value: 24.776999999999997 - type: map_at_3 value: 21.59 - type: map_at_5 value: 23.017000000000003 - type: mrr_at_1 value: 21.36669874879692 - type: mrr_at_10 value: 28.47930702598651 - type: mrr_at_100 value: 29.504734721457147 - type: mrr_at_1000 value: 29.58610606296599 - type: mrr_at_20 value: 29.06239382160277 - type: mrr_at_3 value: 25.85819698427977 - type: mrr_at_5 value: 27.523259544433753 - type: ndcg_at_1 value: 21.367 - type: ndcg_at_10 value: 28.765 - type: ndcg_at_100 value: 34.772999999999996 - type: ndcg_at_1000 value: 37.924 - type: ndcg_at_20 value: 30.891999999999996 - type: ndcg_at_3 value: 24.248 - type: ndcg_at_5 value: 26.479999999999997 - type: precision_at_1 value: 21.367 - type: precision_at_10 value: 5.351 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.147 - type: precision_at_20 value: 3.325 - type: precision_at_3 value: 11.325000000000001 - type: precision_at_5 value: 8.469999999999999 - type: recall_at_1 value: 17.519000000000002 - type: recall_at_10 value: 38.602 - type: recall_at_100 value: 65.377 - type: recall_at_1000 value: 86.812 - type: recall_at_20 value: 46.161 - type: recall_at_3 value: 25.898 - type: recall_at_5 value: 31.654 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval (default) type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: main_score value: 22.706 - type: map_at_1 value: 12.979 - type: map_at_10 value: 18.898 - type: map_at_100 value: 20.087 - type: map_at_1000 value: 20.223 - type: map_at_20 value: 19.516 - type: map_at_3 value: 16.955000000000002 - type: map_at_5 value: 18.043 - type: mrr_at_1 value: 15.639269406392694 - type: mrr_at_10 value: 22.00872472276581 - type: mrr_at_100 value: 23.052476310365996 - type: mrr_at_1000 value: 23.1408565851826 - type: mrr_at_20 value: 22.589418169993035 - type: mrr_at_3 value: 19.99619482496195 - type: mrr_at_5 value: 21.2062404870624 - type: ndcg_at_1 value: 15.639 - type: ndcg_at_10 value: 22.706 - type: ndcg_at_100 value: 28.477999999999998 - type: ndcg_at_1000 value: 31.756 - type: ndcg_at_20 value: 24.836 - type: ndcg_at_3 value: 19.049 - type: ndcg_at_5 value: 20.807000000000002 - type: precision_at_1 value: 15.639 - type: precision_at_10 value: 4.258 - type: precision_at_100 value: 0.865 - type: precision_at_1000 value: 0.133 - type: precision_at_20 value: 2.7969999999999997 - type: precision_at_3 value: 9.056000000000001 - type: precision_at_5 value: 6.758 - type: recall_at_1 value: 12.979 - type: recall_at_10 value: 31.16 - type: recall_at_100 value: 56.245 - type: recall_at_1000 value: 79.526 - type: recall_at_20 value: 38.696000000000005 - type: recall_at_3 value: 21.302 - type: recall_at_5 value: 25.615 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval (default) type: CQADupstackRetrieval_is_a_combined_dataset config: default split: test revision: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 21.956083333333336 - type: ndcg_at_10 value: 21.956083333333336 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval (default) type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: main_score value: 17.395 - type: map_at_1 value: 10.088 - type: map_at_10 value: 14.539 - type: map_at_100 value: 15.362 - type: map_at_1000 value: 15.464 - type: map_at_20 value: 14.924000000000001 - type: map_at_3 value: 13.136999999999999 - type: map_at_5 value: 13.937 - type: mrr_at_1 value: 11.96319018404908 - type: mrr_at_10 value: 16.467767065926576 - type: mrr_at_100 value: 17.283614344094357 - type: mrr_at_1000 value: 17.375873381573232 - type: mrr_at_20 value: 16.854301079891787 - type: mrr_at_3 value: 15.08179959100204 - type: mrr_at_5 value: 15.848670756646216 - type: ndcg_at_1 value: 11.963 - type: ndcg_at_10 value: 17.395 - type: ndcg_at_100 value: 21.911 - type: ndcg_at_1000 value: 24.796000000000003 - type: ndcg_at_20 value: 18.773999999999997 - type: ndcg_at_3 value: 14.719 - type: ndcg_at_5 value: 16.032 - type: precision_at_1 value: 11.963 - type: precision_at_10 value: 2.96 - type: precision_at_100 value: 0.569 - type: precision_at_1000 value: 0.08800000000000001 - type: precision_at_20 value: 1.817 - type: precision_at_3 value: 6.748 - type: precision_at_5 value: 4.877 - type: recall_at_1 value: 10.088 - type: recall_at_10 value: 24.356 - type: recall_at_100 value: 45.73 - type: recall_at_1000 value: 67.577 - type: recall_at_20 value: 29.534 - type: recall_at_3 value: 16.944 - type: recall_at_5 value: 20.392 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval (default) type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: main_score value: 12.592999999999998 - type: map_at_1 value: 7.1290000000000004 - type: map_at_10 value: 10.302 - type: map_at_100 value: 10.994 - type: map_at_1000 value: 11.118 - type: map_at_20 value: 10.653 - type: map_at_3 value: 9.361 - type: map_at_5 value: 9.803 - type: mrr_at_1 value: 8.843771507226428 - type: mrr_at_10 value: 12.421700040419921 - type: mrr_at_100 value: 13.14928923530448 - type: mrr_at_1000 value: 13.252448388769883 - type: mrr_at_20 value: 12.803691756524197 - type: mrr_at_3 value: 11.344345033264513 - type: mrr_at_5 value: 11.824386327139244 - type: ndcg_at_1 value: 8.844000000000001 - type: ndcg_at_10 value: 12.592999999999998 - type: ndcg_at_100 value: 16.409000000000002 - type: ndcg_at_1000 value: 19.906 - type: ndcg_at_20 value: 13.831 - type: ndcg_at_3 value: 10.7 - type: ndcg_at_5 value: 11.359 - type: precision_at_1 value: 8.844000000000001 - type: precision_at_10 value: 2.33 - type: precision_at_100 value: 0.506 - type: precision_at_1000 value: 0.096 - type: precision_at_20 value: 1.512 - type: precision_at_3 value: 5.116 - type: precision_at_5 value: 3.599 - type: recall_at_1 value: 7.1290000000000004 - type: recall_at_10 value: 17.549999999999997 - type: recall_at_100 value: 35.393 - type: recall_at_1000 value: 61.23800000000001 - type: recall_at_20 value: 22.124 - type: recall_at_3 value: 12.109 - type: recall_at_5 value: 13.832 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval (default) type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: main_score value: 19.111 - type: map_at_1 value: 12.577 - type: map_at_10 value: 16.275000000000002 - type: map_at_100 value: 17.083000000000002 - type: map_at_1000 value: 17.206 - type: map_at_20 value: 16.68 - type: map_at_3 value: 14.783 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 14.645522388059701 - type: mrr_at_10 value: 18.617404051172702 - type: mrr_at_100 value: 19.434952661619388 - type: mrr_at_1000 value: 19.536374825069274 - type: mrr_at_20 value: 19.039596975787966 - type: mrr_at_3 value: 16.977611940298516 - type: mrr_at_5 value: 17.938432835820894 - type: ndcg_at_1 value: 14.646 - type: ndcg_at_10 value: 19.111 - type: ndcg_at_100 value: 23.541999999999998 - type: ndcg_at_1000 value: 26.901999999999997 - type: ndcg_at_20 value: 20.593 - type: ndcg_at_3 value: 16.104 - type: ndcg_at_5 value: 17.577 - type: precision_at_1 value: 14.646 - type: precision_at_10 value: 3.237 - type: precision_at_100 value: 0.607 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 1.9869999999999999 - type: precision_at_3 value: 7.027 - type: precision_at_5 value: 5.187 - type: recall_at_1 value: 12.577 - type: recall_at_10 value: 25.642 - type: recall_at_100 value: 46.296 - type: recall_at_1000 value: 70.901 - type: recall_at_20 value: 31.202 - type: recall_at_3 value: 17.396 - type: recall_at_5 value: 21.046 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval (default) type: mteb/cqadupstack-webmasters config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: main_score value: 22.359 - type: map_at_1 value: 14.202 - 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type: recall_at_1 value: 14.202 - type: recall_at_10 value: 29.507 - type: recall_at_100 value: 52.574 - type: recall_at_1000 value: 77.41799999999999 - type: recall_at_20 value: 35.733 - type: recall_at_3 value: 19.345000000000002 - type: recall_at_5 value: 22.99 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval (default) type: mteb/cqadupstack-wordpress config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: main_score value: 14.59 - type: map_at_1 value: 9.341000000000001 - type: map_at_10 value: 12.495000000000001 - type: map_at_100 value: 13.328000000000001 - type: map_at_1000 value: 13.443 - type: map_at_20 value: 12.919 - type: map_at_3 value: 11.448 - type: map_at_5 value: 12.016 - type: mrr_at_1 value: 10.536044362292053 - type: mrr_at_10 value: 13.933045799958926 - type: mrr_at_100 value: 14.753327128738034 - type: mrr_at_1000 value: 14.84798752653836 - type: mrr_at_20 value: 14.348175993628182 - type: mrr_at_3 value: 12.6309303758472 - 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type: main_score value: 20.433 - type: map_at_1 value: 7.469 - type: map_at_10 value: 13.536999999999999 - type: map_at_100 value: 15.222 - type: map_at_1000 value: 15.424 - type: map_at_20 value: 14.41 - type: map_at_3 value: 10.911999999999999 - type: map_at_5 value: 12.232 - type: mrr_at_1 value: 17.133550488599347 - type: mrr_at_10 value: 27.41355152267201 - type: mrr_at_100 value: 28.50611626391541 - type: mrr_at_1000 value: 28.568789326404005 - type: mrr_at_20 value: 28.08885051017031 - type: mrr_at_3 value: 23.724212812160687 - type: mrr_at_5 value: 25.8707926167209 - type: ndcg_at_1 value: 17.134 - type: ndcg_at_10 value: 20.433 - type: ndcg_at_100 value: 27.783 - type: ndcg_at_1000 value: 31.787 - type: ndcg_at_20 value: 23.108999999999998 - type: ndcg_at_3 value: 15.565999999999999 - type: ndcg_at_5 value: 17.354 - type: precision_at_1 value: 17.134 - type: precision_at_10 value: 6.866 - type: precision_at_100 value: 1.47 - type: precision_at_1000 value: 0.22100000000000003 - type: precision_at_20 value: 4.531000000000001 - type: precision_at_3 value: 11.965 - type: precision_at_5 value: 9.707 - type: recall_at_1 value: 7.469 - type: recall_at_10 value: 26.285999999999998 - type: recall_at_100 value: 52.376999999999995 - type: recall_at_1000 value: 75.261 - type: recall_at_20 value: 34.035 - type: recall_at_3 value: 14.526 - type: recall_at_5 value: 19.306 - task: type: Retrieval dataset: name: MTEB ClimateFEVERHardNegatives (default) type: mteb/ClimateFEVER_test_top_250_only_w_correct-v2 config: default split: test revision: 3a309e201f3c2c4b13bd4a367a8f37eee2ec1d21 metrics: - type: main_score value: 22.605 - type: map_at_1 value: 8.6 - type: map_at_10 value: 15.281 - type: map_at_100 value: 17.282 - type: map_at_1000 value: 17.522 - type: map_at_20 value: 16.339000000000002 - type: map_at_3 value: 12.429 - type: map_at_5 value: 13.922 - type: mrr_at_1 value: 18.5 - type: mrr_at_10 value: 29.625952380952388 - type: mrr_at_100 value: 30.77892068089231 - type: mrr_at_1000 value: 30.83853566495065 - 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type: precision_at_3 value: 15.562999999999999 - type: precision_at_5 value: 10.014000000000001 - type: recall_at_1 value: 38.412 - type: recall_at_10 value: 54.417 - type: recall_at_100 value: 69.15 - type: recall_at_1000 value: 84.515 - type: recall_at_20 value: 59.185 - type: recall_at_3 value: 46.69 - type: recall_at_5 value: 50.072 - task: type: Retrieval dataset: name: MTEB CodeFeedbackST (default) type: CoIR-Retrieval/codefeedback-st config: default split: test revision: d213819e87aab9010628da8b73ab4eb337c89340 metrics: - type: main_score value: 45.592 - type: map_at_1 value: 34.54 - type: map_at_10 value: 41.855 - type: map_at_100 value: 42.528 - type: map_at_1000 value: 42.587 - type: map_at_20 value: 42.239 - type: map_at_3 value: 39.985 - type: map_at_5 value: 41.075 - type: mrr_at_1 value: 34.42151664217722 - type: mrr_at_10 value: 41.78311069737728 - type: mrr_at_100 value: 42.45614518998771 - type: mrr_at_1000 value: 42.51517890438335 - type: mrr_at_20 value: 42.16798542795404 - type: mrr_at_3 value: 39.90715304840442 - type: mrr_at_5 value: 41.00055367448295 - type: ndcg_at_1 value: 34.54 - type: ndcg_at_10 value: 45.592 - type: ndcg_at_100 value: 49.13 - type: ndcg_at_1000 value: 50.885999999999996 - type: ndcg_at_20 value: 46.989999999999995 - type: ndcg_at_3 value: 41.754000000000005 - type: ndcg_at_5 value: 43.714999999999996 - type: precision_at_1 value: 34.54 - type: precision_at_10 value: 5.74 - type: precision_at_100 value: 0.746 - type: precision_at_1000 value: 0.089 - type: precision_at_20 value: 3.1460000000000004 - type: precision_at_3 value: 15.623999999999999 - type: precision_at_5 value: 10.325 - type: recall_at_1 value: 34.54 - type: recall_at_10 value: 57.403999999999996 - type: recall_at_100 value: 74.577 - type: recall_at_1000 value: 88.801 - type: recall_at_20 value: 62.927 - type: recall_at_3 value: 46.873 - type: recall_at_5 value: 51.623 - task: type: Retrieval dataset: name: MTEB CosQA (default) type: CoIR-Retrieval/cosqa config: default split: test revision: bc5efb7e9d437246ce393ed19d772e08e4a79535 metrics: - 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type: precision_at_5 value: 2.04 - type: recall_at_1 value: 3.2 - type: recall_at_10 value: 13.8 - type: recall_at_100 value: 39.2 - type: recall_at_1000 value: 78.0 - type: recall_at_20 value: 19.0 - type: recall_at_3 value: 7.6 - type: recall_at_5 value: 10.2 - task: type: Retrieval dataset: name: MTEB DBPedia (default) type: mteb/dbpedia config: default split: dev revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 29.817 - type: map_at_1 value: 6.151 - type: map_at_10 value: 12.292 - type: map_at_100 value: 18.139 - type: map_at_1000 value: 19.84 - type: map_at_20 value: 14.495 - type: map_at_3 value: 8.426 - type: map_at_5 value: 10.192 - type: mrr_at_1 value: 46.26865671641791 - type: mrr_at_10 value: 57.92466240227434 - type: mrr_at_100 value: 58.67349319471301 - type: mrr_at_1000 value: 58.68212283999546 - type: mrr_at_20 value: 58.47241542478595 - type: mrr_at_3 value: 54.726368159203986 - type: mrr_at_5 value: 57.33830845771145 - type: ndcg_at_1 value: 38.06 - type: ndcg_at_10 value: 29.817 - type: ndcg_at_100 value: 36.472 - type: ndcg_at_1000 value: 45.576 - type: ndcg_at_20 value: 30.009000000000004 - type: ndcg_at_3 value: 32.839 - type: ndcg_at_5 value: 32.301 - type: precision_at_1 value: 46.269 - type: precision_at_10 value: 25.820999999999998 - type: precision_at_100 value: 8.552 - type: precision_at_1000 value: 1.576 - type: precision_at_20 value: 20.075000000000003 - type: precision_at_3 value: 35.821 - type: precision_at_5 value: 34.327999999999996 - type: recall_at_1 value: 6.151 - type: recall_at_10 value: 16.838 - type: recall_at_100 value: 48.427 - type: recall_at_1000 value: 77.018 - type: recall_at_20 value: 26.147 - type: recall_at_3 value: 9.221 - type: recall_at_5 value: 12.453 - task: type: Retrieval dataset: name: MTEB DBPedia (default) type: mteb/dbpedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: main_score value: 27.377000000000002 - type: map_at_1 value: 5.527 - type: map_at_10 value: 12.384 - type: map_at_100 value: 17.660999999999998 - type: map_at_1000 value: 18.98 - type: map_at_20 value: 14.424999999999999 - type: map_at_3 value: 8.484 - type: map_at_5 value: 10.174 - type: mrr_at_1 value: 44.25 - type: mrr_at_10 value: 55.620238095238086 - type: mrr_at_100 value: 56.311713506324445 - type: mrr_at_1000 value: 56.33739917164095 - type: mrr_at_20 value: 56.11873017655717 - type: mrr_at_3 value: 52.95833333333334 - type: mrr_at_5 value: 54.595833333333324 - type: ndcg_at_1 value: 31.75 - type: ndcg_at_10 value: 27.377000000000002 - type: ndcg_at_100 value: 32.164 - type: ndcg_at_1000 value: 40.050000000000004 - type: ndcg_at_20 value: 27.424 - type: ndcg_at_3 value: 28.683999999999997 - type: ndcg_at_5 value: 28.283 - type: precision_at_1 value: 44.25 - type: precision_at_10 value: 24.45 - type: precision_at_100 value: 7.704999999999999 - type: precision_at_1000 value: 1.5970000000000002 - type: precision_at_20 value: 18.462 - type: precision_at_3 value: 35.167 - type: precision_at_5 value: 30.95 - type: recall_at_1 value: 5.527 - type: recall_at_10 value: 18.016 - type: recall_at_100 value: 41.656 - type: recall_at_1000 value: 67.38300000000001 - type: recall_at_20 value: 24.21 - type: recall_at_3 value: 9.936 - type: recall_at_5 value: 13.187999999999999 - task: type: Retrieval dataset: name: MTEB DBPediaHardNegatives (default) type: mteb/DBPedia_test_top_250_only_w_correct-v2 config: default split: test revision: 943ec7fdfef3728b2ad1966c5b6479ff9ffd26c9 metrics: - type: main_score value: 30.444 - type: map_at_1 value: 5.722 - type: map_at_10 value: 13.688 - type: map_at_100 value: 22.032 - type: map_at_1000 value: 25.386999999999997 - type: map_at_20 value: 16.307 - type: map_at_3 value: 9.008 - type: map_at_5 value: 11.056000000000001 - type: mrr_at_1 value: 47.0 - type: mrr_at_10 value: 59.097420634920624 - type: mrr_at_100 value: 59.821938296328106 - type: mrr_at_1000 value: 59.83760663887243 - type: mrr_at_20 value: 59.59489258161859 - type: mrr_at_3 value: 56.458333333333336 - type: mrr_at_5 value: 58.18333333333333 - type: ndcg_at_1 value: 33.5 - type: ndcg_at_10 value: 30.444 - type: ndcg_at_100 value: 40.474 - type: ndcg_at_1000 value: 51.964 - type: ndcg_at_20 value: 31.356 - type: ndcg_at_3 value: 30.772 - type: ndcg_at_5 value: 30.576999999999998 - type: precision_at_1 value: 47.0 - type: precision_at_10 value: 27.975 - type: precision_at_100 value: 12.055 - type: precision_at_1000 value: 2.9579999999999997 - type: precision_at_20 value: 22.275 - type: precision_at_3 value: 38.083 - type: precision_at_5 value: 33.75 - type: recall_at_1 value: 5.722 - type: recall_at_10 value: 20.571 - type: recall_at_100 value: 55.967999999999996 - type: recall_at_1000 value: 91.362 - type: recall_at_20 value: 28.526 - type: recall_at_3 value: 10.761999999999999 - type: recall_at_5 value: 14.854999999999999 - task: type: Retrieval dataset: name: MTEB FEVER (default) type: mteb/fever config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: main_score value: 42.997 - type: map_at_1 value: 22.017 - type: map_at_10 value: 35.199000000000005 - type: map_at_100 value: 36.254999999999995 - type: map_at_1000 value: 36.298 - type: map_at_20 value: 35.855 - type: map_at_3 value: 31.072 - type: map_at_5 value: 33.461 - type: mrr_at_1 value: 23.582358235823584 - type: mrr_at_10 value: 37.35784888012616 - type: mrr_at_100 value: 38.36344206815839 - type: mrr_at_1000 value: 38.39238175644681 - type: mrr_at_20 value: 38.01212529885376 - type: mrr_at_3 value: 33.098309830982956 - type: mrr_at_5 value: 35.579557955795615 - type: ndcg_at_1 value: 23.582 - type: ndcg_at_10 value: 42.997 - type: ndcg_at_100 value: 47.979 - type: ndcg_at_1000 value: 48.994 - type: ndcg_at_20 value: 45.35 - type: ndcg_at_3 value: 34.579 - type: ndcg_at_5 value: 38.851 - type: precision_at_1 value: 23.582 - type: precision_at_10 value: 7.1290000000000004 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_20 value: 4.0840000000000005 - 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type: main_score value: 20.793 - type: map_at_1 value: 5.779 - type: map_at_10 value: 14.64 - type: map_at_100 value: 16.412 - type: map_at_1000 value: 16.478 - type: map_at_20 value: 15.638 - type: map_at_3 value: 10.545 - type: map_at_5 value: 12.82 - type: mrr_at_1 value: 5.093046033300686 - type: mrr_at_10 value: 14.511255693919727 - type: mrr_at_100 value: 16.266082070705043 - type: mrr_at_1000 value: 16.333152055297443 - type: mrr_at_20 value: 15.49481390088696 - type: mrr_at_3 value: 10.381978452497567 - type: mrr_at_5 value: 12.74975514201763 - type: ndcg_at_1 value: 5.779 - type: ndcg_at_10 value: 20.793 - type: ndcg_at_100 value: 30.137000000000004 - type: ndcg_at_1000 value: 31.706 - type: ndcg_at_20 value: 24.431 - type: ndcg_at_3 value: 12.264 - type: ndcg_at_5 value: 16.35 - type: precision_at_1 value: 5.779 - type: precision_at_10 value: 4.099 - type: precision_at_100 value: 0.8630000000000001 - type: precision_at_1000 value: 0.098 - type: precision_at_20 value: 2.769 - type: precision_at_3 value: 5.762 - type: precision_at_5 value: 5.436 - type: recall_at_1 value: 5.779 - type: recall_at_10 value: 40.989 - type: recall_at_100 value: 86.337 - type: recall_at_1000 value: 98.335 - type: recall_at_20 value: 55.387 - type: recall_at_3 value: 17.287 - type: recall_at_5 value: 27.179 - task: type: Retrieval dataset: name: MTEB FeedbackQARetrieval (default) type: lt2c/fqa config: default split: test revision: 1ee1cd0 metrics: - type: main_score value: 27.159 - type: map_at_1 value: 27.159 - type: map_at_10 value: 36.533 - type: map_at_100 value: 37.653999999999996 - type: map_at_1000 value: 37.719 - type: map_at_20 value: 37.19 - type: map_at_3 value: 33.650999999999996 - type: map_at_5 value: 35.338 - type: mrr_at_1 value: 27.158634538152608 - type: mrr_at_10 value: 36.53293730477466 - type: mrr_at_100 value: 37.65359357224721 - type: mrr_at_1000 value: 37.71854110065475 - type: mrr_at_20 value: 37.18989930977979 - type: mrr_at_3 value: 33.65127175368139 - type: mrr_at_5 value: 35.33801874163323 - type: ndcg_at_1 value: 27.159 - type: ndcg_at_10 value: 41.756 - type: ndcg_at_100 value: 47.424 - type: ndcg_at_1000 value: 49.128 - type: ndcg_at_20 value: 44.111 - type: ndcg_at_3 value: 35.798 - type: ndcg_at_5 value: 38.827 - type: precision_at_1 value: 27.159 - type: precision_at_10 value: 5.848 - type: precision_at_100 value: 0.855 - type: precision_at_1000 value: 0.099 - type: precision_at_20 value: 3.386 - type: precision_at_3 value: 14.005999999999998 - type: precision_at_5 value: 9.869 - type: recall_at_1 value: 27.159 - type: recall_at_10 value: 58.484 - type: recall_at_100 value: 85.492 - type: recall_at_1000 value: 98.845 - type: recall_at_20 value: 67.721 - type: recall_at_3 value: 42.018 - type: recall_at_5 value: 49.347 - task: type: Retrieval dataset: name: MTEB FiQA2018 (default) type: mteb/fiqa config: default split: dev revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 19.159000000000002 - 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type: precision_at_5 value: 7.76 - type: recall_at_1 value: 9.577 - type: recall_at_10 value: 24.362000000000002 - type: recall_at_100 value: 48.222 - type: recall_at_1000 value: 74.358 - type: recall_at_20 value: 30.465999999999998 - type: recall_at_3 value: 16.057 - type: recall_at_5 value: 19.516 - task: type: Retrieval dataset: name: MTEB FiQA2018 (default) type: mteb/fiqa config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: main_score value: 19.986 - type: map_at_1 value: 9.157 - type: map_at_10 value: 14.877 - type: map_at_100 value: 16.185 - type: map_at_1000 value: 16.366 - type: map_at_20 value: 15.551 - type: map_at_3 value: 12.574 - type: map_at_5 value: 13.694999999999999 - type: mrr_at_1 value: 16.51234567901235 - type: mrr_at_10 value: 23.66169410150891 - type: mrr_at_100 value: 24.6092001023614 - type: mrr_at_1000 value: 24.695544929151346 - type: mrr_at_20 value: 24.17998019538123 - type: mrr_at_3 value: 20.98765432098765 - type: mrr_at_5 value: 22.32253086419753 - 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type: precision_at_5 value: 7.465 - type: recall_at_1 value: 8.012 - type: recall_at_10 value: 23.233999999999998 - type: recall_at_100 value: 46.623999999999995 - type: recall_at_1000 value: 74.092 - type: recall_at_20 value: 29.854000000000003 - type: recall_at_3 value: 14.216000000000001 - type: recall_at_5 value: 17.713 - task: type: Retrieval dataset: name: MTEB HellaSwag (default) type: RAR-b/hellaswag config: default split: test revision: a5c990205e017d10761197ccab3000936689c3ae metrics: - type: main_score value: 16.377 - type: map_at_1 value: 8.474 - type: map_at_10 value: 13.479 - type: map_at_100 value: 14.296000000000001 - type: map_at_1000 value: 14.393 - type: map_at_20 value: 13.905000000000001 - type: map_at_3 value: 11.878 - type: map_at_5 value: 12.733 - type: mrr_at_1 value: 8.474407488548099 - type: mrr_at_10 value: 13.47926012335491 - type: mrr_at_100 value: 14.296018190032331 - type: mrr_at_1000 value: 14.39320635735857 - type: mrr_at_20 value: 13.905283977590932 - type: mrr_at_3 value: 11.878443869083188 - type: mrr_at_5 value: 12.733353249684685 - type: ndcg_at_1 value: 8.474 - type: ndcg_at_10 value: 16.377 - type: ndcg_at_100 value: 20.878 - type: ndcg_at_1000 value: 23.878 - type: ndcg_at_20 value: 17.93 - type: ndcg_at_3 value: 13.014999999999999 - type: ndcg_at_5 value: 14.557999999999998 - type: precision_at_1 value: 8.474 - type: precision_at_10 value: 2.571 - type: precision_at_100 value: 0.48 - type: precision_at_1000 value: 0.073 - type: precision_at_20 value: 1.593 - type: precision_at_3 value: 5.437 - type: precision_at_5 value: 4.013 - type: recall_at_1 value: 8.474 - type: recall_at_10 value: 25.712000000000003 - type: recall_at_100 value: 48.008 - type: recall_at_1000 value: 72.52499999999999 - type: recall_at_20 value: 31.856 - type: recall_at_3 value: 16.311 - type: recall_at_5 value: 20.066 - task: type: Retrieval dataset: name: MTEB HotpotQA (default) type: mteb/hotpotqa config: default split: dev revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: main_score value: 49.524 - type: map_at_1 value: 27.583999999999996 - type: map_at_10 value: 40.455000000000005 - type: map_at_100 value: 41.567 - type: map_at_1000 value: 41.665 - type: map_at_20 value: 41.099000000000004 - type: map_at_3 value: 37.438 - type: map_at_5 value: 39.202999999999996 - type: mrr_at_1 value: 55.16798237561961 - type: mrr_at_10 value: 63.83496376336482 - type: mrr_at_100 value: 64.32844309604842 - type: mrr_at_1000 value: 64.35048997347738 - type: mrr_at_20 value: 64.14047945884145 - type: mrr_at_3 value: 61.93929380086919 - type: mrr_at_5 value: 63.0802888440121 - type: ndcg_at_1 value: 55.16799999999999 - type: ndcg_at_10 value: 49.524 - type: ndcg_at_100 value: 53.879 - type: ndcg_at_1000 value: 55.911 - type: ndcg_at_20 value: 51.31 - type: ndcg_at_3 value: 44.527 - type: ndcg_at_5 value: 47.102 - type: precision_at_1 value: 55.16799999999999 - type: precision_at_10 value: 10.718 - type: precision_at_100 value: 1.4160000000000001 - type: precision_at_1000 value: 0.169 - type: precision_at_20 value: 5.935 - type: precision_at_3 value: 28.26 - type: precision_at_5 value: 18.990000000000002 - type: recall_at_1 value: 27.583999999999996 - type: recall_at_10 value: 53.589 - type: recall_at_100 value: 70.782 - type: recall_at_1000 value: 84.276 - type: recall_at_20 value: 59.354 - type: recall_at_3 value: 42.39 - type: recall_at_5 value: 47.476 - task: type: Retrieval dataset: name: MTEB HotpotQAHardNegatives (default) type: mteb/HotpotQA_test_top_250_only_w_correct-v2 config: default split: test revision: 617612fa63afcb60e3b134bed8b7216a99707c37 metrics: - type: main_score value: 50.415 - type: map_at_1 value: 26.8 - type: map_at_10 value: 40.503 - type: map_at_100 value: 42.092 - type: map_at_1000 value: 42.198 - type: map_at_20 value: 41.394999999999996 - type: map_at_3 value: 36.75 - type: map_at_5 value: 38.945 - type: mrr_at_1 value: 53.6 - type: mrr_at_10 value: 63.90658730158732 - type: mrr_at_100 value: 64.46665914282829 - type: mrr_at_1000 value: 64.4775151418674 - type: mrr_at_20 value: 64.20681999545006 - type: mrr_at_3 value: 61.40000000000001 - type: mrr_at_5 value: 62.98000000000005 - type: ndcg_at_1 value: 53.6 - type: ndcg_at_10 value: 50.415 - type: ndcg_at_100 value: 56.48800000000001 - type: ndcg_at_1000 value: 58.388 - type: ndcg_at_20 value: 52.68000000000001 - type: ndcg_at_3 value: 44.165 - type: ndcg_at_5 value: 47.429 - type: precision_at_1 value: 53.6 - type: precision_at_10 value: 11.31 - type: precision_at_100 value: 1.614 - type: precision_at_1000 value: 0.186 - type: precision_at_20 value: 6.375 - type: precision_at_3 value: 28.433000000000003 - type: precision_at_5 value: 19.62 - type: recall_at_1 value: 26.8 - type: recall_at_10 value: 56.55 - type: recall_at_100 value: 80.7 - type: recall_at_1000 value: 93.05 - type: recall_at_20 value: 63.74999999999999 - type: recall_at_3 value: 42.65 - type: recall_at_5 value: 49.05 - task: type: Retrieval dataset: name: MTEB LEMBNarrativeQARetrieval (default) type: dwzhu/LongEmbed config: default split: test revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 47.11 - type: map_at_1 value: 38.176 - type: map_at_10 value: 44.11 - type: map_at_100 value: 44.885999999999996 - type: map_at_1000 value: 45.005 - type: map_at_20 value: 44.486 - type: map_at_3 value: 42.669000000000004 - type: map_at_5 value: 43.441 - type: mrr_at_1 value: 38.17590200019141 - type: mrr_at_10 value: 44.109989260003694 - type: mrr_at_100 value: 44.886475970293596 - type: mrr_at_1000 value: 45.00541901614199 - type: mrr_at_20 value: 44.48565175776022 - type: mrr_at_3 value: 42.66915494305687 - type: mrr_at_5 value: 43.44052062398311 - type: ndcg_at_1 value: 38.176 - type: ndcg_at_10 value: 47.11 - type: ndcg_at_100 value: 51.644999999999996 - type: ndcg_at_1000 value: 54.366 - type: ndcg_at_20 value: 48.475 - type: ndcg_at_3 value: 44.101 - type: ndcg_at_5 value: 45.494 - type: precision_at_1 value: 38.176 - type: precision_at_10 value: 5.6610000000000005 - type: precision_at_100 value: 0.796 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 3.1 - type: precision_at_3 value: 16.078 - type: precision_at_5 value: 10.324 - type: recall_at_1 value: 38.176 - type: recall_at_10 value: 56.608000000000004 - type: recall_at_100 value: 79.644 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 61.995999999999995 - type: recall_at_3 value: 48.234 - type: recall_at_5 value: 51.622 - type: main_score value: 29.847 - type: map_at_1 value: 18.271 - type: map_at_10 value: 25.415 - type: map_at_100 value: 27.086 - type: map_at_1000 value: 27.134000000000004 - type: map_at_20 value: 26.135 - type: map_at_3 value: 22.659000000000002 - type: map_at_5 value: 24.198 - type: mrr_at_1 value: 18.271119842829076 - type: mrr_at_10 value: 25.414678641594147 - type: mrr_at_100 value: 27.086094547163714 - type: mrr_at_1000 value: 27.13383971528746 - type: mrr_at_20 value: 26.13474777243653 - type: mrr_at_3 value: 22.658808120497696 - type: mrr_at_5 value: 24.197773411918757 - type: ndcg_at_1 value: 18.271 - type: ndcg_at_10 value: 29.847 - type: ndcg_at_100 value: 39.669 - type: ndcg_at_1000 value: 40.528999999999996 - type: ndcg_at_20 value: 32.509 - type: ndcg_at_3 value: 24.151 - type: ndcg_at_5 value: 26.927 - type: precision_at_1 value: 18.271 - type: precision_at_10 value: 4.42 - type: precision_at_100 value: 0.9400000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 2.741 - type: precision_at_3 value: 9.496 - type: precision_at_5 value: 7.045999999999999 - type: recall_at_1 value: 18.271 - type: recall_at_10 value: 44.204 - type: recall_at_100 value: 93.975 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 54.813 - type: recall_at_3 value: 28.487000000000002 - type: recall_at_5 value: 35.232 - type: main_score value: 77.203 - type: map_at_1 value: 69.333 - type: map_at_10 value: 74.787 - type: map_at_100 value: 75.348 - type: map_at_1000 value: 75.369 - type: map_at_20 value: 75.16000000000001 - type: map_at_3 value: 73.611 - type: map_at_5 value: 74.42800000000001 - type: mrr_at_1 value: 69.33333333333334 - type: mrr_at_10 value: 74.78743386243384 - type: mrr_at_100 value: 75.34827076805841 - type: mrr_at_1000 value: 75.36876455686495 - type: mrr_at_20 value: 75.16008204758204 - type: mrr_at_3 value: 73.61111111111111 - type: mrr_at_5 value: 74.42777777777778 - type: ndcg_at_1 value: 69.333 - type: ndcg_at_10 value: 77.203 - type: ndcg_at_100 value: 79.87100000000001 - type: ndcg_at_1000 value: 80.286 - type: ndcg_at_20 value: 78.55499999999999 - type: ndcg_at_3 value: 74.917 - type: ndcg_at_5 value: 76.337 - type: precision_at_1 value: 69.333 - type: precision_at_10 value: 8.466999999999999 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.5 - type: precision_at_3 value: 26.222 - type: precision_at_5 value: 16.400000000000002 - type: recall_at_1 value: 69.333 - type: recall_at_10 value: 84.667 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 90.0 - type: recall_at_3 value: 78.667 - type: recall_at_5 value: 82.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_1024 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 94.667 - type: map_at_100 value: 94.667 - type: map_at_1000 value: 94.667 - type: map_at_20 value: 94.667 - type: map_at_3 value: 94.667 - type: map_at_5 value: 94.667 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 94.66666666666666 - type: mrr_at_100 value: 94.66666666666666 - type: mrr_at_1000 value: 94.66666666666666 - type: mrr_at_20 value: 94.66666666666666 - type: mrr_at_3 value: 94.66666666666666 - type: mrr_at_5 value: 94.66666666666666 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 96.04700000000001 - type: ndcg_at_100 value: 96.04700000000001 - type: ndcg_at_1000 value: 96.04700000000001 - type: ndcg_at_20 value: 96.04700000000001 - type: ndcg_at_3 value: 96.04700000000001 - type: ndcg_at_5 value: 96.04700000000001 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_16384 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 94.167 - type: map_at_100 value: 94.167 - type: map_at_1000 value: 94.167 - type: map_at_20 value: 94.167 - type: map_at_3 value: 93.667 - type: map_at_5 value: 94.167 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 94.16666666666666 - type: mrr_at_100 value: 94.16666666666666 - type: mrr_at_1000 value: 94.16666666666666 - type: mrr_at_20 value: 94.16666666666666 - type: mrr_at_3 value: 93.66666666666666 - type: mrr_at_5 value: 94.16666666666666 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 95.647 - type: ndcg_at_100 value: 95.647 - type: ndcg_at_1000 value: 95.647 - type: ndcg_at_20 value: 95.647 - type: ndcg_at_3 value: 94.786 - type: ndcg_at_5 value: 95.647 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 32.667 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 98.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_2048 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 92.0 - type: map_at_1 value: 92.0 - type: map_at_10 value: 95.667 - type: map_at_100 value: 95.667 - type: map_at_1000 value: 95.667 - type: map_at_20 value: 95.667 - type: map_at_3 value: 95.667 - type: map_at_5 value: 95.667 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 95.66666666666666 - type: mrr_at_100 value: 95.66666666666666 - type: mrr_at_1000 value: 95.66666666666666 - type: mrr_at_20 value: 95.66666666666666 - type: mrr_at_3 value: 95.66666666666666 - type: mrr_at_5 value: 95.66666666666666 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 96.786 - type: ndcg_at_100 value: 96.786 - type: ndcg_at_1000 value: 96.786 - type: ndcg_at_20 value: 96.786 - type: ndcg_at_3 value: 96.786 - type: ndcg_at_5 value: 96.786 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 92.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_256 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.0 - type: map_at_1 value: 90.0 - type: map_at_10 value: 95.0 - type: map_at_100 value: 95.0 - type: map_at_1000 value: 95.0 - type: map_at_20 value: 95.0 - type: map_at_3 value: 95.0 - type: map_at_5 value: 95.0 - type: mrr_at_1 value: 90.0 - type: mrr_at_10 value: 95.0 - type: mrr_at_100 value: 95.0 - type: mrr_at_1000 value: 95.0 - type: mrr_at_20 value: 95.0 - type: mrr_at_3 value: 95.0 - type: mrr_at_5 value: 95.0 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 96.309 - type: ndcg_at_100 value: 96.309 - type: ndcg_at_1000 value: 96.309 - type: ndcg_at_20 value: 96.309 - type: ndcg_at_3 value: 96.309 - type: ndcg_at_5 value: 96.309 - type: precision_at_1 value: 90.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 90.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_32768 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 84.0 - type: map_at_1 value: 84.0 - type: map_at_10 value: 91.167 - type: map_at_100 value: 91.167 - type: map_at_1000 value: 91.167 - type: map_at_20 value: 91.167 - type: map_at_3 value: 90.667 - type: map_at_5 value: 91.167 - type: mrr_at_1 value: 84.0 - type: mrr_at_10 value: 91.16666666666666 - type: mrr_at_100 value: 91.16666666666666 - type: mrr_at_1000 value: 91.16666666666666 - type: mrr_at_20 value: 91.16666666666666 - type: mrr_at_3 value: 90.66666666666666 - type: mrr_at_5 value: 91.16666666666666 - type: ndcg_at_1 value: 84.0 - type: ndcg_at_10 value: 93.43299999999999 - type: ndcg_at_100 value: 93.43299999999999 - type: ndcg_at_1000 value: 93.43299999999999 - type: ndcg_at_20 value: 93.43299999999999 - type: ndcg_at_3 value: 92.571 - type: ndcg_at_5 value: 93.43299999999999 - type: precision_at_1 value: 84.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 32.667 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 84.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 98.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_4096 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 92.0 - type: map_at_1 value: 92.0 - type: map_at_10 value: 96.0 - type: map_at_100 value: 96.0 - type: map_at_1000 value: 96.0 - type: map_at_20 value: 96.0 - type: map_at_3 value: 96.0 - type: map_at_5 value: 96.0 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 96.0 - type: mrr_at_100 value: 96.0 - type: mrr_at_1000 value: 96.0 - type: mrr_at_20 value: 96.0 - type: mrr_at_3 value: 96.0 - type: mrr_at_5 value: 96.0 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 97.04700000000001 - type: ndcg_at_100 value: 97.04700000000001 - type: ndcg_at_1000 value: 97.04700000000001 - type: ndcg_at_20 value: 97.04700000000001 - type: ndcg_at_3 value: 97.04700000000001 - type: ndcg_at_5 value: 97.04700000000001 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 92.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_512 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 94.0 - type: map_at_1 value: 94.0 - type: map_at_10 value: 97.0 - type: map_at_100 value: 97.0 - type: map_at_1000 value: 97.0 - type: map_at_20 value: 97.0 - type: map_at_3 value: 97.0 - type: map_at_5 value: 97.0 - type: mrr_at_1 value: 94.0 - type: mrr_at_10 value: 97.0 - type: mrr_at_100 value: 97.0 - type: mrr_at_1000 value: 97.0 - type: mrr_at_20 value: 97.0 - type: mrr_at_3 value: 97.0 - type: mrr_at_5 value: 97.0 - type: ndcg_at_1 value: 94.0 - type: ndcg_at_10 value: 97.786 - type: ndcg_at_100 value: 97.786 - type: ndcg_at_1000 value: 97.786 - type: ndcg_at_20 value: 97.786 - type: ndcg_at_3 value: 97.786 - type: ndcg_at_5 value: 97.786 - type: precision_at_1 value: 94.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 94.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - type: main_score value: 100.0 - type: map_at_1 value: 100.0 - type: map_at_10 value: 100.0 - type: map_at_100 value: 100.0 - type: map_at_1000 value: 100.0 - type: map_at_20 value: 100.0 - type: map_at_3 value: 100.0 - type: map_at_5 value: 100.0 - type: mrr_at_1 value: 100.0 - type: mrr_at_10 value: 100.0 - type: mrr_at_100 value: 100.0 - type: mrr_at_1000 value: 100.0 - type: mrr_at_20 value: 100.0 - type: mrr_at_3 value: 100.0 - type: mrr_at_5 value: 100.0 - type: ndcg_at_1 value: 100.0 - type: ndcg_at_10 value: 100.0 - type: ndcg_at_100 value: 100.0 - type: ndcg_at_1000 value: 100.0 - type: ndcg_at_20 value: 100.0 - type: ndcg_at_3 value: 100.0 - type: ndcg_at_5 value: 100.0 - type: precision_at_1 value: 100.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBNeedleRetrieval (default) type: dwzhu/LongEmbed config: default split: test_8192 revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 88.0 - type: map_at_1 value: 88.0 - type: map_at_10 value: 93.167 - type: map_at_100 value: 93.167 - type: map_at_1000 value: 93.167 - type: map_at_20 value: 93.167 - type: map_at_3 value: 92.667 - type: map_at_5 value: 93.167 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.16666666666666 - type: mrr_at_100 value: 93.16666666666666 - type: mrr_at_1000 value: 93.16666666666666 - type: mrr_at_20 value: 93.16666666666666 - type: mrr_at_3 value: 92.66666666666666 - type: mrr_at_5 value: 93.16666666666666 - type: ndcg_at_1 value: 88.0 - type: ndcg_at_10 value: 94.90899999999999 - type: ndcg_at_100 value: 94.90899999999999 - type: ndcg_at_1000 value: 94.90899999999999 - type: ndcg_at_20 value: 94.90899999999999 - type: ndcg_at_3 value: 94.047 - type: ndcg_at_5 value: 94.90899999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - 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type: precision_at_10 value: 10.0 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 5.0 - type: precision_at_3 value: 33.333 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 100.0 - type: recall_at_10 value: 100.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 100.0 - type: recall_at_3 value: 100.0 - type: recall_at_5 value: 100.0 - task: type: Retrieval dataset: name: MTEB LEMBSummScreenFDRetrieval (default) type: dwzhu/LongEmbed config: default split: validation revision: 6e346642246bfb4928c560ee08640dc84d074e8c metrics: - type: main_score value: 90.19 - type: map_at_1 value: 81.548 - type: map_at_10 value: 87.57300000000001 - type: map_at_100 value: 87.703 - type: map_at_1000 value: 87.703 - type: map_at_20 value: 87.703 - type: map_at_3 value: 86.657 - type: map_at_5 value: 87.178 - type: mrr_at_1 value: 81.54761904761905 - type: mrr_at_10 value: 87.57345993953139 - 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type: precision_at_20 value: 4.583 - type: precision_at_3 value: 20.286 - type: precision_at_5 value: 14.798 - type: recall_at_1 value: 40.909 - type: recall_at_10 value: 84.596 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 91.667 - type: recall_at_3 value: 60.858999999999995 - type: recall_at_5 value: 73.99 - task: type: Retrieval dataset: name: MTEB LegalBenchCorporateLobbying (default) type: mteb/legalbench_corporate_lobbying config: default split: test revision: f69691c650464e62546d7f2a4536f8f87c891e38 metrics: - type: main_score value: 89.377 - type: map_at_1 value: 79.706 - type: map_at_10 value: 86.652 - type: map_at_100 value: 86.756 - type: map_at_1000 value: 86.76 - type: map_at_20 value: 86.749 - type: map_at_3 value: 85.784 - type: map_at_5 value: 86.387 - type: mrr_at_1 value: 79.70588235294119 - type: mrr_at_10 value: 86.65184407096169 - type: mrr_at_100 value: 86.75604621101796 - type: mrr_at_1000 value: 86.76035993650815 - 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type: precision_at_20 value: 5.176 - type: precision_at_3 value: 21.009 - type: precision_at_5 value: 15.211 - type: recall_at_1 value: 37.468 - type: recall_at_10 value: 69.607 - type: recall_at_100 value: 91.57 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 74.553 - type: recall_at_3 value: 51.856 - type: recall_at_5 value: 60.309999999999995 - task: type: Retrieval dataset: name: MTEB LitSearchRetrieval (default) type: princeton-nlp/LitSearch config: default split: test revision: 9573fb284a1026c998df47024b888a163f0f0e25 metrics: - type: main_score value: 25.226 - type: map_at_1 value: 15.326999999999998 - type: map_at_10 value: 21.394 - type: map_at_100 value: 22.343 - type: map_at_1000 value: 22.429 - type: map_at_20 value: 21.941 - type: map_at_3 value: 19.123 - type: map_at_5 value: 20.14 - type: mrr_at_1 value: 15.745393634840871 - type: mrr_at_10 value: 21.745699396453173 - type: mrr_at_100 value: 22.713350239272813 - type: mrr_at_1000 value: 22.79392349547639 - 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type: precision_at_3 value: 57.364000000000004 - type: precision_at_5 value: 56.279 - type: recall_at_1 value: 1.055 - type: recall_at_10 value: 8.778 - type: recall_at_100 value: 36.775999999999996 - type: recall_at_1000 value: 72.783 - type: recall_at_20 value: 14.529 - type: recall_at_3 value: 3.019 - type: recall_at_5 value: 4.987 - task: type: Retrieval dataset: name: MTEB MSMARCOHardNegatives (default) type: mteb/MSMARCO_test_top_250_only_w_correct-v2 config: default split: test revision: 67c0b4f7f15946e0b15cf6cf3b8993d04cb3efc6 metrics: - type: main_score value: 49.17 - type: map_at_1 value: 1.871 - type: map_at_10 value: 11.155 - type: map_at_100 value: 38.995000000000005 - type: map_at_1000 value: 58.543 - type: map_at_20 value: 16.564 - type: map_at_3 value: 5.378 - type: map_at_5 value: 7.403 - type: mrr_at_1 value: 69.76744186046511 - type: mrr_at_10 value: 78.7624584717608 - type: mrr_at_100 value: 78.97387496224707 - type: mrr_at_1000 value: 78.97387496224707 - type: mrr_at_20 value: 78.97387496224707 - 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type: precision_at_5 value: 12.461 - type: recall_at_1 value: 23.926 - type: recall_at_10 value: 73.48599999999999 - type: recall_at_100 value: 88.86699999999999 - type: recall_at_1000 value: 97.89999999999999 - type: recall_at_20 value: 79.541 - type: recall_at_3 value: 50.586 - type: recall_at_5 value: 62.305 - task: type: Retrieval dataset: name: MTEB NFCorpus (default) type: mteb/nfcorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: main_score value: 30.044999999999998 - type: map_at_1 value: 5.015 - type: map_at_10 value: 10.93 - type: map_at_100 value: 13.592 - type: map_at_1000 value: 14.890999999999998 - type: map_at_20 value: 12.005 - type: map_at_3 value: 8.518 - type: map_at_5 value: 9.646 - type: mrr_at_1 value: 41.17647058823529 - type: mrr_at_10 value: 49.96486313823774 - type: mrr_at_100 value: 50.73871199227761 - type: mrr_at_1000 value: 50.788364180879874 - type: mrr_at_20 value: 50.53695651632227 - type: mrr_at_3 value: 47.936016511867905 - 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type: precision_at_20 value: 3.023 - type: precision_at_3 value: 8.033999999999999 - type: precision_at_5 value: 6.524000000000001 - type: recall_at_1 value: 9.2 - type: recall_at_10 value: 38.775 - type: recall_at_100 value: 76.188 - type: recall_at_1000 value: 94.56599999999999 - type: recall_at_20 value: 50.9 - type: recall_at_3 value: 20.676 - type: recall_at_5 value: 27.810000000000002 - task: type: Retrieval dataset: name: MTEB NQHardNegatives (default) type: mteb/NQ_test_top_250_only_w_correct-v2 config: default split: test revision: d700fe4f167a5db8e6c9b03e8c26e7eaf66faf97 metrics: - type: main_score value: 25.072 - type: map_at_1 value: 9.767000000000001 - type: map_at_10 value: 18.782 - type: map_at_100 value: 20.74 - type: map_at_1000 value: 20.821 - type: map_at_20 value: 19.819 - type: map_at_3 value: 15.089 - type: map_at_5 value: 16.929 - type: mrr_at_1 value: 11.0 - type: mrr_at_10 value: 20.55162698412698 - type: mrr_at_100 value: 22.24001716934204 - type: mrr_at_1000 value: 22.296375463042438 - 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type: precision_at_20 value: 1.572 - type: precision_at_3 value: 5.024 - type: precision_at_5 value: 3.83 - type: recall_at_1 value: 7.073 - type: recall_at_10 value: 24.81 - type: recall_at_100 value: 48.422 - type: recall_at_1000 value: 67.35600000000001 - type: recall_at_20 value: 31.447000000000003 - type: recall_at_3 value: 15.071000000000002 - type: recall_at_5 value: 19.151 - task: type: Retrieval dataset: name: MTEB Quail (default) type: RAR-b/quail config: default split: test revision: 1851bc536f8bdab29e03e29191c4586b1d8d7c5a metrics: - type: main_score value: 3.605 - type: map_at_1 value: 0.882 - type: map_at_10 value: 2.467 - type: map_at_100 value: 2.9659999999999997 - type: map_at_1000 value: 3.052 - type: map_at_20 value: 2.711 - type: map_at_3 value: 1.746 - type: map_at_5 value: 2.0629999999999997 - type: mrr_at_1 value: 0.8823529411764706 - type: mrr_at_10 value: 2.4645337301587333 - type: mrr_at_100 value: 2.9670174083352596 - type: mrr_at_1000 value: 3.0527771606810092 - 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type: precision_at_5 value: 21.279999999999998 - type: recall_at_1 value: 60.195 - type: recall_at_10 value: 87.35300000000001 - type: recall_at_100 value: 97.055 - type: recall_at_1000 value: 99.669 - type: recall_at_20 value: 91.628 - type: recall_at_3 value: 76.40599999999999 - type: recall_at_5 value: 81.636 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval (default) type: mteb/quora config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: main_score value: 77.529 - type: map_at_1 value: 60.492000000000004 - type: map_at_10 value: 72.82499999999999 - type: map_at_100 value: 73.668 - type: map_at_1000 value: 73.706 - type: map_at_20 value: 73.351 - type: map_at_3 value: 69.887 - type: map_at_5 value: 71.66 - type: mrr_at_1 value: 69.59 - type: mrr_at_10 value: 76.96056349206305 - type: mrr_at_100 value: 77.29271487249463 - type: mrr_at_1000 value: 77.30133384825908 - type: mrr_at_20 value: 77.1902356844216 - type: mrr_at_3 value: 75.39499999999957 - 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type: precision_at_3 value: 32.067 - type: precision_at_5 value: 21.279999999999998 - type: recall_at_1 value: 60.285999999999994 - type: recall_at_10 value: 86.75399999999999 - type: recall_at_100 value: 97.328 - type: recall_at_1000 value: 99.551 - type: recall_at_20 value: 91.85900000000001 - type: recall_at_3 value: 75.408 - type: recall_at_5 value: 80.353 - task: type: Retrieval dataset: name: MTEB RARbCode (default) type: RAR-b/humanevalpack-mbpp-pooled config: default split: test revision: 25f7d11a7ac12dcbb8d3836eb2de682b98c825e4 metrics: - type: main_score value: 10.407 - type: map_at_1 value: 6.132 - type: map_at_10 value: 8.741 - type: map_at_100 value: 9.198 - type: map_at_1000 value: 9.264 - type: map_at_20 value: 8.991 - type: map_at_3 value: 7.637 - type: map_at_5 value: 8.21 - type: mrr_at_1 value: 6.132075471698113 - type: mrr_at_10 value: 8.741389637616054 - type: mrr_at_100 value: 9.19843044441297 - type: mrr_at_1000 value: 9.263870046532622 - type: mrr_at_20 value: 8.991124756997893 - 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type: precision_at_20 value: 1.633 - type: precision_at_3 value: 7.781000000000001 - type: precision_at_5 value: 5.102 - type: recall_at_1 value: 19.686999999999998 - type: recall_at_10 value: 28.438000000000002 - type: recall_at_100 value: 45.165 - type: recall_at_1000 value: 65.865 - type: recall_at_20 value: 32.663 - type: recall_at_3 value: 23.342 - type: recall_at_5 value: 25.509999999999998 - task: type: Retrieval dataset: name: MTEB SCIDOCS (default) type: mteb/scidocs config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics: - type: main_score value: 13.222999999999999 - type: map_at_1 value: 2.9979999999999998 - type: map_at_10 value: 7.475 - type: map_at_100 value: 8.903 - type: map_at_1000 value: 9.16 - type: map_at_20 value: 8.136000000000001 - type: map_at_3 value: 5.329 - type: map_at_5 value: 6.411 - type: mrr_at_1 value: 14.7 - type: mrr_at_10 value: 22.86599206349206 - type: mrr_at_100 value: 24.016847471793167 - type: mrr_at_1000 value: 24.09878143285336 - 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type: precision_at_3 value: 0.136 - type: precision_at_5 value: 0.10200000000000001 - type: recall_at_1 value: 0.307 - type: recall_at_10 value: 0.819 - type: recall_at_100 value: 2.968 - type: recall_at_1000 value: 29.017 - type: recall_at_20 value: 1.484 - type: recall_at_3 value: 0.409 - type: recall_at_5 value: 0.512 - task: type: Retrieval dataset: name: MTEB SciFact (default) type: mteb/scifact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: main_score value: 59.348 - type: map_at_1 value: 45.306000000000004 - type: map_at_10 value: 54.547000000000004 - type: map_at_100 value: 55.535000000000004 - type: map_at_1000 value: 55.582 - type: map_at_20 value: 55.242000000000004 - type: map_at_3 value: 51.763000000000005 - type: map_at_5 value: 53.27499999999999 - type: mrr_at_1 value: 47.0 - type: mrr_at_10 value: 55.67632275132276 - type: mrr_at_100 value: 56.50056008171798 - type: mrr_at_1000 value: 56.54270500751058 - type: mrr_at_20 value: 56.277193298903846 - 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type: precision_at_3 value: 3.273 - type: precision_at_5 value: 2.672 - type: recall_at_1 value: 4.317 - type: recall_at_10 value: 19.344 - type: recall_at_100 value: 50.973 - type: recall_at_1000 value: 79.35900000000001 - type: recall_at_20 value: 26.255 - type: recall_at_3 value: 9.82 - type: recall_at_5 value: 13.358999999999998 - task: type: Retrieval dataset: name: MTEB TempReasonL2Fact (default) type: RAR-b/TempReason-l2-fact config: default split: test revision: 13758bcf978613b249d0de4d0840f57815122bdf metrics: - type: main_score value: 6.4430000000000005 - type: map_at_1 value: 1.538 - type: map_at_10 value: 4.381 - type: map_at_100 value: 5.583 - type: map_at_1000 value: 5.755 - type: map_at_20 value: 4.888 - type: map_at_3 value: 3.042 - type: map_at_5 value: 3.6929999999999996 - type: mrr_at_1 value: 1.5378914211599035 - type: mrr_at_10 value: 4.380615627141467 - type: mrr_at_100 value: 5.582481340163152 - type: mrr_at_1000 value: 5.7543358690140085 - type: mrr_at_20 value: 4.888341522384855 - 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type: main_score value: 10.59 - type: map_at_1 value: 4.3 - type: map_at_10 value: 8.134 - type: map_at_100 value: 8.967 - type: map_at_1000 value: 9.154 - type: map_at_20 value: 8.498999999999999 - type: map_at_3 value: 6.550000000000001 - type: map_at_5 value: 7.385 - type: mrr_at_1 value: 4.3 - type: mrr_at_10 value: 8.133968253968261 - type: mrr_at_100 value: 8.966515392145464 - type: mrr_at_1000 value: 9.153606984319149 - type: mrr_at_20 value: 8.49921878517081 - type: mrr_at_3 value: 6.550000000000002 - type: mrr_at_5 value: 7.385000000000004 - type: ndcg_at_1 value: 4.3 - type: ndcg_at_10 value: 10.59 - type: ndcg_at_100 value: 15.728 - type: ndcg_at_1000 value: 21.025 - type: ndcg_at_20 value: 11.944 - type: ndcg_at_3 value: 7.282 - type: ndcg_at_5 value: 8.797 - type: precision_at_1 value: 4.3 - type: precision_at_10 value: 1.8599999999999999 - type: precision_at_100 value: 0.45199999999999996 - type: precision_at_1000 value: 0.087 - type: precision_at_20 value: 1.2 - type: precision_at_3 value: 3.1329999999999996 - type: precision_at_5 value: 2.62 - type: recall_at_1 value: 4.3 - type: recall_at_10 value: 18.6 - type: recall_at_100 value: 45.2 - type: recall_at_1000 value: 87.4 - type: recall_at_20 value: 24.0 - type: recall_at_3 value: 9.4 - type: recall_at_5 value: 13.100000000000001 - task: type: Retrieval dataset: name: MTEB Touche2020 (default) type: mteb/touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: main_score value: 22.398 - type: map_at_1 value: 2.067 - type: map_at_10 value: 8.579 - type: map_at_100 value: 15.012 - type: map_at_1000 value: 16.706 - type: map_at_20 value: 10.653 - type: map_at_3 value: 3.909 - type: map_at_5 value: 6.077 - type: mrr_at_1 value: 28.57142857142857 - type: mrr_at_10 value: 43.01830255911888 - type: mrr_at_100 value: 43.96547082263703 - type: mrr_at_1000 value: 43.96547082263703 - type: mrr_at_20 value: 43.71604198403339 - type: mrr_at_3 value: 37.41496598639456 - type: mrr_at_5 value: 41.496598639455776 - type: ndcg_at_1 value: 24.490000000000002 - type: ndcg_at_10 value: 22.398 - type: ndcg_at_100 value: 36.604 - type: ndcg_at_1000 value: 48.111 - type: ndcg_at_20 value: 23.369999999999997 - type: ndcg_at_3 value: 21.378 - type: ndcg_at_5 value: 23.685000000000002 - type: precision_at_1 value: 28.571 - type: precision_at_10 value: 21.224 - type: precision_at_100 value: 8.408 - type: precision_at_1000 value: 1.59 - type: precision_at_20 value: 16.735 - type: precision_at_3 value: 23.128999999999998 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.067 - type: recall_at_10 value: 15.182 - type: recall_at_100 value: 50.768 - type: recall_at_1000 value: 86.29299999999999 - type: recall_at_20 value: 22.32 - type: recall_at_3 value: 4.865 - type: recall_at_5 value: 9.24 - task: type: Retrieval dataset: name: MTEB Touche2020Retrieval.v3 (default) type: mteb/webis-touche2020-v3 config: default split: test revision: 431886eaecc48f067a3975b70d0949ea2862463c metrics: - type: main_score value: 52.575 - type: map_at_1 value: 2.026 - type: map_at_10 value: 15.136 - type: map_at_100 value: 31.539 - type: map_at_1000 value: 34.672 - type: map_at_20 value: 21.477 - type: map_at_3 value: 5.931 - type: map_at_5 value: 9.476999999999999 - type: mrr_at_1 value: 63.26530612244898 - type: mrr_at_10 value: 77.57045675413023 - type: mrr_at_100 value: 77.75598551108757 - type: mrr_at_1000 value: 77.75598551108757 - type: mrr_at_20 value: 77.75598551108757 - type: mrr_at_3 value: 75.85034013605441 - type: mrr_at_5 value: 77.27891156462586 - type: ndcg_at_1 value: 54.081999999999994 - type: ndcg_at_10 value: 52.575 - type: ndcg_at_100 value: 55.051 - type: ndcg_at_1000 value: 67.027 - type: ndcg_at_20 value: 46.561 - type: ndcg_at_3 value: 58.48799999999999 - type: ndcg_at_5 value: 57.115 - type: precision_at_1 value: 63.26500000000001 - type: precision_at_10 value: 56.531 - type: precision_at_100 value: 18.898 - type: precision_at_1000 value: 3.084 - type: precision_at_20 value: 44.082 - type: precision_at_3 value: 68.027 - type: precision_at_5 value: 65.714 - type: recall_at_1 value: 2.026 - type: recall_at_10 value: 19.494 - type: recall_at_100 value: 59.349 - type: recall_at_1000 value: 89.84 - type: recall_at_20 value: 29.953000000000003 - type: recall_at_3 value: 6.819999999999999 - type: recall_at_5 value: 11.386000000000001 - task: type: Retrieval dataset: name: MTEB WinoGrande (default) type: RAR-b/winogrande config: default split: test revision: f74c094f321077cf909ddfb8bccc1b5912a4ac28 metrics: - type: main_score value: 61.800999999999995 - type: map_at_1 value: 29.044999999999998 - type: map_at_10 value: 51.514 - type: map_at_100 value: 51.896 - type: map_at_1000 value: 51.897000000000006 - type: map_at_20 value: 51.842 - type: map_at_3 value: 46.803 - type: map_at_5 value: 49.957 - type: mrr_at_1 value: 29.36069455406472 - type: mrr_at_10 value: 51.617619423460056 - type: mrr_at_100 value: 52.01415572431666 - type: mrr_at_1000 value: 52.014274589675495 - type: mrr_at_20 value: 51.95996098874236 - type: mrr_at_3 value: 46.86924493554325 - type: mrr_at_5 value: 50.04209418574064 - type: ndcg_at_1 value: 29.044999999999998 - type: ndcg_at_10 value: 61.800999999999995 - type: ndcg_at_100 value: 63.315 - type: ndcg_at_1000 value: 63.324000000000005 - type: ndcg_at_20 value: 62.986 - type: ndcg_at_3 value: 52.525 - type: ndcg_at_5 value: 58.160999999999994 - type: precision_at_1 value: 29.044999999999998 - type: precision_at_10 value: 9.361 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_20 value: 4.913 - type: precision_at_3 value: 23.02 - type: precision_at_5 value: 16.527 - type: recall_at_1 value: 29.044999999999998 - type: recall_at_10 value: 93.607 - type: recall_at_100 value: 99.921 - type: recall_at_1000 value: 100.0 - type: recall_at_20 value: 98.264 - type: recall_at_3 value: 69.06099999999999 - type: recall_at_5 value: 82.636 --- # Static Embeddings with BERT uncased tokenizer finetuned on various datasets This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [squad](https://huggingface.co/datasets/sentence-transformers/squad), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli), [paq](https://huggingface.co/datasets/sentence-transformers/paq), [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets), [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual), [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa), [miracl](https://huggingface.co/datasets/sentence-transformers/miracl), [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) and [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and is designed to be used for semantic search. Read our [Static Embeddings blogpost](https://huggingface.co/blog/static-embeddings) to learn more about this model and how it was trained. * **0 Active Parameters:** This model does not use any active parameters, instead consisting exclusively of averaging pre-computed token embeddings. * **100x to 400x faster:** On CPU, this model is 100x to 400x faster than common options like [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). On GPU, it's 10x to 25x faster. * **Matryoshka:** This model was trained with a [Matryoshka loss](https://huggingface.co/blog/matryoshka), allowing you to truncate the embeddings for faster retrieval at minimal performance costs. * **Evaluations:** See [Evaluations](#evaluation) for details on performance on NanoBEIR, embedding speed, and Matryoshka dimensionality truncation. In short, this model is **87.4%** as performant as the commonly used [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). * **Training Script:** See [train.py](train.py) for the training script used to train this model from scratch. See [`static-similarity-mrl-multilingual-v1`](https://huggingface.co/sentence-transformers/static-similarity-mrl-multilingual-v1) for a general-purpose multilingual static embedding model. It's been trained for semantic textual similarity, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) - [squad](https://huggingface.co/datasets/sentence-transformers/squad) - [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) - [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) - [paq](https://huggingface.co/datasets/sentence-transformers/paq) - [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) - [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) - [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) - [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) - [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) - [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/static-retrieval-mrl-en-v1") # Run inference sentences = [ 'Gadofosveset-enhanced MR angiography of carotid arteries: does steady-state imaging improve accuracy of first-pass imaging?', 'To evaluate the diagnostic accuracy of gadofosveset-enhanced magnetic resonance (MR) angiography in the assessment of carotid artery stenosis, with digital subtraction angiography (DSA) as the reference standard, and to determine the value of reading first-pass, steady-state, and "combined" (first-pass plus steady-state) MR angiograms.', 'In a longitudinal study we investigated in vivo alterations of CVO during neuroinflammation, applying Gadofluorine M- (Gf) enhanced magnetic resonance imaging (MRI) in experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. SJL/J mice were monitored by Gadopentate dimeglumine- (Gd-DTPA) and Gf-enhanced MRI after adoptive transfer of proteolipid-protein-specific T cells. Mean Gf intensity ratios were calculated individually for different CVO and correlated to the clinical disease course. Subsequently, the tissue distribution of fluorescence-labeled Gf as well as the extent of cellular inflammation was assessed in corresponding histological slices.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` This model was trained with Matryoshka loss, allowing this model to be used with lower dimensionalities with minimal performance loss (See [Matryoshka Evaluations](#matryoshka-evaluations) for evaluations). Notably, a lower dimensionality allows for much faster and cheaper information retrieval. You can specify a lower dimensionality with the `truncate_dim` argument when initializing the Sentence Transformer model: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/static-retrieval-mrl-en-v1", truncate_dim=256) embeddings = model.encode([ "what is the difference between chronological order and spatial order?", "can lavender grow indoors?" ]) print(embeddings.shape) # => (2, 256) ```    ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.32 | 0.7 | 0.46 | 0.28 | 0.64 | 0.18 | 0.42 | 0.24 | 0.8 | 0.28 | 0.1 | 0.52 | 0.5714 | | cosine_accuracy@3 | 0.52 | 0.84 | 0.8 | 0.44 | 0.82 | 0.42 | 0.56 | 0.44 | 0.96 | 0.48 | 0.46 | 0.6 | 0.898 | | cosine_accuracy@5 | 0.6 | 0.9 | 0.84 | 0.54 | 0.86 | 0.5 | 0.62 | 0.58 | 1.0 | 0.54 | 0.56 | 0.62 | 0.9796 | | cosine_accuracy@10 | 0.78 | 0.94 | 0.94 | 0.64 | 0.96 | 0.66 | 0.72 | 0.7 | 1.0 | 0.7 | 0.74 | 0.76 | 1.0 | | cosine_precision@1 | 0.32 | 0.7 | 0.46 | 0.28 | 0.64 | 0.18 | 0.42 | 0.24 | 0.8 | 0.28 | 0.1 | 0.52 | 0.5714 | | cosine_precision@3 | 0.1933 | 0.5867 | 0.2667 | 0.1933 | 0.3733 | 0.14 | 0.3733 | 0.1467 | 0.3867 | 0.2267 | 0.1533 | 0.2067 | 0.6054 | | cosine_precision@5 | 0.14 | 0.544 | 0.18 | 0.16 | 0.26 | 0.1 | 0.32 | 0.124 | 0.248 | 0.188 | 0.112 | 0.132 | 0.6204 | | cosine_precision@10 | 0.104 | 0.452 | 0.1 | 0.104 | 0.148 | 0.066 | 0.244 | 0.076 | 0.13 | 0.14 | 0.074 | 0.084 | 0.5306 | | cosine_recall@1 | 0.1467 | 0.0805 | 0.4367 | 0.1519 | 0.32 | 0.18 | 0.0428 | 0.24 | 0.7107 | 0.0597 | 0.1 | 0.485 | 0.0398 | | cosine_recall@3 | 0.239 | 0.1605 | 0.7467 | 0.2983 | 0.56 | 0.42 | 0.0984 | 0.43 | 0.9253 | 0.1417 | 0.46 | 0.57 | 0.1236 | | cosine_recall@5 | 0.279 | 0.218 | 0.8033 | 0.3793 | 0.65 | 0.5 | 0.1196 | 0.58 | 0.9627 | 0.1947 | 0.56 | 0.595 | 0.2095 | | cosine_recall@10 | 0.4197 | 0.3143 | 0.9033 | 0.4838 | 0.74 | 0.66 | 0.1389 | 0.69 | 0.9793 | 0.2887 | 0.74 | 0.75 | 0.337 | | **cosine_ndcg@10** | **0.3309** | **0.5681** | **0.6922** | **0.3651** | **0.6547** | **0.4041** | **0.3242** | **0.4534** | **0.8951** | **0.2643** | **0.4078** | **0.6111** | **0.5703** | | cosine_mrr@10 | 0.4453 | 0.7854 | 0.6397 | 0.3915 | 0.7485 | 0.3245 | 0.5041 | 0.3764 | 0.88 | 0.3998 | 0.3034 | 0.5837 | 0.744 | | cosine_map@100 | 0.2598 | 0.4335 | 0.6205 | 0.3024 | 0.5798 | 0.3389 | 0.1449 | 0.389 | 0.8594 | 0.205 | 0.3151 | 0.5683 | 0.447 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.424 | | cosine_accuracy@3 | 0.6337 | | cosine_accuracy@5 | 0.703 | | cosine_accuracy@10 | 0.8108 | | cosine_precision@1 | 0.424 | | cosine_precision@3 | 0.2963 | | cosine_precision@5 | 0.2406 | | cosine_precision@10 | 0.1733 | | cosine_recall@1 | 0.2303 | | cosine_recall@3 | 0.398 | | cosine_recall@5 | 0.4655 | | cosine_recall@10 | 0.5727 | | **cosine_ndcg@10** | **0.5032** | | cosine_mrr@10 | 0.5482 | | cosine_map@100 | 0.4203 | We've evaluated [sentence-transformers/static-retrieval-mrl-en-v1](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) on NanoBEIR and plotted it against the inference speed computed on a RTX 3090 and i7-13700K. For the inference speed tests, we calculated the number of computed query embeddings of the [GooAQ dataset](https://huggingface.co/datasets/sentence-transformers/gooaq) per second, either on CPU or GPU. We evaluate against 3 types of models: 1. Attention-based dense embedding models, e.g. traditional Sentence Transformer models like [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [`bge-base-en-v1.5`](https://huggingface.co/BAAI/bge-base-en-v1.5), and [`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5). 2. Static Embedding-based models, e.g. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1), [`potion-base-8M`](https://huggingface.co/minishlab/potion-base-8M), [`M2V_base_output`](https://huggingface.co/minishlab/M2V_base_output), and [`glove.6B.300d`](https://huggingface.co/sentence-transformers/average_word_embeddings_glove.6B.300d). 3. Sparse bag-of-words model, BM25, often a strong baseline. <details><summary>Click to expand BM25 implementation details</summary> I relied on the highly efficient [bm25s](https://github.com/xhluca/bm25s) implementation, using `model.get_scores()` on tokens after tokenization and stemming with the English `PyStemmer`. </details> > **NOTE:** Many of the attention-based dense embedding models are finetuned on the training splits of the (Nano)BEIR evaluation datasets. This gives the models an unfair advantage in this benchmark and can result in lower downstream performance on real retrieval tasks. > > [static-retrieval-mrl-en-v1](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is purposefully not trained on any of these datasets. ##### GPU ![NanoBEIR performance vs inference speed](img/nano_beir_vs_speed_gpu.png) ##### CPU ![NanoBEIR performance vs inference speed](img/nano_beir_vs_speed_cpu.png) We can draw some notable conclusions from these figures: 1. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) outperforms all other Static Embedding models. 2. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is the only Static Embedding model to outperform BM25. 3. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is * **87.4%** as performant as the commonly used [`all-mpnet-base-v2`](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), * **24x** faster on GPU, * **397x** faster on CPU. 4. [`static-retrieval-mrl-en-v1`](https://huggingface.co/sentence-transformers/static-retrieval-mrl-en-v1) is quicker on CPU than on GPU: This model can run extraordinarily quickly everywhere, including consumer-grade PCs, tiny servers, phones, or in-browser. #### Matryoshka Evaluations We experimented with the results on NanoBEIR performance when we performed Matryoshka-style dimensionality reduction by truncating the output embeddings to a lower dimensionality. | Dimensionality | NanoBEIR_mean | NanoArguAna | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoSciFact | NanoTouche2020 | |----------------|---------------|-------------|------------------|-------------|-----------|--------------|--------------|-------------|--------------|--------|--------------------|-------------|-------------|----------------| | 1024 | **0.5031** | 0.4077 | 0.3308 | 0.5681 | 0.6921 | 0.3651 | 0.6547 | 0.4040 | 0.3241 | 0.4533 | 0.8950 | 0.2642 | 0.6111 | 0.5702 | | 512 | **0.4957** | 0.3878 | 0.3360 | 0.5626 | 0.6945 | 0.3517 | 0.6280 | 0.3892 | 0.3206 | 0.4505 | 0.8986 | 0.2657 | 0.5953 | 0.5635 | | 256 | **0.4819** | 0.3855 | 0.3203 | 0.5407 | 0.6734 | 0.3518 | 0.6027 | 0.4144 | 0.2860 | 0.4254 | 0.8948 | 0.2466 | 0.5620 | 0.5605 | | 128 | **0.4622** | 0.4001 | 0.2982 | 0.5266 | 0.6273 | 0.3188 | 0.5606 | 0.4025 | 0.2693 | 0.4021 | 0.8930 | 0.2283 | 0.5447 | 0.5368 | | 64 | **0.4176** | 0.3424 | 0.2809 | 0.5022 | 0.5480 | 0.2831 | 0.4680 | 0.3739 | 0.2153 | 0.3845 | 0.8525 | 0.1680 | 0.5045 | 0.5050 | | 32 | **0.3532** | 0.2866 | 0.1870 | 0.4292 | 0.4193 | 0.2292 | 0.3602 | 0.3587 | 0.1444 | 0.3525 | 0.8325 | 0.1525 | 0.3983 | 0.4408 | ![NanoBEIR performance vs Matryoshka dimensionality reduction](img/nano_beir_matryoshka.png) These findings show that reducing the dimensionality by e.g. 2x only has a 1.47% reduction in performance (0.5031 NDCG@10 vs 0.4957 NDCG@10), while realistically resulting in a 2x speedup in retrieval speed.   ## Training Details ### Training Datasets <details><summary>gooaq</summary> * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco</summary> * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 characters</li><li>mean: 33.26 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 96 characters</li><li>mean: 356.24 characters</li><li>max: 1006 characters</li></ul> | <ul><li>min: 68 characters</li><li>mean: 327.52 characters</li><li>max: 995 characters</li></ul> | * Samples: | query | positive | negative | |:---------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>when was the sullivan acts</code> | <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> | <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> | | <code>can lavender grow indoors</code> | <code>Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.</code> | <code>Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.</code> | | <code>what kind of barley do you malt</code> | <code>Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.</code> | <code>Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>squad</summary> * Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) * Size: 87,599 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 14 characters</li><li>mean: 59.66 characters</li><li>max: 150 characters</li></ul> | <ul><li>min: 156 characters</li><li>mean: 769.53 characters</li><li>max: 3706 characters</li></ul> | * Samples: | question | answer | |:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What did Business Insider call San Diego in 2013?</code> | <code>San Diego was ranked as the 20th-safest city in America in 2013 by Business Insider. According to Forbes magazine, San Diego was the ninth-safest city in the top 10 list of safest cities in the U.S. in 2010. Like most major cities, San Diego had a declining crime rate from 1990 to 2000. Crime in San Diego increased in the early 2000s. In 2004, San Diego had the sixth lowest crime rate of any U.S. city with over half a million residents. From 2002 to 2006, the crime rate overall dropped 0.8%, though not evenly by category. While violent crime decreased 12.4% during this period, property crime increased 1.1%. Total property crimes per 100,000 people were lower than the national average in 2008.</code> | | <code>What did the Spanish call this region?</code> | <code>The name Montana comes from the Spanish word Montaña, meaning "mountain", or more broadly, "mountainous country". Montaña del Norte was the name given by early Spanish explorers to the entire mountainous region of the west. The name Montana was added to a bill by the United States House Committee on Territories, which was chaired at the time by Rep. James Ashley of Ohio, for the territory that would become Idaho Territory. The name was successfully changed by Representatives Henry Wilson (Massachusetts) and Benjamin F. Harding (Oregon), who complained that Montana had "no meaning". When Ashley presented a bill to establish a temporary government in 1864 for a new territory to be carved out of Idaho, he again chose Montana Territory. This time Rep. Samuel Cox, also of Ohio, objected to the name. Cox complained that the name was a misnomer given that most of the territory was not mountainous and that a Native American name would be more appropriate than a Spanish one. Other names such as...</code> | | <code>Small missiles were designed that could be mounted on what?</code> | <code>As this process continued, the missile found itself being used for more and more of the roles formerly filled by guns. First to go were the large weapons, replaced by equally large missile systems of much higher performance. Smaller missiles soon followed, eventually becoming small enough to be mounted on armored cars and tank chassis. These started replacing, or at least supplanting, similar gun-based SPAAG systems in the 1960s, and by the 1990s had replaced almost all such systems in modern armies. Man-portable missiles, MANPADs as they are known today, were introduced in the 1960s and have supplanted or even replaced even the smallest guns in most advanced armies.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>s2orc</summary> * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 90,000 training samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: | | title | abstract | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 31 characters</li><li>mean: 80.02 characters</li><li>max: 185 characters</li></ul> | <ul><li>min: 84 characters</li><li>mean: 635.31 characters</li><li>max: 1023 characters</li></ul> | * Samples: | title | abstract | |:----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions</code> | <code>The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.</code> | | <code>Enlightening on medical administration by clinical governance in British</code> | <code>Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.</code> | | <code>APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION</code> | <code>Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>allnli</summary> * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 557,850 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 34.88 characters</li><li>max: 193 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 46.49 characters</li><li>max: 181 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 50.47 characters</li><li>max: 204 characters</li></ul> | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>paq</summary> * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 25 characters</li><li>mean: 50.56 characters</li><li>max: 104 characters</li></ul> | <ul><li>min: 509 characters</li><li>mean: 620.96 characters</li><li>max: 773 characters</li></ul> | * Samples: | query | answer | |:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>in veetla visheshanga ganesh is the husband of</code> | <code>Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him</code> | | <code>when did simon property group became a publicly traded company</code> | <code>of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly</code> | | <code>what was the nationality of antoine faivre</code> | <code>Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>trivia</summary>_qa * Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 73,346 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 21 characters</li><li>mean: 76.91 characters</li><li>max: 455 characters</li></ul> | <ul><li>min: 136 characters</li><li>mean: 3273.89 characters</li><li>max: 4096 characters</li></ul> | * Samples: | query | answer | |:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What type of rock is formed by the solidification of molten magma?</code> | <code>igneous rock - Dictionary Definition : Vocabulary.com igneous rock n rock formed by the solidification of molten magma Types: a rare type of peridotite that sometimes contains diamonds; found in South Africa and Siberia Type of: material consisting of the aggregate of minerals like those making up the Earth's crust Word Family Usage Examples Sign up, it's free! Whether you're a student, an educator, or a life-long learner, Vocabulary.com can put you on the path to systematic vocabulary improvement.</code> | | <code>Which river flows through the town of Shrewsbury?</code> | <code>River Severn | river, Wales and England, United Kingdom | Britannica.com river, Wales and England, United Kingdom Written By: Wales River Severn, Welsh Hafren, Britain’s longest river from source to tidal waters—about 180 miles (290 km) long, with the Severn estuary adding some 40 miles (64 km) to its total length. The Severn rises near the River Wye on the northeastern slopes of Plynlimon (Welsh: Pumlumon), Wales , and follows a semicircular course basically southward to the Bristol Channel and the Atlantic Ocean . It drains an area of 4,350 square miles (11,266 square km) with an average discharge at Bewdley of 2,170 cubic feet (61.5 cubic m) per second. River Severn at Shrewsbury, Shropshire, Eng. Chris Bayley The river’s course is at first southeasterly, descending from an elevation of 2,000 feet (600 m) at its source to 500 feet (150 m) at the Welsh town of Llanidloes. There it turns sharply northeastward, following the Vale of Powys past Newtown and Welshpool . At Llanymynech the...</code> | | <code>Which band's name was inspired by a novel by Herman Hesse?</code> | <code>23 Band Names Inspired by Literature :: Books :: Lists :: Paste 23 Band Names Inspired by Literature By Wyndham Wyeth | April 24, 2011 | 10:52pm Share Tweet Submit Pin At Paste, we look for “Signs of Life” in all forms of art. And while we value each artform for its unique merits, it’s always a treat when they overlap. So we decided to take a look at bands that derived their names from literature. The works that inspired several of the entries are probably obvious, but a few of them will most certainly surprise you. It may also surprise you to see which genres favor the written word. (Who knew metalheads were such scholars?) Photo by Max Blau New Jersey punks Titus Andronicus take their name from the greatest wordsmith of them all, William Shakespeare . Titus Andronicus is thought to be the famous playwright’s first tragedy. It is also his bloodiest and most violent work. 2. The Doors Source: The Doors of Perception by Aldous Huxley When The Doors formed in 1965, they decided to na...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco_10m</summary> * Dataset: [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2) * Size: 10,000,000 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 characters</li><li>mean: 33.47 characters</li><li>max: 158 characters</li></ul> | <ul><li>min: 53 characters</li><li>mean: 353.76 characters</li><li>max: 948 characters</li></ul> | <ul><li>min: 67 characters</li><li>mean: 343.74 characters</li><li>max: 1063 characters</li></ul> | * Samples: | query | positive | negative | |:-------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is ged equivalent</code> | <code>A GED is equivalent to a high school diploma however colleges do not look at it the same. A huge part of high school is commitment and dedication, therefore if you choose to drop out of high school you will probably not get accepted into any top colleges. However, you could always start at a community college and work your way up. Good Luck</code> | <code>If you are not far along in your Army career, education is an especially good way to boost your chances of promotion. Continuing your education at any level can earn you points. For completing your GED or bachelor's degree, you earn 10 points.</code> | | <code>foods that help with diverticulitis</code> | <code>Diet for Diverticulitis. Gradually you can ease back into a regular diet. Your doctor may advise you to start with low-fiber foods (white bread, meat, poultry, fish, eggs, and dairy products) before introducing high-fiber foods. Fiber softens and adds bulk to stools, helping them pass more easily through the colon.</code> | <code>During an attack of diverticulitis, your doctor may recommend a clear liquid diet or a low-fiber diet. This helps the area of infection to heal. Foods allowed on a clear-liquid diet include: 1 Plain water.iverticulitis occurs when small, bulging pouches (diverticula) in your colon become infected and inflamed â causing severe abdominal pain, nausea, and fever. The treatment of a diverticulitis attack will depend on the severity of the symptoms and whether this is your first attack.</code> | | <code>calories burned in turbo kick class</code> | <code>The American Council on Exercise did a study to find out how many calories are burned during a turbo kick class. The study looked at 15 women in a turbo kick class who weighed about 135 lbs and found that they burned between 6.45 to 8.3 calories a minute. This comes out to about 350 to 450 calories an hour.</code> | <code>Popular Calories Burned Searches: 1 Calories Burned For Intervals of run/walk or walk/jog: 6 mph or slower (> 10 minutes per mile) 2 Calories Burned For Walking: 3.5 mph (17 minutes per mile) 3 Calories Burned For Walking: 4 mph (15 minutes per mile) Calories Burned For Walking: 4.6 mph (13 minutes per mile)</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>swim_ir</summary> * Dataset: [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) at [834c20f](https://huggingface.co/datasets/nthakur/swim-ir-monolingual/tree/834c20f0ceef6a68e029fb4447d17d20bb0288c3) * Size: 501,538 training samples * Columns: <code>query</code> and <code>text</code> * Approximate statistics based on the first 1000 samples: | | query | text | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 0 characters</li><li>mean: 59.98 characters</li><li>max: 189 characters</li></ul> | <ul><li>min: 208 characters</li><li>mean: 525.9 characters</li><li>max: 2743 characters</li></ul> | * Samples: | query | text | |:------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>How many blocked kicks did Williams have in his second year at Bowling Green State University?</code> | <code>Williams accepted a football scholarship from Bowling Green State University, where he became one of the best special teams players in school history. As a redshirt freshman, he was a wide receiver on the scout team. The next year, he played mainly on special teams and had 3 blocked kicks.</code> | | <code>How many town councils are there in the metropolitan borough?</code> | <code>Horwich, Westhoughton and Blackrod are now constituted as civil parishes. There are three town councils in the metropolitan borough, Westhoughton Town Council, Horwich Town Council and Blackrod Town Council. The rest of the metropolitan borough, Bolton, Farnworth, Kearsley, Little Lever, and South Turton, have remained unparished areas since 1974.</code> | | <code>What is the name of the person selected to lead BART’s 296-member police force?</code> | <code>In 2009, the hiring of two independent organizations reviewed BART's policies and procedures in the process of overseeing the BART Police. The two independent firms investigated the matters of BART Police Shooting of Oscar Grant and were charged with making recommendations to the board. Ward Allen formulated and chaired BART's first Police Department Review Committee, and as a result, BART made sweeping changes on many security measures, as well as corrected and implemented several policies and procedures. BPD Review Committee has led to the re-training of all officers on use of force, diversity re-training and other issues. Ward Allen hired Kenton Rainey, the person selected to lead BART’s 296-member police force, to take command as Chief of Police.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>pubmedqa</summary> * Dataset: [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) at [a1ef0b5](https://huggingface.co/datasets/sentence-transformers/pubmedqa/tree/a1ef0b513b16ed490e807ac11da40e436d3a54c3) * Size: 1,660 training samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, and <code>negative_20</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | | details | <ul><li>min: 25 characters</li><li>mean: 94.06 characters</li><li>max: 213 characters</li></ul> | <ul><li>min: 5 characters</li><li>mean: 409.42 characters</li><li>max: 1582 characters</li></ul> | <ul><li>min: 5 characters</li><li>mean: 325.57 characters</li><li>max: 1300 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 299.5 characters</li><li>max: 1352 characters</li></ul> | <ul><li>min: 21 characters</li><li>mean: 317.37 characters</li><li>max: 1590 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 334.43 characters</li><li>max: 1536 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 335.49 characters</li><li>max: 1247 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 336.16 characters</li><li>max: 1383 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 319.98 characters</li><li>max: 1501 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 337.33 characters</li><li>max: 1493 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 324.82 characters</li><li>max: 1058 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 336.76 characters</li><li>max: 1457 characters</li></ul> | <ul><li>min: 10 characters</li><li>mean: 355.4 characters</li><li>max: 1748 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 344.26 characters</li><li>max: 1705 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 335.11 characters</li><li>max: 1593 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 353.83 characters</li><li>max: 1374 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 328.01 characters</li><li>max: 1755 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 337.74 characters</li><li>max: 1579 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 336.94 characters</li><li>max: 1325 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 319.49 characters</li><li>max: 1410 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 340.91 characters</li><li>max: 1680 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 330.34 characters</li><li>max: 1509 characters</li></ul> | * Samples: | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | 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| <code>Visceral adipose tissue area measurement at a single level: can it represent visceral adipose tissue volume?</code> | <code>Measurement of visceral adipose tissue (VAT) needs to be accurate and sensitive to change for risk monitoring. The purpose of this study is to determine the CT slice location where VAT area can best reflect changes in VAT volume and body weight.</code> | <code>46 patients with psoriasis and 46 sex- and age-matched control patients were included in this study. The abdominal fat area [visceral fat area (VFA), subcutaneous fat area (SFA) and total fat area (TFA)] at the level of the umbilicus was evaluated by computed tomography.</code> | <code>A retrospective review of CRC patients who received adjuvant chemotherapy at a single center during the period 2006-2009 identified from a prospectively maintained database. Visceral adiposity was determined by measuring visceral fat area (VFA) on preoperative staging CT. All patients were followed up to study completion or death.</code> | <code>A total of 1941 participants without known cardiovascular disease were enrolled from the Korean Genome and Epidemiology Study. Visceral fat area (VFA) was assessed by computed tomography. Appendicular skeletal muscle mass (ASM) was estimated by dual-energy X-ray absorptiometry and was used as a percentage of body weight (ASM/Wt). LV structure and function were assessed by tissue Doppler imaging (TDI) echocardiography.</code> | <code>One hundred and forty nonobese patients (BMI <25 kg/m2) were enrolled. EFV and visceral fat area were measured by MDCT. Patients were classified according to the plaque components (noncalcified, mixed and calcified) and severity of CAD. Inflammatory biomarkers were also measured, and compared with each CT parameter.</code> | <code>The blood gas level in each pulmonary vein (PV) was measured in supine subjects with diverse body mass index (BMI) values, to determine whether there was a regional insufficiency in gas exchange depending on the subject's BMI.</code> | <code>Magnetic resonance imaging (MRI) of 163 patients with cholecystolithiasis and 163 non-cholecystolithiasis control subjects admitted to our institution between March 2011 and September 2013 were included in this cross-sectional evaluation. There were 98 women and 65 men in cholecystolithiasis group with an average age of 57±16 years (range 25-86 years). There were 87 women and 76 men in the control group with an average age of 41±16 years (range 14-77 years). Visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (SAT) and total abdominal adipose tissue (TAT) of all the subjects at navel level were measured on abdominal MRI. According to the visceral adipose area (cut-off point VAT = 100 cm2), study subjects were divided into 1) increased accumulation of intra-abdominal fat and 2) normal distribution of intra-abdominal fat. Logistic regression was used to assess the association of fat with the presence of cholecystolithiasis, adjusted for age and sex.</code> | <code>We reviewed the medical records of 106 patients undergoing LA or LESS-A at our institution. Total fat area (TFA) and visceral fat area (VFA) were measured at the level of the L4 vertebra by computed tomography. To categorize the type of obesity, the VFA/TFA ratio was calculated. Multiple logistic regression analyses were performed to identify independent predictors of prolonged operative time.</code> | <code>The weight gain was 5.2% greater in rats exposed to fructose than in controls (P = 0.042). Total and visceral adipose tissue volumes were 5.2 cm3 (P = 0.017) and 3.1 cm3 (P = 0.019) greater, respectively, while lean tissue volumes did not differ. The level of triglycerides and apolipoprotein A-I was higher (P = 0.034, P = 0.005, respectively) in fructose-exposed rats.</code> | <code>A total of 1593 middle-aged to older patients participated in this cross-sectional study. Brachial-to-ankle pulse wave velocity (baPWV) was measured as an index of arterial stiffness. Second PP (PP2) at the second peak of radial SBP was used to estimate central PP. Radial augmentation index was calculated as PP2/PP. Thigh muscle cross-sectional area and abdominal visceral fat area were quantified by computed tomography. Patients were classified as sarcopenic if their hand grip strength or skeletal muscle mass (measured by bioelectrical impedance) was more than 1 SD lower than the mean of those in a reference group aged below 50 years, or in the lowest 20% of the studied population. Visceral obesity was defined as visceral fat area greater than 100 cm.</code> | <code>Visceral adiposity is linked with sleep-disordered breathing (SDB) (called Syndrome Z), and both correlate with coronary artery disease (CAD). The aim of the present study was to determine the significance of excess visceral fat, SDB and circulating levels of biomarkers in CAD in Japanese men.</code> | <code>There are no published studies on the impact of visceral adipose tissue (VAT) change on outcomes of restorative proctocolectomy and ileal pouch-anal anastomosis (IPAA). The aim of this historic cohort study was to evaluate the impact of excessive VAT gain on the outcomes of inflammatory bowel disease (IBD) patients with IPAA.</code> | <code>A subgroup of 46 men (n = 20, aged 29.1-33.4 years) and women (n = 26, aged 29.1-33.8 years) were recruited from an ongoing population study at our institution. Anthropometric variables including weight, height, and waist circumference were measured using standard procedures, and body mass index was calculated (kg/m(2)). Visceral adipose tissue (VAT) was measured with magnetic resonance imaging. Plasma apolipoproteins, lipids, glucose, and insulin were measured after an overnight fasting.</code> | <code>Our aim was to describe adipose tissue content and distribution in ALS patients.</code> | <code>This was a cross-sectional study of 140 Japanese patients with type 2 diabetes (mean age 65 ± 11 year; 44.6% women). Visceral fat area (VFA; cm(2) ) and liver attenuation index (LAI) were assessed by abdominal computed tomography. The patients were divided into four groups by VFA and body mass index (BMI; kg/m(2) ) as follows: BMI <25 kg/m(2) and VFA <100 cm(2) (OB[-]VA[-]), BMI ≥25 kg/m(2) and VFA <100 cm(2) (OB[+]VA[-]), BMI <25 kg/m(2) and VFA ≥100 cm(2) (OB[-]VA[+]), and BMI ≥25 kg/m(2) and VFA ≥100 cm(2) (OB[+]VA[+]). Multivariate linear regression and logistic regression analysis were carried out to determine the impact of OB(-)VA(+) on LAI.</code> | <code>Epicardial adipose tissue represents visceral adiposity, the early detection of which could be helpful for assessing subclinical target organ damage. Although previous studies have reported a relationship between epicardial fat thickness (EFT) and carotid intima-media thickness, there have been no studies detailing the relationship between EFT and brachial-ankle pulse wave velocity (baPWV).</code> | <code>Higher adiponectin levels were associated with lower risk of diabetes (P < 0.001). Visceral fat was the only adiposity measure associated with diabetes after adjusting for BMI (odds ratio 3.0 [2.1-4.3] in women and 1.3 [1.0-1.6] in men, P < 0.001 between-sex comparison). Adipocytokines attenuated the association between visceral fat and diabetes for both sexes but more strongly in men (women 2.3 [1.5-3.3], men 1.1 [0.9-1.4]). In men, adiponectin, IL-6, and PAI-1 remained independently associated with diabetes after adjusting for fat depots; in women, adiponectin was the only independently associated adipocytokine. Controlling for insulin, HDL, triglycerides, and blood pressure did not change these results.</code> | <code>Ninety obesity patients and 95 non-obesity Uygur individuals were enrolled in this study. CD68 levels in abdominal subcutaneous and omental adipose tissues were detected by immunohistochemistry. The cytokine expression levels of adiponectin (APMI) and visfatin in serum were measured by enzyme-linked immunosorbent assay. Infection of 3T3-L1 cells with Ad36 was performed. Real-time PCR was performed to determine expression levels of APMI and Visfatin genes in the 3T3-L1 preadipocytes infected with Ad36.</code> | <code>Serum CEA levels correlated with visceral fat area, fasting glucose, and triglyceride levels after adjusting for age and BMI. The mean visceral fat area increased significantly with the increasing CEA tirtiles. In a step-wise multiple regression analysis, age (β = 0.26, p<0.01) and visceral fat area (β = 0.19, p = 0.03) were identified as explanatory variables for serum CEA level.</code> | <code>The visceral adiposity index (VAI) has proved to be a marker of visceral adipose dysfunction, strongly associated with insulin sensitivity in both the general and specific populations of patients at metabolic risk.</code> | <code>Visceral adiposity is associated with hepatic steatosis, inflammation, and fibrosis in non-alcoholic fatty liver disease (NAFLD). The visceral adiposity index (VAI), a novel marker of visceral fat distribution and dysfunction, has been correlated with histology in hepatitis C. We assessed the ability of VAI to predict disease severity in NAFLD and hence its role as a non-invasive marker of liver damage.</code> | | <code>Do general practitioner hospitals reduce the utilisation of general hospital beds?</code> | <code>Observational study comparing the total rates of admissions and of occupied bed days in general hospitals between populations with and without access to GP hospitals. Comparisons were also made separately for diagnoses commonly encountered in GP hospitals.</code> | <code>To study the prevalence of GERD comorbidities in a tertiary care hospital.</code> | <code>The population studied was a sample of 10% of the patients 65 years or older registered with a general practitioner contributing to the General Practice Research Database between 1988 and 1996.</code> | <code>Tertiary University Hospitals.</code> | <code>University hospital and district general hospital.</code> | <code>Inpatient rehabilitation facilities.</code> | <code>Inpatient rehabilitation facilities.</code> | <code>General medical service at a teaching hospital.</code> | <code>To examine the hypothesis that nursing homes responding to these changes in demand shifted the balance of resources from hotel to clinical activities.</code> | <code>Outpatient practices of general practitioners in the United Kingdom who contribute to the General Practice Research Database.</code> | <code>Hospital rehabilitation programs.</code> | <code>Surgical department of a large district general hospital.</code> | <code>Hospital-based case-control study.</code> | <code>Patients' homes.</code> | <code>the relationship between proximity to death and the amount of care provided by general practitioners (GPs) is largely unknown.</code> | <code>Public hospital, primary care clinic.</code> | <code>A teaching hospital and a district general hospital.</code> | <code>District General Hospital in the UK.</code> | <code>Academic general internal medicine practice.</code> | <code>A hospital-based study was conducted.</code> | <code>To evaluate the impact of participation in a trial on General Practitioners management and patient behaviour.</code> | | <code>"Occult" posttraumatic lesions of the knee: can magnetic resonance substitute for diagnostic arthroscopy?</code> | <code>We identified three types of occult post-traumatic injuries by morpho-topographic and signal intensity patterns: bone bruises (no. 25), subchondral (no. 33) and osteochondral (no. 35) injuries. Arthroscopy depicted 45 osteochondral and 19 chondral injuries. A bone bruise was defined as a typical subcortical area of signal loss, with various shapes, on T1-weighted images and of increased signal intensity on T2-weighted and FIR images. The cortical bone and articular cartilage were normal in all cases, while osteochondral injuries exhibited associated bone and cartilage damage with the same abnormal MR signal intensity. Sprain was the mechanism of injury in 52 cases, bruise in 12 and stress in 6. In 52 sprains (30 in valgus), the injury site was the lateral compartment in 92.3% of cases (100% in valgus), associated with meniscal damage in 73% of cases (90% in valgus) and with ligament injury in 90.4% (100% in valgus). In 12 bruises, the injury site was the lateral compartment in 58.3% of...</code> | <code>Preoperative range of motion (ROM) has been regarded as one of the most important factors in predicting postoperative ROM following total knee arthroplasty (TKA). Mobile-bearing TKA designs have been suggested to possibly improve the knee kinematics compared to fixed-bearing designs. The purpose of this study was to examine the difference in postoperative flexion as a function of preoperative flexion in a consecutive series of TKAs done using a posterior-stabilized rotating-platform prosthesis.</code> | <code>To assess the association of underlying diagnosis with outcomes after revision total knee arthroplasty (TKA).</code> | <code>We identified 37 knees diagnosed with osteoarthritis with a preoperative knee flexion ≥120° but a 12-month postoperative range of motion (ROM) ≤110°. A random sample of 111 patients (1:3) from the same database, whose knees had a preoperative and 12-month postoperative ROM ≥120°, based on a diagnosis of primary osteoarthritis and no previous open knee surgery, were selected as the controls.</code> | <code>This study reports a series of patients operated on by anterior cruciate ligament (ACL) reconstruction combined with valgus high tibial osteotomy (HTO) for chronic anterior knee instability associated with medial tibiofemoral osteoarthritis. It was hypothesized that the combined surgery would enable return to sport, stabilize the knee and relieve medial pain.</code> | <code>To determine the prevalence and factors associated with knee osteoarthritis (OA) defined by magnetic resonance imaging (MRI) and specific OA features on MRI 1 year after anterior cruciate ligament reconstruction (ACLR).</code> | <code>In anterior ankle arthroscopy, the anterior working area (AWA) is restricted by the presence of the dorsalis pedis artery (DPA) and tendons. Pseudoaneurysms caused by iatrogenic damage to the DPA are difficult to identify intraoperatively. In knee arthroscopy, risk of popliteal artery damage is reduced in the flexed position [1]. This study investigates how DPA movement is affected by dorsiflexion and plantarflexion with the aim of identifying the positions providing the greatest AWA.</code> | <code>To investigate whether sex affects the trajectory of functional recovery after total knee arthroplasty (TKA).</code> | <code>To determine whether magnetic resonance imaging (MRI) evidence of tendinopathy in early rheumatoid arthritis (RA) could be used to predict the course of tendon involvement in later disease and specifically the risk of tendon rupture.</code> | <code>With the advent of MRI (Magnetic Resonance Imaging), Synovial lesions around knee are being more and more easily detected. Synovial lesions of knee present with boggy swelling, effusion, pain, and restriction of motion. Differential diagnoses of such lesions include pigmented villonodular synovitis, synovial lipoma, synovial chondromatosis, rheumatoid arthritis, synovial hemangioma, amyloid arthropathy, xanthomata and lipoma arborescens. CT and MRI often help in diagnosis of such lesions. MRI of Lipoma Arborescens has been regarded to have characteristic diagnostic appearance - it includes a synovial mass with frond-like architecture and fat signal intensity on all pulse sequences. Sometimes Lipoma Arborescens can present in conjunction with inflammatory arthritis. Synovectomy is often curative for such conditions.</code> | <code>Joint trauma can lead to a spectrum of acute lesions, including cartilage degradation, ligament or meniscus tears, and synovitis, all potentially associated with osteoarthritis (OA). This study was undertaken to generate and validate a murine model of knee joint trauma following noninvasive controlled injurious compression in vivo.</code> | <code>To determine which subregions of the knee joint have a high prevalence of pre-radiographic osteoarthritic changes, i.e., cartilage damage and osteophytes that can only be detected by magnetic resonance imaging (MRI), in radiographically normal knees.</code> | <code>78 of initially 84 patients (80 of 86 knees) were clinically and radiographically reassessed 5 (5.1-5.9) years after conventional, image-based, and image-free total knee arthroplasty. The methodology was identical to that used preoperatively and at 2 years, including the Knee Society score (KSS) and the functional score (FS), and AP and true lateral standard radiographs.</code> | <code>In adult patients with trauma, an increase in the thickness of the retropharyngeal soft tissues is commonly used as a potential indicator of occult injury, but no studies have examined this parameter using computed tomography (CT) as a screening modality.</code> | <code>To evaluate the thickness of cartilage at the posterior aspect of the medial and lateral condyle in Osteoarthritis (OA) knees compared to non-OA knees using computed tomography arthrography (CTA).</code> | <code>Total knee arthroplasty (TKA) successfully alleviates pain from knee osteoarthritis; but deficits in function can persist long term. Despite these well-known deficits, there is little evidence supporting the use of rehabilitation interventions following TKA.</code> | <code>The anterior intermeniscal ligament of the knee is at risk during knee arthroscopy, anterior cruciate ligament reconstruction, and tibial nail insertion.</code> | <code>It has been previously demonstrated that radiographic severity of arthritis predicts outcome following knee replacement. In certain circumstances, patients may undergo arthroplasty without severe radiographic disease. An example may be the patient with significant chondral damage unsuccessfully treated with arthroscopy. This patient may proceed to joint replacement when their radiographs would not normally merit such intervention. We investigated whether these findings were also applicable to total ankle replacements (TARs).</code> | <code>There is an increasing body of evidence that magnetic resonance imaging-occult tissue damage is an important component of primary progressive multiple sclerosis (PPMS) pathology. Proton magnetic resonance spectroscopy (1H-MRS) can be used to measure in vivo whole-brain N-acetylaspartate (WBNAA) concentrations, the decrease of whose levels is considered a marker of neuronal-axonal injury.</code> | <code>Meniscectomy and articular cartilage damage have been found to increase the prevalence of osteoarthritis after anterior cruciate ligament reconstruction, but the effect of knee range of motion has not been extensively studied.</code> | <code>Patients 50 years and older with knee osteoarthritis who underwent arthroscopy between 1998 and 2010 were retrospectively identified and an annual arthroscopy rate was calculated from 1998 through 2002 and from 2006 through 2010. Patients who underwent knee arthroplasty within 2 years of arthroscopy during each period were identified, and a 2-year conversion to arthroplasty rate was calculated.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>miracl</summary> * Dataset: [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) at [07e2b62](https://huggingface.co/datasets/sentence-transformers/miracl/tree/07e2b629250bf4185f4c87f640fac15949b8aa73) * Size: 789,900 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 characters</li><li>mean: 39.23 characters</li><li>max: 129 characters</li></ul> | <ul><li>min: 72 characters</li><li>mean: 745.86 characters</li><li>max: 4292 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 649.52 characters</li><li>max: 3570 characters</li></ul> | * Samples: | anchor | positive | negative | |:-------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Who created The Walking Dead comic books?</code> | <code>Days Gone Bye (The Walking Dead) Robert Kirkman, the creator of the eponymous series of comic books, considered the idea of creating a television show based on the comic series, but did not move forward. Frank Darabont expressed interest in developing the series for television. In January 2010, AMC formally announced that it had ordered a pilot for a possible series adapted from "The Walking Dead" comic book. In the announcement, the executives stated that Darabont would serve as writer, director, and an executive producer alongside Gale Anne Hurd.</code> | <code>Living Dead The Walking Dead</code> | | <code>When was the first car invented?</code> | <code>Car In 1879, Benz was granted a patent for his first engine, which had been designed in 1878. Many of his other inventions made the use of the internal combustion engine feasible for powering a vehicle. His first "Motorwagen" was built in 1885 in Mannheim, Germany. He was awarded the patent for its invention as of his application on 29 January 1886 (under the auspices of his major company, Benz & Cie., which was founded in 1883). Benz began promotion of the vehicle on 3 July 1886, and about 25 Benz vehicles were sold between 1888 and 1893, when his first four-wheeler was introduced along with a model intended for affordability. They also were powered with four-stroke engines of his own design. Emile Roger of France, already producing Benz engines under license, now added the Benz car to his line of products. Because France was more open to the early cars, initially more were built and sold in France through Roger than Benz sold in Germany. In August 1888 Bertha Benz, the wife of Karl B...</code> | <code>Elwood Haynes In 1905, three years after the Apperson brothers split from Haynes, Haynes-Apperson was renamed the Haynes Automobile Company and Haynes launched a series of publicity campaigns. A parade of 2,000 cars was organized in New York City during 1908 and Haynes, whom many recognized as the inventor of the American automobile, led the parade down Broadway riding in the "Pioneer". He was followed by ten Haynes cars, a model from each year to display the advancement in technology. On his way to the parade, Haynes was unaware of the city's newly established speeding laws and was arrested for driving too fast—in a car with a top speed of 15 mph (17 km/h)—and taken to jail. He was soon able to see a magistrate who released him after learning that he was Elwood Haynes and had come to lead the parade. The celebration was intended to be a ten-year commemoration of the invention of the automobile, although earlier self-vehicles dated back nearly twenty years in Europe. Haynes donated the...</code> | | <code>How many doctors are in Doctor Who?</code> | <code>The Doctor (Doctor Who) The Doctor is the title character in the long-running BBC science fiction television programme "Doctor Who". Since the show's inception in 1963, the character has been portrayed by thirteen lead actors. In the programme, "the Doctor" is the alias assumed by a centuries-old alien—a Time Lord from the planet Gallifrey—who travels through space and time in the TARDIS, frequently with companions. The transition to each succeeding actor is explained within the show's narrative through the plot device of "regeneration", a biological function of the Time Lord race that allows a change of cellular structure and appearance with recovery following a potentially fatal injury.</code> | <code>Sixth Doctor The Sixth Doctor is an incarnation of the Doctor, the protagonist of the BBC science fiction television series "Doctor Who". He is portrayed by Colin Baker. Although his televisual time on the series was comparatively brief and turbulent, Baker has continued as the Sixth Doctor in Big Finish's range of original "Doctor Who" audio adventures. Within the series' narrative, the Doctor is a centuries-old Time Lord alien from the planet Gallifrey who travels in time and space in his TARDIS, frequently with companions. At the end of life, the Doctor can regenerate his body; in doing so, his physical appearance and personality change. Baker portrays the sixth such incarnation, an arrogant, flamboyant character in brightly coloured, mismatched clothes whose brash, often patronising personality set him apart from all his previous incarnations.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mldr</summary> * Dataset: [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) at [40ad767](https://huggingface.co/datasets/sentence-transformers/mldr/tree/40ad7672817ebee49e00dd25aed00e1c401881d6) * Size: 200,000 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 17 characters</li><li>mean: 65.31 characters</li><li>max: 210 characters</li></ul> | <ul><li>min: 2432 characters</li><li>mean: 20354.29 characters</li><li>max: 123500 characters</li></ul> | <ul><li>min: 3035 characters</li><li>mean: 16236.77 characters</li><li>max: 166364 characters</li></ul> | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>When was the art museum in Santa Barbara first opened to the public?</code> | <code>The Santa Barbara Museum of Art (SBMA) is an art museum located in downtown Santa Barbara, California. Founded in 1941, it is home to both permanent and special collections, the former of which includes Asian, American, and European art that spans 4,000 years from ancient to modern. History The Santa Barbara Museum of Art opened to the public on June 5, 1941, in a building that was at one time the Santa Barbara Post Office (1914–1932). The idea for an art museum first came from the local artist Colin Campbell Cooper when he learned that the post office was going to be sold. In a letter to the editor published in the Santa Barbara News-Press in July 1937, Cooper proposed that the impressive Italianate structure should be transformed into a museum. After gaining momentum in town and with the support of local businesses, politicians and art collectors the Santa Barbara Museum of Art was officially established just four years after Cooper's letter was published. The renowned Chicago arc...</code> | <code>The Knott's Berry Farm amusement park in Orange County, California, originated from a berry farm owned by Walter Knott (1889–1981). In the 1920s, Knott and his wife, Cordelia, sold berries, berry preserves and pies from a roadside stand beside State Route 39, near the small town of Buena Park. In 1932, on a visit to Rudolph Boysen's farm in nearby Anaheim, Walter Knott was introduced to a new hybrid berry of a blackberry, a red raspberry, and a loganberry cross-bred by Boysen, who gave Walter his last six wilted berry-hybrid plants. Walter planted and cultivated them, then the family sold the berries at their roadside stand. When people asked what kind they were, he called them "boysenberries". In 1934, to make ends meet, Knott's wife Cordelia (1890–1974) reluctantly began serving fried chicken dinners on their wedding china. For dessert, Knott's signature Boysenberry Pie was also served to guests dining in the small tea room. As Southern California developed, Highway 39 became the ma...</code> | | <code>What is the objective of Opération Chammal?</code> | <code>Opération Chammal is a French military operation in Iraq and Syria in an attempt to contain the expansion of the Islamic State of Iraq and the Levant and to support the Iraqi Army. Its name comes from the Shamal (Chammal in French), a northwesterly wind that blows over Iraq and the Persian Gulf states. Airstrikes over Iraq started 19 September 2014 and airstrikes over Syria started by the end of September 2015. The French operation is limited to airstrikes; French president François Hollande has reiterated that no ground troops would be used in the conflict. Additionally, the French frigate has joined the United States Navy's Commander Task Force 50 (CTF 50) as an escort. On 14 November 2015, ISIL claimed that the attacks that took place in Paris the previous day were retaliation for Opération Chammal. In response, French forces increased their attacks against ISIL in Syria. Background On 10 June 2014, the terrorist group of the Islamic State of Iraq and the Levant and several ot...</code> | <code>CMA CGM S.A. is a French container transportation and shipping company. It is the world’s 3rd largest container shipping company, using 257 shipping routes between 420 ports in 160 different countries. Its headquarters are in Marseille, France and its North American headquarters are in Norfolk, Virginia, United States. The name is an acronym of two predecessor companies, Compagnie Maritime d'Affrètement (CMA) and Compagnie Générale Maritime (CGM), which translate as "Maritime Freighting Company" and "General Maritime Company". History The history of CMA CGM can be traced back to the middle of the 19th century, when two major French shipping lines were created, respectively Messageries Maritimes (MM) in 1851 and Compagnie Générale Maritime (CGM) in 1855, soon renamed Compagnie Générale Transatlantique in 1861. Both companies were created partly with the backing of the French State, through the award of mail contracts to various destinations, French colonies and overseas territories a...</code> | | <code>What was the reason for Williams-Franklin's decision to become a vegan during her time as a player?</code> | <code>Taj McWilliams-Franklin (born October 20, 1970) is a former American professional women's basketball player. A two-time WNBA champion with the Detroit Shock and Minnesota Lynx and six-time all-star, McWilliams-Franklin's professional career has spanned three decades, and began before the WNBA was founded. She retired from the WNBA after the 2012 season. College years After attending T. W. Josey High School in Augusta, Georgia, McWilliams-Franklin attended Georgia State University in 1989 and played on the school's basketball team for one season. However, she had become pregnant during her senior year in high school, and after the coach who recruited her to Georgia State was let go, the incoming staff told her "school was no place for kids." McWilliams-Franklin moved to Austin, Texas, where a friend connected her with St. Edward's University coach Dave McKey. She enrolled at St. Edwards as a Rhetoric major. While at St. Edward's, she set school records and individual achievements, in...</code> | <code>Spider-Woman (Gwendolyne Maxine Stacy; colloquial: "Spider-Gwen" or "Ghost-Spider") is a superhero appearing in American comic books published by Marvel Comics. She was created by Jason Latour and Robbi Rodriguez. The character debuted in Edge of Spider-Verse issue #2 as part of the 2014–15 "Spider-Verse" comic book storyline, leading to the ongoing series Spider-Gwen that began in 2015. Spider-Woman is a variant of Spider-Man and an alternate-universe version of Gwen Stacy. She lives on Earth-65, where Gwen Stacy is bitten by a radioactive spider and becomes a superhero instead of Peter Parker becoming Spider-Man. The character's various enemies include Earth-65 versions of Matt Murdock and Frank Castle. Gwen Stacy's Spider-Woman harbors much of Peter's personality and conflicts along with his powers and abilities. Spider-Woman was met with positive reviews from critics, with them applauding her design—cited as a popular choice for cosplay—and a feminist perspective. For promotion, ...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mr_tydi</summary> * Dataset: [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) at [abbdf55](https://huggingface.co/datasets/sentence-transformers/mr-tydi/tree/abbdf55c630352da943f779610c3ce6268118351) * Size: 354,700 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 12 characters</li><li>mean: 38.85 characters</li><li>max: 95 characters</li></ul> | <ul><li>min: 64 characters</li><li>mean: 645.85 characters</li><li>max: 4067 characters</li></ul> | <ul><li>min: 21 characters</li><li>mean: 626.85 characters</li><li>max: 2870 characters</li></ul> | * Samples: | anchor | positive | negative | |:----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Is amnesia real?</code> | <code>Amnesia Amnesia is a deficit in memory caused by brain damage, disease, or psychological trauma.[1] Amnesia can also be caused temporarily by the use of various sedatives and hypnotic drugs. The memory can be either wholly or partially lost due to the extent of damage that was caused.[2] There are two main types of amnesia: retrograde amnesia and anterograde amnesia. Retrograde amnesia is the inability to retrieve information that was acquired before a particular date, usually the date of an accident or operation.[3] In some cases the memory loss can extend back decades, while in others the person may lose only a few months of memory. Anterograde amnesia is the inability to transfer new information from the short-term store into the long-term store. People with this type of amnesia cannot remember things for long periods of time. These two types are not mutually exclusive; both can occur simultaneously.</code> | <code>Amnesia Head trauma is a very broad range as it deals with any kind of injury or active action toward the brain which might cause amnesia. Retrograde and anterograde amnesia is more often seen from events like this, an exact example of a cause of the two would be electroconvulsive therapy, which would cause both briefly for the receiving patient. Traumatic events are more subjective. What is traumatic is dependent on what the person finds to be traumatic. Regardless, a traumatic event is an event where something so distressing occurs that the mind chooses to forget rather than deal with the stress. A common example of amnesia that is caused by traumatic events is dissociative amnesia, which occurs when the person forgets an event that has deeply disturbed them.[8] An example would be a person forgetting a fatal and graphic car accident involving their loved ones. Physical deficiencies are different from head trauma because physical deficiencies lean more toward passive physical issues.</code> | | <code>What is the largest naval base in the world?</code> | <code>Naval Station Norfolk Naval Station Norfolk, is a United States Navy base in Norfolk, Virginia. It supports naval forces in the United States Fleet Forces Command,[1] those operating in the Atlantic Ocean, Mediterranean Sea, and the Indian Ocean. The installation occupies about 4 miles (6.4km) of waterfront space and 11 miles (18km) of pier and wharf space of the Hampton Roads peninsula known as Sewell's Point. It is the world's largest naval station, with the largest concentration of U.S. Navy forces through 75 ships alongside 14 piers and with 134 aircraft and 11 aircraft hangars at the adjacently operated Chambers Field and [2] Port Services controls more than 3,100 ships' movements annually as they arrive and depart their berths.</code> | <code>Clark Air Base Clark Air Base was arguably the most urbanized military facility in history and was the largest American base overseas. At its peak around 1990, it had a permanent population of 15,000. It had a base exchange, a large commissary, a small shopping arcade, a branch department store, cafeterias, teen centers, a hotel, miniature golf, riding stables, zoo, and other concessions.</code> | | <code>What is the power of the Red Lantern?</code> | <code>Red Lantern Corps In Final Crisis: Rage of the Red Lanterns, Atrocitus is shown in a flashback as having apparently formed a central power battery by using the blood of the other Inversions in blood magic rituals. The battery stands before a great lake of blood from which he forms his red power ring (crystallized by his anger), as well as other rings and batteries used to form the Red Lantern Corps. Harnessing the red light of rage, he sends his rings out into the universe; however, upon accepting the rings, his recruits' hearts are rendered useless. Their blood spoils from within, forcing them to expel the violently flammable and corrosive material from their mouths. Additionally, the Red Lanterns are reduced to an almost animalistic state, with only Atrocitus appearing to be in full control of himself. Once Atrocitus assembles a sufficient force, he leads them on a mission to capture Sinestro (who is being transferred to Korugar for his execution). Coincidentally, the Sinestro Corps ...</code> | <code>Green Lantern in other media John Stewart is a member of the Justice League in the "Justice League" animated series. In this series, Stewart's ring was initially constrained to permitting him to fly, generating a protective force field, creating walls, and firing energy blasts; this limitation was established as being due to Stewart's mindset, not an inherent limitation of the ring itself (the series' version of Stewart is a former U.S. Marine, not an architect). After being berated by Katma Tui for his unimaginative use of the ring, Stewart has learned to generate complex tools (to defuse a bomb in one instance) and weapons. (He was also shown to be more creative when transformed into a child in the episode "Kids Stuff".) In a development not seen in any other version of the Green Lantern mythos, Stewart's eyes glow green when wearing his charged power ring. The glow fades when the ring runs out of power. The series has been inconsistent about the ring's effectiveness against yellow; ...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> ### Evaluation Datasets <details><summary>gooaq</summary> * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco</summary> * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 characters</li><li>mean: 33.36 characters</li><li>max: 137 characters</li></ul> | <ul><li>min: 67 characters</li><li>mean: 347.87 characters</li><li>max: 906 characters</li></ul> | <ul><li>min: 57 characters</li><li>mean: 318.18 characters</li><li>max: 906 characters</li></ul> | * Samples: | query | positive | negative | |:-------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>is cabinet refacing worth the cost?</code> | <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> | <code>Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.</code> | | <code>is the fovea ethmoidalis a bone</code> | <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> | <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> | | <code>average pitches per inning</code> | <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> | <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>squad</summary> * Dataset: [squad](https://huggingface.co/datasets/sentence-transformers/squad) at [d84c8c2](https://huggingface.co/datasets/sentence-transformers/squad/tree/d84c8c2ef64693264c890bb242d2e73fc0a46c40) * Size: 87,599 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 1 characters</li><li>mean: 60.25 characters</li><li>max: 161 characters</li></ul> | <ul><li>min: 152 characters</li><li>mean: 761.88 characters</li><li>max: 2525 characters</li></ul> | * Samples: | question | answer | |:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>When did the Russian Empire begin to question the existence of the Ottoman Empire?</code> | <code>In 1853 the Russian Empire on behalf of the Slavic Balkan states began to question the very existence of the Ottoman Empire. The result was the Crimean War, 1853–1856, in which the British Empire and the French Empire supported the Ottoman Empire in its struggle against the incursions of the Russian Empire. Eventually, the Ottoman Empire lost control of the Balkan region.</code> | | <code>How would one describe the control of universities before nation-states in the 17th century?</code> | <code>The propagation of universities was not necessarily a steady progression, as the 17th century was rife with events that adversely affected university expansion. Many wars, and especially the Thirty Years' War, disrupted the university landscape throughout Europe at different times. War, plague, famine, regicide, and changes in religious power and structure often adversely affected the societies that provided support for universities. Internal strife within the universities themselves, such as student brawling and absentee professors, acted to destabilize these institutions as well. Universities were also reluctant to give up older curricula, and the continued reliance on the works of Aristotle defied contemporary advancements in science and the arts. This era was also affected by the rise of the nation-state. As universities increasingly came under state control, or formed under the auspices of the state, the faculty governance model (begun by the University of Paris) became more and m...</code> | | <code>When did Jewish law recognize copyright?</code> | <code>The concept's origins can potentially be traced back further. Jewish law includes several considerations whose effects are similar to those of modern intellectual property laws, though the notion of intellectual creations as property does not seem to exist – notably the principle of Hasagat Ge'vul (unfair encroachment) was used to justify limited-term publisher (but not author) copyright in the 16th century. In 500 BCE, the government of the Greek state of Sybaris offered one year's patent "to all who should discover any new refinement in luxury".</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>s2orc</summary> * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 10,000 evaluation samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: | | title | abstract | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 31 characters</li><li>mean: 80.04 characters</li><li>max: 198 characters</li></ul> | <ul><li>min: 96 characters</li><li>mean: 653.93 characters</li><li>max: 1023 characters</li></ul> | * Samples: | title | abstract | |:-------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna</code> | <code>The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.</code> | | <code>Subclinical organ damage and cardiovascular risk prediction</code> | <code>AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...</code> | | <code>A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan</code> | <code>In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>allnli</summary> * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 6,584 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 15 characters</li><li>mean: 72.82 characters</li><li>max: 300 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 34.11 characters</li><li>max: 126 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 36.38 characters</li><li>max: 121 characters</li></ul> | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>paq</summary> * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 25 characters</li><li>mean: 51.3 characters</li><li>max: 108 characters</li></ul> | <ul><li>min: 504 characters</li><li>mean: 623.09 characters</li><li>max: 835 characters</li></ul> | * Samples: | query | answer | |:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>when did season 3 of the voice brasil start</code> | <code>The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on</code> | | <code>when did the little ranger first come out</code> | <code>Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures</code> | | <code>what is the name of rachel's sister in ninjaaiden</code> | <code>her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>trivia_qa</summary> * Dataset: [trivia_qa](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 73,346 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 26 characters</li><li>mean: 77.62 characters</li><li>max: 258 characters</li></ul> | <ul><li>min: 135 characters</li><li>mean: 3169.71 characters</li><li>max: 4096 characters</li></ul> | * Samples: | query | answer | |:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>In which country is 'Ninety Mile Beach'?</code> | <code>Ninety (90) Mile Beach, Gippsland, Victoria - Tourism Australia Gippsland Find travel information on Ninety Mile Beach, one of the longest uninterrupted beaches in the world, located outside of Melbourne at Gippsland Lakes. Ninety Mile Beach, located in the Gippsland region on Victoria's south-eastern coastline, is one of the longest uninterrupted beaches in the world. Stand on the beach and watch the beach disappear into the salty sea spray in the distance. You might find that your footprints are the only ones in the sand that day. This is one of the most natural and unspoilt beaches in the world and is ideal for activities from beach fishing and swimming to walking, whale and dolphin-spotting or just lazing in the sun. Sun, sand and lush national parks all create the perfect holiday environment. Victoria's Ninety Mile Beach is a 90-mile long stretch of pristine golden sand that separates the Gippsland Lakes from Bass Strait. Stretching as far as the eye can see it is one of the most ...</code> | | <code>What country gets nearly 75% of its electricity from nuclear power?</code> | <code>Nuclear Power in France | French Nuclear Energy - World Nuclear Association Nuclear Power in France (Updated November 2016) France derives about 75% of its electricity from nuclear energy, due to a long-standing policy based on energy security. This share may be reduced to 50% by 2025. France is the world's largest net exporter of electricity due to its very low cost of generation, and gains over €3 billion per year from this. France has been very active in developing nuclear technology. Reactors and especially fuel products and services are a significant export. About 17% of France's electricity is from recycled nuclear fuel. In 2014 French electricity generation was 541 TWh gross. Consumption in 2012 was 454 TWh – 6600 kWh per person. Winter demand varies by 2300 MWe per degree C. Over the last decade France has exported up to 70 billion kWh net each year and EdF expects exports to continue at 55-70 TWh/yr. In 2014 they were principally to Italy, UK, Switzerland, and Belgium, as ...</code> | | <code>Which Spaniard led an expedition which reached Tenochtitlan, the Aztec capital in 1519?</code> | <code>The Spanish Conquest (1519-1521) : Mexico History History | See all articles tagged history The Spanish Conquest (1519-1521) Tweet April 21, 1519--the year Ce Acatl (One Reed) by Aztec reckoning-- marked the opening of a short but decisive chapter in Mexico's history. On that day a fleet of 11 Spanish galleons sailing along the eastern gulf coast dropped anchor just off the wind-swept beach on the island of San Juan de Ulúa. Under the command of the wily, daring Hernán Cortés, the vessels bore 550 Spanish soldiers and sailors, as well as 16 horses, the first of the species to tread the American continent. The party disembarked to set up camp on the dunes behind the beach. In a friendly reception from the native Totonac Indians, greetings and gifts were exchanged. Cognizant of the existence of a great inland Empire, Cortés promptly dispatched a message requesting an audience with Aztec ruler Moctezuma II . (The term "Aztec" will be used throughout, although some historians prefer the ...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>msmarco_10m</summary> * Dataset: [msmarco_10m](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets) at [8c5139a](https://huggingface.co/datasets/bclavie/msmarco-10m-triplets/tree/8c5139a245a5997992605792faa49ec12a6eb5f2) * Size: 10,000,000 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 characters</li><li>mean: 33.98 characters</li><li>max: 131 characters</li></ul> | <ul><li>min: 56 characters</li><li>mean: 353.39 characters</li><li>max: 1029 characters</li></ul> | <ul><li>min: 85 characters</li><li>mean: 339.79 characters</li><li>max: 983 characters</li></ul> | * Samples: | query | positive | negative | |:----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what does a dental hygienist do</code> | <code>During a dental appointment, a hygienist typically removes soft and hard deposits from a patient's teeth; examines the gums and teeth to discern the presence of disease or oral abnormality; and strips the teeth of calculus (tartar), stains and plaque. dental hygienist takes on a somewhat academic role as well; he or she educates dental patients on how to establish and maintain suitable oral hygiene, often with the aid of teeth models to give the patient a visual sense.</code> | <code>Michelly in Tennessee said: Can anyone tell me how much you get paid hourly working as a dental hygienist. I make $36 dollars an hour and I just graduated from school. Should I be making more. Try to visit this link www.payscale.com/research/US/Job=Dental_Hygienist/Hourly_Rate so you will know how much dental hygienist usually get based on experience.</code> | | <code>average annual temperature by florida county</code> | <code>Lake County Weather. The average temperature of Lake County is 70.90Â°F, which is about the same as the Florida average temperature of 71.80Â°F and is much higher than the national average temperature of 54.45Â°F. Historical Weather. Heating Cost Index, #29.</code> | <code>average rn salary in fl the average annual salary for a registered nurse in the state of florida in 2011 was $ 64020 the average does fluctuate throughout the state with median rn salaries at their highest in metro areas</code> | | <code>what does amortization mean</code> | <code>What is 'Amortization'. Amortization is the paying off of debt with a fixed repayment schedule in regular installments over a period of time for example with a mortgage or a car loan.</code> | <code>First off, your EBIT is the same as your operating profit, but you can also calculate it by subtracting interest and tax from net income: $100,000 / ($10,000 + $25,000) = $65,000 To get EBITDA, you need to add back in depreciation and amortization:</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>swim_ir</summary> * Dataset: [swim_ir](https://huggingface.co/datasets/nthakur/swim-ir-monolingual) at [834c20f](https://huggingface.co/datasets/nthakur/swim-ir-monolingual/tree/834c20f0ceef6a68e029fb4447d17d20bb0288c3) * Size: 501,538 evaluation samples * Columns: <code>query</code> and <code>text</code> * Approximate statistics based on the first 1000 samples: | | query | text | |:--------|:-----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 4 characters</li><li>mean: 59.74 characters</li><li>max: 165 characters</li></ul> | <ul><li>min: 206 characters</li><li>mean: 522.53 characters</li><li>max: 3079 characters</li></ul> | * Samples: | query | text | |:------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Where was he born?</code> | <code>He was born in Brownsville, Edmonson County, Kentucky, March 28, 1890; attended the public schools, Western Kentucky State Teachers College at Bowling Green, and the law department of the University of Kentucky at Lexington; was admitted to the bar in 1915 and commenced practice in Brownsville, Ky.; county judge of Edmonson County, Ky., 1916-1918.</code> | | <code>What was Channon's National Service?</code> | <code>Channon completed his National Service in the Royal Horse Guards (the Blues) from 1955 to 1956, serving in Cyprus during the 1956 Cyprus emergency. In London, he was a member of the set around Princess Margaret, and then attended Christ Church, Oxford from 1956. He was president of the Oxford University Conservative Association.</code> | | <code>What is the role of Immunoglobulin A in the immune system?</code> | <code>Immunoglobulin heavy constant alpha 1 is a immunoglobulin gene with symbol "IGHA1". It encodes a constant (C) segment of Immunoglobulin A heavy chain. Immunoglobulin A is an antibody that plays a critical role in immune function in the mucous membranes. IgA shows the same typical structure of other antibody classes, with two heavy chains and two light chains, and four distinct domains: one variable region, and three variable regions. As a major class of immunoglobulin in body secretions, IgA plays a role in defending against infection, as well as preventing the access of foreign antigens to the immunologic system.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>pubmedqa</summary> * Dataset: [pubmedqa](https://huggingface.co/datasets/sentence-transformers/pubmedqa) at [a1ef0b5](https://huggingface.co/datasets/sentence-transformers/pubmedqa/tree/a1ef0b513b16ed490e807ac11da40e436d3a54c3) * Size: 1,660 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, <code>negative_1</code>, <code>negative_2</code>, <code>negative_3</code>, <code>negative_4</code>, <code>negative_5</code>, <code>negative_6</code>, <code>negative_7</code>, <code>negative_8</code>, <code>negative_9</code>, <code>negative_10</code>, <code>negative_11</code>, <code>negative_12</code>, <code>negative_13</code>, <code>negative_14</code>, <code>negative_15</code>, <code>negative_16</code>, <code>negative_17</code>, <code>negative_18</code>, <code>negative_19</code>, and <code>negative_20</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | 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| type | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | string | | details | <ul><li>min: 38 characters</li><li>mean: 88.92 characters</li><li>max: 140 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 372.72 characters</li><li>max: 1113 characters</li></ul> | <ul><li>min: 26 characters</li><li>mean: 371.1 characters</li><li>max: 1185 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 326.89 characters</li><li>max: 1084 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 334.62 characters</li><li>max: 1477 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 347.64 characters</li><li>max: 1310 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 337.96 characters</li><li>max: 1221 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 303.39 characters</li><li>max: 953 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 328.25 characters</li><li>max: 1168 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 318.59 characters</li><li>max: 989 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 284.81 characters</li><li>max: 853 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 333.89 characters</li><li>max: 875 characters</li></ul> | <ul><li>min: 26 characters</li><li>mean: 327.33 characters</li><li>max: 1041 characters</li></ul> | <ul><li>min: 19 characters</li><li>mean: 354.74 characters</li><li>max: 1705 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 334.33 characters</li><li>max: 1593 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 346.0 characters</li><li>max: 1374 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 367.52 characters</li><li>max: 1625 characters</li></ul> | <ul><li>min: 17 characters</li><li>mean: 357.96 characters</li><li>max: 1126 characters</li></ul> | <ul><li>min: 23 characters</li><li>mean: 322.84 characters</li><li>max: 1060 characters</li></ul> | <ul><li>min: 25 characters</li><li>mean: 312.1 characters</li><li>max: 805 characters</li></ul> | <ul><li>min: 22 characters</li><li>mean: 362.97 characters</li><li>max: 1002 characters</li></ul> | <ul><li>min: 22 characters</li><li>mean: 328.62 characters</li><li>max: 1310 characters</li></ul> | * Samples: | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | negative_9 | negative_10 | negative_11 | negative_12 | negative_13 | negative_14 | negative_15 | negative_16 | negative_17 | negative_18 | negative_19 | negative_20 | 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| <code>Chronic functional somatic symptoms: a single syndrome?</code> | <code>Observational study, with a comparison control group.</code> | <code>Nasal nitric oxide (NO) and olfactory function are decreased in patients with chronic inflammatory sinonasal disease, suggesting a link between these two parameters. The aim of the study was to investigate nasal NO levels in patients with olfactory dysfunction due to different causes.</code> | <code>Fibromyalgia (FM) is a worldwide diffuse musculoskeletal chronic pain condition that affects up to 5% of the general population. Many symptoms associated with mitochondrial diseases are reported in patients with FM such as exercise intolerance, fatigue, myopathy and mitochondrial dysfunction. In this study, we report a mutation in cytochrome b gene of mitochondrial DNA (mtDNA) in a family with FM with inflammasome complex activation.</code> | <code>Chronic fatigue syndrome (CFS) has an uncertain pathogenesis. Allergies have been suggested as one cause.</code> | <code>Patient's self-reported symptoms on a structured case history questionnaire.</code> | <code>Chronic multisymptom illness (reporting at least one symptom in at least two of the following symptom constructs: general fatigue; mood and cognition problems; and musculoskeletal discomfort) was assessed, differentiating by potential burn pit exposure, among deployers who completed 2004 and 2007 Millennium Cohort questionnaires.</code> | <code>functional decline and death.</code> | <code>Olfactory loss is a debilitating symptom of chronic rhinosinusitis (CRS). The pathophysiology of inflammatory olfactory dysfunction likely involves both conductive and sensorineural components. To study the interaction of CRS-associated inflammatory cytokines with the olfactory epithelium (OE), a transgenic mouse model was developed that allows temporally-controlled local gene expression. Interferon-gamma (IFN-γ) is a prototypical T helper 1 (Th1) cytokine linked to nonpolypoid CRS (CRSsNP), as well as sinonasal viral and bacterial infections. In this study, the effects of chronic IFN-γ expression on olfactory histology and function were investigated.</code> | <code>Monosomy 1p36 syndrome is the most commonly observed subtelomeric deletion syndrome. Patients with this syndrome typically have common clinical features, such as intellectual disability, epilepsy, and characteristic craniofacial features.</code> | <code>Migraine is frequently accompanied by symptoms consistent with functional gastrointestinal disorders (FGIDs). This study evaluated the prevalence of functional gastrointestinal symptoms and assessed the symptoms' relationship with the concomitant functional symptoms of anxiety, depression, and headache-related disability.</code> | <code>The 2003 Canadian Consensus Criteria for chronic fatigue syndrome (CFS) are often assumed to suggest low-grade systemic inflammation, but have never been formally validated. This study explored the content validity of the Criteria in a sample of adolescents with CFS selected according to a wide case definition.</code> | <code>Irritable pouch syndrome (IPS) is a functional disorder in patients with ileal pouch-anal anastomosis (IPAA), which presents with symptoms in the absence of structural abnormalities of the pouch. Thus, it resembles other functional disorders, such as irritable bowel syndrome characterized by visceral hypersensitivity in the presence of normal rectal biomechanics. The aim was to assess pouch biomechanics and perception of balloon distension in different groups of subjects with IPAA and to correlate the findings with clinical features.</code> | <code>Fibromyalgia (FM) and chronic fatigue syndrome (CFS) frequently overlap clinically and have been considered variants of one common disorder. We have recently shown that CFS is associated with a short corrected electrocardiographic QT interval (QTc). In the present study, we evaluated whether FM and CFS can be distinguished by QTc.</code> | <code>Single case with clinical follow-up over 2 years.</code> | <code>Fibromyalgia syndrome (FMS) is a disease of unknown pathogenesis characterized by chronic musculoskeletal pain. FMS has been also associated with altered endocrinological responses, but findings are inconsistent.</code> | <code>Primary Sjögren's syndrome (pSS) is a systemic rheumatic disease in which gastrointestinal (GI) symptoms are common. Faecal calprotectin (FC) is a non-invasive biomarker that has been suggested to discriminate organic intestinal disease from functional disorders. The purpose of this study was to explore the usefulness of FC testing in patients with pSS.</code> | <code>An inactive lifestyle has been associated with functional somatic symptoms (FSS), but findings are contradictory. Moreover, mediating factors in this relationship are unclear. We examined whether low physical activity was related to FSS in adolescents, and whether this association was mediated by low physical fitness.</code> | <code>Nephrotic syndrome is a common kidney disease in both children and adults that is characterized by dramatic structural changes in the actin-rich foot processes of glomerular podocytes. Although glucocorticoids are the primary treatment for nephrotic syndrome, neither the target cell nor mechanism of action of glucocorticoids in nephrotic syndrome is known. For the last 30 years glucocorticoids have been presumed to act by reducing the release of soluble mediators of disease by circulating lymphocytes. In contrast, we hypothesized that glucocorticoids exert their beneficial effects in nephrotic syndrome by direct action on podocytes.</code> | <code>Chronic sclerosing sialadenitis is a fibroinflammatory disease of the salivary glands, characteristically of the submandibular gland. One prior Asian study proposed that chronic sclerosing sialadenitis is a part of the spectrum of IgG4-associated disease. This association has not been confirmed in Western populations. We therefore, investigated the relationship between IgG4 and chronic sclerosing sialadenitis, and compared the histomorphologic features of this condition with those of chronic sialadenitis-not otherwise specified, Sjögren syndrome, and lymphoepithelial sialadenitis.</code> | <code>Presence of GI symptoms.</code> | <code>Disrupted-in schizophrenia 1 (DISC1), identified in a pedigree with a familial psychosis with the chromosome translocation (1:11), is a putative susceptibility gene for psychoses such as schizophrenia and major depressive disorder (MDD). Patients with chronic fatigue syndrome (CFS) report having continuous severe fatigue and many overlapping symptoms with MDD; however, the mechanism and effective treatment of CFS are still unclear. We focused on the overlapping symptoms between CFS and MDD and performed an association study of the functional single-nucleotide polymorphism (SNP) in the DISC1 gene with CFS.</code> | | <code>Does sonographic needle guidance affect the clinical outcome of intraarticular injections?</code> | <code>In total, 148 painful joints were randomized to IA triamcinolone acetonide injection by conventional palpation-guided anatomic injection or sonographic image-guided injection enhanced with a one-handed control syringe (the reciprocating device). A one-needle, 2-syringe technique was used, where the first syringe was used to introduce the needle, aspirate any effusion, and anesthetize and dilate the IA space with lidocaine. After IA placement and synovial space dilation were confirmed, a syringe exchange was performed, and corticosteroid was injected with the second syringe through the indwelling IA needle. Baseline pain, procedural pain, pain at outcome (2 weeks), and changes in pain scores were measured with a 0-10 cm visual analog pain scale (VAS).</code> | <code>We used a blinded, longitudinal observational design of effectiveness in an effort to determine the accuracy of intra-articular injections and the effect of that accuracy on pain and functional outcomes in patients with various shoulder pathologies.</code> | <code>Data from standardized procedure notes and postprocedure chest radiographs were extracted and individually reviewed to verify the presence of pneumothorax or misplacement, and any intervention performed for either complication. The overall success rate of ultrasound-guided right internal jugular vein central venous catheter placement was 96.9% with an average of 1.3 attempts. There was only one pneumothorax (0.1% [95% CI, 0-0.4%]), and the rate of catheter misplacement requiring repositioning or replacement was 1.0% (95% CI, 0.6-1.7%). There were no arterial placements found on chest radiographs. Multivariate regression analysis showed no correlation between high-risk patient characteristics and composite complication rate.</code> | <code>Real-time ultrasound-guided techniques allow for improved cannulation of the internal jugular vein and femoral vein for hemodialysis; however, these techniques require extra sterilization procedures, specialized probes, or needle guides. A simpler ultrasound vessel localization method was performed to investigate whether this alternative approach would aid in the cannulation of the femoral vein for patients in whom temporary angioaccess was required for hemodialysis.</code> | <code>To determine whether duration or venue of intravenous antibiotic administration affect lung function.</code> | <code>Needle electromyography (NEE) would be more valuable if it could predict outcomes after lumbar epidural steroid injections (LESIs) in lumbosacral radiculopathy (LSR).</code> | <code>Among patients with Wilkes stage III and IV disease undergoing arthroscopic lysis and lavage, does the use of an intra-articular injection of sodium hyaluronate (SH), when compared with Ringer lavage, result in better postoperative pain control and temporomandibular joint (TMJ) function?</code> | <code>Intraoperative MR imaging and sonography are used for navigation during neurosurgical procedures. The purpose of this experimental study was to evaluate the potential of high-resolution sonography using superparamagnetic iron oxide (SPIO) particles as a contrast medium to delineate brain tumors and to relate these findings with those of MR imaging.</code> | <code>Academic general internal medicine practice.</code> | <code>To evaluate how guidance on water-intake impacts the degree of nocturia.</code> | <code>Intra-articular knee injections are commonly performed in clinical practice for treating various knee joint disorders such as osteoarthritis and rheumatoid arthritis. When selecting the portal for injection, not only intra-articular needle accuracy but also procedural pain should be taken into consideration. The purpose of this study was to determine whether injection through anterolateral portal provokes less pain and provides better pain relief compared to superolateral portal.</code> | <code>Ultrasound guidance reduces the required local anesthetic volume for successful peripheral nerve blockade, but it is unclear whether this impacts postoperative analgesia. This prospective, randomized, observer-blinded study tested the hypothesis that a low-volume ultrasound-guided ankle block would provide similar analgesia after foot surgery compared with a conventional-volume surface landmark technique.</code> | <code>The purpose of this study was to evaluate how intravascular ultrasound-determined thickness and reflectivity of the inner echogenic layer of coronary artery plaque is affected by changes in collagen, elastin, proteoglycan, calcium and lipid content in the intima and media.</code> | <code>The anesthetic requirement is decreased in animals with head injury, but there are no data regarding the effect of intracranial tumor on the potency for intravenous anesthetics. The authors compared the quantal dose-response curves for propofol in patients having large (> or = 30 mm, mass effect) brain tumor with those having smaller (< 30 mm) lesions and with control patients undergoing noncranial surgery.</code> | <code>To develop digital multimedia instruction on intraoral suturing.</code> | <code>To evaluate a long term efficiency of a deep sclerectomy with T-Flux implant on intraocular pressure</code> | <code>In bilateral cochlear implant users, electrodes mapped to the same frequency range in each ear may stimulate different places in each cochlea due to an insertion depth difference of electrode arrays. This interaural place of stimulation mismatch can lead to problems with auditory image fusion and sensitivity to binaural cues, which may explain the large localization errors seen in many patients. Previous work has shown that interaural place of stimulation mismatch can lead to off-centered auditory images being perceived even though interaural time and level differences (ITD and ILD, respectively) were zero. Large interaural mismatches reduced the ability to use ITDs for auditory image lateralization. In contrast, lateralization with ILDs was still possible but the mapping of ILDs to spatial locations was distorted. This study extends the previous work by systematically investigating the effect of interaural place of stimulation mismatch on ITD and ILD sensitivity directly and examining...</code> | <code>Mathematical modeling and use of real world clinical inputs.</code> | <code>The use of ultrasonography in regional anesthetic blocks has rapidly evolved over the past few years. It has been speculated that ultrasound guidance might increase success rates and reduce complications. The aim of our study is to compare the success rate and quality of interscalene brachial plexus blocks performed either with direct ultrasound visualization or with the aid of nerve stimulation to guide needle placement.</code> | <code>Intra-operative nerve monitoring (IONM) of the recurrent laryngeal nerve (RLN) during thyroid and parathyroid surgery is thought to aid in identification and dissection of the RLN. While utilization of IONM is increasing, one area of variability in its application is the assessment of adequate endotracheal tube electrode placement for IONM during the case. The main objective of this study is to assess the overall success of utilizing respiratory variation to confirm proper endotracheal tube placement for RLN monitoring.</code> | <code>Time-dependent development of intracochlear impedances was investigated in 4 different groups of adult patients up to 4 years after implantation. Additionally, during rehabilitation period just after first fitting, impedances before and after stimulation were measured as to investigate the influence of electrical stimulation on the impedances. Results from standard Nucleus 24 Contour (control), standard Nucleus 24 Contour with intraoperative application of steroids, iridium-coated Nucleus 24 Contour, and iridium-coated Nucleus 24 Contour with intraoperative application of steroids were compared.</code> | | <code>Does type 1 diabetes mellitus affect Achilles tendon response to a 10 km run?</code> | <code>Participants were 7 individuals with T1DM and 10 controls. All regularly ran distances greater than 5 km and VISA-A scores indicated good tendon function (T1DM = 94 ± 11, control = 94 ± 10). There were no diabetic complications and HbA1c was 8.7 ± 2.6 mmol/mol for T1DM and 5.3 ± 0.4 mmol/mol for control groups. Baseline tendon structure was similar in T1DM and control groups - UTC echo-types (I-IV) and anterior-posterior thickness were all p > 0.05. No response to load was seen in either T1DM or control group over the 4-days post exercise.</code> | <code>The inability to produce insulin endogenously precipitates the clinical symptoms of type 1 diabetes mellitus. However, the dynamic trajectory of beta cell destruction following onset remains unclear. Using model-based inference, the severity of beta cell destruction at onset decreases with age where, on average, a 40% reduction in beta cell mass was sufficient to precipitate clinical symptoms at 20 years of age. While plasma C-peptide provides a surrogate measure of endogenous insulin production post-onset, it is unclear as to whether plasma C-peptide represents changes in beta cell mass or beta cell function. The objective of this paper was to determine the relationship between beta cell mass and endogenous insulin production post-onset.</code> | <code>Seven healthy individuals with type 1 diabetes were tested on two separate occasions, during which either a 30-min MOD or IHE protocol was performed. MOD consisted of continuous exercise at 40% Vo(2peak), while the IHE protocol involved a combination of continuous exercise at 40% Vo(2peak) interspersed with 4-s sprints performed every 2 min to simulate the activity patterns of team sports.</code> | <code>It is unclear how genetic type 1 diabetes mellitus (DM) influences infarct size when blood glucose is tightly controlled. The aim of this study was to determine the effect of genetic type 1 DM, as occurs in BB rats, on infarct size after transient unilateral middle cerebral artery occlusion (MCAO) in male and female rats. In addition, studies suggest that male type 1 DM rats have a higher incidence of end-organ complications than do females. A second aim of this study was to determine the effect of chronic 17beta-estradiol (E(2)) administration on infarct size in male BB rats.</code> | <code>Studies on bone mineral characteristics in children with type 1 diabetes mellitus (T1DM) have generated conflicting results.</code> | <code>It has recently been reported that the risk of developing nephropathy in patients with insulin dependent (type 1) diabetes mellitus is strongly associated with synergism between poor glycaemic control and carriage of the hypertension associated angiotensin II (type 1) receptor C1166 allele. The same report also revealed an increase in risk of nephropathy in diabetic patients carrying a specific angiotensin II (type 1) receptor haplotype, i.e. C1166/140-bp microsatellite allele (major allele).</code> | <code>The angiotensin II (type 1) (AT1) receptor mediates many biological effects of the renin-angiotensin system (RAS), leading to remodelling of cardiac tissue. The present study was designed to analyse changes in the function and expression of the AT1 receptor as principal effector of the RAS in myocardium from type 2 diabetic patients compared with non-diabetic myocardium as control. In addition, we determined the effect of treatment with ACE inhibitors or AT1 receptor blockers on expression levels of the receptor in diabetic patients.</code> | <code>Type 2 diabetes mellitus increases the risk of atherosclerotic cardiovascular disease. Antioxidative properties of high density lipoprotein (HDL) are important for atheroprotection. This study investigated whether the antioxidative functionality of HDL is altered in type 2 diabetes mellitus and aimed to identify potential determinants of this parameter.</code> | <code>Life-long insulin is the standard treatment for type 1 diabetes mellitus (T1DM). The role of traditional Chinese medicine (TCM) in T1DM is still not clear. The aim of this study is to explore the prescription pattern of TCM and its impact on the risk of diabetic ketoacidosis (DKA) in patients with T1DM.</code> | <code>In type 1 diabetes, lung diffusing capacity for carbon monoxide (DL(CO)) may be impaired, and insulin has been shown to be beneficial in cases in which near-normal metabolic control is achieved. An influence of insulin, per se, on the alveolar-capillary membrane conductance is unexplored. We aimed at testing this possibility.</code> | <code>aIMT, but not cIMT, was significantly greater in the children with type 1 diabetes mellitus than in control subjects (P < .001). In children with type 1 diabetes mellitus, aIMT correlated with glycosylated hemoglobin (r = 0.31, P = .01) and was independently associated with age (beta = 0.38, P = .001) and low-density lipoprotein cholesterol level (beta = 0.38, P = .001). Vascular function (GTN) was worse in children with type 1 diabetes mellitus who had an aIMT >95th percentile, as defined with the control subjects.</code> | <code>Type 1 diabetes mellitus (T1DM) is caused by specific destruction of the pancreatic beta cells in the islets of Langerhans. Increased sensitivity to cytokines, in particular to interleukin-1beta (IL-1beta) seems to be an acquired trait during beta-cell maturation. In response to cytokines both protective and deleterious mechanisms are induced in beta cells, and when the deleterious prevail, T1DM develops. The aims of this study were to identify perturbation in protein patterns (PiPP) associated with beta-cell maturation, and compare these changes to previous analyses of IL-1beta exposed rat islets. For this purpose, proteome analyses were carried out using a cell-line, which matures from a glucagon-producing pre-beta-cell phenotype (NHI-glu) to an insulin-producing beta-cell phenotype (NHI-ins). We have previously shown that this maturation is accompanied by acquired sensitivity to the toxic effects of IL-1beta.</code> | <code>Type 1 diabetes mellitus is a T-cell-mediated autoimmune disease that leads to a major loss of insulin-secreting beta cells. The further decline of beta-cell function after clinical onset might be prevented by treatment with CD3 monoclonal antibodies, as suggested by the results of a phase 1 study. To provide proof of this therapeutic principle at the metabolic level, we initiated a phase 2 placebo-controlled trial with a humanized antibody, an aglycosylated human IgG1 antibody directed against CD3 (ChAglyCD3).</code> | <code>Type 2 diabetes mellitus (T2DM) affects approximately 10% of Americans, while 79 million Americans are estimated to have glucose intolerance or prediabetes (pre-DM). The present study was designed to determine whether obese patients with pre-DM or T2DM would lose weight as effectively as obese normoglycemic patients, in a medically supervised high-protein, low-calorie-weight management program.</code> | <code>We have previously shown that long-term type 1 diabetes affects the structural organization, contractile apparatus and extracellular matrix (ECM) of the myometrium during early pregnancy in mice.</code> | <code>In Achilles injured participants, there was a significant difference between soleus and lateral gastrocnemius offset times during running compared to the asymptomatic controls (p<0.05). There were no significant differences in triceps surae muscle activity between the footwear only and footwear and orthoses condition in the Achilles injured participants.</code> | <code>Nine adolescents with type 1 diabetes mellitus (five males, four females, aged 16 +/- 1.8 yr, diabetes duration 8.2 +/- 4.1 yr, hemoglobin A1c 7.8 +/- 0.8%, mean +/- SD) were subjected on two different occasions to a rest or 45 min of exercise at 95% of their lactate threshold. Insulin was administered iv at a rate based on their usual insulin dose, with similar plasma insulin levels for both studies (82.1 +/- 19.0, exercise vs. 82.7 +/- 16.4 pmol/liter, rest). Glucose was infused to maintain euglycemia for 18 h.</code> | <code>Type 1 diabetes mellitus (T1DM) is influenced by genetic as well as environmental factors. Its incidence has risen considerably since the 1950s.</code> | <code>Insulin resistance has been associated with hypertension and with renal complications in patients with type 1 diabetes mellitus. Causal relationships have not been fully explained.</code> | <code>Diabetes mellitus (DM) is a common metabolic disease among the middle-aged and older population, which leads to an increase of stroke incidence and poor stroke recovery. The present study was designed to investigate the impact of DM on brain damage and on ischemic brain repair after stroke in aging animals.</code> | <code>Type 1 diabetes (T1D) is an autoimmune disease with multiple susceptibility genes. The aim of this study was to determine whether combining IDDM1/HLA and IDDM2/ insulin( INS) 5' variable number of tandem repeat locus (VNTR) genotypes improves T1D risk assessment.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>miracl</summary> * Dataset: [miracl](https://huggingface.co/datasets/sentence-transformers/miracl) at [07e2b62](https://huggingface.co/datasets/sentence-transformers/miracl/tree/07e2b629250bf4185f4c87f640fac15949b8aa73) * Size: 789,900 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 characters</li><li>mean: 38.08 characters</li><li>max: 86 characters</li></ul> | <ul><li>min: 93 characters</li><li>mean: 746.58 characters</li><li>max: 3851 characters</li></ul> | <ul><li>min: 18 characters</li><li>mean: 648.12 characters</li><li>max: 2554 characters</li></ul> | * Samples: | anchor | positive | negative | |:------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What was Paul Heyman's first production?</code> | <code>Paul Heyman On May 23, 2005, Heyman returned in a segment with Vince McMahon and Eric Bischoff announcing ECW One Night Stand, with Heyman in charge. On the May 22 episode of "Raw", Heyman appeared as ECW Representative promoting "One Night Stand". On May 25, 2006 it was announced that ECW would relaunch, as a third WWE brand. Heyman was in charge of the new brand on-camera but had minimal creative input off-camera as well. On the May 29 episode of "Raw", during a face-off with Mick Foley, Heyman announced that he was granted a draft pick from both Raw and SmackDown! by Vince McMahon. His Raw draft pick was former ECW wrestler (and Money in the Bank contract holder) Rob Van Dam, and his SmackDown! draft pick was Kurt Angle. Heyman predicted that Van Dam would defeat John Cena at "One Night Stand" for the WWE Championship and then declare himself the new ECW World Heavyweight Champion.</code> | <code>Jay Wolpert Wolpert left Goodson-Todman to form his own production company, and his first game show was the 1979 series "Whew!" for CBS. Wolpert produced the series with Burt Sugarman for most of its run. "Whew!" was canceled in 1980 and Wolpert did not return to television with a series until January 1983, despite shooting several pilots in the interim. On January 3, 1983, Wolpert's "Hit Man" debuted on NBC with Peter Tomarken as its host. "Hit Man" lasted thirteen weeks on the air.</code> | | <code>When was Locke born?</code> | <code>Adria Locke Langley Locke was born in Iowa, 1899, as the youngest of three children. When she was young her family moved to Stanton, Nebraska and that is where she grew up. Her father William Locke, was president of the Omaha livestock market and a Quaker. He had certain ideas of what a woman should be. Because of this Adria's grandfather, Thomas Glendenning, took over in teaching her what would later be a great social consciousness.</code> | <code>Keith Locke Locke was born and grew up in Christchurch, to Jack and Elsie Locke, prominent lifelong political activists for a wide variety of causes. Their four children were brought up in this environment and followed their parents into a life of activism, (as well as Keith, his sister Maire Leadbeater is a well-known activist and former city councillor for Auckland City Council). His father Jack was under surveillance during the 1951 New Zealand waterfront dispute. Former Prime Minister Robert Muldoon is said to have described the Lockes as the most "notorious Communist family in New Zealand". The Lockes lived in the Avon Loop area of the Christchurch Central City and were very active in the community notably organising Avon River clean-ups and native tree planting and arguing against development of the area, and in favour of retaining the character of the area.</code> | | <code>How do plants get blight?</code> | <code>Bacterial blight of soybean Bacterial blight of soybeans can enter leaves through wounds or natural openings such as stomata. After gaining entrance to the host leaves, "Pseudomonas syringae pv. glycinea" multiplies in the leaf intercellular fluid. The pathogen must then overcome the plants defenses. "Pseudomonas syringae pv. glycinea" accomplishes this by using the type three secretion system to inject a variety of pathogenicity effector proteins (Hrp proteins) into the plant cell cytoplasm. These proteins act by interfering with effector-triggered immunity and producing phytohormones/toxins that suppress plant defenses. The expression of these virulence factors depends on the environmental conditions at the time of infection (see "environment section). Furthermore, expression of virulence factors will only take place when a sufficiently large population of bacteria is present, which is determined through quorum sensing. When successful, the common symptoms of bacterial blight will be...</code> | <code>Gummy stem blight Gummy Stem Blight is a cucurbit-rot disease caused by the fungal plant pathogen "Didymella bryoniae" (anamorph "Phoma cucurbitacearum"). Gummy Stem Blight can affect a host at any stage of growth in its development and affects all parts of the host including leaves, stems and fruits. Symptoms generally consist of circular dark tan lesions that blight the leaf, water soaked leaves, stem cankers, and gummy brown ooze that exudes from cankers, giving it the name Gummy Stem Blight. Gummy Stem Blight reduces yields of edible cucurbits by devastating the vines and leaves and rotting the fruits.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mldr</summary> * Dataset: [mldr](https://huggingface.co/datasets/sentence-transformers/mldr) at [40ad767](https://huggingface.co/datasets/sentence-transformers/mldr/tree/40ad7672817ebee49e00dd25aed00e1c401881d6) * Size: 200,000 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 16 characters</li><li>mean: 66.52 characters</li><li>max: 527 characters</li></ul> | <ul><li>min: 3223 characters</li><li>mean: 20386.58 characters</li><li>max: 131268 characters</li></ul> | <ul><li>min: 3611 characters</li><li>mean: 15772.93 characters</li><li>max: 145135 characters</li></ul> | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What is an Aboriginal reserve and how was it created?</code> | <code>An Aboriginal reserve, also called simply reserve, was a government-sanctioned settlement for Aboriginal Australians, created under various state and federal legislation. Along with missions and other institutions, they were used from the 19th century to the 1960s to keep Aboriginal people separate from the white Australian population, for various reasons perceived by the government of the day. The Aboriginal reserve laws gave governments much power over all aspects of Aboriginal people’s lives. Protectors of Aborigines and (later) Aboriginal Protection Boards were appointed to look after the interests of the Aboriginal people. History Aboriginal reserves were used from the nineteenth century to keep Aboriginal people separate from the white Australian population, often ostensibly for their protection. Protectors of Aborigines had been appointed from as early as 1836 in South Australia (with Matthew Moorhouse as the first permanent appointment as Chief Protector in 1839), with the G...</code> | <code>The Nature Reserve () is a nature reserve located in the municipalities of Sorsele and Storuman in Västerbotten County of Swedish Lapland. It is the largest natural reserve in Sweden and one of the largest protected areas in Europe, totaling 562,772 ha (approx. 5,628 km2). Most of the reserve is made up of several Scandinavian Mountains, the main ones being Artfjället, Norra Storfjället, Ammarfjället and Björkfjället. Most of the landscapes of the Swedish mountains are represented. This ranges from the pronounced alpine character of Norra Storfjället, which includes the highlight of the reserve, the Norra Sytertoppen (1,768 m), to the plateau and plains near the base of the mountains. The differences in elevation highlight the diversity of rocks in the mountains. Among the mountains are the valleys and waterways of the Ume River drainage basin. This includes a portion of the Vindel River, after which the reserve is named. Towards the east, the elevation decreases and the mountains gi...</code> | | <code>What is the English translation of the song "Bosanac"?</code> | <code>This is a list of Bosnian patriotic songs. {| class="wikitable sortable" |+ !Title!!English translation!!Lyricist!!Composer!!Arranger!!Year!!scope="col" class="unsortable"|Description |- |"Bosanac"||'Bosnian Man'||||||||style="text-align:center"|1984||song sung by Bosnian singer Lepa Brena and her band Slatki Greh on their 1984 album Bato, Bato |- |"Bosanac"||'Bosnian [Man]'||||||||style="text-align:center"|1991||folk song sung by singer Enes Begović |- |"Bosanac"||'Bosnian [Man]'||Mirko Šenkovski||Mirko Šenkovski||Mirko Šenkovski||style="text-align:center"|2012||turbo-folk song sung by singer Elvira Rahić and DJ Deny |- |"Bosna"||'Bosnia'||||||||||song about the Bosnia and Herzegovina national basketball team, also used when the KK Bosna team plays games |- |"Bosna će vječno živjeti u nama"||'Bosnia Will Live in Us Forever'||Besim Spahić||Besim Spahić||Besim Spahić||||song sung by Bosnian musician and scientist |- |"Bosna ima jedna samo"||'Bosnia [There] Is One [And] Only'||Enver Ša...</code> | <code>"Quiéreme mucho" is a criolla-bolero composed in 1911 by Gonzalo Roig with lyrics by Ramón Gollury and Agustín Rodríguez. The song was inspired by Roig's wife, Blanca Becerra, and premiered in Havana in 1911 without much success. In 1917, it was included in the sainete El servicio militar obligatorio and performed by Becerra and Rafael Llorens to critical acclaim. Roig published and sold the rights to the song in 1921, and the first recording was made in the United States by singer Tito Schipa in 1923. The English version, "Yours", was published in 1931 in the United States. It featured lyrics in English written by Albert Gamse and Jack Sherr. Both versions have been extensively recorded and arranged by different musicians, becoming Latin music standards. Composition "Quiéreme mucho" was composed by Gonzalo Roig at 21 years of age in 1911, before he had finished his music studies. He wrote the melody and played it on his piano, without making any further arrangements. Roig had been co...</code> | | <code>Who were the representatives that declined to run for reelection to the Vermont House of Representatives in 2016?</code> | <code>Selene Colburn is an American politician currently serving in the Vermont House of Representatives from the Chittenden-6-4 district since 2017 as a member of the Vermont Progressive Party. Prior to her tenure in the State House she served on the city council in Burlington, Vermont. She is the first female chair of the House Progressive Caucus. Colburn was born in Burlington, and educated at Burlington High School, Bennington College, and Simmons University. She became active in politics in her youth when she joined anti-war demonstrations. Colburn was first elected to office with her election to the Burlington city council in the 2014 election and she won reelection in the 2015 and 2017 elections. She was elected to the state house alongside Brian Cina in the 2016 election with the nominations of the Progressive and Democratic parties and was reelected in the 2018 and 2020 elections. She was selected to serve as assistant chair of the Vermont Progressive Party's caucus in the state h...</code> | <code>The 2016 United States Senate election in Florida was held November 8, 2016 to elect a member of the United States Senate to represent the State of Florida, concurrently with the 2016 U.S. presidential election, as well as other elections to the United States Senate in other states and elections to the United States House of Representatives and various state and local elections. The primary elections for both the Republicans and Democrats took place on August 30, 2016. Incumbent Republican Senator Marco Rubio ran for another term but faced well-funded Republican primary opposition after initially announcing he would not seek re-election to his Senate seat. He had openly considered whether to seek re-election or run for president in 2016. He stated in April 2014 that he would not run for both the Senate and president in 2016, as Florida law prohibits a candidate from simultaneously appearing twice on a ballot, but did not rule out running for either office. However, in April 2015, Ru...</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> <details><summary>mr_tydi</summary> * Dataset: [mr_tydi](https://huggingface.co/datasets/sentence-transformers/mr-tydi) at [abbdf55](https://huggingface.co/datasets/sentence-transformers/mr-tydi/tree/abbdf55c630352da943f779610c3ce6268118351) * Size: 354,700 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 13 characters</li><li>mean: 38.6 characters</li><li>max: 101 characters</li></ul> | <ul><li>min: 71 characters</li><li>mean: 667.4 characters</li><li>max: 21297 characters</li></ul> | <ul><li>min: 10 characters</li><li>mean: 619.16 characters</li><li>max: 3134 characters</li></ul> | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>What is the largest city in Sardinia?</code> | <code>Sardinia Sardinia is politically a region of Italy, whose official name is Regione Autonoma della Sardegna / Regione Autònoma de Sardigna (Autonomous Region of Sardinia),[3] and enjoys some degree of domestic autonomy granted by a specific Statute.[4] It is divided into four provinces and a metropolitan city, with Cagliari being the region's capital and its largest city as well. Sardinia's indigenous language and the other minority languages (Sassarese, Corsican Gallurese, Algherese Catalan and Ligurian Tabarchino) spoken on the island are recognized by the regional law and enjoy "equal dignity" with Italian.[5]</code> | <code>History of Cagliari Caralis (or Karales) was the capital of the Roman province of Sardinia and Corsica and was elevated to the rank of "Municipium", a result of the civil war between Julius Caesar and Pompey when Caesar himself granted this status in gratitude to the city for its fidelity during the bloody war. All Caralitani obtained Roman citizenship and were enrolled in the tribe Quirina. The territory of the city included the campidano plain, likely becoming Sanluri. With about 20,000 inhabitants Caralis was the largest and most populous city of the island and the most important of the western Mediterranean basin of the Republic, and later of the Roman Empire. The city was equipped with important road links to the main towns of the island such as Sulki, with the coastal road and with that running through the valley of the Cixerri, Olbia and Tibula along the east coast, and Turris and Tibula along the road modeled on the current "Carlo Felice", and finally a road through the centre ...</code> | | <code>What activism did Katherine Schmidt participate in?</code> | <code>Katherine Schmidt Katherine Schmidt (February 6, 1899 – April 18, 1978) was an American artist and art activist. Early in her career the figure studies, landscapes, and still lifes she painted drew praise for their "purity and clarity of color," "sound draftsmanship," and "individual choice of subject and its handling."[3] During the 1930s she was known mainly for the quality of her still life paintings which showed, one critic said, "impeccable artistry."[4] At the end of her career, in the 1960s and 1970s, she produced specialized and highly disciplined still lifes of objects such as dead leaves and pieces of crumpled paper, which, said a critic, approached a "magical realism."[5] As an art activist she helped promote the rights of artists for fair remuneration.[6]</code> | <code>Caroline Katzenstein Caroline Katzenstein (1888–1968) was an American suffragist, activist, advocate for equal rights, insurance agent, and author. She was active in the local Philadelphia suffragist movement through the Pennsylvania branch of the National American Woman Suffrage Association and the Equal Franchise Society of Philadelphia. She played a role in the formation of the Congressional Union for Women Suffrage, which later became the National Women's Party. Katzenstein was also active in the movement for equal rights, serving on the Women's Joint Legislative Committee with Alice Paul, and championing the cause for the Equal Rights Amendment. She was the author of "Lifting the Curtain: the State and National Woman Suffrage Campaigns in Pennsylvania as I Saw Them" (1955).</code> | | <code>Who made the first wristwatch?</code> | <code>History of watches From the beginning, wristwatches were almost exclusively worn by women, while men used pocketwatches up until the early 20th century. The concept of the wristwatch goes back to the production of the very earliest watches in the 16th century. Some people say the world's first wristwatch was created by Abraham-Louis Breguet for Caroline Murat, Queen of Naples, in 1810.[21][22][23][24][25] Elizabeth I of England received a wristwatch from Robert Dudley in 1571, described as an arm watch. By the mid nineteenth century, most watchmakers produced a range of wristwatches, often marketed as bracelets, for women.[26]</code> | <code>Rolex In 1908, Wilsdorf registered the trademark "Rolex" and opened an office in La Chaux-de-Fonds, Switzerland.[14][15] The book The Best of Time: Rolex Wristwatches: An Unauthorized History by Jeffrey P. Hess and James Dowling says that the name was just made up.[16] One story, never confirmed by Wilsdorf, recounts that the name came from the French phrase horlogerie exquise, meaning "exquisite clockwork"[9] or as a contraction of "horological excellence". Wilsdorf was said to want his watch brand's name to be easily pronounceable in any language.[17] He also thought that the name "Rolex" was onomatopoeic, sounding like a watch being wound. It is easily pronounceable in many languages and, as all its upper-case letters have the same size, can be written symmetrically. It was also short enough to fit on the face of a watch.[17]</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` </details> ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs <details><summary>Click to expand</summary> | Epoch | Step | Training Loss | gooaq loss | msmarco loss | squad loss | s2orc loss | allnli loss | paq loss | trivia qa loss | msmarco 10m loss | swim ir loss | pubmedqa loss | miracl loss | mldr loss | mr tydi loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:-----:|:-------------:|:----------:|:------------:|:----------:|:----------:|:-----------:|:--------:|:--------------:|:----------------:|:------------:|:-------------:|:-----------:|:---------:|:------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.0741 | 0.3518 | 0.2118 | 0.0793 | 0.3538 | 0.3200 | 0.1954 | 0.1589 | 0.6759 | 0.1532 | 0.0945 | 0.4296 | 0.1455 | 0.2495 | | 0.0000 | 1 | 32.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0064 | 250 | 22.7851 | 8.3992 | 17.7191 | 17.6791 | 16.6296 | 18.7913 | 12.1404 | 18.9992 | 12.1891 | 11.6795 | 26.3440 | 8.5795 | 19.3571 | 9.5985 | 0.2366 | 0.5129 | 0.6004 | 0.1960 | 0.6334 | 0.3941 | 0.2713 | 0.3392 | 0.7977 | 0.2416 | 0.3819 | 0.5448 | 0.4679 | 0.4321 | | 0.0127 | 500 | 9.6296 | 4.6987 | 13.6254 | 11.7605 | 12.5290 | 17.2038 | 6.9342 | 12.0873 | 7.3539 | 9.1374 | 23.1663 | 4.2482 | 14.5991 | 3.3365 | 0.2929 | 0.5509 | 0.6529 | 0.2890 | 0.6495 | 0.4244 | 0.2873 | 0.3690 | 0.8830 | 0.2373 | 0.3815 | 0.5802 | 0.5422 | 0.4723 | | 0.0191 | 750 | 6.7008 | 3.6302 | 11.7061 | 10.1299 | 11.2366 | 15.1612 | 5.5833 | 10.6967 | 5.7074 | 8.8117 | 23.2404 | 3.5115 | 12.5734 | 2.4346 | 0.3101 | 0.5565 | 0.6684 | 0.3406 | 0.6354 | 0.4111 | 0.2972 | 0.3894 | 0.8611 | 0.2513 | 0.3613 | 0.5840 | 0.5578 | 0.4788 | | 0.0255 | 1000 | 5.8282 | 2.9789 | 11.2050 | 9.5095 | 10.6029 | 14.7717 | 5.0173 | 9.6170 | 5.1146 | 9.0596 | 22.3746 | 3.0912 | 12.2982 | 2.2626 | 0.3066 | 0.5514 | 0.6654 | 0.3252 | 0.6390 | 0.4139 | 0.2917 | 0.4168 | 0.8678 | 0.2590 | 0.3884 | 0.6214 | 0.5614 | 0.4852 | | 0.0318 | 1250 | 5.3975 | 2.8335 | 11.0393 | 9.3407 | 9.6014 | 14.5350 | 4.8262 | 9.4577 | 4.9009 | 8.9271 | 21.8053 | 3.2513 | 12.2634 | 1.6880 | 0.3090 | 0.5449 | 0.6607 | 0.3432 | 0.6243 | 0.4145 | 0.3026 | 0.4392 | 0.8801 | 0.2608 | 0.3760 | 0.6102 | 0.5768 | 0.4879 | | 0.0382 | 1500 | 5.3077 | 2.7030 | 10.6366 | 8.9914 | 9.8588 | 14.6669 | 4.6253 | 9.3728 | 4.5863 | 9.1788 | 22.0617 | 2.8378 | 10.9618 | 1.8702 | 0.3240 | 0.5421 | 0.7026 | 0.3507 | 0.6227 | 0.4134 | 0.3150 | 0.3996 | 0.8776 | 0.2493 | 0.3625 | 0.6120 | 0.5642 | 0.4874 | | 0.0445 | 1750 | 4.9354 | 2.6691 | 10.6339 | 8.9606 | 9.7095 | 14.9174 | 4.5880 | 9.3114 | 4.1786 | 8.2898 | 22.8332 | 2.6850 | 11.3781 | 1.6352 | 0.3092 | 0.5591 | 0.6615 | 0.3253 | 0.6363 | 0.3926 | 0.3165 | 0.4057 | 0.9019 | 0.2600 | 0.3685 | 0.6030 | 0.5563 | 0.4843 | | 0.0509 | 2000 | 4.8017 | 2.5867 | 10.0547 | 8.8155 | 9.6765 | 14.7973 | 4.3931 | 9.2721 | 4.0193 | 7.7955 | 23.4468 | 1.9884 | 11.4315 | 1.8009 | 0.3274 | 0.5615 | 0.7024 | 0.3531 | 0.6481 | 0.3959 | 0.3134 | 0.4183 | 0.8849 | 0.2505 | 0.3694 | 0.5991 | 0.5664 | 0.4916 | | 0.0573 | 2250 | 4.8193 | 2.4974 | 9.9855 | 8.8389 | 9.6763 | 14.4220 | 4.3112 | 9.1019 | 4.0176 | 8.4064 | 22.7034 | 2.7534 | 11.5256 | 2.3585 | 0.3149 | 0.5392 | 0.6689 | 0.3344 | 0.6495 | 0.4080 | 0.3058 | 0.3953 | 0.8857 | 0.2588 | 0.3426 | 0.5986 | 0.5756 | 0.4829 | | 0.0636 | 2500 | 4.8773 | 2.7116 | 10.2180 | 8.6935 | 9.7664 | 14.3161 | 4.2722 | 8.9829 | 4.2454 | 9.1911 | 22.8367 | 2.6666 | 11.6110 | 2.0147 | 0.3377 | 0.5455 | 0.6547 | 0.3130 | 0.6396 | 0.4259 | 0.3256 | 0.4226 | 0.8825 | 0.2491 | 0.3908 | 0.5852 | 0.5656 | 0.4875 | | 0.0700 | 2750 | 4.5856 | 2.7758 | 9.9754 | 8.6197 | 9.6282 | 14.4828 | 4.1534 | 8.8766 | 4.2312 | 9.5281 | 21.9368 | 2.9119 | 9.7259 | 1.9405 | 0.3384 | 0.5763 | 0.6646 | 0.3068 | 0.6740 | 0.4195 | 0.3155 | 0.4301 | 0.8809 | 0.2366 | 0.3965 | 0.5963 | 0.5714 | 0.4928 | | 0.0764 | 3000 | 4.3725 | 2.5993 | 10.1291 | 8.7361 | 8.9502 | 14.8227 | 4.1163 | 8.8371 | 4.1014 | 9.2259 | 23.4047 | 3.2715 | 10.3051 | 2.3604 | 0.3040 | 0.5703 | 0.6836 | 0.3006 | 0.6355 | 0.3993 | 0.3318 | 0.4236 | 0.9001 | 0.2579 | 0.3966 | 0.5902 | 0.5770 | 0.4900 | | 0.0827 | 3250 | 4.4409 | 2.5753 | 10.0879 | 8.5131 | 8.7106 | 14.8015 | 4.0560 | 8.8296 | 4.1868 | 9.3069 | 22.5793 | 2.3810 | 8.6639 | 2.0435 | 0.3147 | 0.5806 | 0.6766 | 0.3342 | 0.6293 | 0.4134 | 0.3208 | 0.4089 | 0.8834 | 0.2656 | 0.3784 | 0.6119 | 0.5731 | 0.4916 | | 0.0891 | 3500 | 4.6192 | 2.4352 | 9.9932 | 8.5716 | 9.2016 | 14.1559 | 4.0585 | 8.9413 | 3.8278 | 8.6089 | 22.9941 | 2.5541 | 9.4271 | 1.7271 | 0.3052 | 0.5531 | 0.6921 | 0.3284 | 0.6391 | 0.4027 | 0.3288 | 0.4235 | 0.8938 | 0.2565 | 0.3928 | 0.5848 | 0.5741 | 0.4904 | | 0.0955 | 3750 | 4.4805 | 2.5370 | 10.0723 | 8.4652 | 8.8024 | 14.4678 | 4.0045 | 8.8487 | 3.6855 | 8.4129 | 22.5177 | 2.5961 | 9.1362 | 1.6572 | 0.2996 | 0.5669 | 0.7051 | 0.3007 | 0.6433 | 0.3822 | 0.3127 | 0.4419 | 0.8853 | 0.2741 | 0.3696 | 0.5911 | 0.5796 | 0.4886 | | 0.1018 | 4000 | 4.3246 | 2.4130 | 10.0235 | 8.4203 | 9.1794 | 14.4445 | 3.9667 | 8.8021 | 3.6692 | 8.0637 | 22.1590 | 2.2690 | 9.5487 | 1.4625 | 0.2987 | 0.5659 | 0.6905 | 0.3103 | 0.6280 | 0.4004 | 0.3025 | 0.4273 | 0.9026 | 0.2728 | 0.3710 | 0.6004 | 0.5746 | 0.4881 | | 0.1082 | 4250 | 4.547 | 2.2318 | 10.0773 | 8.4970 | 8.6540 | 13.9845 | 3.9893 | 8.7805 | 3.4480 | 8.1038 | 21.2066 | 2.4344 | 9.2932 | 1.4761 | 0.3037 | 0.5760 | 0.7118 | 0.3239 | 0.6135 | 0.4056 | 0.3170 | 0.4323 | 0.8860 | 0.2620 | 0.3887 | 0.6211 | 0.5824 | 0.4941 | | 0.1145 | 4500 | 4.3008 | 2.3243 | 9.8733 | 8.5042 | 9.0158 | 14.0935 | 3.9014 | 8.8306 | 3.5557 | 8.4240 | 21.2823 | 2.6280 | 9.4869 | 1.8310 | 0.3076 | 0.5788 | 0.6867 | 0.3187 | 0.6190 | 0.3936 | 0.3181 | 0.4160 | 0.8895 | 0.2564 | 0.3960 | 0.6148 | 0.5736 | 0.4899 | | 0.1209 | 4750 | 4.2386 | 2.4259 | 9.8799 | 8.3964 | 9.1116 | 13.9412 | 3.8572 | 8.7955 | 3.6524 | 9.6881 | 21.3812 | 2.2282 | 8.9280 | 1.5408 | 0.3393 | 0.5783 | 0.7190 | 0.3137 | 0.6239 | 0.3953 | 0.3044 | 0.4231 | 0.8768 | 0.2636 | 0.3828 | 0.6043 | 0.5662 | 0.4916 | | 0.1273 | 5000 | 4.141 | 2.4005 | 9.9973 | 8.2741 | 9.1627 | 14.4273 | 3.7931 | 8.7825 | 3.6856 | 9.0001 | 21.6595 | 2.2374 | 9.2771 | 1.4845 | 0.3243 | 0.5705 | 0.6858 | 0.3304 | 0.6328 | 0.3888 | 0.3145 | 0.4096 | 0.8775 | 0.2492 | 0.3769 | 0.6001 | 0.5600 | 0.4862 | | 0.1336 | 5250 | 4.3221 | 2.4200 | 9.8792 | 8.2559 | 9.0431 | 13.9564 | 3.8055 | 8.5773 | 3.6137 | 8.1900 | 21.6272 | 2.2271 | 8.1229 | 1.6308 | 0.3207 | 0.5876 | 0.6945 | 0.3449 | 0.6232 | 0.4072 | 0.3011 | 0.4084 | 0.8894 | 0.2557 | 0.3668 | 0.5905 | 0.5582 | 0.4883 | | 0.1400 | 5500 | 4.2121 | 2.3857 | 10.1277 | 8.3257 | 9.0878 | 13.8545 | 3.7696 | 8.6034 | 3.5613 | 8.7845 | 21.5562 | 2.3611 | 7.5145 | 1.9243 | 0.3160 | 0.5683 | 0.7024 | 0.3382 | 0.6244 | 0.4038 | 0.3075 | 0.4316 | 0.8784 | 0.2603 | 0.3876 | 0.5866 | 0.5650 | 0.4900 | | 0.1464 | 5750 | 4.1071 | 2.4265 | 9.9555 | 8.0947 | 9.1289 | 14.0017 | 3.7337 | 8.6306 | 3.4562 | 8.3132 | 21.7894 | 2.1157 | 8.1967 | 1.5567 | 0.3317 | 0.5604 | 0.7101 | 0.3645 | 0.6460 | 0.3857 | 0.2987 | 0.4236 | 0.8830 | 0.2535 | 0.3867 | 0.5767 | 0.5612 | 0.4909 | | 0.1527 | 6000 | 4.1189 | 2.3586 | 10.0799 | 8.0905 | 9.0291 | 14.0232 | 3.7064 | 8.5220 | 3.4742 | 8.3858 | 21.5903 | 2.1663 | 7.6242 | 1.4405 | 0.3264 | 0.5614 | 0.6825 | 0.3668 | 0.6296 | 0.3972 | 0.2863 | 0.4296 | 0.8869 | 0.2482 | 0.3809 | 0.6004 | 0.5556 | 0.4886 | | 0.1591 | 6250 | 4.0873 | 2.2906 | 9.9813 | 8.1351 | 8.5907 | 13.8665 | 3.7028 | 8.5648 | 3.5042 | 8.1623 | 21.6688 | 2.2940 | 7.6652 | 1.5228 | 0.3444 | 0.5666 | 0.7035 | 0.3415 | 0.6188 | 0.3992 | 0.2989 | 0.4318 | 0.8816 | 0.2504 | 0.4014 | 0.6042 | 0.5637 | 0.4928 | | 0.1654 | 6500 | 3.9586 | 2.2378 | 9.9318 | 8.0887 | 8.7977 | 14.1260 | 3.6614 | 8.5028 | 3.3178 | 8.3118 | 21.5718 | 2.2074 | 8.0905 | 1.7266 | 0.3299 | 0.5828 | 0.6994 | 0.3505 | 0.6375 | 0.3988 | 0.3225 | 0.4173 | 0.8891 | 0.2448 | 0.3930 | 0.6034 | 0.5614 | 0.4946 | | 0.1718 | 6750 | 4.1981 | 2.2863 | 9.5475 | 7.6881 | 8.5462 | 13.6929 | 3.6704 | 8.6660 | 3.3401 | 8.9262 | 21.8933 | 2.0578 | 8.4832 | 1.6796 | 0.3456 | 0.5600 | 0.6983 | 0.3549 | 0.6445 | 0.3844 | 0.3139 | 0.4263 | 0.8952 | 0.2626 | 0.3877 | 0.5820 | 0.5597 | 0.4935 | | 0.1782 | 7000 | 4.0528 | 2.3292 | 9.5129 | 7.8273 | 8.7743 | 13.6930 | 3.6284 | 8.6346 | 3.3430 | 8.5204 | 21.3129 | 2.3350 | 8.8695 | 1.9034 | 0.3457 | 0.5673 | 0.6850 | 0.3274 | 0.6321 | 0.3981 | 0.3171 | 0.4252 | 0.8830 | 0.2643 | 0.3901 | 0.5888 | 0.5590 | 0.4910 | | 0.1845 | 7250 | 4.0547 | 2.2386 | 9.5373 | 7.9214 | 8.7896 | 13.6151 | 3.6172 | 8.5316 | 3.3128 | 9.3566 | 21.4568 | 2.3743 | 9.1696 | 1.7235 | 0.3528 | 0.5597 | 0.6931 | 0.3369 | 0.6327 | 0.3951 | 0.3111 | 0.4368 | 0.8787 | 0.2552 | 0.3758 | 0.5911 | 0.5515 | 0.4900 | | 0.1909 | 7500 | 4.3005 | 2.2273 | 9.4397 | 7.9013 | 8.8606 | 13.3847 | 3.6401 | 8.4134 | 3.2583 | 8.3415 | 21.4206 | 2.4573 | 9.2348 | 1.4832 | 0.3557 | 0.5642 | 0.7145 | 0.3380 | 0.6412 | 0.3772 | 0.3085 | 0.4278 | 0.8792 | 0.2522 | 0.3738 | 0.5843 | 0.5587 | 0.4904 | | 0.1973 | 7750 | 4.0054 | 2.2277 | 9.4653 | 7.9297 | 8.5999 | 13.6106 | 3.6049 | 8.3861 | 3.2335 | 9.3198 | 21.6595 | 2.4730 | 8.7335 | 1.6145 | 0.3396 | 0.5535 | 0.6901 | 0.3556 | 0.6311 | 0.3867 | 0.3182 | 0.4308 | 0.8692 | 0.2590 | 0.3654 | 0.5925 | 0.5558 | 0.4883 | | 0.2036 | 8000 | 3.8426 | 2.2970 | 9.4352 | 7.9532 | 8.6501 | 13.9004 | 3.5835 | 8.3664 | 3.2109 | 8.4302 | 21.0340 | 2.1047 | 9.0103 | 1.1751 | 0.3420 | 0.5695 | 0.6868 | 0.3746 | 0.6434 | 0.4042 | 0.3193 | 0.4259 | 0.8847 | 0.2623 | 0.3785 | 0.5945 | 0.5702 | 0.4966 | | 0.2100 | 8250 | 3.9404 | 2.2417 | 9.5349 | 7.8978 | 8.6899 | 13.8131 | 3.5697 | 8.3664 | 3.1548 | 8.6003 | 21.6214 | 2.0881 | 9.1829 | 0.9559 | 0.3306 | 0.5651 | 0.6959 | 0.3448 | 0.6455 | 0.3857 | 0.3123 | 0.4431 | 0.9009 | 0.2580 | 0.3981 | 0.6073 | 0.5748 | 0.4971 | | 0.2164 | 8500 | 3.9522 | 2.2103 | 9.6575 | 7.9030 | 8.3617 | 14.0083 | 3.5433 | 8.3198 | 3.2148 | 8.5004 | 20.8166 | 2.3194 | 8.0428 | 1.2475 | 0.3343 | 0.5688 | 0.7054 | 0.3411 | 0.6625 | 0.3919 | 0.3148 | 0.4213 | 0.8965 | 0.2665 | 0.3454 | 0.6133 | 0.5782 | 0.4954 | | 0.2227 | 8750 | 3.9665 | 2.1659 | 9.7216 | 7.8772 | 8.6394 | 13.9406 | 3.5289 | 8.3360 | 3.1863 | 8.7835 | 21.2033 | 2.1874 | 8.4683 | 1.2399 | 0.3231 | 0.5715 | 0.6794 | 0.3378 | 0.6684 | 0.3966 | 0.3171 | 0.4048 | 0.8887 | 0.2660 | 0.3461 | 0.6094 | 0.5559 | 0.4896 | | 0.2291 | 9000 | 4.0217 | 2.1042 | 9.6765 | 7.8951 | 8.4255 | 13.8092 | 3.5314 | 8.3896 | 3.1097 | 8.0204 | 21.4246 | 2.0600 | 8.7244 | 1.3343 | 0.3218 | 0.5696 | 0.6931 | 0.3569 | 0.6654 | 0.3978 | 0.3159 | 0.4193 | 0.9000 | 0.2827 | 0.3750 | 0.5890 | 0.5796 | 0.4974 | | 0.2354 | 9250 | 4.0008 | 2.0865 | 9.4154 | 7.9689 | 8.5298 | 13.6352 | 3.5371 | 8.4191 | 3.0414 | 8.4828 | 21.5173 | 1.9966 | 7.6465 | 1.1097 | 0.3282 | 0.5675 | 0.6934 | 0.3476 | 0.6559 | 0.3907 | 0.3272 | 0.4132 | 0.9038 | 0.2712 | 0.3891 | 0.5951 | 0.5716 | 0.4965 | | 0.2418 | 9500 | 3.8041 | 2.0969 | 9.4478 | 7.9720 | 8.6298 | 13.7493 | 3.5003 | 8.4702 | 3.0939 | 8.5108 | 21.6929 | 1.9457 | 7.9947 | 1.2784 | 0.3291 | 0.5655 | 0.6915 | 0.3533 | 0.6495 | 0.3949 | 0.3291 | 0.4313 | 0.9007 | 0.2641 | 0.3910 | 0.5838 | 0.5710 | 0.4965 | | 0.2482 | 9750 | 3.9483 | 2.1627 | 9.5085 | 7.8994 | 8.7048 | 13.4591 | 3.4941 | 8.3342 | 3.1202 | 8.7011 | 20.9101 | 1.8594 | 7.8214 | 1.1181 | 0.3312 | 0.5665 | 0.6870 | 0.3608 | 0.6530 | 0.4038 | 0.3267 | 0.4511 | 0.8973 | 0.2613 | 0.4063 | 0.5832 | 0.5660 | 0.4996 | | 0.2545 | 10000 | 3.9843 | 2.1229 | 9.4348 | 7.9006 | 8.2377 | 13.5086 | 3.4905 | 8.2574 | 3.0114 | 8.3314 | 20.5157 | 1.9033 | 6.7485 | 1.1358 | 0.3365 | 0.5704 | 0.6858 | 0.3616 | 0.6561 | 0.3953 | 0.3263 | 0.4322 | 0.8833 | 0.2690 | 0.3995 | 0.6004 | 0.5691 | 0.4989 | | 0.2609 | 10250 | 3.8779 | 2.1442 | 9.4343 | 7.9411 | 8.4677 | 13.5196 | 3.4558 | 8.2314 | 3.0592 | 8.6126 | 20.3879 | 1.9378 | 6.8287 | 1.1270 | 0.3192 | 0.5688 | 0.7051 | 0.3669 | 0.6577 | 0.3913 | 0.3167 | 0.4479 | 0.8978 | 0.2619 | 0.4030 | 0.5966 | 0.5699 | 0.5002 | | 0.2673 | 10500 | 3.8708 | 2.1487 | 9.6030 | 7.9352 | 8.4657 | 13.3417 | 3.4574 | 8.3005 | 3.0167 | 8.4265 | 20.5354 | 1.9044 | 6.9217 | 1.1079 | 0.3255 | 0.5730 | 0.7020 | 0.3445 | 0.6527 | 0.3861 | 0.3178 | 0.4322 | 0.8859 | 0.2644 | 0.3997 | 0.5969 | 0.5739 | 0.4965 | | 0.2736 | 10750 | 3.8153 | 2.1486 | 9.2440 | 7.8432 | 8.5271 | 13.4366 | 3.4702 | 8.3222 | 2.9680 | 8.4780 | 20.6855 | 1.8790 | 6.6261 | 1.0838 | 0.3240 | 0.5727 | 0.6970 | 0.3487 | 0.6546 | 0.3897 | 0.3294 | 0.4421 | 0.8908 | 0.2548 | 0.3802 | 0.6028 | 0.5744 | 0.4970 | | 0.2800 | 11000 | 3.9693 | 2.1542 | 9.4072 | 7.8122 | 8.6226 | 13.1083 | 3.4426 | 8.2961 | 3.0086 | 8.6255 | 20.5001 | 1.9112 | 6.6305 | 1.1554 | 0.3273 | 0.5631 | 0.6912 | 0.3502 | 0.6431 | 0.4012 | 0.3173 | 0.4422 | 0.8834 | 0.2740 | 0.3829 | 0.6117 | 0.5745 | 0.4971 | | 0.2864 | 11250 | 3.7596 | 2.1288 | 9.4115 | 7.8479 | 8.6295 | 13.1814 | 3.4282 | 8.2609 | 2.9531 | 8.6024 | 20.7827 | 1.8339 | 7.0503 | 1.0273 | 0.3301 | 0.5714 | 0.6814 | 0.3457 | 0.6447 | 0.3962 | 0.3142 | 0.4545 | 0.8751 | 0.2687 | 0.3827 | 0.5921 | 0.5492 | 0.4928 | | 0.2927 | 11500 | 3.7377 | 2.1764 | 9.2284 | 7.7482 | 8.6753 | 13.2556 | 3.4186 | 8.2092 | 2.9631 | 8.2251 | 20.9522 | 1.8887 | 6.8783 | 1.1493 | 0.3184 | 0.5710 | 0.6896 | 0.3620 | 0.6411 | 0.4016 | 0.3141 | 0.4598 | 0.8871 | 0.2660 | 0.4035 | 0.5946 | 0.5680 | 0.4982 | | 0.2991 | 11750 | 3.645 | 2.1757 | 9.2386 | 7.7988 | 8.4091 | 13.3105 | 3.3986 | 8.1770 | 3.0392 | 8.3319 | 20.8279 | 1.8464 | 7.2340 | 1.2369 | 0.3066 | 0.5680 | 0.6938 | 0.3413 | 0.6498 | 0.4035 | 0.3119 | 0.4692 | 0.8776 | 0.2697 | 0.4018 | 0.5937 | 0.5725 | 0.4969 | | 0.3054 | 12000 | 3.8302 | 2.1730 | 9.2542 | 7.8031 | 8.3773 | 13.3083 | 3.4245 | 8.2024 | 2.9451 | 8.4451 | 20.3890 | 1.8992 | 7.1868 | 1.3149 | 0.3022 | 0.5639 | 0.7047 | 0.3580 | 0.6585 | 0.3953 | 0.3193 | 0.4512 | 0.8757 | 0.2667 | 0.4016 | 0.5815 | 0.5653 | 0.4957 | | 0.3118 | 12250 | 3.7341 | 2.1580 | 9.1449 | 7.7487 | 8.2782 | 13.4871 | 3.4325 | 8.1531 | 2.8524 | 8.0765 | 20.4420 | 1.8084 | 7.4004 | 1.1942 | 0.3217 | 0.5648 | 0.7022 | 0.3658 | 0.6597 | 0.4010 | 0.3204 | 0.4470 | 0.8778 | 0.2668 | 0.4062 | 0.5841 | 0.5764 | 0.4995 | | 0.3182 | 12500 | 3.6937 | 2.2003 | 9.0298 | 7.7776 | 8.3088 | 13.3345 | 3.4198 | 8.0772 | 2.8139 | 8.7163 | 20.4754 | 1.8802 | 7.2714 | 1.2016 | 0.3086 | 0.5676 | 0.6930 | 0.3609 | 0.6532 | 0.4067 | 0.3228 | 0.4341 | 0.8737 | 0.2723 | 0.4014 | 0.5783 | 0.5799 | 0.4964 | | 0.3245 | 12750 | 3.6917 | 2.1808 | 9.0285 | 7.7770 | 8.3062 | 13.5737 | 3.3953 | 8.1291 | 2.8232 | 8.3426 | 20.9837 | 1.9420 | 7.1199 | 1.2568 | 0.3288 | 0.5620 | 0.6855 | 0.3512 | 0.6575 | 0.4069 | 0.3309 | 0.4372 | 0.8906 | 0.2709 | 0.4106 | 0.5928 | 0.5874 | 0.5009 | | 0.3309 | 13000 | 3.6376 | 2.1621 | 9.0474 | 7.7871 | 8.1721 | 13.4874 | 3.3904 | 8.1507 | 2.8109 | 8.5607 | 21.0805 | 1.9734 | 7.0553 | 1.3466 | 0.3250 | 0.5686 | 0.6850 | 0.3520 | 0.6630 | 0.4044 | 0.3258 | 0.4424 | 0.8666 | 0.2684 | 0.4038 | 0.5769 | 0.5725 | 0.4965 | | 0.3373 | 13250 | 3.7786 | 2.1146 | 9.1181 | 7.7333 | 8.2758 | 13.4782 | 3.3906 | 8.2021 | 2.8320 | 8.3097 | 21.1471 | 1.8529 | 7.3608 | 1.2242 | 0.3282 | 0.5649 | 0.6997 | 0.3761 | 0.6680 | 0.4097 | 0.3242 | 0.4143 | 0.8873 | 0.2784 | 0.3958 | 0.5956 | 0.5745 | 0.5013 | | 0.3436 | 13500 | 3.8654 | 2.2053 | 9.0632 | 7.6973 | 8.4055 | 13.2312 | 3.3747 | 8.1627 | 2.8245 | 8.4075 | 20.2899 | 1.7553 | 7.1383 | 1.1577 | 0.3316 | 0.5672 | 0.6693 | 0.3901 | 0.6695 | 0.4017 | 0.3213 | 0.4138 | 0.8953 | 0.2703 | 0.4023 | 0.5856 | 0.5821 | 0.5000 | | 0.3500 | 13750 | 3.7545 | 2.1424 | 9.0522 | 7.6998 | 8.3319 | 13.5322 | 3.3625 | 8.1303 | 2.8320 | 8.1860 | 20.4538 | 1.7997 | 7.0770 | 1.2512 | 0.3385 | 0.5654 | 0.6937 | 0.3856 | 0.6659 | 0.4025 | 0.3225 | 0.4156 | 0.8994 | 0.2675 | 0.4113 | 0.5886 | 0.5770 | 0.5026 | | 0.3564 | 14000 | 3.715 | 2.1826 | 8.9396 | 7.6755 | 8.3111 | 13.3344 | 3.3331 | 8.1973 | 2.8760 | 8.8218 | 20.6306 | 1.9014 | 7.3386 | 1.2366 | 0.3256 | 0.5675 | 0.6903 | 0.3846 | 0.6706 | 0.4032 | 0.3300 | 0.4426 | 0.8876 | 0.2647 | 0.4082 | 0.5874 | 0.5770 | 0.5030 | | 0.3627 | 14250 | 3.6348 | 2.1393 | 9.0032 | 7.7843 | 8.3942 | 13.2654 | 3.3368 | 8.1124 | 2.8874 | 8.5999 | 20.8261 | 1.8395 | 7.5311 | 1.1380 | 0.3339 | 0.5642 | 0.7175 | 0.3780 | 0.6600 | 0.3965 | 0.3229 | 0.4305 | 0.8915 | 0.2714 | 0.3914 | 0.5816 | 0.5732 | 0.5009 | | 0.3691 | 14500 | 3.604 | 2.1709 | 8.9648 | 7.7166 | 8.4281 | 13.5507 | 3.3138 | 8.1362 | 2.8989 | 8.3940 | 20.6827 | 1.9298 | 7.3468 | 1.2705 | 0.3416 | 0.5594 | 0.7198 | 0.3776 | 0.6605 | 0.3965 | 0.3295 | 0.4341 | 0.8838 | 0.2632 | 0.3995 | 0.5884 | 0.5693 | 0.5018 | | 0.3754 | 14750 | 3.5398 | 2.1451 | 9.0195 | 7.7407 | 8.4766 | 13.6169 | 3.2839 | 8.1386 | 2.9178 | 8.2585 | 21.1674 | 1.9484 | 7.5143 | 1.2747 | 0.3418 | 0.5592 | 0.6971 | 0.3763 | 0.6539 | 0.4060 | 0.3264 | 0.4214 | 0.8805 | 0.2524 | 0.3855 | 0.5872 | 0.5735 | 0.4970 | | 0.3818 | 15000 | 3.7153 | 2.1038 | 8.9847 | 7.7279 | 8.2667 | 13.4033 | 3.2862 | 8.1232 | 2.8999 | 8.3224 | 20.9361 | 1.9569 | 7.3440 | 1.3036 | 0.3407 | 0.5687 | 0.7066 | 0.3497 | 0.6532 | 0.4043 | 0.3317 | 0.4300 | 0.8830 | 0.2564 | 0.3884 | 0.6013 | 0.5752 | 0.4992 | | 0.3882 | 15250 | 3.752 | 2.0765 | 9.0293 | 7.7275 | 8.3168 | 13.2782 | 3.3105 | 8.0286 | 2.8404 | 8.2745 | 20.9582 | 1.7465 | 7.5436 | 1.2770 | 0.3313 | 0.5710 | 0.6945 | 0.3742 | 0.6699 | 0.4022 | 0.3327 | 0.4277 | 0.8903 | 0.2640 | 0.3872 | 0.5821 | 0.5662 | 0.4995 | | 0.3945 | 15500 | 3.7794 | 2.0409 | 9.0264 | 7.7501 | 8.3903 | 13.3169 | 3.3058 | 8.0128 | 2.8463 | 8.5307 | 21.5663 | 1.7179 | 7.7448 | 1.1827 | 0.3286 | 0.5684 | 0.6961 | 0.3692 | 0.6743 | 0.4031 | 0.3267 | 0.4267 | 0.8927 | 0.2549 | 0.3777 | 0.5810 | 0.5686 | 0.4975 | | 0.4009 | 15750 | 3.7444 | 2.0122 | 9.0629 | 7.7541 | 8.0134 | 13.2091 | 3.2956 | 8.0839 | 2.8528 | 8.1722 | 20.9378 | 1.7628 | 7.8655 | 1.2965 | 0.3282 | 0.5744 | 0.6839 | 0.3807 | 0.6644 | 0.4032 | 0.3277 | 0.4451 | 0.8896 | 0.2706 | 0.3916 | 0.5851 | 0.5685 | 0.5010 | | 0.4073 | 16000 | 3.7817 | 2.0448 | 9.1787 | 7.7705 | 8.0529 | 13.1694 | 3.3128 | 8.1419 | 2.8104 | 8.2099 | 21.0454 | 1.7436 | 7.2934 | 1.2463 | 0.3137 | 0.5640 | 0.6884 | 0.3692 | 0.6600 | 0.3978 | 0.3215 | 0.4314 | 0.8937 | 0.2719 | 0.4094 | 0.6031 | 0.5697 | 0.4995 | | 0.4136 | 16250 | 3.7293 | 2.0586 | 9.2379 | 7.7514 | 8.1877 | 13.1981 | 3.2983 | 8.0763 | 2.8564 | 8.6500 | 20.9279 | 1.8403 | 7.2051 | 1.1732 | 0.3405 | 0.5648 | 0.6944 | 0.3620 | 0.6614 | 0.3953 | 0.3286 | 0.4245 | 0.8921 | 0.2698 | 0.3946 | 0.5915 | 0.5770 | 0.4997 | | 0.4200 | 16500 | 3.6243 | 2.0477 | 9.1718 | 7.6943 | 7.9493 | 13.3019 | 3.2908 | 8.0963 | 2.8306 | 8.7436 | 20.6790 | 1.8745 | 7.4356 | 1.1781 | 0.3272 | 0.5657 | 0.6944 | 0.3726 | 0.6848 | 0.3974 | 0.3318 | 0.4274 | 0.8939 | 0.2636 | 0.3948 | 0.5950 | 0.5746 | 0.5018 | | 0.4263 | 16750 | 3.5071 | 2.0483 | 9.2054 | 7.7004 | 8.1887 | 13.3662 | 3.2727 | 7.9229 | 2.8256 | 8.2771 | 21.1584 | 1.8469 | 7.6476 | 1.2131 | 0.3318 | 0.5660 | 0.7000 | 0.3801 | 0.6902 | 0.3938 | 0.3224 | 0.4191 | 0.8969 | 0.2643 | 0.4052 | 0.6106 | 0.5684 | 0.5038 | | 0.4327 | 17000 | 3.6337 | 2.0383 | 9.1228 | 7.7337 | 8.2262 | 13.2250 | 3.2714 | 7.9983 | 2.7662 | 8.4949 | 20.8407 | 1.8184 | 7.7876 | 1.2807 | 0.3251 | 0.5595 | 0.6957 | 0.3897 | 0.6697 | 0.3933 | 0.3123 | 0.4334 | 0.8934 | 0.2663 | 0.3935 | 0.5931 | 0.5700 | 0.4996 | | 0.4391 | 17250 | 3.5075 | 2.0327 | 8.9777 | 7.7194 | 8.2392 | 13.4002 | 3.2678 | 7.9239 | 2.7551 | 8.2470 | 21.1674 | 1.7744 | 7.9402 | 1.3115 | 0.3418 | 0.5595 | 0.7123 | 0.3790 | 0.6625 | 0.3978 | 0.3174 | 0.4232 | 0.8866 | 0.2683 | 0.3926 | 0.5876 | 0.5764 | 0.5004 | | 0.4454 | 17500 | 3.6595 | 2.0419 | 8.9509 | 7.6536 | 8.3099 | 13.3537 | 3.2766 | 7.9939 | 2.7604 | 8.3880 | 20.8993 | 1.8358 | 7.6156 | 1.2238 | 0.3311 | 0.5643 | 0.7049 | 0.3564 | 0.6686 | 0.3932 | 0.3099 | 0.4350 | 0.8872 | 0.2687 | 0.3826 | 0.5854 | 0.5665 | 0.4964 | | 0.4518 | 17750 | 3.5743 | 2.0049 | 9.0019 | 7.6693 | 8.3489 | 13.3261 | 3.2758 | 8.0051 | 2.7881 | 8.4247 | 20.8115 | 1.8714 | 7.7491 | 1.1884 | 0.3341 | 0.5614 | 0.6972 | 0.3571 | 0.6625 | 0.3893 | 0.3036 | 0.4368 | 0.8911 | 0.2625 | 0.3861 | 0.5734 | 0.5681 | 0.4941 | | 0.4582 | 18000 | 3.6038 | 1.9810 | 9.0106 | 7.7162 | 8.3584 | 13.1195 | 3.2674 | 8.0266 | 2.8184 | 8.4383 | 20.5199 | 1.8854 | 7.8663 | 1.1762 | 0.3318 | 0.5583 | 0.7086 | 0.3591 | 0.6509 | 0.3878 | 0.3137 | 0.4247 | 0.8831 | 0.2601 | 0.3921 | 0.5978 | 0.5648 | 0.4948 | | 0.4645 | 18250 | 3.6903 | 2.0005 | 9.0187 | 7.6743 | 8.4280 | 13.0108 | 3.2733 | 7.8845 | 2.7810 | 8.3511 | 20.1457 | 1.7802 | 8.0015 | 1.0885 | 0.3407 | 0.5657 | 0.7033 | 0.3626 | 0.6644 | 0.3841 | 0.3299 | 0.4358 | 0.8844 | 0.2642 | 0.3904 | 0.5871 | 0.5632 | 0.4981 | | 0.4709 | 18500 | 3.7208 | 1.9972 | 9.1020 | 7.6472 | 8.1589 | 13.0717 | 3.2601 | 8.0039 | 2.7673 | 8.3361 | 20.0231 | 1.8054 | 7.7381 | 1.1832 | 0.3328 | 0.5652 | 0.7043 | 0.3473 | 0.6693 | 0.3810 | 0.3313 | 0.4293 | 0.8770 | 0.2633 | 0.3946 | 0.5914 | 0.5634 | 0.4962 | | 0.4773 | 18750 | 3.6357 | 2.0069 | 9.1473 | 7.6843 | 8.2110 | 13.1578 | 3.2540 | 7.9856 | 2.7390 | 8.6913 | 20.3263 | 1.8252 | 7.9545 | 1.0354 | 0.3285 | 0.5631 | 0.7093 | 0.3648 | 0.6685 | 0.3842 | 0.3285 | 0.4361 | 0.8918 | 0.2744 | 0.4065 | 0.5814 | 0.5610 | 0.4998 | | 0.4836 | 19000 | 3.5737 | 1.9755 | 9.1397 | 7.6784 | 8.2604 | 13.3462 | 3.2391 | 7.9876 | 2.7643 | 8.4540 | 20.2047 | 1.7528 | 7.6572 | 1.0906 | 0.3284 | 0.5631 | 0.7083 | 0.3657 | 0.6660 | 0.3795 | 0.3186 | 0.4393 | 0.8876 | 0.2613 | 0.4056 | 0.5835 | 0.5637 | 0.4977 | | 0.4900 | 19250 | 3.5325 | 2.0232 | 9.1987 | 7.6517 | 8.2727 | 13.1941 | 3.2276 | 7.9841 | 2.7238 | 8.4698 | 19.9076 | 1.8015 | 7.2144 | 1.1134 | 0.3337 | 0.5635 | 0.7076 | 0.3570 | 0.6572 | 0.3891 | 0.3297 | 0.4360 | 0.8864 | 0.2689 | 0.3997 | 0.5813 | 0.5608 | 0.4978 | | 0.4963 | 19500 | 3.4782 | 2.0033 | 9.1235 | 7.7026 | 8.3383 | 13.1817 | 3.2308 | 8.0122 | 2.6934 | 8.4448 | 19.7121 | 1.7192 | 6.9417 | 0.9934 | 0.3222 | 0.5656 | 0.7009 | 0.3562 | 0.6654 | 0.3792 | 0.3292 | 0.4544 | 0.8803 | 0.2703 | 0.4027 | 0.5892 | 0.5611 | 0.4982 | | 0.5027 | 19750 | 3.7141 | 1.9830 | 9.2166 | 7.6724 | 8.3188 | 13.1514 | 3.2465 | 8.0979 | 2.7005 | 8.1956 | 19.8289 | 1.6910 | 7.1995 | 1.0668 | 0.3323 | 0.5632 | 0.7209 | 0.3442 | 0.6745 | 0.3864 | 0.3287 | 0.4255 | 0.8828 | 0.2751 | 0.4097 | 0.5785 | 0.5597 | 0.4986 | | 0.5091 | 20000 | 3.7058 | 1.9625 | 9.1729 | 7.6554 | 8.3323 | 13.0269 | 3.2368 | 8.0075 | 2.7159 | 8.6974 | 20.1650 | 1.6862 | 7.3387 | 1.1706 | 0.3414 | 0.5637 | 0.7214 | 0.3498 | 0.6745 | 0.3817 | 0.3309 | 0.4290 | 0.8861 | 0.2711 | 0.3861 | 0.5854 | 0.5757 | 0.4998 | | 0.5154 | 20250 | 3.502 | 1.9983 | 9.1304 | 7.6354 | 8.3832 | 13.2376 | 3.2262 | 7.9229 | 2.7119 | 8.7638 | 20.0759 | 1.6633 | 6.9686 | 1.1490 | 0.3296 | 0.5703 | 0.7116 | 0.3409 | 0.6717 | 0.3847 | 0.3294 | 0.4198 | 0.8859 | 0.2655 | 0.3924 | 0.5836 | 0.5647 | 0.4962 | | 0.5218 | 20500 | 3.6424 | 1.9867 | 9.0935 | 7.6470 | 8.4280 | 13.0866 | 3.2338 | 7.9608 | 2.7225 | 8.4571 | 20.1605 | 1.6184 | 6.8877 | 1.1519 | 0.3377 | 0.5682 | 0.7128 | 0.3479 | 0.6646 | 0.3900 | 0.3255 | 0.4408 | 0.8863 | 0.2655 | 0.4184 | 0.5862 | 0.5627 | 0.5005 | | 0.5282 | 20750 | 3.6085 | 1.9589 | 9.1559 | 7.6282 | 8.3675 | 13.1289 | 3.2393 | 8.0050 | 2.7199 | 8.3705 | 20.4945 | 1.6721 | 6.8649 | 1.2063 | 0.3328 | 0.5719 | 0.7054 | 0.3526 | 0.6755 | 0.3974 | 0.3280 | 0.4204 | 0.8926 | 0.2686 | 0.4145 | 0.5783 | 0.5553 | 0.4995 | | 0.5345 | 21000 | 3.5763 | 1.9392 | 9.1139 | 7.6917 | 8.2745 | 13.3345 | 3.2333 | 7.9848 | 2.7010 | 8.4815 | 20.5463 | 1.7049 | 7.0751 | 1.1276 | 0.3251 | 0.5710 | 0.7128 | 0.3561 | 0.6642 | 0.4022 | 0.3225 | 0.4394 | 0.8997 | 0.2737 | 0.4106 | 0.5732 | 0.5592 | 0.5008 | | 0.5409 | 21250 | 3.5401 | 1.9744 | 9.0583 | 7.5955 | 8.2868 | 13.2877 | 3.2309 | 7.9686 | 2.6641 | 8.2829 | 20.3974 | 1.6843 | 6.9287 | 1.0128 | 0.3322 | 0.5692 | 0.7117 | 0.3348 | 0.6715 | 0.3834 | 0.3269 | 0.4372 | 0.8869 | 0.2662 | 0.4097 | 0.5797 | 0.5587 | 0.4975 | | 0.5473 | 21500 | 3.489 | 1.9417 | 8.9543 | 7.6468 | 8.3612 | 13.3847 | 3.2314 | 7.9631 | 2.6635 | 8.4673 | 20.5367 | 1.7459 | 6.7037 | 1.0989 | 0.3328 | 0.5741 | 0.7097 | 0.3508 | 0.6660 | 0.3916 | 0.3277 | 0.4391 | 0.8860 | 0.2632 | 0.4100 | 0.5910 | 0.5578 | 0.5000 | | 0.5536 | 21750 | 3.555 | 1.9533 | 8.9916 | 7.6360 | 8.3586 | 13.3764 | 3.2284 | 7.9575 | 2.7214 | 8.3429 | 20.5891 | 1.7569 | 6.6890 | 1.1157 | 0.3339 | 0.5744 | 0.7114 | 0.3392 | 0.6636 | 0.3964 | 0.3259 | 0.4504 | 0.8925 | 0.2651 | 0.4031 | 0.5792 | 0.5646 | 0.5000 | | 0.5600 | 22000 | 3.586 | 1.9301 | 8.9806 | 7.6738 | 8.3535 | 13.2453 | 3.2316 | 7.9946 | 2.6907 | 8.2869 | 20.5606 | 1.6573 | 6.8912 | 1.1068 | 0.3239 | 0.5788 | 0.7121 | 0.3537 | 0.6589 | 0.4023 | 0.3243 | 0.4499 | 0.8891 | 0.2644 | 0.4083 | 0.5802 | 0.5616 | 0.5006 | | 0.5663 | 22250 | 3.5084 | 1.9406 | 9.0040 | 7.6810 | 8.3815 | 13.1792 | 3.2167 | 7.9700 | 2.7316 | 8.6409 | 20.6425 | 1.6145 | 6.8099 | 1.1404 | 0.3174 | 0.5772 | 0.7177 | 0.3519 | 0.6613 | 0.3950 | 0.3324 | 0.4362 | 0.8894 | 0.2648 | 0.4173 | 0.5857 | 0.5626 | 0.5007 | | 0.5727 | 22500 | 3.5095 | 1.9202 | 8.9619 | 7.6717 | 8.3825 | 13.1359 | 3.2147 | 7.9776 | 2.7114 | 8.2541 | 20.5107 | 1.6855 | 6.9227 | 1.1935 | 0.3335 | 0.5726 | 0.7134 | 0.3574 | 0.6578 | 0.3959 | 0.3262 | 0.4401 | 0.8968 | 0.2604 | 0.4105 | 0.5826 | 0.5620 | 0.5007 | | 0.5791 | 22750 | 3.5059 | 1.9225 | 8.9956 | 7.6775 | 8.3968 | 13.0329 | 3.2114 | 7.9836 | 2.7107 | 8.4843 | 20.6793 | 1.6821 | 7.0987 | 1.0579 | 0.3273 | 0.5703 | 0.7118 | 0.3689 | 0.6490 | 0.3850 | 0.3257 | 0.4320 | 0.8824 | 0.2622 | 0.4119 | 0.5898 | 0.5605 | 0.4982 | | 0.5854 | 23000 | 3.4047 | 1.9716 | 9.0017 | 7.6435 | 8.4379 | 13.0467 | 3.1957 | 7.9843 | 2.7017 | 8.5995 | 20.8783 | 1.5818 | 7.1997 | 1.0067 | 0.3272 | 0.5718 | 0.7132 | 0.3728 | 0.6544 | 0.3913 | 0.3255 | 0.4377 | 0.8869 | 0.2583 | 0.4107 | 0.5916 | 0.5539 | 0.4996 | | 0.5918 | 23250 | 3.4732 | 1.9518 | 8.9827 | 7.6143 | 8.3925 | 13.2800 | 3.1877 | 7.9616 | 2.7122 | 8.5013 | 21.0376 | 1.6291 | 7.0831 | 1.0816 | 0.3316 | 0.5689 | 0.7129 | 0.3705 | 0.6536 | 0.3847 | 0.3211 | 0.4495 | 0.8844 | 0.2622 | 0.4141 | 0.5916 | 0.5551 | 0.5000 | | 0.5982 | 23500 | 3.4271 | 1.9688 | 9.0092 | 7.5830 | 8.3763 | 13.2765 | 3.1857 | 7.9625 | 2.6794 | 8.4899 | 20.8080 | 1.6519 | 7.1604 | 1.1423 | 0.3346 | 0.5716 | 0.7054 | 0.3637 | 0.6562 | 0.3844 | 0.3249 | 0.4346 | 0.8912 | 0.2577 | 0.4009 | 0.5872 | 0.5697 | 0.4986 | | 0.6045 | 23750 | 3.4701 | 2.0238 | 9.0036 | 7.5173 | 8.4058 | 13.2881 | 3.1791 | 7.9275 | 2.7149 | 8.8465 | 20.6630 | 1.7025 | 7.2286 | 1.1973 | 0.3308 | 0.5670 | 0.7040 | 0.3754 | 0.6609 | 0.3936 | 0.3252 | 0.4475 | 0.8948 | 0.2542 | 0.4062 | 0.5877 | 0.5717 | 0.5015 | | 0.6109 | 24000 | 3.6199 | 2.0084 | 8.9869 | 7.5146 | 8.3790 | 13.1350 | 3.1771 | 7.9137 | 2.7032 | 8.5424 | 20.5441 | 1.7652 | 6.8017 | 1.1617 | 0.3224 | 0.5656 | 0.7045 | 0.3643 | 0.6643 | 0.3864 | 0.3174 | 0.4577 | 0.8873 | 0.2571 | 0.3735 | 0.5939 | 0.5611 | 0.4966 | | 0.6173 | 24250 | 3.408 | 2.0137 | 9.0355 | 7.5305 | 8.3597 | 13.1604 | 3.1735 | 7.9201 | 2.7078 | 9.0787 | 20.5226 | 1.7341 | 6.7525 | 1.0237 | 0.3310 | 0.5676 | 0.7061 | 0.3643 | 0.6602 | 0.3995 | 0.3240 | 0.4588 | 0.8936 | 0.2572 | 0.3868 | 0.5928 | 0.5718 | 0.5011 | | 0.6236 | 24500 | 3.4651 | 1.9564 | 8.9636 | 7.5635 | 8.3731 | 13.3261 | 3.1737 | 7.9529 | 2.6638 | 8.5231 | 20.4787 | 1.7792 | 6.7317 | 1.0516 | 0.3291 | 0.5670 | 0.7060 | 0.3572 | 0.6594 | 0.3982 | 0.3253 | 0.4400 | 0.8917 | 0.2577 | 0.3886 | 0.5951 | 0.5662 | 0.4986 | | 0.6300 | 24750 | 3.6988 | 1.9668 | 8.9583 | 7.5875 | 8.4316 | 12.9644 | 3.1769 | 7.9801 | 2.6750 | 8.5780 | 20.3883 | 1.7045 | 6.9010 | 1.0783 | 0.3300 | 0.5668 | 0.7040 | 0.3653 | 0.6550 | 0.3957 | 0.3293 | 0.4570 | 0.8878 | 0.2621 | 0.3816 | 0.5932 | 0.5665 | 0.4996 | | 0.6363 | 25000 | 3.4365 | 1.9782 | 8.9306 | 7.5829 | 8.4123 | 13.0937 | 3.1640 | 7.9870 | 2.6806 | 8.6281 | 20.2045 | 1.7244 | 6.9685 | 1.0365 | 0.3289 | 0.5651 | 0.6956 | 0.3703 | 0.6494 | 0.4025 | 0.3294 | 0.4511 | 0.8841 | 0.2634 | 0.3894 | 0.5970 | 0.5605 | 0.4990 | | 0.6427 | 25250 | 3.6097 | 1.9653 | 8.9386 | 7.5722 | 8.4185 | 13.0207 | 3.1629 | 7.9910 | 2.6867 | 8.7326 | 20.2092 | 1.7321 | 6.8303 | 1.0569 | 0.3273 | 0.5643 | 0.7024 | 0.3587 | 0.6534 | 0.4018 | 0.3282 | 0.4401 | 0.8872 | 0.2614 | 0.3860 | 0.5966 | 0.5703 | 0.4983 | | 0.6491 | 25500 | 3.5379 | 1.9518 | 8.9189 | 7.5241 | 8.4156 | 13.0924 | 3.1557 | 7.9747 | 2.6468 | 8.6005 | 20.3848 | 1.7474 | 6.9006 | 1.0193 | 0.3186 | 0.5692 | 0.7032 | 0.3581 | 0.6429 | 0.4068 | 0.3288 | 0.4426 | 0.8937 | 0.2584 | 0.4055 | 0.5963 | 0.5667 | 0.4993 | | 0.6554 | 25750 | 3.6223 | 1.9609 | 8.9459 | 7.5554 | 8.4227 | 12.8817 | 3.1483 | 7.9813 | 2.6559 | 8.8423 | 20.3294 | 1.6381 | 6.8535 | 1.0140 | 0.3252 | 0.5722 | 0.7104 | 0.3689 | 0.6554 | 0.4087 | 0.3291 | 0.4293 | 0.8894 | 0.2632 | 0.3940 | 0.5995 | 0.5702 | 0.5012 | | 0.6618 | 26000 | 3.402 | 1.9602 | 8.9366 | 7.5236 | 8.2409 | 12.9294 | 3.1424 | 7.9619 | 2.6323 | 8.6640 | 20.2959 | 1.6627 | 6.7331 | 1.0115 | 0.3276 | 0.5676 | 0.7031 | 0.3652 | 0.6493 | 0.4030 | 0.3263 | 0.4298 | 0.8830 | 0.2655 | 0.4030 | 0.6017 | 0.5724 | 0.4998 | | 0.6682 | 26250 | 3.4876 | 1.9929 | 8.9621 | 7.4947 | 8.1977 | 12.8814 | 3.1350 | 7.8663 | 2.6554 | 8.5629 | 20.3156 | 1.7019 | 6.7602 | 0.9582 | 0.3253 | 0.5639 | 0.7127 | 0.3621 | 0.6544 | 0.4146 | 0.3278 | 0.4350 | 0.8895 | 0.2613 | 0.4056 | 0.5997 | 0.5635 | 0.5012 | | 0.6745 | 26500 | 3.4301 | 1.9752 | 8.9474 | 7.5246 | 8.1379 | 12.7941 | 3.1369 | 7.8999 | 2.6256 | 8.5223 | 20.4417 | 1.6627 | 6.7999 | 0.9626 | 0.3232 | 0.5661 | 0.7094 | 0.3719 | 0.6474 | 0.4077 | 0.3299 | 0.4533 | 0.8849 | 0.2620 | 0.3993 | 0.6008 | 0.5665 | 0.5017 | | 0.6809 | 26750 | 3.482 | 1.9646 | 8.9353 | 7.5199 | 8.2066 | 12.6512 | 3.1284 | 7.9080 | 2.6464 | 8.6207 | 20.4063 | 1.6852 | 6.7490 | 1.0089 | 0.3172 | 0.5689 | 0.7121 | 0.3734 | 0.6461 | 0.4089 | 0.3287 | 0.4516 | 0.8854 | 0.2601 | 0.3965 | 0.5993 | 0.5634 | 0.5009 | | 0.6873 | 27000 | 3.5073 | 1.9431 | 8.9478 | 7.5188 | 8.0549 | 12.6486 | 3.1310 | 7.9241 | 2.6508 | 8.5172 | 20.4459 | 1.6855 | 6.8390 | 1.0012 | 0.3222 | 0.5705 | 0.6936 | 0.3675 | 0.6511 | 0.3970 | 0.3286 | 0.4467 | 0.8875 | 0.2587 | 0.3986 | 0.6177 | 0.5690 | 0.5007 | | 0.6936 | 27250 | 3.5565 | 1.9438 | 8.9610 | 7.5044 | 8.0383 | 12.4879 | 3.1277 | 7.9219 | 2.6321 | 8.4003 | 20.6229 | 1.7421 | 6.7256 | 1.0533 | 0.3327 | 0.5737 | 0.6955 | 0.3653 | 0.6486 | 0.4067 | 0.3277 | 0.4368 | 0.8901 | 0.2589 | 0.3877 | 0.6135 | 0.5696 | 0.5005 | | 0.7000 | 27500 | 3.4506 | 1.9639 | 8.9673 | 7.4644 | 8.1455 | 12.4523 | 3.1171 | 7.9159 | 2.6772 | 8.5339 | 20.7734 | 1.7690 | 6.6677 | 1.0708 | 0.3260 | 0.5730 | 0.6960 | 0.3562 | 0.6419 | 0.4053 | 0.3310 | 0.4395 | 0.8871 | 0.2594 | 0.4010 | 0.6155 | 0.5679 | 0.5000 | | 0.7063 | 27750 | 3.4875 | 1.9177 | 8.9276 | 7.4754 | 8.1375 | 12.5163 | 3.1315 | 7.8697 | 2.6247 | 8.4426 | 20.4950 | 1.7047 | 6.6303 | 1.0030 | 0.3244 | 0.5690 | 0.6887 | 0.3499 | 0.6471 | 0.4057 | 0.3309 | 0.4464 | 0.8893 | 0.2570 | 0.4038 | 0.6038 | 0.5701 | 0.4989 | | 0.7127 | 28000 | 3.5298 | 1.8939 | 8.9022 | 7.4889 | 8.1322 | 12.5461 | 3.1409 | 7.8621 | 2.5963 | 8.4173 | 20.4948 | 1.7078 | 6.4516 | 0.9874 | 0.3380 | 0.5721 | 0.6934 | 0.3631 | 0.6439 | 0.4046 | 0.3272 | 0.4486 | 0.8893 | 0.2593 | 0.4020 | 0.6017 | 0.5687 | 0.5009 | | 0.7191 | 28250 | 3.3329 | 1.8970 | 8.9172 | 7.5001 | 8.1758 | 12.5515 | 3.1333 | 7.9075 | 2.5928 | 8.5025 | 20.3447 | 1.6879 | 6.5795 | 0.9658 | 0.3247 | 0.5729 | 0.7000 | 0.3710 | 0.6468 | 0.4069 | 0.3336 | 0.4564 | 0.8933 | 0.2637 | 0.4105 | 0.6012 | 0.5749 | 0.5043 | | 0.7254 | 28500 | 3.3897 | 1.8966 | 8.9038 | 7.5265 | 8.2199 | 12.6173 | 3.1244 | 7.8882 | 2.5974 | 8.3728 | 20.3628 | 1.6804 | 6.6887 | 0.9733 | 0.3278 | 0.5750 | 0.7001 | 0.3645 | 0.6528 | 0.4145 | 0.3310 | 0.4669 | 0.8964 | 0.2638 | 0.4032 | 0.6086 | 0.5713 | 0.5058 | | 0.7318 | 28750 | 3.4588 | 1.8934 | 8.8948 | 7.5026 | 8.2374 | 12.4866 | 3.1287 | 7.8941 | 2.5811 | 8.3781 | 20.4334 | 1.7109 | 6.6195 | 0.9699 | 0.3251 | 0.5707 | 0.7083 | 0.3702 | 0.6411 | 0.4086 | 0.3252 | 0.4553 | 0.8935 | 0.2641 | 0.4068 | 0.6063 | 0.5592 | 0.5026 | | 0.7382 | 29000 | 3.3675 | 1.8959 | 8.8925 | 7.5043 | 8.2628 | 12.6063 | 3.1174 | 7.9132 | 2.5908 | 8.2436 | 20.3771 | 1.6740 | 6.7151 | 0.9895 | 0.3262 | 0.5725 | 0.7069 | 0.3694 | 0.6495 | 0.4063 | 0.3169 | 0.4622 | 0.8962 | 0.2609 | 0.4124 | 0.6101 | 0.5635 | 0.5041 | | 0.7445 | 29250 | 3.3886 | 1.8976 | 8.9215 | 7.4975 | 8.2401 | 12.4978 | 3.1127 | 7.8615 | 2.5814 | 8.2715 | 20.4379 | 1.6740 | 6.7705 | 0.9492 | 0.3269 | 0.5683 | 0.7061 | 0.3760 | 0.6554 | 0.4089 | 0.3209 | 0.4508 | 0.8982 | 0.2658 | 0.3982 | 0.6053 | 0.5668 | 0.5037 | | 0.7509 | 29500 | 3.4826 | 1.8855 | 8.9320 | 7.5047 | 8.2470 | 12.5056 | 3.1168 | 7.8967 | 2.5742 | 8.3762 | 20.4917 | 1.7122 | 6.3650 | 0.9882 | 0.3380 | 0.5681 | 0.7061 | 0.3727 | 0.6521 | 0.4071 | 0.3222 | 0.4771 | 0.8963 | 0.2701 | 0.3943 | 0.6010 | 0.5624 | 0.5052 | | 0.7572 | 29750 | 3.4268 | 1.8725 | 8.9332 | 7.5158 | 8.2481 | 12.4708 | 3.1146 | 7.9010 | 2.5617 | 8.2478 | 20.3435 | 1.6860 | 6.4246 | 0.9801 | 0.3331 | 0.5691 | 0.7061 | 0.3767 | 0.6523 | 0.4068 | 0.3195 | 0.4589 | 0.9097 | 0.2627 | 0.4113 | 0.6059 | 0.5641 | 0.5059 | | 0.7636 | 30000 | 3.2621 | 1.8813 | 8.9125 | 7.5203 | 8.2736 | 12.5555 | 3.1094 | 7.9083 | 2.5488 | 8.3690 | 20.3392 | 1.7015 | 6.5219 | 0.9901 | 0.3366 | 0.5686 | 0.6941 | 0.3696 | 0.6465 | 0.4062 | 0.3228 | 0.4570 | 0.8967 | 0.2657 | 0.3975 | 0.6054 | 0.5702 | 0.5028 | | 0.7700 | 30250 | 3.3289 | 1.8893 | 8.9211 | 7.5322 | 8.1753 | 12.4890 | 3.1063 | 7.9272 | 2.5320 | 8.4628 | 20.3169 | 1.6841 | 6.5986 | 0.9798 | 0.3302 | 0.5719 | 0.6944 | 0.3745 | 0.6471 | 0.4007 | 0.3172 | 0.4741 | 0.9046 | 0.2685 | 0.4126 | 0.6014 | 0.5666 | 0.5049 | | 0.7763 | 30500 | 3.5363 | 1.8836 | 8.8909 | 7.5323 | 8.2197 | 12.4000 | 3.1064 | 7.9088 | 2.5494 | 8.3271 | 20.3110 | 1.7132 | 6.4502 | 0.9974 | 0.3298 | 0.5705 | 0.6995 | 0.3671 | 0.6511 | 0.4057 | 0.3204 | 0.4560 | 0.8949 | 0.2613 | 0.4153 | 0.6011 | 0.5738 | 0.5036 | | 0.7827 | 30750 | 3.3557 | 1.8824 | 8.9002 | 7.5249 | 8.2047 | 12.4618 | 3.1055 | 7.9265 | 2.5501 | 8.2708 | 20.2254 | 1.7222 | 6.4927 | 0.9813 | 0.3333 | 0.5678 | 0.6926 | 0.3781 | 0.6575 | 0.4005 | 0.3225 | 0.4497 | 0.8991 | 0.2649 | 0.4018 | 0.6051 | 0.5698 | 0.5033 | | 0.7891 | 31000 | 3.4481 | 1.8725 | 8.9077 | 7.5043 | 8.2095 | 12.5197 | 3.1095 | 7.9124 | 2.5216 | 8.1396 | 20.0618 | 1.6962 | 6.4808 | 0.9764 | 0.3321 | 0.5691 | 0.6941 | 0.3650 | 0.6464 | 0.4013 | 0.3239 | 0.4522 | 0.9035 | 0.2657 | 0.4058 | 0.6041 | 0.5639 | 0.5021 | | 0.7954 | 31250 | 3.3888 | 1.8596 | 8.9249 | 7.5248 | 8.2429 | 12.4622 | 3.1054 | 7.9247 | 2.5351 | 8.3011 | 19.9880 | 1.7062 | 6.5598 | 0.9628 | 0.3428 | 0.5694 | 0.7008 | 0.3761 | 0.6564 | 0.4013 | 0.3223 | 0.4589 | 0.9024 | 0.2633 | 0.4111 | 0.6052 | 0.5670 | 0.5059 | | 0.8018 | 31500 | 3.3989 | 1.8662 | 8.9299 | 7.5206 | 8.1957 | 12.4667 | 3.1043 | 7.9089 | 2.5465 | 8.2149 | 19.8897 | 1.6830 | 6.6131 | 0.9204 | 0.3452 | 0.5658 | 0.7002 | 0.3709 | 0.6570 | 0.4004 | 0.3233 | 0.4588 | 0.9015 | 0.2619 | 0.4044 | 0.6015 | 0.5654 | 0.5043 | | 0.8082 | 31750 | 3.3603 | 1.8671 | 8.9158 | 7.5135 | 8.1945 | 12.5201 | 3.1009 | 7.9130 | 2.5539 | 8.3876 | 19.9818 | 1.6685 | 6.6359 | 0.9307 | 0.3516 | 0.5659 | 0.6928 | 0.3767 | 0.6534 | 0.4004 | 0.3245 | 0.4627 | 0.9021 | 0.2619 | 0.4252 | 0.6019 | 0.5699 | 0.5068 | | 0.8145 | 32000 | 3.3783 | 1.8588 | 8.8993 | 7.4994 | 8.2058 | 12.5200 | 3.0980 | 7.8902 | 2.5448 | 8.3444 | 19.9960 | 1.6815 | 6.7156 | 0.9509 | 0.3358 | 0.5665 | 0.7012 | 0.3759 | 0.6551 | 0.4002 | 0.3259 | 0.4505 | 0.8960 | 0.2621 | 0.4113 | 0.6059 | 0.5716 | 0.5045 | | 0.8209 | 32250 | 3.4379 | 1.8693 | 8.8896 | 7.4639 | 8.0939 | 12.5840 | 3.0894 | 7.8559 | 2.5590 | 8.4227 | 19.9565 | 1.6855 | 6.6835 | 0.9650 | 0.3433 | 0.5663 | 0.7004 | 0.3717 | 0.6527 | 0.4076 | 0.3255 | 0.4491 | 0.8990 | 0.2643 | 0.4127 | 0.5984 | 0.5741 | 0.5050 | | 0.8272 | 32500 | 3.4962 | 1.8838 | 8.8634 | 7.4636 | 8.1354 | 12.5144 | 3.0866 | 7.8561 | 2.5410 | 8.4169 | 20.0794 | 1.6625 | 6.6897 | 0.9849 | 0.3461 | 0.5678 | 0.7024 | 0.3808 | 0.6564 | 0.4083 | 0.3200 | 0.4531 | 0.9015 | 0.2622 | 0.4116 | 0.6061 | 0.5685 | 0.5065 | | 0.8336 | 32750 | 3.4927 | 1.8696 | 8.8655 | 7.4274 | 8.0888 | 12.5431 | 3.0874 | 7.8320 | 2.5324 | 8.3181 | 20.0095 | 1.6403 | 6.7182 | 0.9837 | 0.3439 | 0.5657 | 0.7078 | 0.3771 | 0.6580 | 0.4078 | 0.3210 | 0.4543 | 0.9087 | 0.2625 | 0.4184 | 0.6095 | 0.5691 | 0.5080 | | 0.8400 | 33000 | 3.33 | 1.8743 | 8.8776 | 7.4438 | 8.1127 | 12.5571 | 3.0793 | 7.8474 | 2.5423 | 8.3195 | 20.1064 | 1.6546 | 6.7761 | 1.0118 | 0.3400 | 0.5684 | 0.6938 | 0.3760 | 0.6560 | 0.4077 | 0.3248 | 0.4586 | 0.8965 | 0.2613 | 0.4085 | 0.6094 | 0.5704 | 0.5055 | | 0.8463 | 33250 | 3.3431 | 1.8687 | 8.8074 | 7.4577 | 8.1313 | 12.5392 | 3.0816 | 7.8629 | 2.5306 | 8.3183 | 20.1216 | 1.6653 | 6.8390 | 1.0199 | 0.3233 | 0.5685 | 0.7031 | 0.3770 | 0.6493 | 0.4075 | 0.3243 | 0.4586 | 0.9015 | 0.2600 | 0.4068 | 0.6050 | 0.5659 | 0.5039 | | 0.8527 | 33500 | 3.4455 | 1.8618 | 8.8108 | 7.4587 | 8.1421 | 12.5672 | 3.0800 | 7.8533 | 2.5364 | 8.2589 | 20.0766 | 1.6537 | 6.8825 | 1.0161 | 0.3346 | 0.5647 | 0.7003 | 0.3761 | 0.6523 | 0.4075 | 0.3239 | 0.4598 | 0.9088 | 0.2641 | 0.4134 | 0.5976 | 0.5702 | 0.5056 | | 0.8591 | 33750 | 3.3189 | 1.8578 | 8.8046 | 7.4684 | 8.1488 | 12.6076 | 3.0817 | 7.8637 | 2.5249 | 8.2008 | 20.1733 | 1.6486 | 6.9228 | 1.0004 | 0.3428 | 0.5684 | 0.7019 | 0.3782 | 0.6483 | 0.4049 | 0.3229 | 0.4525 | 0.9039 | 0.2641 | 0.4088 | 0.6114 | 0.5669 | 0.5058 | | 0.8654 | 34000 | 3.3815 | 1.8587 | 8.7921 | 7.4675 | 8.0968 | 12.6359 | 3.0800 | 7.8680 | 2.5266 | 8.3492 | 20.1037 | 1.6352 | 6.8485 | 1.0061 | 0.3412 | 0.5655 | 0.6945 | 0.3806 | 0.6510 | 0.4061 | 0.3178 | 0.4583 | 0.9085 | 0.2640 | 0.4078 | 0.6105 | 0.5689 | 0.5058 | | 0.8718 | 34250 | 3.3381 | 1.8586 | 8.7828 | 7.4672 | 8.1115 | 12.6341 | 3.0783 | 7.8743 | 2.5275 | 8.3363 | 20.0616 | 1.6445 | 6.8898 | 1.0146 | 0.3255 | 0.5645 | 0.6941 | 0.3784 | 0.6488 | 0.4027 | 0.3190 | 0.4518 | 0.9039 | 0.2637 | 0.4079 | 0.6115 | 0.5730 | 0.5034 | | 0.8782 | 34500 | 3.3992 | 1.8597 | 8.7906 | 7.4658 | 8.1316 | 12.6647 | 3.0781 | 7.8643 | 2.5249 | 8.3280 | 20.0237 | 1.6348 | 6.8277 | 1.0203 | 0.3283 | 0.5645 | 0.6925 | 0.3744 | 0.6525 | 0.4027 | 0.3250 | 0.4593 | 0.9040 | 0.2625 | 0.4097 | 0.6121 | 0.5676 | 0.5042 | | 0.8845 | 34750 | 3.2951 | 1.8608 | 8.8033 | 7.4729 | 8.1063 | 12.6896 | 3.0719 | 7.8671 | 2.5243 | 8.3368 | 20.0587 | 1.6455 | 6.7907 | 1.0106 | 0.3274 | 0.5651 | 0.6925 | 0.3738 | 0.6527 | 0.4045 | 0.3219 | 0.4592 | 0.9041 | 0.2619 | 0.3985 | 0.6120 | 0.5679 | 0.5032 | | 0.8909 | 35000 | 3.262 | 1.8677 | 8.8024 | 7.4317 | 8.1170 | 12.7225 | 3.0671 | 7.8519 | 2.5303 | 8.4655 | 20.1156 | 1.6405 | 6.7997 | 1.0141 | 0.3288 | 0.5667 | 0.6935 | 0.3738 | 0.6554 | 0.4049 | 0.3196 | 0.4532 | 0.9041 | 0.2626 | 0.4116 | 0.6123 | 0.5653 | 0.5040 | | 0.8972 | 35250 | 3.3218 | 1.8698 | 8.7992 | 7.4495 | 8.1390 | 12.7198 | 3.0627 | 7.8528 | 2.5399 | 8.4549 | 20.1652 | 1.6288 | 6.7857 | 1.0261 | 0.3328 | 0.5666 | 0.6951 | 0.3686 | 0.6538 | 0.4043 | 0.3248 | 0.4596 | 0.9042 | 0.2646 | 0.4097 | 0.6123 | 0.5725 | 0.5053 | | 0.9036 | 35500 | 3.3529 | 1.8633 | 8.7964 | 7.4339 | 8.1347 | 12.7295 | 3.0657 | 7.8262 | 2.5256 | 8.4119 | 20.1363 | 1.6165 | 6.7987 | 1.0260 | 0.3268 | 0.5660 | 0.6951 | 0.3695 | 0.6543 | 0.4042 | 0.3232 | 0.4511 | 0.9068 | 0.2666 | 0.4047 | 0.6112 | 0.5701 | 0.5038 | | 0.9100 | 35750 | 3.2205 | 1.8626 | 8.7751 | 7.4335 | 8.1346 | 12.7532 | 3.0654 | 7.8318 | 2.5077 | 8.3883 | 20.0738 | 1.5839 | 6.7819 | 1.0282 | 0.3291 | 0.5659 | 0.6951 | 0.3702 | 0.6530 | 0.4045 | 0.3241 | 0.4493 | 0.9067 | 0.2622 | 0.4192 | 0.6112 | 0.5710 | 0.5047 | | 0.9163 | 36000 | 3.3671 | 1.8521 | 8.7693 | 7.4500 | 8.1155 | 12.8041 | 3.0671 | 7.8451 | 2.4994 | 8.3053 | 20.0666 | 1.5984 | 6.7696 | 1.0377 | 0.3383 | 0.5667 | 0.6946 | 0.3739 | 0.6536 | 0.4049 | 0.3262 | 0.4457 | 0.9065 | 0.2625 | 0.4143 | 0.6112 | 0.5719 | 0.5054 | | 0.9227 | 36250 | 3.4074 | 1.8555 | 8.7683 | 7.4607 | 8.1465 | 12.7545 | 3.0621 | 7.8488 | 2.5080 | 8.3816 | 20.0420 | 1.6043 | 6.7991 | 1.0539 | 0.3367 | 0.5685 | 0.6935 | 0.3714 | 0.6523 | 0.4068 | 0.3237 | 0.4471 | 0.9065 | 0.2645 | 0.4176 | 0.6110 | 0.5692 | 0.5053 | | 0.9291 | 36500 | 3.4806 | 1.8543 | 8.7833 | 7.4584 | 8.1070 | 12.7444 | 3.0605 | 7.8475 | 2.5166 | 8.3809 | 19.3340 | 1.6181 | 6.7663 | 1.0555 | 0.3271 | 0.5693 | 0.6935 | 0.3733 | 0.6560 | 0.3990 | 0.3229 | 0.4530 | 0.9065 | 0.2618 | 0.4105 | 0.6129 | 0.5654 | 0.5039 | | 0.9354 | 36750 | 3.4848 | 1.8558 | 8.7967 | 7.4512 | 8.1175 | 12.6960 | 3.0616 | 7.8420 | 2.5137 | 8.4267 | 19.4008 | 1.6181 | 6.7376 | 1.0629 | 0.3284 | 0.5697 | 0.6922 | 0.3723 | 0.6530 | 0.4048 | 0.3234 | 0.4534 | 0.9065 | 0.2641 | 0.4088 | 0.6115 | 0.5635 | 0.5040 | | 0.9418 | 37000 | 3.1947 | 1.8574 | 8.8026 | 7.4555 | 8.1222 | 12.7155 | 3.0591 | 7.8391 | 2.5180 | 8.4551 | 19.4094 | 1.6134 | 6.7200 | 1.0657 | 0.3318 | 0.5681 | 0.6922 | 0.3711 | 0.6550 | 0.4043 | 0.3234 | 0.4534 | 0.9037 | 0.2645 | 0.4090 | 0.6113 | 0.5685 | 0.5043 | | 0.9482 | 37250 | 3.3557 | 1.8569 | 8.7972 | 7.4535 | 8.1200 | 12.7427 | 3.0585 | 7.8460 | 2.5167 | 8.5049 | 19.4395 | 1.6141 | 6.7080 | 1.0700 | 0.3308 | 0.5699 | 0.6930 | 0.3708 | 0.6513 | 0.4048 | 0.3247 | 0.4589 | 0.9039 | 0.2645 | 0.4087 | 0.6113 | 0.5677 | 0.5046 | | 0.9545 | 37500 | 3.369 | 1.8580 | 8.7958 | 7.4558 | 8.1193 | 12.7407 | 3.0579 | 7.8509 | 2.5139 | 8.5231 | 19.4691 | 1.6139 | 6.7228 | 1.0696 | 0.3388 | 0.5683 | 0.6922 | 0.3696 | 0.6556 | 0.4050 | 0.3236 | 0.4460 | 0.8965 | 0.2624 | 0.4145 | 0.6113 | 0.5682 | 0.5040 | | 0.9609 | 37750 | 3.398 | 1.8564 | 8.7955 | 7.4567 | 8.1174 | 12.7478 | 3.0589 | 7.8415 | 2.5086 | 8.5030 | 19.4719 | 1.5989 | 6.7117 | 1.0689 | 0.3381 | 0.5678 | 0.6922 | 0.3652 | 0.6544 | 0.4049 | 0.3234 | 0.4460 | 0.9039 | 0.2599 | 0.3998 | 0.6113 | 0.5658 | 0.5025 | | 0.9672 | 38000 | 3.3699 | 1.8534 | 8.8011 | 7.4604 | 8.1135 | 12.7689 | 3.0574 | 7.8412 | 2.5097 | 8.4866 | 19.4973 | 1.5966 | 6.7309 | 1.0655 | 0.3372 | 0.5680 | 0.6922 | 0.3651 | 0.6553 | 0.4038 | 0.3224 | 0.4560 | 0.9025 | 0.2675 | 0.4061 | 0.6111 | 0.5712 | 0.5045 | | 0.9736 | 38250 | 3.4483 | 1.8540 | 8.8045 | 7.4506 | 8.0942 | 12.7725 | 3.0569 | 7.8379 | 2.5135 | 8.4817 | 19.5038 | 1.5995 | 6.7289 | 1.0693 | 0.3378 | 0.5682 | 0.6922 | 0.3724 | 0.6550 | 0.4038 | 0.3243 | 0.4534 | 0.9025 | 0.2645 | 0.4058 | 0.6111 | 0.5710 | 0.5048 | | 0.9800 | 38500 | 3.254 | 1.8546 | 8.8026 | 7.4530 | 8.1004 | 12.7796 | 3.0559 | 7.8378 | 2.5086 | 8.4538 | 19.5061 | 1.5997 | 6.7390 | 1.0724 | 0.3381 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2642 | 0.4064 | 0.6111 | 0.5677 | 0.5034 | | 0.9863 | 38750 | 3.2759 | 1.8549 | 8.8024 | 7.4545 | 8.0996 | 12.7875 | 3.0562 | 7.8400 | 2.5054 | 8.4401 | 19.5192 | 1.6018 | 6.7433 | 1.0737 | 0.3382 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3241 | 0.4534 | 0.8951 | 0.2642 | 0.4078 | 0.6111 | 0.5689 | 0.5036 | | 0.9927 | 39000 | 3.3273 | 1.8548 | 8.8017 | 7.4536 | 8.0908 | 12.7885 | 3.0557 | 7.8396 | 2.5052 | 8.4335 | 19.5169 | 1.6002 | 6.7439 | 1.0716 | 0.3382 | 0.5681 | 0.6922 | 0.3647 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2641 | 0.4078 | 0.6111 | 0.5703 | 0.5037 | | 0.9991 | 39250 | 3.3902 | 1.8540 | 8.8010 | 7.4543 | 8.0872 | 12.7890 | 3.0561 | 7.8412 | 2.5040 | 8.3945 | 19.5165 | 1.5997 | 6.7462 | 1.0718 | 0.3309 | 0.5681 | 0.6922 | 0.3651 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2643 | 0.4078 | 0.6111 | 0.5703 | 0.5032 | | 1.0 | 39287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3309 | 0.5681 | 0.6922 | 0.3651 | 0.6547 | 0.4041 | 0.3242 | 0.4534 | 0.8951 | 0.2643 | 0.4078 | 0.6111 | 0.5703 | 0.5032 | </details> ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 2.611 kWh - **Carbon Emitted**: 1.015 kg of CO2 - **Hours Used**: 17.883 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.45.2 - PyTorch: 2.5.0.dev20240807+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.1-dev.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION", "TRANSLATION", "SUMMARIZATION" ]

[ "CAS", "CRAFT", "PCR", "PUBMEDQA", "SCIFACT" ]

sentence-transformers-testing/stsb-bert-tiny-lora

sentence-transformers-testing

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers-testing/stsb-bert-tiny-safetensors", "base_model:finetune:sentence-transformers-testing/stsb-bert-tiny-safetensors", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-11-08T13:55:33

2024-11-11T10:23:08

0

3

--- base_model: sentence-transformers-testing/stsb-bert-tiny-safetensors datasets: - sentence-transformers/gooaq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. co2_eq_emissions: emissions: 9.679189270737199 energy_consumed: 0.024901310697493708 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.15 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: stsb-bert-tiny adapter finetuned on GooAQ pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.26 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.38 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.05600000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.056666666666666664 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08666666666666668 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11166666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.17833333333333332 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1412311142763055 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.19938095238095235 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.11363345611144926 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.72 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.86 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.34 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.344 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.29 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.02634308391586433 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06038926804951766 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.10265977040056268 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.19610280190566398 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.34151812101104584 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5504126984126985 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21133731615809154 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.22 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.044000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.18 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.22 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.34 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21218661613500586 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.17491269841269838 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.18857101300669993 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.1 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.28 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.04 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.032 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.044000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.06199999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.12488888888888887 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.15574603174603174 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10395695406287388 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.10821428571428571 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.08041090092126037 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.36 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.62 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.36 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.31 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.35 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.39 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3504958855767756 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4476349206349205 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.29308037158200173 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.06 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.36 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.06 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.036000000000000004 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.36 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.21417075898440763 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16666666666666663 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.19159156983842277 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.44 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.00377949106046741 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.007274949456892388 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.012714784638321257 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.019303285579015287 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.09870502263453415 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2538809523809524 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.018928657854150332 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.18 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.2 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.42 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.06 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.04 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.042 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.19 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2051878697694875 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1506904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16101738947158584 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.7 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.32 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.22399999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.624 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7719999999999999 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.866 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8993333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7992844609162323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7798333333333335 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7635205205527187 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.036000000000000004 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.07466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.09466666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13466666666666666 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.1348403477257659 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.24209523809523809 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.10255365352032365 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.06400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.04 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.32 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2375425714519515 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.1856666666666667 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1985205474177431 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.22 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.3 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.32 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.07333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.064 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.034 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.195 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.28 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.19370675821369307 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.16466666666666668 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.1653693334513147 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.20408163265306123 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5102040816326531 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7551020408163265 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8775510204081632 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20408163265306123 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.25170068027210885 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.25306122448979596 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24489795918367346 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.014397370082893721 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.04876234248655414 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.0792610922160282 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.14648888406884147 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2485959675297849 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4082118561710398 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.16376385142142616 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.20646781789638935 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.33924646781789636 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.41039246467817886 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5121193092621665 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.20646781789638935 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.1419256933542648 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11762009419152278 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08822291993720567 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.10809127782506864 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.19128922256356135 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2362967591905488 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.30153648743329886 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.25241711140675877 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2947898009020458 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2040229677928606 name: Cosine Map@100 --- # stsb-bert-tiny adapter finetuned on GooAQ pairs This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors) on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. This model was trained using [train_script.py](train_script.py). ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers-testing/stsb-bert-tiny-safetensors](https://huggingface.co/sentence-transformers-testing/stsb-bert-tiny-safetensors)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 128 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 128, '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, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-lora") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 128] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 | | cosine_accuracy@3 | 0.22 | 0.62 | 0.18 | 0.1 | 0.52 | 0.26 | 0.26 | 0.18 | 0.82 | 0.26 | 0.26 | 0.22 | 0.5102 | | cosine_accuracy@5 | 0.26 | 0.72 | 0.22 | 0.2 | 0.54 | 0.32 | 0.3 | 0.2 | 0.88 | 0.32 | 0.32 | 0.3 | 0.7551 | | cosine_accuracy@10 | 0.38 | 0.86 | 0.36 | 0.28 | 0.62 | 0.36 | 0.44 | 0.42 | 0.94 | 0.4 | 0.4 | 0.32 | 0.8776 | | cosine_precision@1 | 0.14 | 0.42 | 0.12 | 0.06 | 0.36 | 0.06 | 0.2 | 0.08 | 0.7 | 0.18 | 0.08 | 0.08 | 0.2041 | | cosine_precision@3 | 0.08 | 0.34 | 0.06 | 0.04 | 0.2067 | 0.0867 | 0.12 | 0.06 | 0.32 | 0.12 | 0.0867 | 0.0733 | 0.2517 | | cosine_precision@5 | 0.056 | 0.344 | 0.044 | 0.048 | 0.14 | 0.064 | 0.096 | 0.04 | 0.224 | 0.092 | 0.064 | 0.064 | 0.2531 | | cosine_precision@10 | 0.05 | 0.29 | 0.036 | 0.032 | 0.078 | 0.036 | 0.08 | 0.042 | 0.118 | 0.066 | 0.04 | 0.034 | 0.2449 | | cosine_recall@1 | 0.0567 | 0.0263 | 0.12 | 0.044 | 0.18 | 0.06 | 0.0038 | 0.08 | 0.624 | 0.036 | 0.08 | 0.08 | 0.0144 | | cosine_recall@3 | 0.0867 | 0.0604 | 0.18 | 0.062 | 0.31 | 0.26 | 0.0073 | 0.17 | 0.772 | 0.0747 | 0.26 | 0.195 | 0.0488 | | cosine_recall@5 | 0.1117 | 0.1027 | 0.22 | 0.1249 | 0.35 | 0.32 | 0.0127 | 0.19 | 0.866 | 0.0947 | 0.32 | 0.28 | 0.0793 | | cosine_recall@10 | 0.1783 | 0.1961 | 0.34 | 0.1557 | 0.39 | 0.36 | 0.0193 | 0.4 | 0.8993 | 0.1347 | 0.4 | 0.3 | 0.1465 | | **cosine_ndcg@10** | **0.1412** | **0.3415** | **0.2122** | **0.104** | **0.3505** | **0.2142** | **0.0987** | **0.2052** | **0.7993** | **0.1348** | **0.2375** | **0.1937** | **0.2486** | | cosine_mrr@10 | 0.1994 | 0.5504 | 0.1749 | 0.1082 | 0.4476 | 0.1667 | 0.2539 | 0.1507 | 0.7798 | 0.2421 | 0.1857 | 0.1647 | 0.4082 | | cosine_map@100 | 0.1136 | 0.2113 | 0.1886 | 0.0804 | 0.2931 | 0.1916 | 0.0189 | 0.161 | 0.7635 | 0.1026 | 0.1985 | 0.1654 | 0.1638 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2065 | | cosine_accuracy@3 | 0.3392 | | cosine_accuracy@5 | 0.4104 | | cosine_accuracy@10 | 0.5121 | | cosine_precision@1 | 0.2065 | | cosine_precision@3 | 0.1419 | | cosine_precision@5 | 0.1176 | | cosine_precision@10 | 0.0882 | | cosine_recall@1 | 0.1081 | | cosine_recall@3 | 0.1913 | | cosine_recall@5 | 0.2363 | | cosine_recall@10 | 0.3015 | | **cosine_ndcg@10** | **0.2524** | | cosine_mrr@10 | 0.2948 | | cosine_map@100 | 0.204 |   ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 60.48 tokens</li><li>max: 138 tokens</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 8 tokens</li><li>mean: 11.88 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 61.03 tokens</li><li>max: 127 tokens</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.1174 | 0.3053 | 0.1405 | 0.0440 | 0.2821 | 0.2297 | 0.0773 | 0.1708 | 0.7830 | 0.1181 | 0.2017 | 0.1447 | 0.1642 | 0.2138 | | 0.0010 | 1 | 3.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0256 | 25 | 3.6146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0512 | 50 | 3.6074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0768 | 75 | 3.5997 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1024 | 100 | 3.5737 | 2.0205 | 0.1178 | 0.3061 | 0.1477 | 0.0461 | 0.2837 | 0.2291 | 0.0804 | 0.1713 | 0.7791 | 0.1205 | 0.2049 | 0.1534 | 0.1731 | 0.2164 | | 0.1279 | 125 | 3.5644 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1535 | 150 | 3.4792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1791 | 175 | 3.4743 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2047 | 200 | 3.4169 | 1.9114 | 0.1336 | 0.3084 | 0.1446 | 0.0604 | 0.2965 | 0.2350 | 0.0847 | 0.1650 | 0.7806 | 0.1270 | 0.2141 | 0.1633 | 0.1835 | 0.2228 | | 0.2303 | 225 | 3.3535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2559 | 250 | 3.3336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2815 | 275 | 3.3038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3071 | 300 | 3.2576 | 1.8114 | 0.1359 | 0.3260 | 0.1733 | 0.0752 | 0.3167 | 0.2323 | 0.0851 | 0.1753 | 0.7843 | 0.1266 | 0.2218 | 0.1752 | 0.2012 | 0.2330 | | 0.3327 | 325 | 3.2304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3582 | 350 | 3.2133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3838 | 375 | 3.1369 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4094 | 400 | 3.1412 | 1.7379 | 0.1389 | 0.3298 | 0.1930 | 0.0934 | 0.3261 | 0.2310 | 0.0852 | 0.1760 | 0.7850 | 0.1349 | 0.2235 | 0.1863 | 0.2118 | 0.2396 | | 0.4350 | 425 | 3.0782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 450 | 3.0948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4862 | 475 | 3.0696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5118 | 500 | 3.0641 | 1.6850 | 0.1373 | 0.3307 | 0.1945 | 0.0937 | 0.3301 | 0.2365 | 0.0931 | 0.1950 | 0.7933 | 0.1359 | 0.2231 | 0.1885 | 0.2289 | 0.2447 | | 0.5374 | 525 | 3.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5629 | 550 | 2.9927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5885 | 575 | 2.9796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6141 | 600 | 2.9624 | 1.6475 | 0.1397 | 0.3321 | 0.2058 | 0.0999 | 0.3422 | 0.2276 | 0.1014 | 0.1901 | 0.7971 | 0.1393 | 0.2258 | 0.1918 | 0.2342 | 0.2482 | | 0.6397 | 625 | 2.9508 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6653 | 650 | 2.958 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6909 | 675 | 2.9428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7165 | 700 | 2.9589 | 1.6209 | 0.1425 | 0.3344 | 0.2061 | 0.1050 | 0.3427 | 0.2295 | 0.1001 | 0.1868 | 0.7955 | 0.1342 | 0.2298 | 0.1922 | 0.2343 | 0.2487 | | 0.7421 | 725 | 2.9152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7677 | 750 | 2.9056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7932 | 775 | 2.9111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8188 | 800 | 2.9107 | 1.6037 | 0.1415 | 0.3401 | 0.2064 | 0.1053 | 0.3523 | 0.2153 | 0.1001 | 0.1934 | 0.7976 | 0.1340 | 0.2302 | 0.1946 | 0.2461 | 0.2505 | | 0.8444 | 825 | 2.8675 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8700 | 850 | 2.9175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8956 | 875 | 2.8592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9212 | 900 | 2.86 | 1.5941 | 0.1411 | 0.3415 | 0.2180 | 0.1048 | 0.3506 | 0.2210 | 0.0987 | 0.2052 | 0.7988 | 0.1349 | 0.2302 | 0.1946 | 0.2464 | 0.2528 | | 0.9468 | 925 | 2.8603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9724 | 950 | 2.8909 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 975 | 2.8819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 977 | - | - | 0.1412 | 0.3415 | 0.2122 | 0.1040 | 0.3505 | 0.2142 | 0.0987 | 0.2052 | 0.7993 | 0.1348 | 0.2375 | 0.1937 | 0.2486 | 0.2524 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.025 kWh - **Carbon Emitted**: 0.010 kg of CO2 - **Hours Used**: 0.15 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.46.2 - PyTorch: 2.5.0+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.3 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

ilhamdprastyo/jina-embeddings-v3-tei

ilhamdprastyo

feature-extraction

[ "transformers", "pytorch", "onnx", "safetensors", "xlm-roberta", "feature-extraction", "sentence-similarity", "mteb", "sentence-transformers", "custom_code", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "arxiv:2409.10173", "license:cc-by-nc-4.0", "model-index", "text-embeddings-inference", "region:us" ]

2024-11-15T03:45:30

2024-11-15T06:59:50

0

1

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type: v_measure value: 39.96377851800628 - type: v_measure_std value: 0.9793033243093288 - task: type: Clustering dataset: name: MTEB CLSClusteringS2S (default) type: C-MTEB/CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics: - type: main_score value: 38.788850224595784 - type: v_measure value: 38.788850224595784 - type: v_measure_std value: 1.0712604145916924 - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics: - type: map value: 77.95952507806115 - type: mrr value: 80.8643253968254 - type: main_score value: 77.95952507806115 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics: - type: map value: 78.21522500165045 - type: mrr value: 81.28194444444443 - type: main_score value: 78.21522500165045 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval (default) type: mteb/cqadupstack-android config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 33.377 - type: map_at_10 value: 46.371 - type: map_at_100 value: 47.829 - type: map_at_1000 value: 47.94 - type: map_at_20 value: 47.205000000000005 - type: map_at_3 value: 42.782 - type: map_at_5 value: 44.86 - type: mrr_at_1 value: 41.345 - type: mrr_at_10 value: 52.187 - type: mrr_at_100 value: 52.893 - type: mrr_at_1000 value: 52.929 - type: mrr_at_20 value: 52.637 - type: mrr_at_3 value: 49.714000000000006 - type: mrr_at_5 value: 51.373000000000005 - type: ndcg_at_1 value: 41.345 - type: ndcg_at_10 value: 52.946000000000005 - type: ndcg_at_100 value: 57.92699999999999 - type: ndcg_at_1000 value: 59.609 - type: ndcg_at_20 value: 54.900999999999996 - type: ndcg_at_3 value: 48.357 - type: ndcg_at_5 value: 50.739000000000004 - type: precision_at_1 value: 41.345 - type: precision_at_10 value: 10.186 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.2 - type: precision_at_20 value: 5.959 - type: precision_at_3 value: 23.796 - type: precision_at_5 value: 17.024 - type: recall_at_1 value: 33.377 - type: recall_at_10 value: 65.067 - type: recall_at_100 value: 86.04899999999999 - type: recall_at_1000 value: 96.54899999999999 - type: recall_at_20 value: 72.071 - type: recall_at_3 value: 51.349999999999994 - type: recall_at_5 value: 58.41 - type: main_score value: 52.946000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval (default) type: mteb/cqadupstack-english config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 31.097 - type: map_at_10 value: 42.183 - type: map_at_100 value: 43.580999999999996 - type: map_at_1000 value: 43.718 - type: map_at_20 value: 42.921 - type: map_at_3 value: 38.963 - type: map_at_5 value: 40.815 - type: mrr_at_1 value: 39.745000000000005 - type: mrr_at_10 value: 48.736000000000004 - type: mrr_at_100 value: 49.405 - type: mrr_at_1000 value: 49.452 - type: mrr_at_20 value: 49.118 - type: mrr_at_3 value: 46.497 - type: mrr_at_5 value: 47.827999999999996 - type: ndcg_at_1 value: 39.745000000000005 - type: ndcg_at_10 value: 48.248000000000005 - type: ndcg_at_100 value: 52.956 - type: ndcg_at_1000 value: 54.99699999999999 - type: ndcg_at_20 value: 50.01 - type: ndcg_at_3 value: 43.946000000000005 - type: ndcg_at_5 value: 46.038000000000004 - type: precision_at_1 value: 39.745000000000005 - type: precision_at_10 value: 9.229 - type: precision_at_100 value: 1.5070000000000001 - type: precision_at_1000 value: 0.199 - type: precision_at_20 value: 5.489999999999999 - type: precision_at_3 value: 21.38 - type: precision_at_5 value: 15.274 - type: recall_at_1 value: 31.097 - type: recall_at_10 value: 58.617 - type: recall_at_100 value: 78.55199999999999 - type: recall_at_1000 value: 91.13900000000001 - type: recall_at_20 value: 64.92 - type: recall_at_3 value: 45.672000000000004 - type: recall_at_5 value: 51.669 - type: main_score value: 48.248000000000005 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval (default) type: mteb/cqadupstack-gaming config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.745000000000005 - type: map_at_10 value: 52.063 - type: map_at_100 value: 53.077 - type: map_at_1000 value: 53.13 - type: map_at_20 value: 52.66 - type: map_at_3 value: 48.662 - type: map_at_5 value: 50.507000000000005 - type: mrr_at_1 value: 45.391999999999996 - type: mrr_at_10 value: 55.528 - type: mrr_at_100 value: 56.16100000000001 - type: mrr_at_1000 value: 56.192 - type: mrr_at_20 value: 55.923 - type: mrr_at_3 value: 52.93600000000001 - type: mrr_at_5 value: 54.435 - type: ndcg_at_1 value: 45.391999999999996 - type: ndcg_at_10 value: 58.019 - type: ndcg_at_100 value: 61.936 - type: ndcg_at_1000 value: 63.015 - type: ndcg_at_20 value: 59.691 - type: ndcg_at_3 value: 52.294 - type: ndcg_at_5 value: 55.017 - type: precision_at_1 value: 45.391999999999996 - type: precision_at_10 value: 9.386 - type: precision_at_100 value: 1.232 - type: precision_at_1000 value: 0.136 - type: precision_at_20 value: 5.223 - type: precision_at_3 value: 23.177 - type: precision_at_5 value: 15.9 - type: recall_at_1 value: 39.745000000000005 - type: recall_at_10 value: 72.08099999999999 - type: recall_at_100 value: 88.85300000000001 - type: recall_at_1000 value: 96.569 - type: recall_at_20 value: 78.203 - type: recall_at_3 value: 56.957 - type: recall_at_5 value: 63.63100000000001 - type: main_score value: 58.019 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval (default) type: mteb/cqadupstack-gis config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 26.651999999999997 - type: map_at_10 value: 35.799 - type: map_at_100 value: 36.846000000000004 - type: map_at_1000 value: 36.931000000000004 - type: map_at_20 value: 36.341 - type: map_at_3 value: 32.999 - type: map_at_5 value: 34.597 - type: mrr_at_1 value: 28.814 - type: mrr_at_10 value: 37.869 - type: mrr_at_100 value: 38.728 - type: mrr_at_1000 value: 38.795 - type: mrr_at_20 value: 38.317 - type: mrr_at_3 value: 35.235 - type: mrr_at_5 value: 36.738 - type: ndcg_at_1 value: 28.814 - type: ndcg_at_10 value: 41.028 - type: ndcg_at_100 value: 46.162 - type: ndcg_at_1000 value: 48.15 - type: ndcg_at_20 value: 42.824 - type: ndcg_at_3 value: 35.621 - type: ndcg_at_5 value: 38.277 - type: precision_at_1 value: 28.814 - type: precision_at_10 value: 6.361999999999999 - type: precision_at_100 value: 0.9450000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_20 value: 3.6159999999999997 - type: precision_at_3 value: 15.140999999999998 - type: precision_at_5 value: 10.712000000000002 - type: recall_at_1 value: 26.651999999999997 - type: recall_at_10 value: 55.038 - type: recall_at_100 value: 78.806 - type: recall_at_1000 value: 93.485 - type: recall_at_20 value: 61.742 - type: recall_at_3 value: 40.682 - type: recall_at_5 value: 46.855000000000004 - type: main_score value: 41.028 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval (default) type: mteb/cqadupstack-mathematica config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 17.627000000000002 - type: map_at_10 value: 26.436999999999998 - type: map_at_100 value: 27.85 - type: map_at_1000 value: 27.955999999999996 - type: map_at_20 value: 27.233 - type: map_at_3 value: 23.777 - type: map_at_5 value: 25.122 - type: mrr_at_1 value: 22.387999999999998 - type: mrr_at_10 value: 31.589 - type: mrr_at_100 value: 32.641999999999996 - type: mrr_at_1000 value: 32.696999999999996 - type: mrr_at_20 value: 32.201 - type: mrr_at_3 value: 28.98 - type: mrr_at_5 value: 30.342000000000002 - type: ndcg_at_1 value: 22.387999999999998 - type: ndcg_at_10 value: 32.129999999999995 - type: ndcg_at_100 value: 38.562999999999995 - type: ndcg_at_1000 value: 40.903 - type: ndcg_at_20 value: 34.652 - type: ndcg_at_3 value: 27.26 - type: ndcg_at_5 value: 29.235 - type: precision_at_1 value: 22.387999999999998 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 1.068 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_20 value: 3.6999999999999997 - type: precision_at_3 value: 13.267000000000001 - type: precision_at_5 value: 9.403 - type: recall_at_1 value: 17.627000000000002 - type: recall_at_10 value: 44.71 - type: recall_at_100 value: 72.426 - type: recall_at_1000 value: 88.64699999999999 - type: recall_at_20 value: 53.65 - type: recall_at_3 value: 30.989 - type: recall_at_5 value: 36.237 - type: main_score value: 32.129999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval (default) type: mteb/cqadupstack-physics config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 30.891000000000002 - type: map_at_10 value: 41.519 - type: map_at_100 value: 42.896 - type: map_at_1000 value: 42.992999999999995 - type: map_at_20 value: 42.287 - type: map_at_3 value: 37.822 - type: map_at_5 value: 39.976 - type: mrr_at_1 value: 37.921 - type: mrr_at_10 value: 47.260999999999996 - type: mrr_at_100 value: 48.044 - type: mrr_at_1000 value: 48.08 - type: mrr_at_20 value: 47.699999999999996 - type: mrr_at_3 value: 44.513999999999996 - type: mrr_at_5 value: 46.064 - type: ndcg_at_1 value: 37.921 - type: ndcg_at_10 value: 47.806 - type: ndcg_at_100 value: 53.274 - type: ndcg_at_1000 value: 55.021 - type: ndcg_at_20 value: 49.973 - type: ndcg_at_3 value: 42.046 - type: ndcg_at_5 value: 44.835 - type: precision_at_1 value: 37.921 - type: precision_at_10 value: 8.767999999999999 - type: precision_at_100 value: 1.353 - type: precision_at_1000 value: 0.168 - type: precision_at_20 value: 5.135 - type: precision_at_3 value: 20.051 - type: precision_at_5 value: 14.398 - type: recall_at_1 value: 30.891000000000002 - type: recall_at_10 value: 60.897999999999996 - type: recall_at_100 value: 83.541 - type: recall_at_1000 value: 94.825 - type: recall_at_20 value: 68.356 - type: recall_at_3 value: 44.65 - type: recall_at_5 value: 51.919000000000004 - type: main_score value: 47.806 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval (default) type: mteb/cqadupstack-programmers config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 27.654 - type: map_at_10 value: 38.025999999999996 - type: map_at_100 value: 39.425 - type: map_at_1000 value: 39.528 - type: map_at_20 value: 38.838 - type: map_at_3 value: 34.745 - type: map_at_5 value: 36.537 - type: mrr_at_1 value: 34.018 - type: mrr_at_10 value: 43.314 - type: mrr_at_100 value: 44.283 - type: mrr_at_1000 value: 44.327 - type: mrr_at_20 value: 43.929 - type: mrr_at_3 value: 40.868 - type: mrr_at_5 value: 42.317 - type: ndcg_at_1 value: 34.018 - type: ndcg_at_10 value: 43.887 - type: ndcg_at_100 value: 49.791000000000004 - type: ndcg_at_1000 value: 51.834 - type: ndcg_at_20 value: 46.376 - type: ndcg_at_3 value: 38.769999999999996 - type: ndcg_at_5 value: 41.144 - type: precision_at_1 value: 34.018 - type: precision_at_10 value: 8.001999999999999 - type: precision_at_100 value: 1.2630000000000001 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.737 - type: precision_at_3 value: 18.417 - type: precision_at_5 value: 13.150999999999998 - type: recall_at_1 value: 27.654 - type: recall_at_10 value: 56.111 - type: recall_at_100 value: 81.136 - type: recall_at_1000 value: 94.788 - type: recall_at_20 value: 65.068 - type: recall_at_3 value: 41.713 - type: recall_at_5 value: 48.106 - type: main_score value: 43.887 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval (default) type: CQADupstackRetrieval_is_a_combined_dataset config: default split: test revision: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 42.58858333333333 - type: ndcg_at_10 value: 42.58858333333333 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval (default) type: mteb/cqadupstack-stats config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.501 - type: map_at_10 value: 32.814 - type: map_at_100 value: 33.754 - type: map_at_1000 value: 33.859 - type: map_at_20 value: 33.324 - type: map_at_3 value: 30.758000000000003 - type: map_at_5 value: 31.936999999999998 - type: mrr_at_1 value: 27.761000000000003 - type: mrr_at_10 value: 35.662 - type: mrr_at_100 value: 36.443999999999996 - type: mrr_at_1000 value: 36.516999999999996 - type: mrr_at_20 value: 36.085 - type: mrr_at_3 value: 33.742 - type: mrr_at_5 value: 34.931 - type: ndcg_at_1 value: 27.761000000000003 - type: ndcg_at_10 value: 37.208000000000006 - type: ndcg_at_100 value: 41.839 - type: ndcg_at_1000 value: 44.421 - type: ndcg_at_20 value: 38.917 - type: ndcg_at_3 value: 33.544000000000004 - type: ndcg_at_5 value: 35.374 - type: precision_at_1 value: 27.761000000000003 - type: precision_at_10 value: 5.92 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 3.4130000000000003 - type: precision_at_3 value: 15.031 - type: precision_at_5 value: 10.306999999999999 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 47.579 - type: recall_at_100 value: 69.045 - type: recall_at_1000 value: 88.032 - type: recall_at_20 value: 54.125 - type: recall_at_3 value: 37.202 - type: recall_at_5 value: 41.927 - type: main_score value: 37.208000000000006 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval (default) type: mteb/cqadupstack-tex config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 18.29 - type: map_at_10 value: 26.183 - type: map_at_100 value: 27.351999999999997 - type: map_at_1000 value: 27.483999999999998 - type: map_at_20 value: 26.798 - type: map_at_3 value: 23.629 - type: map_at_5 value: 24.937 - type: mrr_at_1 value: 22.299 - type: mrr_at_10 value: 30.189 - type: mrr_at_100 value: 31.098 - type: mrr_at_1000 value: 31.177 - type: mrr_at_20 value: 30.697000000000003 - type: mrr_at_3 value: 27.862 - type: mrr_at_5 value: 29.066 - type: ndcg_at_1 value: 22.299 - type: ndcg_at_10 value: 31.202 - type: ndcg_at_100 value: 36.617 - type: ndcg_at_1000 value: 39.544000000000004 - type: ndcg_at_20 value: 33.177 - type: ndcg_at_3 value: 26.639000000000003 - type: ndcg_at_5 value: 28.526 - type: precision_at_1 value: 22.299 - type: precision_at_10 value: 5.8020000000000005 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_20 value: 3.505 - type: precision_at_3 value: 12.698 - type: precision_at_5 value: 9.174 - type: recall_at_1 value: 18.29 - type: recall_at_10 value: 42.254999999999995 - type: recall_at_100 value: 66.60000000000001 - type: recall_at_1000 value: 87.31400000000001 - type: recall_at_20 value: 49.572 - type: recall_at_3 value: 29.342000000000002 - type: recall_at_5 value: 34.221000000000004 - type: main_score value: 31.202 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval (default) type: mteb/cqadupstack-unix config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 27.722 - type: map_at_10 value: 37.698 - type: map_at_100 value: 38.899 - type: map_at_1000 value: 38.998 - type: map_at_20 value: 38.381 - type: map_at_3 value: 34.244 - type: map_at_5 value: 36.295 - type: mrr_at_1 value: 32.183 - type: mrr_at_10 value: 41.429 - type: mrr_at_100 value: 42.308 - type: mrr_at_1000 value: 42.358000000000004 - type: mrr_at_20 value: 41.957 - type: mrr_at_3 value: 38.401999999999994 - type: mrr_at_5 value: 40.294999999999995 - type: ndcg_at_1 value: 32.183 - type: ndcg_at_10 value: 43.519000000000005 - type: ndcg_at_100 value: 48.786 - type: ndcg_at_1000 value: 50.861999999999995 - type: ndcg_at_20 value: 45.654 - type: ndcg_at_3 value: 37.521 - type: ndcg_at_5 value: 40.615 - type: precision_at_1 value: 32.183 - type: precision_at_10 value: 7.603 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_20 value: 4.408 - type: precision_at_3 value: 17.071 - type: precision_at_5 value: 12.668 - type: recall_at_1 value: 27.722 - type: recall_at_10 value: 57.230000000000004 - type: recall_at_100 value: 79.97999999999999 - type: recall_at_1000 value: 94.217 - 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type: ndcg_at_100 value: 80.75 - type: ndcg_at_1000 value: 81.026 - type: ndcg_at_20 value: 79.092 - type: ndcg_at_3 value: 72.81 - type: ndcg_at_5 value: 74.22999999999999 - type: precision_at_1 value: 71.829 - type: precision_at_10 value: 17.717 - type: precision_at_100 value: 2.031 - type: precision_at_1000 value: 0.207 - type: precision_at_20 value: 9.399000000000001 - type: precision_at_3 value: 44.458999999999996 - type: precision_at_5 value: 31.535000000000004 - type: recall_at_1 value: 46.444 - type: recall_at_10 value: 86.275 - type: recall_at_100 value: 98.017 - type: recall_at_1000 value: 99.8 - type: recall_at_20 value: 90.935 - type: recall_at_3 value: 70.167 - type: recall_at_5 value: 78.2 --- <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> The embedding model trained by <a href="https://jina.ai/">Jina AI</a>. jina-embeddings-v3: Multilingual Embeddings With Task LoRA ## Quick Start [Blog](https://jina.ai/news/jina-embeddings-v3-a-frontier-multilingual-embedding-model/#parameter-dimensions) | [Azure](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/jinaai.jina-embeddings-v3) | [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-kdi3xkt62lo32) | [API](https://jina.ai/embeddings) ## Intended Usage & Model Info `jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications. Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co/jinaai/xlm-roberta-flash-implementation), this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. ### Key Features: - **Extended Sequence Length:** Supports up to 8192 tokens with RoPE. - **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options: - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks - `separation`: Used for embeddings in clustering and re-ranking applications - `classification`: Used for embeddings in classification tasks - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks - **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application. ### Supported Languages: While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: **Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.** ## Usage **<details><summary>Apply mean pooling when integrating the model.</summary>** ### Why Use Mean Pooling? Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. This approach has been shown to produce high-quality sentence embeddings. We provide an `encode` function that handles this for you automatically. However, if you're working with the model directly, outside of the `encode` function, you'll need to apply mean pooling manually. Here's how you can do it: ```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 = ["How is the weather today?", "What is the current weather like today?"] tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3") model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") task = 'retrieval.query' task_id = model._adaptation_map[task] adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32) with torch.no_grad(): model_output = model(**encoded_input, adapter_mask=adapter_mask) embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) embeddings = F.normalize(embeddings, p=2, dim=1) ``` </details> The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/). Alternatively, you can use `jina-embeddings-v3` directly via Transformers package: ```bash !pip install transformers torch einops !pip install 'numpy<2' ``` If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100), ```bash !pip install flash-attn --no-build-isolation ``` ```python from transformers import AutoModel # Initialize the model model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) texts = [ "Follow the white rabbit.", # English "Sigue al conejo blanco.", # Spanish "Suis le lapin blanc.", # French "跟着白兔走。", # Chinese "اتبع الأرنب الأبيض.", # Arabic "Folge dem weißen Kaninchen.", # German ] # When calling the `encode` function, you can choose a `task` based on the use case: # 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching' # Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used. embeddings = model.encode(texts, task="text-matching") # Compute similarities print(embeddings[0] @ embeddings[1].T) ``` By default, the model supports a maximum sequence length of 8192 tokens. However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode(["Very long ... document"], max_length=2048) ``` In case you want to use **Matryoshka embeddings** and switch to a different dimension, you can adjust it by passing the `truncate_dim` parameter to the `encode` function: ```python embeddings = model.encode(['Sample text'], truncate_dim=256) ``` The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`: ```bash !pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) task = "retrieval.query" embeddings = model.encode( ["What is the weather like in Berlin today?"], task=task, prompt_name=task, ) ``` You can fine-tune `jina-embeddings-v3` using [SentenceTransformerTrainer](https://sbert.net/docs/package_reference/sentence_transformer/trainer.html). To fine-tune for a specific task, you should set the task before passing the model to the ST Trainer, either during initialization: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'default_task': 'classification'}) ``` Or afterwards: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) model[0].default_task = 'classification' ``` This way you can fine-tune the LoRA adapter for the chosen task. However, If you want to fine-tune the entire model, make sure the main parameters are set as trainable when loading the model: ```python model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True, model_kwargs={'lora_main_params_trainable': True}) ``` This will allow fine-tuning the whole model instead of just the LoRA adapters. **<details><summary>ONNX Inference.</summary>** You can use ONNX for efficient inference with `jina-embeddings-v3`: ```python import onnxruntime import numpy as np from transformers import AutoTokenizer, PretrainedConfig # Load tokenizer and model config tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3') config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3') # Tokenize input input_text = tokenizer('sample text', return_tensors='np') # ONNX session model_path = 'jina-embeddings-v3/onnx/model.onnx' session = onnxruntime.InferenceSession(model_path) # Prepare inputs for ONNX model task_type = 'text-matching' task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64) inputs = { 'input_ids': input_text['input_ids'], 'attention_mask': input_text['attention_mask'], 'task_id': task_id } # Run model outputs = session.run(None, inputs)[0] # Apply mean pooling to 'outputs' to get a single representation of each text ``` </details> ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## License `jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/). ## Citation If you find `jina-embeddings-v3` useful in your research, please cite the following paper: ```bibtex @misc{sturua2024jinaembeddingsv3multilingualembeddingstask, title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao}, year={2024}, eprint={2409.10173}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.10173}, } ```

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

oMarquess/nahara-dataset-model

oMarquess

question-answering

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "question-answering", "en", "dataset:oMarquess/nahara-dataset-2010n", "license:apache-2.0", "endpoints_compatible", "region:us" ]

2024-11-17T16:05:17

2024-11-17T18:29:38

0

--- base_model: unsloth/meta-llama-3.1-8b-bnb-4bit datasets: - oMarquess/nahara-dataset-2010n language: - en license: apache-2.0 pipeline_tag: question-answering tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Nahara Dataset Model - **Developed by:** Redeemer Salami Okekale, BMS - **License:** apache-2.0 - **Finetuned from model:** unsloth/meta-llama-3.1-8b-bnb-4bit - **Training Loss:** 1.181600 **Model Description:** The **nahara-dataset-model** is a fine-tuned version of Meta's LLaMA series, specifically optimized for low-precision (4-bit) operations to enhance efficiency in both memory usage and computational resources. It was fine-tuned on the Nahara dataset and achieved a training loss of **1.181600**, ensuring strong performance on medical data. - **Model Type:** Transformer-based Language Model - **Size:** 8 billion parameters - **Precision:** 4-bit quantization using bnb (bits and bytes), improving memory efficiency and making the model suitable for resource-constrained environments. **Intended Use:** This model serves as a highly adaptable AI copilot for medical professionals, ideal for providing real-time recommendations and decision support. It can assist with: - Medical diagnostics and treatment suggestions - Summarization of clinical data - Generation of medical reports and documentation - Assistance with medical coding and research data preparation **Performance:** - **Training Loss:** 1.181600 - **Fine-tuning Data:** Medical and clinical datasets enhanced through data augmentation techniques to handle sparsity and variability, making it applicable across various healthcare contexts. **Applications:** The nahara-dataset-model is suited for: - Clinical decision support systems - AI copilots for medical professionals - Research data analysis and augmentation - Medical record summarization and automated report generation **Limitations and Considerations:** - The model is trained on medical data but may not encompass all clinical expertise nuances. It should be used to **augment decision-making**, not replace professional judgment. - Ethical considerations, including **data privacy** and **bias** in healthcare applications, must be strictly followed. - While efficiency is boosted by quantizing to 4-bit, there may be **trade-offs in performance** for complex tasks compared to higher precision models. **Future Improvements:** The model will undergo further optimization and refinement in **Phase 2**, including expanding the dataset, improving real-world adaptability, and fine-tuning the AI copilot for specific medical specializations. **Contributors:** - Emmanuel Akomanin Asiamah, PhD - Elli Banini - Felix Coker - Philip Attram, BMS - Schandorf Osam-Frimpong, MD - Daniel Mawuenyega Gohoho - Vitus Amenorpe - Aaron Kofi Gayi - Julius Richard Ogbey - Cherryln Asiwome Ahiable - Ama Quashie - Andrew Kojo Mensah-Onumah - Edith Zikpi - Azumah Benson, MD

[ "SUMMARIZATION" ]

[ "MEDICAL DATA" ]

HIT-TMG/KaLM-embedding-multilingual-max-instruct-v1

HIT-TMG

null

[ "mteb", "model-index", "region:us" ]

2024-11-21T06:26:54

2025-01-07T12:09:46

0

9

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type: similarity_f1_threshold value: 65.37638306617737 - type: similarity_precision value: 51.91637630662021 - type: similarity_recall value: 97.38562091503267 --- Development Version: Scheduled for Release Post-Optimization

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

Aleph-Alpha/Pharia-1-Embedding-4608-control

Aleph-Alpha

null

[ "license:other", "region:us" ]

2024-11-21T14:47:12

2024-12-19T13:51:34

0

1

--- license: other license_name: open-aleph-license license_link: LICENSE --- # Model Card for Pharia-1-Embedding-4608-control This model card provides an overview of Pharia-1-Embedding-4608-control, an embedding model developed by Aleph Alpha Research*. Pharia-1-Embedding-4608-control has been built on top of Pharia-1-LLM-7B-control. For additional training details, including architecture, tokenization, tokenizer fertility, pre-training, instruction fine-tuning and resource usage we refer to the model card of [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control). Due to being trained with a diverse set of instructions, Pharia-1-Embedding-4608-control can deliver customized embeddings at runtime without further finetuning. Pharia-1-Embedding-4608-control was trained on carefully curated data in compliance with applicable EU and national regulations, including copyright and data privacy laws. Furthermore it shows strong cross-lingual performance allowing for prompting and text to be embedded written in different languages. The finetuning was always performed using English instructions. ## Model Overview - **Developed by:** Aleph Alpha Research   - **Model type/architecture:** Embedding adapter on top of Pharia-1-LLM-7B-control trained with representational instruction-tuning (inspired by the approach of GritLM). - **Language(s) (NLP):** Trained on English, German, French, Spanish.   - **USP:** Model exhibits superior quality in pure cross-lingual tasks for (German, English, French & Spanish pairings, see evaluation below) ### Model Description |Model |Embedding Size|Description| |--------------------------------|--------------|-----------| |Pharia-1-Embedding-4608-control |4608|Pharia-1-Embedding-4608-control is an Embedding model optimized for German, French and Spanish and designed for customizable embeddings at runtime via instructions (prompts)|  ### Model Access We provide access to our models through the channels listed below. - On-premise installation: Our customers are supplied with our full LLM and Embedding model stack, including model weights and inference runtime. Contact us for options to deploy Pharia-1-Embedding-4608-control in any cloud or on-premise environment. We provide our customers with open access to our full model checkpoint including weights and code for commercial use. Downloadable from Huggingface: An HF-adapted version of our model can be found in our Huggingface repo (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf) together with code snippets that make the model easy to use. Please refer to the changelog for updates to the models served. We do not deprecate officially released versions of old model generations when we release newer versions, so users can continue to have access to available models. No prompt data is stored when using our systems, which means that we do not collect PII (personally identifiable information) for any of our public API users as detailed in our Terms & Conditions. We do not log user inputs to the models. We do not train on user data. - **Note**: The same models are made available to users regardless of their geographic location, and the input language but subject to sanction regimes, technology export regulations, and other restrictions that may apply. The same offering is provided to all countries within and external to the European Union if no legal restrictions apply. ### Intended Use Pharia-1-Embedding-4608-control is intended to be deployed as components of AI systems or applications. Use-cases and the model's capabilities include but are not limited to: information retrieval, semantic search, re-ranking and clustering. #### Out-of-Scope Use Pharia-1-Embedding-4608-control is not to be used for illegal or unlawful actions of any kind and with any illegal or unlawful content. This includes in particular prohibited activities such as engaging in terrorism, violence, human trafficking, illegal distribution of materials to minors, sexual solicitation, any other criminal activities, harassment, discrimination, creating or promoting malicious code or activities risking death or harm, including those related to military or nuclear applications, and activities not in compliance with sanction regimes, technology export regulations, and other restrictions that may apply. The models are to be used following ethical standards. The utilization of our technology is always governed by, and may be limited in accordance with, our Terms of Use, the Open Aleph License, or any specific agreement we might have established with you. For non-anonymous reports, we also provide an appeals mechanism for usage policy violations via our dedicated contact address [[email protected]]([email protected]) to communicate with us. Customers and partners are enabled to use our ticketing system [ticketing system](https://servicedesk.aleph-alpha.de/external) for appeals, claims and feedback. ### Use limitations Beyond the risks & limitations stated in the original [Pharia-1-LLM-7B-control](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control), the following limitation applies: - Pharia-1-Embedding-4608-control has been optimized on embedding computation only. Therefore, we do not recommend usage for text generation purposes. ## How to Use We provide two access pathways for our Pharia4608 embedding model. The first one leverages the HF ecosystem and can be found here: https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control-hf. The code snippet in the box below demonstrates its use. As soon as the model class is invoked, the model will we loaded from the repo and is ready for use. The other access pathway is through our public scaling code base. In this version the model weights were not converted to HF format and the repo https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control can be cloned as is. The model path has to be adjusted to the local path where the model was downloaded. The model cards in the corresponding repositories only the code snippet which applies to the specific repo. ### Use with scaling inference code base To perform inference with the original model files, you’ll first need to install the [Scaling library](https://github.com/Aleph-Alpha/scaling). Follow the installation instructions provided in the repository's README file. After installation, download the model weights and use the Scaling inference module to load the checkpoint, vocabulary, and configuration files. ``` from pathlib import Path from torch.nn import CosineSimilarity from scaling.transformer.inference import TransformerInferenceModule MODEL_PATH = "/path/to/model" inference_model = TransformerInferenceModule.from_checkpoint( checkpoint_dir=Path(MODEL_PATH), ) # embed the query: query = "Which country is Galileo from?" query_embeddings = inference_model.encode_queries(query, convert_to_tensor=True) print(f"Type of embeddings: {type(query_embeddings)},\n\ shape of query embeddings: {query_embeddings.shape}") # embed the documents: document_1 = "Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql." document_embeddings_1 = inference_model.encode_corpus(document_1, convert_to_tensor=True) document_2 = "Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy." document_embeddings_2 = inference_model.encode_corpus(document_2, convert_to_tensor=True) # customized embeddings steering the query: instruction = "Represent the question about TV shows to find a paragraph that answers it." steered_query_embeddings = inference_model.encode_queries(query, instruction=instruction, convert_to_tensor=True) # compute similarity between steered query and both documents cossim = CosineSimilarity(dim=1, eps=1e-6) sim1 = round(cossim(document_embeddings_1, steered_query_embeddings).item(), 3) sim2 = round(cossim(document_embeddings_2, steered_query_embeddings).item(), 3) print("Steered embedding causes higher similarity of query to TV show:") print(f"Similarity query/TV show ({sim1}) > similarity query/Italian polymath: ({sim2})") ``` Disclaimer: For the official evaluation scores we used the Scaling compatible checkpoint available under Pharia-1-Embedding-4608-control (https://huggingface.co/Aleph-Alpha/Pharia-1-Embedding-4608-control) ### Example for instruction embedding Pharia-1-Embedding-4608-control is useful for any use-case that relates to estimating the similarity/relevance between text fragments. This is relevant for use-cases such as information retrieval, semantic search, re-ranking and clustering. We use the task of information retrieval as a guiding example where we assume the following query: “Which country is Galileo from?” and two documents: - Galileo is a German television program series produced and broadcast on ProSieben television network. It is also sold to broadcasters in other countries (namely Russia and Poland). The first show was broadcast in 1998, and is now stored in the Arctic World Archive in Svalbard, Norway, after being transferred to special film created by Piql. - Galileo di Vincenzo Bonaiuti de' Galilei (15 February 1564 - 8 January 1642), commonly referred to as Galileo Galilei or mononymously as Galileo, was an Italian (Florentine) astronomer, physicist and engineer, sometimes described as a polymath. He was born in the city of Pisa, then part of the Duchy of Florence and present-day Italy. Source: Wikipedia For our guiding example we assume the context of this use-case is a Question-Answer system for movies and TV shows. **Step 1:** Embed the Query ``` "input": "Which country is Galileo from?" ``` → Embedding: ```[-0.6780134, 0.61449033, 0.102911085, ...]``` **Step 2:** Embed the Documents "input": "Galileo is a German television program series ..." → Embedding: ```[-0.36119246, 0.7793595, -0.38735497, ...]``` "input": "Galileo di Vincenzo Bonaiuti de' Galilei ..." → Embedding: ```[-0.25108248, 1.0496024, -0.20945309, ...]``` **Step 3:** Compare the similarity A typical similarity measure between vectors is cosine similarity. Higher numbers indicate more similar vectors and by extension capture the concept of relevance. In a RAG application these scores determine the ranking during the retrieval step. In this example, we obtain the following cosine similarities: Query vs. German TV show: ~0.661 Query vs. Italian polymath: ~0.757 This implies that the paragraph about the Italian polymath would be ranked higher than the paragraph about the German TV show which is the one we’re interested in. #### Customized Embeddings To further improve performance you can use instructions to steer the model. Instructions can help the model understand nuances of your specific data and ultimately lead to embeddings that are more useful for your use-case. In this case, we aim to get embeddings that would lead to ranking the paragraph about the German TV Show higher than the paragraph about the Italian polymath. **Step 1:** Embed the Query with an Instruction ```"instruction": "Represent the question about TV shows to find a paragraph that answers it."``` ```"input": "input": "Which country is Galileo from?"``` → Embedding: ```[-0.6310919, 1.4309896, -0.85546875, ...]``` **Step 2:** Compare the similarity We leave the embeddings of the documents untouched and now obtain the following cosine similarities: Query vs. German TV show: ~0.632 Query vs. Italian polymath: ~0.512 These new cosine similarities imply that the ranking has indeed changed and the paragraph about the German TV show is **now more relevant**. This shows that instructions can help the model understand nuances in the data better and ultimately lead to embeddings that are more useful for your use-case. #### Tips on using the model - First try and ideally evaluate the model on your data without instructions to see whether performance aligns with your expectations out-of-the-box - If you decide to use an instruction with the aim of further boosting performance we suggest using this template as a guideline * ```Template: Represent the [X] to find a [Y] that [describe how the X and Y relate]``` * Examples 1. Represent the newspaper paragraph to find a newspaper paragraph with the same topic 2. Represent the sentence to find another sentence with the same meaning - In cases where the two texts to compare are different in nature (e.g. query and document) – also called “asymmetric” – we suggest to first add an instruction to query texts only. Again, try and ideally evaluate the model in this setting. Then, if your aim is to further boost performance, we suggest that you add instructions to document texts as well where [X] and [Y] are flipped accordingly. ## Evaluation ### Evaluations on cross-lingual capabilities There are important use cases where one wants to retrieve multiple documents on a topic or answering questions that are formulated in a different language than the query. This increases recall and information retrieval coverage. For testing on cross-lingual capabilities we evaluated Pharia-1-Embedding-4608-control, GritLM, Nvidia-Embed-v2 and BGE-Multilingual-Gemma2 on the MLQA-V1 datasets (Facebook) for German/English and English/Spanish language pairings. For German/French we used the CLSD-WMT19 dataset providing correct and adversarial translations of a sentence in the corresponding pair language. In order to check quality over a larger range of sample size we did the accuracy computations for varying number of samples taken from the MLQA-V1 dataset. For the CLSD-WMT19 evaluation we employed the full set of data (2900 samples available). #### MLQA-V1 Ger/Eng cross-lingual accuracies for the considered models |# of samples|Pharia4608|GritLM|Nvidia-Embed-v2|BGE-Gemma2| |:---:|:---:|:---:|:---:|:---:| |1000|86.0%|82.5%|77.0%|87.0%| |2000|79.5%|73.4%|69.4%|76.8%| |4000|65.3%|59.2%|56.0%|62.7%| |6000|54.3%|48.6%|45.6%|52.6%| |10000|38.6%|32.8%|32.8%|39.4%| #### MLQA-V1 Eng/Esp cross-lingual accuracies for the considered models |# samples|Pharia4608|GritLM|NV-Embed-v2|BGE-Gemma2| |:---:|:---:|:---:|:---:|:---:| |1000|87.5%|82.0%|81.5%|87.0%| |2000|78.5%|73.9%|70.7%|77.0%| |4000|65.5%|59.3%|56.9%|64.2%| |6000|55.3%|49.2%|46.2%|53.4%| |10000|41.7%|35.5%|33.2%|40.0%| #### CLSD-WMT19 Ger/Fra (2900 samples) cross-lingual evaluation for the considered models |Model Name | accuracy | |:-----------------------------:|:--------------------------------:| |Pharia-1-Embedding-4608-control|95.1% | |GritLM-7B |94.2% | |Nvidia-Embed-v2 |93.4% | |BGE-Gemma2 |95.4% | ## Evaluations on MTEB tasks To evaluate our models multilingual capabilities we evaluate it against other source-available, high-performing embedding models listen in the MTEB leaderboard. For the following evaluations we compare the following models: - NVEmbed-V2: The highest scoring model in the MTEB leaderboard at time of the release - BGE-Multilingual-Gemma2: The highest scoring multilingual model in the MTEB leaderboard at the time of release. - GritLM: A generative representational instruction tuned language model. #### Methodology for Multilingual Evaluations (European languages) * Context: MTEB is a collection of tasks across many task types (e.g. classification, retrieval etc.). Furthermore, tasks can have N subsets on different languages. Subsets itself can also contain N languages, e.g. translation-related tasks. Base script actually comes from [gritlm/evaluation/eval_mteb.py at main · ContextualAI/gritlm](https://github.com/ContextualAI/gritlm/blob/main/evaluation/eval_mteb.py) and includes Medi2-style instructions for many MTEB Tasks. The instructions are all in English. All evaluations use Medi2-style instructions except for the “no instructions” case (see above). If a task does not have Medi2-style instructions, we skip the task. As European languages for MTEB tests German, Italian, Spanish, Portuguese and French were used. * For our Multilingual Evaluations (European languages) we use the tasks from [mteb/scripts/task_selection/europe_tasks.csv at main · embeddings-benchmark/mteb](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/task_selection/europe_tasks.csv) and then filter for tasks where there is at least one subset with at least one of the European languages. * We skip BibleNLPBitextMining and FloresBitextMining because they don’t have ‘test’ splits, only ‘train’ split which we don’t want to use for evaluation (→ training data contamination likely) * We evaluate subsets which contain at least one of the European languages → that’s why there is also an “English” language column because there are subsets that are e.g. En ↔︎ De and are thus considered * The tasks that remain are - AmazonCounterfactualClassification - BUCC.v2 - DiaBlaBitextMining - MassiveScenarioClassification - NTREXBitextMining - STS17 * For NTREXBitextMining the subsets are further filtered down to only pairs of the European languages instead of at least one European language - i.e. this gives 20-2=18 translation pair subsets between the 5 languages. -2 because Italian ↔︎ German doesn’t exist. - this is done because otherwise there are 250 translation pair subsets which are not as relevant (e.g. they contain Vietnamese ↔︎ Portuguese) We used the official scores reported in MTEB Leaderboard if reported, but for some models and subset we created the scores ourselves with the official Huggingface checkpoints and instructions referenced in the Paper or Model card. #### Europe by task | Model Name | AmazonCounterfactualClassification | BUCC.v2 | DiaBlaBitextMining | MassiveScenarioClassification | NTREXBitextMining | STS17 | Average | |-------------------------------------------------------|-------------------------------------:|----------:|---------------------:|--------------------------------:|--------------------:|---------:|----------:| | Pharia-1-Embedding-4608-control | 72.49 | 99.19 | 86.51 | 75.58 | 98.24 | 87.67 | 86.61 | | GritLM-7B | 76.64 | 99.43 | 86.45 | 78.93 | 98.46 | 88.07 | 87.99 | | BGE-Multilingual-Gemma2 | 69.72 | 99.38 | 86.90 | 78.57 | 98.58 | 86.69 | 86.64 | | Nvidia-Embed-v2 | 70.72 | 99.14 | 73.22 | 75.21 | 96.65 | 87.36 | 83.72 | #### Europe by language | Model Name | deu-Latn | eng-Latn | fra-Latn | por-Latn | ita-Latn | spa-Latn | Average | |-------------------------------------------------------|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|----------:| | Pharia-1-Embedding-4608-control | 0.925309 | 0.902113 | 0.937961 | 0.953719 | 0.942352 | 0.945642 | 0.934516 | | GritLM-7B | 0.934603 | 0.905669 | 0.942364 | 0.962042 | 0.949731 | 0.947428 | 0.940306 | | BGE-Multilingual-Gemma2| 93.07 | 92.17 | 94.91 | 94.64 | 96.28 | 94.94 | 94.35 | | Nvidia-Embed-v2 | 91.58 | 88.85 | 90.51 | 93.94 | 95.08 | 93.78| 92.29 | #### MTEB – English only | |Retrieval|Classification|STS|Summarization|PairClassification|Clustering|Reranking|Average| |---|--|--|--|--|--|--|--|--| |Nvidia-Embed-v2|62.65|90.37|84.31|30.7|88.67|58.46|60.65|72.31| |BGE-Multilingual-Gemma2|59.24|88.08|83.88|31.2|85.84|54.65|59.72|69.88| |GritLM-7B|57.36|78.65|83.35|30.39|87.29|50.61|60.48|66.58| |Pharia-1-Embedding-4608-control|39.15 |74.40|82.7 |30.95 |81.73|46.23|57.45|58.94| #### Ablation for “No Instruction” case We ablate how performance changes when not using task-specific instructions for the embeddings. |Model Name|ArguAna|AskUbuntuDupQuestions|BIOSSES|Banking77Classification|EmotionClassification|MedrxivClusteringS2S|NFCorpus|STS17|STSBenchmark|SciFact|SummEval|TwitterSemEval2015|Average| |--|--|--|--|--|--|--|--|--|--|--|--|--|--| |Instruction |51.09|61.71|84.56|86.37|51.77|34.29|37.82|89.56|87.08|69.7 |30.95|70.97|**62.99**| |No Instruction |50.23|60.31|84.45|86.36|50.6 |31.87|37.58|88.75|86.39|71.28|31.00|68.92|**62.31**| |Relative Δ|-1.71%|-2.32%|-0.13%|-0.01%|-2.31%|-7.59%|-0.64%|-0.91%|-0.80%|2.22%|0.16%|-2.97%|**-1.09%**| We observe slightly reduced performance across most tasks when not using task-specific instructions with an average loss in performance of roughly 1%. ## Training Details ### Model architecture | | | |-------|-------| |Number of layers|27| |Number of attention heads|36| |Head size|128| |Number of Key-Value heads|4| |Size hidden dimension|4608| |MLP expansion factor|4| |MLP type|Standard| |Vocabulary size|128,000| |Rotary base|1,000,000| |Total parameter count|7,041,544,704| ### Training Pharia-1-Embedding-4608-control is an adapter on top of Pharia-1-LLM-7B-control, trained with a context window of 2048 Tokens. Pharia-1-Embedding-4608-control was trained with representational instruction-tuning (inspired by the approach of GritLM) and a contrastive learning approach. The final layer is an embedding head with weighted mean pooling. The train set consisted of a blend of open-source and proprietary datasets. Further postprocessing was used to optimize for downstream use and multilinguality. ### Tokenization Tokenization taking place in this embedding model takes full advantage of the one in [Pharia-1-LLM-7B-control model](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control)

[ "TRANSLATION", "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

Godefroyduchalard/solone-embedding-final2

Godefroyduchalard

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:19485", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:OrdalieTech/Solon-embeddings-large-0.1", "base_model:finetune:OrdalieTech/Solon-embeddings-large-0.1", "doi:10.57967/hf/3679", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-11-22T16:43:39

2024-12-02T08:35:44

0

--- base_model: OrdalieTech/Solon-embeddings-large-0.1 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:19485 - loss:MultipleNegativesRankingLoss widget: - source_sentence: chef de bord sentences: - Personne responsable du pilotage d'un navire. - Le chef de bord est une personne responsable du contrôle des dépenses et de l'organisation des réceptions dans un établissement hôtelier. - Procédure suivie par une juridiction lorsqu'elle doit trancher un litige par un acte juridictionnel. - source_sentence: dotation de solidarité rurale sentences: - Dispositif de défiscalisation concernant les propriétaires de logements acquis neufs ou en l'état futur d'achèvement, entre le 1er janvier 1999 et le 2 avril 2003, qui peuvent demander à bénéficier d'une déduction au titre de l'amortissement. - La dotation de solidarité rurale est une aide financière attribuée aux communes urbaines pour compenser les coûts supplémentaires liés à l'accueil des populations rurales qui viennent s'installer dans ces villes, en raison de la pénurie de logements disponibles dans leurs villages d'origine. - Dotation attribuée à certaines communes et à certains chefs-lieux d'arrondissement, en fonction du nombre d'habitants, pour tenir compte, d'une part, des charges qu'ils supportent pour contribuer au maintien de la vie sociale en milieu rural, d'autre part, de l'insuffisance de leurs ressources fiscales. - source_sentence: monument commémoratif sentences: - Les pensions de réversion sont destinées à garantir au survivant du couple un niveau de vie correct en lui versant une fraction de la pension principale dont bénéficiait ou aurait bénéficié son conjoint. Tous les régimes de retraite versent des pensions de réversion, à différents taux et sous des conditions variables. - Monument servant à commémorer un événement ou à honorer une ou plusieurs personnes. - Un monument commémoratif est un dispositif administratif permettant de définir et de gérer les budgets alloués à des événements ou des personnalités, sans nécessairement les honorer. - source_sentence: ozonosphère sentences: - Gestion visant à anticiper l’impact des réformes, à adapter les modes de gestion des ressources humaines, à enrichir et valoriser les compétences des agents publics. Dans son approche pluriannuelle de la GRH, elle se fonde en amont sur les orientations stratégiques de la politique RH découlant notamment des évolutions prévisibles des services (missions, organisation, ressources…) et sur l’analyse de données quantitatives et qualitatives relatives à la gestion prévisionnelle des emplois des effectifs et des compétences. Elle conduit à l’élaboration de plans d’actions qui portent sur l’ensemble des actes de la GRH. - Couche de la stratosphère terrestre dans laquelle la concentration d'ozone est la plus importante. - L'ozonosphère désigne la couche de l'économie terrestre où les entreprises sont exemptées de taxes sur leurs émissions de gaz à effet de serre. - source_sentence: développement rural sentences: - Gestion du développement humain et orientation des changements technologiques et institutionnels de façon à améliorer l'inclusion, la longévité, les connaissances et les standards de vie dans les zones rurales, et ce dans un contexte d'équité et de durabilité. - Le développement rural est un processus administratif visant à réduire l'urbanisation et à favoriser le déclin économique des zones rurales en leur attribuant une part de la dette nationale, dans le but d'améliorer les conditions de vie des citadins. - Aide financière réelle, qui n'est ni un prêt ni une avance de trésorerie, accordée par l'Etat, une collectivité territoriale ou un organisme privé pour financer ou favoriser le développement d'une activité d'intérêt général ou, à titre de secours, pour subvenir à un cas pressant. --- # SentenceTransformer based on OrdalieTech/Solon-embeddings-large-0.1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [OrdalieTech/Solon-embeddings-large-0.1](https://huggingface.co/OrdalieTech/Solon-embeddings-large-0.1). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [OrdalieTech/Solon-embeddings-large-0.1](https://huggingface.co/OrdalieTech/Solon-embeddings-large-0.1)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("Godefroyduchalard/solone-embedding-final2") # Run inference sentences = [ 'développement rural', "Gestion du développement humain et orientation des changements technologiques et institutionnels de façon à améliorer l'inclusion, la longévité, les connaissances et les standards de vie dans les zones rurales, et ce dans un contexte d'équité et de durabilité.", "Le développement rural est un processus administratif visant à réduire l'urbanisation et à favoriser le déclin économique des zones rurales en leur attribuant une part de la dette nationale, dans le but d'améliorer les conditions de vie des citadins.", ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 19,485 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 4.53 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 28.43 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 40.14 tokens</li><li>max: 71 tokens</li></ul> | * Samples: | anchor | positive | negative | |:-----------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>primo-immigrant</code> | <code>Une personne qui déménage dans un nouveau pays pour la première fois et qui n'a jamais vécu auparavant dans ce pays en tant que résident permanent.</code> | <code>Un primo-immigrant est une personne qui a déjà vécu dans un pays pendant au moins dix ans et qui décide de déménager vers un autre pays pour y acquérir la nationalité.</code> | | <code>AAH</code> | <code>L'Allocation aux Adultes Handicapés (AAH) est une aide financière versée par l'Etat français aux personnes ayant un taux d'incapacité supérieur à 80% ou compris entre 50% et 79% avec une restriction substantielle et durable d'accès à l'emploi.</code> | <code>L'Allocation aux Adultes Handicapés (AAH) est une aide financière versée par les entreprises privées françaises pour récompenser les employeurs qui ont réussi à intégrer des personnes handicapées dans leur effectif.</code> | | <code>ACA</code> | <code>l'ACA est un document administratif qui accompagne une demande d'aide sociale et qui atteste de la situation administrative et financière de la personne concernée</code> | <code>L'ACA est un document administratif qui permet de déclarer officiellement l'indépendance financière d'une personne, attestant ainsi sa capacité à supporter ses propres besoins sans recours à l'aide sociale.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 500 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 500 samples: | | anchor | positive | negative | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 6.66 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 46.25 tokens</li><li>max: 360 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 44.94 tokens</li><li>max: 96 tokens</li></ul> | * Samples: | anchor | positive | negative | |:-----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>commission de surendettement des particuliers</code> | <code>Organisme public, implanté dans chaque département, qu'un particulier peut saisir lorsqu'il rencontre de graves difficultés financières pour rembourser des dettes non professionnelles. La commission a pour mission de préserver les intérêts des particuliers et de leurs créanciers en établissant, lorsque cela est possible, un plan conventionnel de redressement. Ce plan amiable de remboursement est approuvé par le débiteur et les principaux créanciers. En cas d'échec, elle pourra, si le débiteur la saisit à nouveau, établir un second plan en imposant des mesures aux créanciers. Si la situation financière du débiteur rend manifestement impossible la mise en œuvre de ces mesures, la procédure de rétablissement personnel pourra être engagée.</code> | <code>L'organisme public chargé de veiller au respect des règles de surendettement est en réalité une commission qui se charge d'évaluer les capacités financières des entreprises pour déterminer si elles sont aptes à emprunter de l'argent.</code> | | <code>infrastructure ferroviaire</code> | <code>Ensemble des installations permettant la circulation de trains (notamment les voies ferrées, caténaires, équipements de transport de l'énergie, système de signalisation ferroviaire, bâtiments, ouvrages d'art, système de communication radio sol-train et télécommunications).</code> | <code>L'infrastructure ferroviaire désigne l'ensemble des installations permettant aux autorités locales de réguler et de contrôler les mouvements des trains, notamment les voies ferrées, les caténaires, les équipements de transport de l'énergie, le système de signalisation ferroviaire, les bâtiments, les ouvrages d'art, le système de communication radio sol-train et les télécommunications.</code> | | <code>Géophysique</code> | <code>Ensemble de sciences utilisant les techniques de la physique et des sciences de l’ingénieur pour connaître la Terre et principalement ses profondeurs inaccessibles à l’observation directe.</code> | <code>La géophysique est l'ensemble des sciences qui visent à prévenir et à gérer les catastrophes naturelles en utilisant les techniques de la physique et des sciences de l’ingénieur pour anticiper et contrôler les phénomènes météorologiques.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `num_train_epochs`: 10 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `eval_use_gather_object`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.8210 | 1000 | 1.1789 | 0.4142 | | 1.6420 | 2000 | 0.7996 | 0.2781 | | 2.4631 | 3000 | 0.6071 | 0.2901 | | 3.2841 | 4000 | 0.5536 | 0.2241 | | 4.1051 | 5000 | 0.5039 | 0.2887 | | 4.9261 | 6000 | 0.5153 | 0.1972 | | 5.7471 | 7000 | 0.5812 | 0.1732 | | 6.5681 | 8000 | 0.5242 | 0.1657 | | 7.3892 | 9000 | 0.4647 | 0.1542 | | 8.2102 | 10000 | 0.4202 | 0.1820 | | 9.0312 | 11000 | 0.4519 | 0.1430 | | 9.8522 | 12000 | 0.4862 | 0.1488 | ### Framework Versions - Python: 3.11.9 - Sentence Transformers: 3.3.1 - Transformers: 4.44.0 - PyTorch: 2.4.1+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CAS" ]

chandanzeon/setfit_finetuned_iaf_98

chandanzeon

text-classification

[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:BAAI/bge-small-en-v1.5", "base_model:finetune:BAAI/bge-small-en-v1.5", "model-index", "region:us" ]

2024-12-16T10:46:03

2024-12-16T10:46:15

0

--- base_model: BAAI/bge-small-en-v1.5 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Review of Administrative and Disciplinary Records Recent administrative evaluations have revealed irregularities within key operational postings. Investigations were launched in key command areas such as Jalandhar and Secunderabad, focusing on personnel movement, access permissions, and communication lapses. Anomalies in operational reports indicated unauthorized sharing of personnel data with external parties, sparking concerns about internal security and discipline. The South Western Command and Central Military Headquarters are overseeing these investigations. Reports highlight a need for increased supervision of personnel involved in administrative roles, as lapses in information sharing protocols pose a significant risk to mission readiness. In response, administrative retraining programs focused on compliance, confidentiality, and secure communication have been implemented across all units. Improved oversight measures, such as enhanced access control protocols and personnel background checks, are being prioritized to prevent such breaches from occurring in the future. Specialized training sessions have been hosted at key logistical hubs to strengthen accountability and ensure all military officials understand their responsibilities.' - text: 'Advanced Technological Integration into Military Strategies To maintain strategic advantages, the military has integrated cutting-edge technological assets into operational strategies. Innovations such as advanced surveillance drones equipped with night vision cameras and AI-assisted threat detection have enhanced the military''s ability to track adversarial movements. These drones are deployed on both border operations and maritime patrols, enabling continuous and real-time intelligence-gathering without compromising operational security. Furthermore, electronic warfare units have been equipped with advanced jamming devices capable of disrupting electronic communication signals used by insurgents. This capability ensures that adversarial communication networks are neutralized during operational missions, reducing the ability of enemy cells to coordinate and launch attacks.' - text: 'Drones in Target Acquisition and Precision Strikes Beyond surveillance and reconnaissance, drones are increasingly being used in target acquisition and precision strike missions. The integration of guided munitions with UAVs allows for highly accurate strikes on key targets, including terrorist camps, weapons caches, and enemy fortifications. Drones like the Harpy and Predator have been used in similar missions, providing high-precision strikes while minimizing the risk to personnel. The use of drones for precision strikes significantly reduces the collateral damage typically associated with traditional airstrikes and ground-based artillery fire.' - text: 'Strengthening Army Resilience through Infrastructure Upgrades Recent initiatives to modernize military infrastructure are focusing on strategic roadways, railway networks, and key logistical hubs across Northern and Eastern theater areas. Troop movement flexibility has become vital as regional border security remains fragile. Construction projects have been prioritized near operational areas like Leh, Arunachal Pradesh, and parts of the Indo-Nepal border. Specialized engineering battalions are spearheading the construction of advanced bridges and all- weather roadways, particularly through challenging terrains such as the Himalayan foothills and desert corridors. The latest developments include high-capacity bridge-building technology, allowing troops and supplies to be moved rapidly even in the most inaccessible locations. The strategic development of these routes ensures the swift mobility of logistical support, troop reinforcements, and rapid response units. Furthermore, advancements in railway infrastructure are underway to support rapid troop deployment. Railway hubs near key operational zones are being modernized, with emphasis on dual-use infrastructure that allows both civilian and military operations to utilize these networks when necessary.' - text: 'Tactical Coordination and Training Joint training exercises involving armored and artillery units have been conducted to refine battlefield tactics. These exercises, held in the Thar Desert, simulated multi-front conflict scenarios, emphasizing coordination between various branches of the armed forces. Feedback from these exercises has led to the adoption of new operational guidelines, such as optimized deployment patterns for tanks and artillery systems. Post-exercise debriefings at Jodhpur Cantonment highlighted the importance of synchronized maneuvers in achieving tactical superiority.' inference: true model-index: - name: SetFit with BAAI/bge-small-en-v1.5 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.9193548387096774 name: Accuracy --- # SetFit with BAAI/bge-small-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 4 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 3 | <ul><li>'Closing Ceremony and Awards Distribution\nThe event concluded with the closing ceremony on August 18, 2024. The ceremony was attended by senior military officials, including the Chief of Naval Staff and Chief of Air Staff. The athletes were recognized for their outstanding performances with awards presented in various categories, such as Best Athlete, Best Team, and Best Sportsmanship. The Indian Air Force won the Best Team Performance trophy, while Captain Aaryan Verma from the Army was named the Best Athlete for his exceptional performance in athletics. The Indian Army was declared the overall winner of the competition, having secured the most points across all events. A highlight of the ceremony was the traditional military drill performed by the three services, showcasing the discipline and precision that is characteristic of the Indian Armed Forces.'</li><li>'Community Engagement Activities\nIn addition to the aerial demonstrations, the IAF organized several community outreach activities. A special education booth was set up for schoolchildren, focusing on the history of the Indian Air Force, aviation careers, and the importance of air defense. The booth also displayed several educational films and interactive content about the Air Forceâ\x80\x99s role in peacekeeping, disaster relief, and national defense. Additionally, a blood donation camp was established near the main entrance, in collaboration with Bangaloreâ\x80\x99s city hospital, to encourage voluntary blood donation. Visitors were encouraged to participate, with the goal of increasing awareness about health and wellness in the community. The camp ran smoothly and successfully collected over 500 units of blood, which would be distributed to regional hospitals.'</li><li>'Environmental Considerations\nWith the ongoing infrastructure developments, the Indian Air Force has taken steps to minimize the environmental impact of the construction. Measures are being implemented to reduce waste and promote sustainability during the projectâ\x80\x99s execution. Additionally, the base has set up an environmental monitoring system to track air and water quality in the vicinity, ensuring that the baseâ\x80\x99s operations do not adversely affect local ecosystems. Moreover, the wastewater treatment facility at the station is being upgraded to ensure that all waste generated from daily operations is properly treated and does not affect the surrounding areas. This initiative is part of the Air Forceâ\x80\x99s broader commitment to environmental responsibility and sustainability.'</li></ul> | | 1 | <ul><li>'Radio Frequency Allocation Updates\nThe communications division recently conducted a comprehensive review of radio frequency allocations across the northern and northeastern sectors. Adjustments were made to avoid overlaps that could interfere with civilian and military operations. A new allocation plan has been implemented for units stationed at Bagdogra and Dimapur, ensuring seamless communication during both routine and emergency operations. Periodic audits of frequency usage continue to safeguard against potential breaches or overlaps.'</li><li>'Signal Frequency Interference Monitoring\nSignal frequency interference has become a growing concern as electronic warfare threats evolve. The monitoring units, based in key strategic areas such as Karnal, Leh, and Udhampur, have observed unauthorized intrusions into radio communication patterns. Advanced detection technologies have been deployed to analyze this data, with initial results highlighting the need for improved counter-electronic warfare capabilities. The Signal Corps has expanded its focus on electronic jamming threats near key tactical airstrips and operational centers. Units in these regions are conducting surveillance with advanced signal detection systems, ensuring they can identify and neutralize attempts at electronic disruption. Military radar units in Jaisalmer and Pathankot are receiving new upgrades to improve signal detection in operationally sensitive regions. Commanders have emphasized the importance of coordinating electronic warfare drills with these signal monitoring operations to enhance response mechanisms. Coordination between signal analysis teams and field operations ensures timely detection and neutralization of electronic threats.'</li><li>'Naval Assets and Maritime Patrolling Operations\nNaval deployments along key trade routes and strategic maritime chokepoints have seen increased patrols and strategic upgrades. Units have been repositioned in response to recent developments in regional waters, focusing on both counter-terror operations and maintaining freedom of navigation. Surveillance assets, such as Indian Navy frigates and long-range maritime reconnaissance aircraft, are actively monitoring the Malabar Sea and Arabian Sea for any irregular ship movements or unauthorized military deployments. Naval units stationed at key operational ports like Visakhapatnam and Karwar are equipped with advanced sonar and radar systems. Recent deployments emphasize anti-submarine warfare capabilities, leveraging advanced underwater detection technology to identify potential threats from hostile assets or insurgent activity. Coordination with air assets, including the Sea King and P-8I aircraft, has improved naval surveillance effectiveness, with regular joint operations enhancing strategic interoperability.'</li></ul> | | 2 | <ul><li>"Training Manuals for Official Use Only\nThe following manuals were distributed among units for use during December 2024 training sessions: 1. Guidelines for Advanced Vehicle Maintenance: ï\x82· This manual provides detailed procedures for troubleshooting and repairing light utility vehicles commonly used in supply operations. Emphasis is placed on maintaining vehicle efficiency in cold-weather conditions. ï\x82· A new section outlines methods for diagnosing electronic systems, a critical aspect as newer models are introduced into service. 2. Basic Communication Protocols: ï\x82· Designed for new recruits, this guide introduces secure communication techniques, including encryption basics and signal relay procedures. ï\x82· The document also includes practical exercises to simulate field scenarios, enhancing recruits' readiness for real-world applications."</li><li>'Routine Procurement Documents\nThe procurement department at Ambala Air Force Station has finalized contracts for the supply of spare parts for MiG-21 aircraft. The document details the scheduled delivery of parts such as hydraulic actuators, brake systems, and navigation units over the next quarter. These supplies are essential for routine maintenance and ensuring that the aircraft remains in operational condition for non-combat purposes. The report also includes internal memos on supplier performance and cost negotiations, which are classified as Restricted to prevent unauthorized access and ensure smooth contract execution. Highlights of the Competition\nThe athletics events were among the most anticipated, with the fastest runners from each branch competing for medals. The 100m sprint final featured a thrilling race between the top sprinters from the Army, Navy, and Air Force, with Captain Aaryan Verma of the Army securing the gold medal with a time of 10.87 seconds, followed by Lieutenant Neha Mehra of the Navy, who claimed the silver with 11.03 seconds. In the football tournament, the Indian Army emerged as the champions after a tense final match against the Indian Air Force. The game ended with a score of 2-1 in favor of the Army, with Subedar Major Vikram Singh scoring the winning goal in the final minutes. The Army team displayed exceptional teamwork and strategic play, which ultimately led them to victory. The cricket matches were highly competitive, with the Indian Navy defeating the Air Force team in a closely contested T20 match. The final was a nail-biting affair, with Navyâ\x80\x99s Lieutenant Commander Rahul Mehta hitting the winning six in the last over of the game.'</li><li>'Supply Chain and Procurement Documents\nRoutine procurement activities continue to fuel military preparedness. The most recent batch of documents contains procurement orders for various operational materials needed in peripheral zones. These orders range from vehicles used in reconnaissance missions to tactical gear for military units that are not directly involved in combat but are still crucial for maintaining defense capabilities. For example, a recent procurement request was made for a series of high-powered satellite phones that will be issued to units deployed in isolated locations. These phones are essential for ensuring that communication lines remain open in areas where traditional communication infrastructure is unavailable. Similarly, there are ongoing negotiations for acquiring medical supplies, such as portable surgical kits and trauma care equipment, specifically for units working in non-conflict zones where medical infrastructure might be limited. The documentation detailing these procurements includes specifics on supplier agreements, delivery schedules, and operational requirements. This is sensitive data, as it could potentially reveal gaps in military supply chains if accessed by unauthorized individuals. Suppliers are carefully vetted, and any leak of information regarding these supply chains could jeopardize the mission\'s success in certain strategic areas. Cipher Message: Cipher Text: "NQ5P7 QXZ8T 7J6B2 P1M9Y." â\x80\x93 Encrypted procurement details, listing authorized suppliers and material quantities for internal distribution only.'</li></ul> | | 0 | <ul><li>'Enhancement of Aerial Surveillance\nUnmanned Aerial Vehicles (UAVs) have been deployed from the Jorhat Air Force Station to maintain constant surveillance over disputed areas. These UAVs, equipped with high-resolution cameras and thermal sensors, provide real-time imagery of adversarial activities. Regular patrol missions conducted over regions like Kibithu and Walong have been instrumental in identifying unauthorized constructions. The data gathered is relayed to command centers in Shillong for detailed analysis. AI-powered algorithms help in detecting anomalies, ensuring swift decision-making in case of any potential threats. These proactive measures have significantly improved situational awareness. Implementation of AI-Based Border Surveillance\nThe recent deployment of artificial intelligence-driven surveillance mechanisms has introduced cutting-edge technology into border operations. Surveillance drones and AI-powered detection sensors have been positioned along key border regions, including the North Eastern States and the Indian-Pakistani border. These assets are leveraging machine learning algorithms to identify patterns of unusual activity, unauthorized crossings, and changes in terrain anomalies. The AI systems are capable of processing vast quantities of real-time data collected from UAVs, thermal imaging cameras, and ground-based radar installations. Machine learning analysis identifies trends that may go unnoticed by conventional monitoring, such as small troop movements or unauthorized infiltration attempts across the porous Indo-Bangladesh border. These capabilities have already proven effective in detecting early signs of infiltration and cross-border activity. Additionally, intelligence teams are collaborating with AI experts to fine-tune these tools for real- time decision-making support. Advanced signal detection and image recognition capabilities are improving response times and ensuring that border patrols can intercept threats with enhanced accuracy and minimal delay.'</li><li>'Conclusion\nThe integration of advanced technology with strategic realignments across operational zones highlights the dynamic and robust approach adopted by the armed forces. These measures not only bolster defensive capabilities but also reinforce the nationâ\x80\x99s readiness to respond to evolving threats.'</li><li>'New Munitions Deployment\nTo enhance combat effectiveness, advanced munitions tailored for specific operational conditions have been introduced. The recent deployment of guided mortar systems to units stationed in the Siachen Glacier highlights this focus. These munitions, tested under extreme conditions, provide unmatched accuracy and reliability. Additionally, countermeasure systems designed to neutralize enemy drones have been distributed across critical sectors. These systems employ directed energy technology, effectively disrupting the electronic controls of hostile UAVs.'</li></ul> | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.9194 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("chandanzeon/setfit_finetuned_iaf_98") # Run inference preds = model("Tactical Coordination and Training Joint training exercises involving armored and artillery units have been conducted to refine battlefield tactics. These exercises, held in the Thar Desert, simulated multi-front conflict scenarios, emphasizing coordination between various branches of the armed forces. Feedback from these exercises has led to the adoption of new operational guidelines, such as optimized deployment patterns for tanks and artillery systems. Post-exercise debriefings at Jodhpur Cantonment highlighted the importance of synchronized maneuvers in achieving tactical superiority.") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:---------|:----| | Word count | 39 | 130.3317 | 475 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 49 | | 1 | 56 | | 2 | 49 | | 3 | 51 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0010 | 1 | 0.267 | - | | 0.0508 | 50 | 0.2533 | - | | 0.1016 | 100 | 0.2342 | - | | 0.1524 | 150 | 0.2272 | - | | 0.2033 | 200 | 0.2065 | - | | 0.2541 | 250 | 0.1573 | - | | 0.3049 | 300 | 0.1051 | - | | 0.3557 | 350 | 0.0546 | - | | 0.4065 | 400 | 0.011 | - | | 0.4573 | 450 | 0.004 | - | | 0.5081 | 500 | 0.0028 | - | | 0.5589 | 550 | 0.0023 | - | | 0.6098 | 600 | 0.0019 | - | | 0.6606 | 650 | 0.0015 | - | | 0.7114 | 700 | 0.0014 | - | | 0.7622 | 750 | 0.0014 | - | | 0.8130 | 800 | 0.0013 | - | | 0.8638 | 850 | 0.0012 | - | | 0.9146 | 900 | 0.0011 | - | | 0.9654 | 950 | 0.001 | - | | 1.0 | 984 | - | 0.0731 | | 1.0163 | 1000 | 0.001 | - | | 1.0671 | 1050 | 0.0009 | - | | 1.1179 | 1100 | 0.0009 | - | | 1.1687 | 1150 | 0.0008 | - | | 1.2195 | 1200 | 0.0008 | - | | 1.2703 | 1250 | 0.0008 | - | | 1.3211 | 1300 | 0.0008 | - | | 1.3720 | 1350 | 0.0007 | - | | 1.4228 | 1400 | 0.0007 | - | | 1.4736 | 1450 | 0.0007 | - | | 1.5244 | 1500 | 0.0007 | - | | 1.5752 | 1550 | 0.0006 | - | | 1.6260 | 1600 | 0.0006 | - | | 1.6768 | 1650 | 0.0006 | - | | 1.7276 | 1700 | 0.0006 | - | | 1.7785 | 1750 | 0.0006 | - | | 1.8293 | 1800 | 0.0006 | - | | 1.8801 | 1850 | 0.0006 | - | | 1.9309 | 1900 | 0.0006 | - | | 1.9817 | 1950 | 0.0005 | - | | 2.0 | 1968 | - | 0.0762 | | 2.0325 | 2000 | 0.0005 | - | | 2.0833 | 2050 | 0.0005 | - | | 2.1341 | 2100 | 0.0005 | - | | 2.1850 | 2150 | 0.0005 | - | | 2.2358 | 2200 | 0.0005 | - | | 2.2866 | 2250 | 0.0005 | - | | 2.3374 | 2300 | 0.0005 | - | | 2.3882 | 2350 | 0.0005 | - | | 2.4390 | 2400 | 0.0005 | - | | 2.4898 | 2450 | 0.0005 | - | | 2.5407 | 2500 | 0.0005 | - | | 2.5915 | 2550 | 0.0004 | - | | 2.6423 | 2600 | 0.0004 | - | | 2.6931 | 2650 | 0.0004 | - | | 2.7439 | 2700 | 0.0004 | - | | 2.7947 | 2750 | 0.0004 | - | | 2.8455 | 2800 | 0.0004 | - | | 2.8963 | 2850 | 0.0004 | - | | 2.9472 | 2900 | 0.0004 | - | | 2.9980 | 2950 | 0.0004 | - | | 3.0 | 2952 | - | 0.0786 | | 3.0488 | 3000 | 0.0004 | - | | 3.0996 | 3050 | 0.0004 | - | | 3.1504 | 3100 | 0.0004 | - | | 3.2012 | 3150 | 0.0004 | - | | 3.2520 | 3200 | 0.0004 | - | | 3.3028 | 3250 | 0.0004 | - | | 3.3537 | 3300 | 0.0004 | - | | 3.4045 | 3350 | 0.0004 | - | | 3.4553 | 3400 | 0.0004 | - | | 3.5061 | 3450 | 0.0004 | - | | 3.5569 | 3500 | 0.0003 | - | | 3.6077 | 3550 | 0.0004 | - | | 3.6585 | 3600 | 0.0004 | - | | 3.7093 | 3650 | 0.0004 | - | | 3.7602 | 3700 | 0.0003 | - | | 3.8110 | 3750 | 0.0003 | - | | 3.8618 | 3800 | 0.0004 | - | | 3.9126 | 3850 | 0.0003 | - | | 3.9634 | 3900 | 0.0003 | - | | 4.0 | 3936 | - | 0.0813 | | 4.0142 | 3950 | 0.0003 | - | | 4.0650 | 4000 | 0.0003 | - | | 4.1159 | 4050 | 0.0003 | - | | 4.1667 | 4100 | 0.0003 | - | | 4.2175 | 4150 | 0.0003 | - | | 4.2683 | 4200 | 0.0003 | - | | 4.3191 | 4250 | 0.0003 | - | | 4.3699 | 4300 | 0.0003 | - | | 4.4207 | 4350 | 0.0003 | - | | 4.4715 | 4400 | 0.0003 | - | | 4.5224 | 4450 | 0.0003 | - | | 4.5732 | 4500 | 0.0003 | - | | 4.6240 | 4550 | 0.0003 | - | | 4.6748 | 4600 | 0.0003 | - | | 4.7256 | 4650 | 0.0003 | - | | 4.7764 | 4700 | 0.0003 | - | | 4.8272 | 4750 | 0.0003 | - | | 4.8780 | 4800 | 0.0003 | - | | 4.9289 | 4850 | 0.0003 | - | | 4.9797 | 4900 | 0.0003 | - | | 5.0 | 4920 | - | 0.0804 | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.2.1 - Transformers: 4.42.2 - PyTorch: 2.5.1+cu121 - Datasets: 3.2.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

[ "TEXT_CLASSIFICATION" ]

[ "MEDAL" ]

Maxthemacaque/onnx-gte-multilingual-base

Maxthemacaque

sentence-similarity

[ "sentence-transformers", "onnx", "mteb", "transformers", "multilingual", "sentence-similarity", "af", "ar", "az", "be", "bg", "bn", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fr", "gl", "gu", "he", "hi", "hr", "ht", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ky", "lo", "lt", "lv", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "pa", "pl", "pt", "qu", "ro", "ru", "si", "sk", "sl", "so", "sq", "sr", "sv", "sw", "ta", "te", "th", "tl", "tr", "uk", "ur", "vi", "yo", "zh", "arxiv:2407.19669", "arxiv:2210.09984", "arxiv:2402.03216", "arxiv:2007.15207", "arxiv:2104.08663", "arxiv:2402.07440", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2024-12-22T10:14:40

2024-12-22T10:19:11

0

--- language: - af - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - es - et - eu - fa - fi - fr - gl - gu - he - hi - hr - ht - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ky - lo - lt - lv - mk - ml - mn - mr - ms - my - ne - nl - 'no' - pa - pl - pt - qu - ro - ru - si - sk - sl - so - sq - sr - sv - sw - ta - te - th - tl - tr - uk - ur - vi - yo - zh license: apache-2.0 tags: - mteb - sentence-transformers - transformers - multilingual - sentence-similarity model-index: - name: gte-multilingual-base (dense) results: - task: type: Clustering dataset: name: MTEB 8TagsClustering type: PL-MTEB/8tags-clustering config: default split: test revision: None metrics: - type: v_measure value: 33.66681726329994 - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_spearman value: 43.54760696384009 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_spearman value: 48.91186363417501 - task: type: Classification dataset: name: MTEB AllegroReviews type: PL-MTEB/allegro-reviews config: default split: test revision: None metrics: - type: accuracy value: 41.689860834990064 - task: type: Clustering dataset: name: MTEB AlloProfClusteringP2P type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: v_measure value: 54.20241337977897 - type: v_measure value: 44.34083695608643 - task: type: Reranking dataset: name: MTEB AlloprofReranking type: lyon-nlp/mteb-fr-reranking-alloprof-s2p config: default split: test revision: 666fdacebe0291776e86f29345663dfaf80a0db9 metrics: - type: map value: 64.91495250072002 - task: type: Retrieval dataset: name: MTEB AlloprofRetrieval type: lyon-nlp/alloprof config: default split: test revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b metrics: - type: ndcg_at_10 value: 53.638 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.95522388059702 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 80.717625 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.64199999999999 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.108 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.169999999999995 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.56799999999999 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 35.75000000000001 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 33.342000000000006 - task: type: Retrieval dataset: name: MTEB ArguAna type: mteb/arguana config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: ndcg_at_10 value: 58.231 - task: type: Retrieval dataset: name: MTEB ArguAna-PL type: clarin-knext/arguana-pl config: default split: test revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 metrics: - type: ndcg_at_10 value: 53.166000000000004 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - 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type: accuracy value: 71.02579999999999 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 57.60045274476514 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 50.346666699466205 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_ap value: 71.88199004440489 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_ap value: 85.41587779677383 - task: type: Retrieval dataset: name: MTEB VideoRetrieval type: C-MTEB/VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: ndcg_at_10 value: 72.792 - task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 82.58000000000001 - task: type: Retrieval dataset: name: MTEB XPQARetrieval (fr) type: jinaai/xpqa config: fr split: test revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f metrics: - type: ndcg_at_10 value: 67.327 --- ## gte-multilingual-base The **gte-multilingual-base** model is the latest in the [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) (General Text Embedding) family of models, featuring several key attributes: - **High Performance**: Achieves state-of-the-art (SOTA) results in multilingual retrieval tasks and multi-task representation model evaluations when compared to models of similar size. - **Training Architecture**: Trained using an encoder-only transformers architecture, resulting in a smaller model size. Unlike previous models based on decode-only LLM architecture (e.g., gte-qwen2-1.5b-instruct), this model has lower hardware requirements for inference, offering a 10x increase in inference speed. - **Long Context**: Supports text lengths up to **8192** tokens. - **Multilingual Capability**: Supports over **70** languages. - **Elastic Dense Embedding**: Support elastic output dense representation while maintaining the effectiveness of downstream tasks, which significantly reduces storage costs and improves execution efficiency. - **Sparse Vectors**: In addition to dense representations, it can also generate sparse vectors. **Paper**: [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) ## Model Information - Model Size: 305M - Embedding Dimension: 768 - Max Input Tokens: 8192 ## Usage - **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** - **How to use it offline: [new-impl/discussions/2](https://huggingface.co/Alibaba-NLP/new-impl/discussions/2#662b08d04d8c3d0a09c88fa3)** - **How to use with [TEI](https://github.com/huggingface/text-embeddings-inference): [refs/pr/7](https://huggingface.co/Alibaba-NLP/gte-multilingual-base/discussions/7#66bfb82ea03b764ca92a2221)** ### Get Dense Embeddings with Transformers ```python # Requires transformers>=4.36.0 import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModel.from_pretrained(model_name_or_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) dimension=768 # The output dimension of the output embedding, should be in [128, 768] embeddings = outputs.last_hidden_state[:, 0][:dimension] embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) # [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]] ``` ### Use with sentence-transformers ```python # Requires sentence-transformers>=3.0.0 from sentence_transformers import SentenceTransformer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] model_name_or_path="Alibaba-NLP/gte-multilingual-base" model = SentenceTransformer(model_name_or_path, trust_remote_code=True) embeddings = model.encode(input_texts, normalize_embeddings=True) # embeddings.shape (4, 768) # sim scores scores = model.similarity(embeddings[:1], embeddings[1:]) print(scores.tolist()) # [[0.301699697971344, 0.7503870129585266, 0.32030850648880005]] ``` ### Use with infinity Usage via docker and [infinity](https://github.com/michaelfeil/infinity), MIT Licensed. ``` docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \ michaelf34/infinity:0.0.69 \ v2 --model-id Alibaba-NLP/gte-multilingual-base --revision "main" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997 ``` ### Use with custom code to get dense embeddigns and sparse token weights ```python # You can find the script gte_embedding.py in https://huggingface.co/Alibaba-NLP/gte-multilingual-base/blob/main/scripts/gte_embedding.py from gte_embedding import GTEEmbeddidng model_name_or_path = 'Alibaba-NLP/gte-multilingual-base' model = GTEEmbeddidng(model_name_or_path) query = "中国的首都在哪儿" docs = [ "what is the capital of China?", "how to implement quick sort in python?", "北京", "快排算法介绍" ] embs = model.encode(docs, return_dense=True,return_sparse=True) print('dense_embeddings vecs', embs['dense_embeddings']) print('token_weights', embs['token_weights']) pairs = [(query, doc) for doc in docs] dense_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.0) sparse_scores = model.compute_scores(pairs, dense_weight=0.0, sparse_weight=1.0) hybrid_scores = model.compute_scores(pairs, dense_weight=1.0, sparse_weight=0.3) print('dense_scores', dense_scores) print('sparse_scores', sparse_scores) print('hybrid_scores', hybrid_scores) # dense_scores [0.85302734375, 0.257568359375, 0.76953125, 0.325439453125] # sparse_scores [0.0, 0.0, 4.600879669189453, 1.570279598236084] # hybrid_scores [0.85302734375, 0.257568359375, 2.1497951507568356, 0.7965233325958252] ``` ## Evaluation We validated the performance of the **gte-multilingual-base** model on multiple downstream tasks, including multilingual retrieval, cross-lingual retrieval, long text retrieval, and general text representation evaluation on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), among others. ### Retrieval Task Retrieval results on [MIRACL](https://arxiv.org/abs/2210.09984) and [MLDR](https://arxiv.org/abs/2402.03216) (multilingual), [MKQA](https://arxiv.org/abs/2007.15207) (crosslingual), [BEIR](https://arxiv.org/abs/2104.08663) and [LoCo](https://arxiv.org/abs/2402.07440) (English). ![image](./images/mgte-retrieval.png) - Detail results on [MLDR](https://arxiv.org/abs/2402.03216) ![image](./images/mgte-retrieval.png) - Detail results on [LoCo](https://arxiv.org/abs/2402.07440) ### MTEB Results on MTEB English, Chinese, French, Polish ![image](./images/mgte-mteb.png) **More detailed experimental results can be found in the [paper](https://arxiv.org/pdf/2407.19669)**. ## Cloud API Services In addition to the open-source [GTE](https://huggingface.co/collections/Alibaba-NLP/gte-models-6680f0b13f885cb431e6d469) series models, GTE series models are also available as commercial API services on Alibaba Cloud. - [Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-embedding/): Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - [ReRank Models](https://help.aliyun.com/zh/model-studio/developer-reference/general-text-sorting-model/): The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. ## Citation If you find our paper or models helpful, please consider cite: ``` @misc{zhang2024mgte, title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval}, author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang}, year={2024}, eprint={2407.19669}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2407.19669}, } ```

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1433

Lots-of-LoRAs

null

[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "license:mit", "region:us" ]

2024-12-30T23:11:25

2024-12-30T23:11:31

0

--- language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1433  ## Model Details ### Model Description  LoRA trained on task1433_head_qa_language_translation_es_to_en - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional]  - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  https://huggingface.co/datasets/Lots-of-LoRAs/task1433_head_qa_language_translation_es_to_en sourced from https://github.com/allenai/natural-instructions ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "TRANSLATION" ]

[ "HEAD-QA" ]

Lots-of-LoRAs/Mistral-7B-Instruct-v0.2-4b-r16-task1432

Lots-of-LoRAs

null

[ "pytorch", "safetensors", "en", "arxiv:1910.09700", "arxiv:2407.00066", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:mit", "region:us" ]

2025-01-01T14:25:49

2025-01-01T14:25:54

0

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 language: en library_name: pytorch license: mit --- # Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1432  ## Model Details ### Model Description  LoRA trained on task1432_head_qa_language_translation_en_to_es - **Developed by:** bruel - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** LoRA - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional]  - **Repository:** https://github.com/bruel-gabrielsson - **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  https://huggingface.co/datasets/Lots-of-LoRAs/task1432_head_qa_language_translation_en_to_es sourced from https://github.com/allenai/natural-instructions ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** @misc{brüelgabrielsson2024compressserveservingthousands, title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead}, author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon}, year={2024}, eprint={2407.00066}, archivePrefix={arXiv}, primaryClass={cs.DC}, url={https://arxiv.org/abs/2407.00066}, } **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

[ "TRANSLATION" ]

[ "HEAD-QA" ]

ThunderJaw/hu_fasttext_resume_sections

ThunderJaw

text-classification

[ "text-classification", "hu", "dataset:ganchengguang/resume_seven_class", "base_model:facebook/fasttext-hu-vectors", "base_model:finetune:facebook/fasttext-hu-vectors", "region:us" ]

2025-01-03T11:53:48

2025-01-03T12:11:09

0

--- base_model: - facebook/fasttext-hu-vectors datasets: - ganchengguang/resume_seven_class language: - hu pipeline_tag: text-classification --- # Model Card for Resume Section Classifier This model is designed to classify sections within Hungarian resumes into categories such as Skills, Education, Experience, and others. It utilizes the `facebook/fasttext-hu-vectors` model as its base and has been fine-tuned on the `ganchengguang/resume_seven_class` dataset. The dataaset was in English so I translated it into Hungarian. It's not the best approach but it still works. ## Model Details ### Model Description This model leverages the `facebook/fasttext-hu-vectors` pre-trained embeddings to classify Hungarian resume sections into predefined categories. It has been fine-tuned on the `ganchengguang/resume_seven_class` dataset, which includes seven categories: Experience, Education, Knowledge, Project, and others. - **Model type:** Text Classification - **Language(s):** Hungarian - **Finetuned from model:** facebook/fasttext-hu-vectors ## Uses ### Direct Use This model can be used directly to classify sections of Hungarian resumes into categories such as Skills, Education, Experience, and others. It is suitable for applications in recruitment and resume analysis. ### Downstream Use The model can be integrated into larger systems for automated resume screening, assisting HR professionals in efficiently processing and categorizing resume information. ### Out-of-Scope Use This model is not intended for use with resumes in languages other than Hungarian. It may not perform accurately on resumes with non-standard formats or those containing significant amounts of non-Hungarian text. ## Bias, Risks, and Limitations The model has been trained on a specific dataset and may not generalize well to resumes with formats or content significantly different from those in the training data. Users should be aware of potential biases in the training data and the model's limitations in handling diverse resume formats. ### Recommendations Users should validate the model's predictions and consider incorporating human oversight, especially when dealing with resumes that deviate from the standard formats present in the training data. ## How to Get Started with the Model - https://github.com/ssobii2/Wozify-CV-Parser - Check Fasttext Website ## Training Details ### Training Data The model was fine-tuned on the `ganchengguang/resume_seven_class` dataset, which contains English resume sections labeled into seven categories: Experience, Education, Knowledge, Project, and others. I translated the dataset into Hungarian. ### Training Procedure The model was fine-tuned using standard text classification procedures, adjusting hyperparameters to optimize performance on the resume classification task. ## Evaluation ### Testing Data, Factors & Metrics The model's performance was evaluated on a held-out test set from the `ganchengguang/resume_seven_class` dataset, using accuracy and F1-score as evaluation metrics. #### Metrics - **Accuracy:** Measures the proportion of correctly classified sections. - **F1-score:** Harmonic mean of precision and recall, providing a balance between the two. ## Environmental Impact The training of this model was conducted on standard hardware, resulting in minimal carbon emissions. Users should consider the environmental impact of training large models and explore options for model distillation or quantization to reduce energy consumption. ## Technical Specifications ### Model Architecture and Objective The model is based on the `facebook/fasttext-hu-vectors` architecture, fine-tuned for the task of classifying Hungarian resume sections into predefined categories. ### Compute Infrastructure The model was trained my personal gaming laptop. #### Hardware - **GPU:** RTX 4070 Laptop GPU 8GB VRAM - **CPI:** Intel Core-i7-13620H - **RAM:** 16GB

[ "TEXT_CLASSIFICATION" ]

[ "CPI" ]

voyageai/voyage-3-m-exp

voyageai

null

[ "mteb", "model-index", "region:us" ]

2025-01-07T05:56:08

2025-01-21T19:16:04

0

12

--- tags: - mteb model-index: - name: voyageai/voyage-3-m-exp results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 95.7761 - type: f1 value: 93.8227 - type: f1_weighted value: 95.9368 - type: ap value: 82.63589999999999 - type: ap_weighted value: 82.63589999999999 - type: main_score value: 95.7761 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification (default) type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 97.7143 - type: f1 value: 97.7143 - type: f1_weighted value: 97.7143 - type: ap value: 96.5356 - type: ap_weighted value: 96.5356 - type: main_score value: 97.7143 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - 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type: manhattan_f1 value: 72.8159 - type: manhattan_f1_threshold value: 2713.7127 - type: manhattan_precision value: 69.176 - type: manhattan_recall value: 76.8602 - type: manhattan_ap value: 79.1243 - type: euclidean_accuracy value: 87.7988 - type: euclidean_accuracy_threshold value: 71.0715 - type: euclidean_f1 value: 72.9982 - type: euclidean_f1_threshold value: 76.0618 - type: euclidean_precision value: 67.4804 - type: euclidean_recall value: 79.4987 - type: euclidean_ap value: 79.1752 - type: dot_accuracy value: 87.7988 - type: dot_accuracy_threshold value: 74.7442 - type: dot_f1 value: 72.9982 - type: dot_f1_threshold value: 71.073 - type: dot_precision value: 67.4804 - type: dot_recall value: 79.4987 - type: dot_ap value: 79.1752 - type: max_accuracy value: 87.8107 - type: max_f1 value: 72.9982 - type: max_precision value: 69.176 - type: max_recall value: 79.4987 - type: max_ap value: 79.1752 - type: main_score value: 79.1752 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus (default) type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: similarity_accuracy value: 89.4749 - type: similarity_accuracy_threshold value: 73.03659999999999 - type: similarity_f1 value: 79.4416 - type: similarity_f1_threshold value: 70.7413 - type: similarity_precision value: 75.9696 - type: similarity_recall value: 83.2461 - type: similarity_ap value: 87.1156 - type: cosine_accuracy value: 89.4749 - type: cosine_accuracy_threshold value: 73.03659999999999 - type: cosine_f1 value: 79.4416 - type: cosine_f1_threshold value: 70.7413 - type: cosine_precision value: 75.9696 - type: cosine_recall value: 83.2461 - type: cosine_ap value: 87.1156 - type: manhattan_accuracy value: 89.471 - type: manhattan_accuracy_threshold value: 2630.4434 - type: manhattan_f1 value: 79.4078 - type: manhattan_f1_threshold value: 2764.4342 - type: manhattan_precision value: 75.7675 - type: manhattan_recall value: 83.4155 - type: manhattan_ap value: 87.0938 - type: euclidean_accuracy value: 89.4749 - type: euclidean_accuracy_threshold value: 73.4349 - type: euclidean_f1 value: 79.4416 - type: euclidean_f1_threshold value: 76.4966 - type: euclidean_precision value: 75.9696 - type: euclidean_recall value: 83.2461 - type: euclidean_ap value: 87.1156 - type: dot_accuracy value: 89.4749 - type: dot_accuracy_threshold value: 73.03659999999999 - type: dot_f1 value: 79.4416 - type: dot_f1_threshold value: 70.7413 - type: dot_precision value: 75.9696 - type: dot_recall value: 83.2461 - type: dot_ap value: 87.1156 - type: max_accuracy value: 89.4749 - type: max_f1 value: 79.4416 - type: max_precision value: 75.9696 - type: max_recall value: 83.4155 - type: max_ap value: 87.1156 - type: main_score value: 87.1156 --- # voyage-3-m-exp This repo contains the tokenizer and evaluation results of the `voyage-3-m-exp` embedding model. `voyage-3-m-exp` is an intermediate snapshot of voyage general-purpose embeddings that are tailored to datasets similar to MTEB. The training sets of MTEB datasets are used in training `voyage-3-m-exp`. **We note that for production use cases, `voyage-3-large` is highly recommended and likey strictly better than `voyage-3-m-exp`. Please see the [blogpost](https://blog.voyageai.com/2025/01/07/voyage-3-large) for more information about `voyage-3-large`.** `voyage-3-m-exp` can be accessed via the [Voyage API](https://docs.voyageai.com/docs/embeddings) with the model name `"voyage-3-m-exp"`. ## Model Information | Dimension | Model Size | Context Length | |-----------------------|------------|---------------------| | 2048 | 6918M | 32000 | ## Reproduction of MTEB results Similar to most open source models on top of the leaderboard, `voyage-3-m-exp` uses task-specific prompts. To reproduce MTEB results on the leaderboard, please set `input_type` of the API to `None` and prepend the following prompts to the input. For retrieval tasks, please use only `text` without adding the `title` field in front of the text. ```python { "BIOSSES" : { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "SICK-R": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS12": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS13": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS14": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS15": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS16": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STSBenchmark": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS17": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "STS22": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "AmazonCounterfactualClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonPolarityClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "AmazonReviewsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "Banking77Classification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "EmotionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ImdbClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MassiveScenarioClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPDomainClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "MTOPIntentClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ToxicConversationsClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "TweetSentimentExtractionClassification": { "query": "Classify the text: ", "document": "Classify the text: ", }, "ArxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "BiorxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "ArxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "MedrxivClusteringS2S": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "RedditClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "StackExchangeClusteringP2P": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "TwentyNewsgroupsClustering": { "query": "Cluster the text: ", "document": "Cluster the text: ", }, "SprintDuplicateQuestions": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterSemEval2015": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, "TwitterURLCorpus": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'StackOverflowDupQuestions': { 'query': 'Represent the sentence to retrieve similar sentences: ', 'document': 'Represent the sentence to retrieve similar sentences: ', }, 'SciDocsRR': { 'query': 'Represent the paper title to retrieve relevant paper titles: ', 'document': 'Represent the paper title to retrieve relevant paper titles: ', }, 'MindSmallReranking': { 'query': 'Represent the query to retrieve relevant documents: ', 'document': 'Represent the document for retrieval: ', }, 'AskUbuntuDupQuestions': { 'query': 'Represent the question to retrieve similar questions: ', 'document': 'Represent the question to retrieve similar questions: ', }, "SummEval": { "query": "Represent the sentence to retrieve similar sentence: ", "document": "Represent the sentence to retrieve similar sentence: ", }, 'ClimateFEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'HotpotQA': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'FEVER': { 'query': 'Represent the claim for retrieving supporting evidence: ', 'document': 'Represent the evidence for retrieval: ', }, 'MSMARCO': { 'query': 'Represent the question for retrieving evidence documents: ', 'document': 'Represent the document for retrieval: ', }, 'DBPedia': { 'query': 'Represent the entity-based query for retrieving relevant articles: ', 'document': 'Represent the article for retrieval: ', }, 'NQ': { 'query': 'Represent the Wikipedia question for retrieving supporting documents: ', 'document': 'Represent the Wikipedia document for retrieval: ', }, 'QuoraRetrieval': { 'query': 'Represent the quora question for retrieving similar questions: ', 'document': 'Represent the question for retrieval: ', }, 'SCIDOCS': { 'query': 'Represent the paper title for retrieving possible citation documents: ', 'document': 'Represent the scitific paper document for retrieval: ', }, 'TRECCOVID': { 'query': 'Represent the query for retrieving supporting articles: ', 'document': 'Represent the article for retrieval: ', }, 'Touche2020': { 'query': 'Represent the question for retrieving supporting documents: ', 'document': 'Represent the document for retrieval: ', }, "SciFact": { "query": "Represent the scientific fact for retrieving supporting document: ", "document": "Represent the document for retrieval: " }, 'NFCorpus': { 'query': 'Represent the nutrition fact for retrieving evidence documents: ', 'document': 'Represent the pubmed document for retrieval: ', }, 'ArguAna': { 'query': 'Represent the argument for retrieving counterarguments: ', 'document': 'Represent the conterargument for retrieval: ', }, 'FiQA2018': { 'query': "Represent the query for retrieving supporting documents: ", 'document': "Represent the document for retrieval: ", } } ```

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/GTE512_sw

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:24:09

2025-01-09T11:24:19

0

--- tags: - mteb model-index: - name: gte-base-en-v1.5_embs_nofiltering_sortlenTrue_phrase2sent_512_15epoch__adam0.001_accum1_best_epoch_2611200_bs128_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.02985074626866 - type: ap value: 38.2391132433526 - type: f1 value: 69.06974168824816 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 74.00564999999999 - type: ap value: 68.12255640587608 - type: f1 value: 73.88644324572483 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 39.46000000000001 - type: f1 value: 38.77579430134605 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.337 - type: map_at_10 value: 36.104 - type: map_at_100 value: 37.363 - type: map_at_1000 value: 37.38 - type: map_at_3 value: 31.46 - type: map_at_5 value: 33.861000000000004 - type: mrr_at_1 value: 22.119 - type: mrr_at_10 value: 36.379 - type: mrr_at_100 value: 37.644 - type: mrr_at_1000 value: 37.662 - type: mrr_at_3 value: 31.745 - type: mrr_at_5 value: 34.12 - type: ndcg_at_1 value: 21.337 - type: ndcg_at_10 value: 44.557 - type: ndcg_at_100 value: 50.072 - type: ndcg_at_1000 value: 50.499 - type: ndcg_at_3 value: 34.794000000000004 - type: ndcg_at_5 value: 39.125 - type: precision_at_1 value: 21.337 - type: precision_at_10 value: 7.176 - type: precision_at_100 value: 0.962 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 14.817 - type: precision_at_5 value: 10.996 - type: recall_at_1 value: 21.337 - type: recall_at_10 value: 71.764 - type: recall_at_100 value: 96.23 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 44.452000000000005 - type: recall_at_5 value: 54.979 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 38.36878876355172 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 28.19433994044647 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.16001797554904 - type: mrr value: 67.81130457723256 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.1086837226076 - type: cos_sim_spearman value: 80.60966807127197 - type: euclidean_pearson value: 80.73535719827952 - type: euclidean_spearman value: 80.60966807127197 - type: manhattan_pearson value: 79.10544477221981 - type: manhattan_spearman value: 79.59759681777079 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.1525974025974 - type: f1 value: 74.45181803662257 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.568758810321945 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.645931603960374 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.36 - type: map_at_10 value: 29.034 - type: map_at_100 value: 30.197000000000003 - type: map_at_1000 value: 30.36 - type: map_at_3 value: 26.334999999999997 - type: map_at_5 value: 27.894999999999996 - type: mrr_at_1 value: 27.325 - type: mrr_at_10 value: 34.975 - type: mrr_at_100 value: 35.787 - type: mrr_at_1000 value: 35.864000000000004 - type: mrr_at_3 value: 32.761 - type: mrr_at_5 value: 34.083999999999996 - type: ndcg_at_1 value: 27.325 - type: ndcg_at_10 value: 34.302 - type: ndcg_at_100 value: 39.35 - type: ndcg_at_1000 value: 42.516999999999996 - type: ndcg_at_3 value: 30.336000000000002 - type: ndcg_at_5 value: 32.234 - type: precision_at_1 value: 27.325 - type: precision_at_10 value: 6.694999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.17500000000000002 - type: precision_at_3 value: 14.926 - type: precision_at_5 value: 11.044 - type: recall_at_1 value: 21.36 - type: recall_at_10 value: 43.64 - type: recall_at_100 value: 66.219 - type: recall_at_1000 value: 87.675 - type: recall_at_3 value: 31.34 - type: recall_at_5 value: 36.896 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 19.008 - type: map_at_10 value: 25.762 - type: map_at_100 value: 26.819 - type: map_at_1000 value: 26.944000000000003 - type: map_at_3 value: 23.688000000000002 - type: map_at_5 value: 24.844 - type: mrr_at_1 value: 24.204 - type: mrr_at_10 value: 30.325999999999997 - type: mrr_at_100 value: 31.151 - type: mrr_at_1000 value: 31.22 - type: mrr_at_3 value: 28.311999999999998 - type: mrr_at_5 value: 29.424 - type: ndcg_at_1 value: 24.204 - type: ndcg_at_10 value: 30.020999999999997 - type: ndcg_at_100 value: 34.632000000000005 - type: ndcg_at_1000 value: 37.462 - type: ndcg_at_3 value: 26.607999999999997 - type: ndcg_at_5 value: 28.105999999999998 - type: precision_at_1 value: 24.204 - type: precision_at_10 value: 5.624 - type: precision_at_100 value: 1.012 - type: precision_at_1000 value: 0.151 - type: precision_at_3 value: 12.887 - type: precision_at_5 value: 9.159 - type: recall_at_1 value: 19.008 - type: recall_at_10 value: 38.156 - type: recall_at_100 value: 58.158 - type: recall_at_1000 value: 77.471 - type: recall_at_3 value: 27.964 - type: recall_at_5 value: 32.221 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.833999999999996 - type: map_at_10 value: 36.896 - type: map_at_100 value: 38.002 - type: map_at_1000 value: 38.088 - type: map_at_3 value: 34.283 - type: map_at_5 value: 35.754999999999995 - type: mrr_at_1 value: 32.351 - type: mrr_at_10 value: 40.275 - type: mrr_at_100 value: 41.152 - type: mrr_at_1000 value: 41.204 - type: mrr_at_3 value: 37.973 - type: mrr_at_5 value: 39.242 - type: ndcg_at_1 value: 32.351 - type: ndcg_at_10 value: 41.867 - type: ndcg_at_100 value: 47.073 - type: ndcg_at_1000 value: 49.125 - type: ndcg_at_3 value: 37.129 - type: ndcg_at_5 value: 39.361000000000004 - type: precision_at_1 value: 32.351 - type: precision_at_10 value: 6.765000000000001 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.129 - type: precision_at_3 value: 16.489 - type: precision_at_5 value: 11.398 - type: recall_at_1 value: 27.833999999999996 - type: recall_at_10 value: 53.668000000000006 - type: recall_at_100 value: 77.114 - type: recall_at_1000 value: 92.131 - type: recall_at_3 value: 40.745 - type: recall_at_5 value: 46.375 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 12.863 - type: map_at_10 value: 17.881 - type: map_at_100 value: 18.742 - type: map_at_1000 value: 18.86 - type: map_at_3 value: 16.485 - type: map_at_5 value: 17.262 - type: mrr_at_1 value: 13.898 - type: mrr_at_10 value: 19.152 - type: mrr_at_100 value: 20.007 - type: mrr_at_1000 value: 20.116 - type: mrr_at_3 value: 17.759 - type: mrr_at_5 value: 18.544 - type: ndcg_at_1 value: 13.898 - type: ndcg_at_10 value: 20.818 - type: ndcg_at_100 value: 25.342 - type: ndcg_at_1000 value: 28.895 - type: ndcg_at_3 value: 18.034 - type: ndcg_at_5 value: 19.367 - type: precision_at_1 value: 13.898 - type: precision_at_10 value: 3.254 - type: precision_at_100 value: 0.582 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.8340000000000005 - type: precision_at_5 value: 5.446 - type: recall_at_1 value: 12.863 - type: recall_at_10 value: 28.636 - type: recall_at_100 value: 50.112 - type: recall_at_1000 value: 77.828 - type: recall_at_3 value: 21.087 - type: recall_at_5 value: 24.307000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.8020000000000005 - type: map_at_10 value: 11.673 - type: map_at_100 value: 12.462 - type: map_at_1000 value: 12.589 - type: map_at_3 value: 10.035 - type: map_at_5 value: 10.699 - type: mrr_at_1 value: 9.826 - type: mrr_at_10 value: 14.248 - type: mrr_at_100 value: 15.057 - type: mrr_at_1000 value: 15.156 - type: mrr_at_3 value: 12.5 - type: mrr_at_5 value: 13.221 - type: ndcg_at_1 value: 9.826 - type: ndcg_at_10 value: 14.818999999999999 - type: ndcg_at_100 value: 19.309 - type: ndcg_at_1000 value: 22.954 - type: ndcg_at_3 value: 11.535 - type: ndcg_at_5 value: 12.577 - type: precision_at_1 value: 9.826 - type: precision_at_10 value: 2.923 - type: precision_at_100 value: 0.618 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 5.5969999999999995 - type: precision_at_5 value: 4.08 - type: recall_at_1 value: 7.8020000000000005 - type: recall_at_10 value: 22.141 - type: recall_at_100 value: 42.653999999999996 - type: recall_at_1000 value: 70.02199999999999 - type: recall_at_3 value: 13.020000000000001 - type: recall_at_5 value: 15.645999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.532 - type: map_at_10 value: 24.692 - type: map_at_100 value: 26.023000000000003 - type: map_at_1000 value: 26.165 - type: map_at_3 value: 22.522000000000002 - type: map_at_5 value: 23.694000000000003 - type: mrr_at_1 value: 22.618 - type: mrr_at_10 value: 29.334 - type: mrr_at_100 value: 30.348999999999997 - type: mrr_at_1000 value: 30.435000000000002 - type: mrr_at_3 value: 26.997 - type: mrr_at_5 value: 28.282 - type: ndcg_at_1 value: 22.618 - type: ndcg_at_10 value: 29.188 - type: ndcg_at_100 value: 35.213 - type: ndcg_at_1000 value: 38.471 - type: ndcg_at_3 value: 25.313999999999997 - type: ndcg_at_5 value: 27.057 - type: precision_at_1 value: 22.618 - type: precision_at_10 value: 5.38 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 11.741999999999999 - type: precision_at_5 value: 8.662 - type: recall_at_1 value: 18.532 - type: recall_at_10 value: 38.164 - type: recall_at_100 value: 64.197 - type: recall_at_1000 value: 86.75399999999999 - type: recall_at_3 value: 27.262999999999998 - type: recall_at_5 value: 31.651 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.257000000000001 - type: map_at_10 value: 20.762 - type: map_at_100 value: 21.956999999999997 - type: map_at_1000 value: 22.102 - type: map_at_3 value: 18.826999999999998 - type: map_at_5 value: 19.911 - type: mrr_at_1 value: 18.836 - type: mrr_at_10 value: 24.484 - type: mrr_at_100 value: 25.561 - type: mrr_at_1000 value: 25.651000000000003 - type: mrr_at_3 value: 22.546 - type: mrr_at_5 value: 23.613 - type: ndcg_at_1 value: 18.836 - type: ndcg_at_10 value: 24.465999999999998 - type: ndcg_at_100 value: 30.337999999999997 - type: ndcg_at_1000 value: 33.775 - type: ndcg_at_3 value: 21.029 - type: ndcg_at_5 value: 22.576 - type: precision_at_1 value: 18.836 - type: precision_at_10 value: 4.5089999999999995 - type: precision_at_100 value: 0.895 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 9.893 - type: precision_at_5 value: 7.146 - type: recall_at_1 value: 15.257000000000001 - type: recall_at_10 value: 32.062000000000005 - type: recall_at_100 value: 57.577 - type: recall_at_1000 value: 81.75 - type: recall_at_3 value: 22.579 - type: recall_at_5 value: 26.613999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 15.333916666666667 - type: map_at_10 value: 20.908583333333333 - type: map_at_100 value: 21.891333333333332 - type: map_at_1000 value: 22.02225 - type: map_at_3 value: 19.053833333333333 - type: map_at_5 value: 20.053916666666666 - type: mrr_at_1 value: 18.5485 - type: mrr_at_10 value: 24.15733333333333 - type: mrr_at_100 value: 25.01325 - type: mrr_at_1000 value: 25.101000000000003 - type: mrr_at_3 value: 22.34708333333333 - type: mrr_at_5 value: 23.317833333333336 - type: ndcg_at_1 value: 18.5485 - type: ndcg_at_10 value: 24.614 - type: ndcg_at_100 value: 29.431166666666662 - type: ndcg_at_1000 value: 32.6675 - type: ndcg_at_3 value: 21.285083333333336 - type: ndcg_at_5 value: 22.751416666666664 - type: precision_at_1 value: 18.5485 - type: precision_at_10 value: 4.4013333333333335 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.12825 - type: precision_at_3 value: 9.847916666666666 - type: precision_at_5 value: 7.069166666666668 - type: recall_at_1 value: 15.333916666666667 - type: recall_at_10 value: 32.5695 - type: recall_at_100 value: 54.50375 - type: recall_at_1000 value: 78.02300000000001 - type: recall_at_3 value: 23.115000000000002 - type: recall_at_5 value: 26.968416666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.745 - type: map_at_10 value: 16.406000000000002 - type: map_at_100 value: 17.157 - type: map_at_1000 value: 17.249 - type: map_at_3 value: 14.835999999999999 - type: map_at_5 value: 15.803 - type: mrr_at_1 value: 13.804 - type: mrr_at_10 value: 18.55 - type: mrr_at_100 value: 19.306 - type: mrr_at_1000 value: 19.38 - type: mrr_at_3 value: 17.05 - type: mrr_at_5 value: 17.947 - type: ndcg_at_1 value: 13.804 - type: ndcg_at_10 value: 19.339000000000002 - type: ndcg_at_100 value: 23.624000000000002 - type: ndcg_at_1000 value: 26.301999999999996 - type: ndcg_at_3 value: 16.454 - type: ndcg_at_5 value: 17.999000000000002 - type: precision_at_1 value: 13.804 - type: precision_at_10 value: 3.282 - type: precision_at_100 value: 0.598 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 7.413 - type: precision_at_5 value: 5.428999999999999 - type: recall_at_1 value: 11.745 - type: recall_at_10 value: 26.255 - type: recall_at_100 value: 46.888000000000005 - type: recall_at_1000 value: 67.131 - type: recall_at_3 value: 18.434 - type: recall_at_5 value: 22.328 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.119 - type: map_at_10 value: 11.944 - type: map_at_100 value: 12.647 - type: map_at_1000 value: 12.770000000000001 - type: map_at_3 value: 10.612 - type: map_at_5 value: 11.292 - type: mrr_at_1 value: 10.22 - type: mrr_at_10 value: 14.496 - type: mrr_at_100 value: 15.18 - type: mrr_at_1000 value: 15.279000000000002 - type: mrr_at_3 value: 12.979 - type: mrr_at_5 value: 13.755 - type: ndcg_at_1 value: 10.22 - type: ndcg_at_10 value: 14.687 - type: ndcg_at_100 value: 18.543000000000003 - type: ndcg_at_1000 value: 22.099 - type: ndcg_at_3 value: 12.076 - type: ndcg_at_5 value: 13.161999999999999 - type: precision_at_1 value: 10.22 - type: precision_at_10 value: 2.822 - type: precision_at_100 value: 0.565 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 5.781 - type: precision_at_5 value: 4.301 - type: recall_at_1 value: 8.119 - type: recall_at_10 value: 20.527 - type: recall_at_100 value: 38.719 - type: recall_at_1000 value: 65.16300000000001 - type: recall_at_3 value: 13.275 - type: recall_at_5 value: 15.998999999999999 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.334999999999999 - type: map_at_10 value: 18.194 - type: map_at_100 value: 19.049 - type: map_at_1000 value: 19.17 - type: map_at_3 value: 16.625 - type: map_at_5 value: 17.509 - type: mrr_at_1 value: 16.231 - type: mrr_at_10 value: 21.308 - type: mrr_at_100 value: 22.154 - type: mrr_at_1000 value: 22.25 - type: mrr_at_3 value: 19.761 - type: mrr_at_5 value: 20.567 - type: ndcg_at_1 value: 16.231 - type: ndcg_at_10 value: 21.525 - type: ndcg_at_100 value: 26.008 - type: ndcg_at_1000 value: 29.351 - type: ndcg_at_3 value: 18.54 - type: ndcg_at_5 value: 19.916 - type: precision_at_1 value: 16.231 - type: precision_at_10 value: 3.6470000000000002 - type: precision_at_100 value: 0.655 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.488999999999999 - type: precision_at_5 value: 6.007 - type: recall_at_1 value: 13.334999999999999 - type: recall_at_10 value: 28.804999999999996 - type: recall_at_100 value: 49.303000000000004 - type: recall_at_1000 value: 73.95 - type: recall_at_3 value: 20.531 - type: recall_at_5 value: 24.067 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.002 - type: map_at_10 value: 21.282 - type: map_at_100 value: 22.518 - type: map_at_1000 value: 22.728 - type: map_at_3 value: 19.463 - type: map_at_5 value: 20.314 - type: mrr_at_1 value: 19.96 - type: mrr_at_10 value: 25.084 - type: mrr_at_100 value: 26.028000000000002 - type: mrr_at_1000 value: 26.119999999999997 - type: mrr_at_3 value: 23.352999999999998 - type: mrr_at_5 value: 24.203 - type: ndcg_at_1 value: 19.96 - type: ndcg_at_10 value: 25.275 - type: ndcg_at_100 value: 30.574 - type: ndcg_at_1000 value: 34.359 - type: ndcg_at_3 value: 22.281000000000002 - type: ndcg_at_5 value: 23.32 - type: precision_at_1 value: 19.96 - type: precision_at_10 value: 4.920999999999999 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.20400000000000001 - type: precision_at_3 value: 10.408000000000001 - type: precision_at_5 value: 7.352 - type: recall_at_1 value: 16.002 - type: recall_at_10 value: 32.452 - type: recall_at_100 value: 57.297 - type: recall_at_1000 value: 83.332 - type: recall_at_3 value: 23.101 - type: recall_at_5 value: 26.395999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.15 - type: map_at_10 value: 16.377 - type: map_at_100 value: 17.122999999999998 - type: map_at_1000 value: 17.241999999999997 - type: map_at_3 value: 14.935 - type: map_at_5 value: 15.669 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.656 - type: mrr_at_100 value: 18.427 - type: mrr_at_1000 value: 18.537 - type: mrr_at_3 value: 16.174 - type: mrr_at_5 value: 16.932 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 19.061 - type: ndcg_at_100 value: 23.168 - type: ndcg_at_1000 value: 26.700000000000003 - type: ndcg_at_3 value: 16.085 - type: ndcg_at_5 value: 17.342 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.994 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 6.715999999999999 - type: precision_at_5 value: 4.806 - type: recall_at_1 value: 12.15 - type: recall_at_10 value: 26.328000000000003 - type: recall_at_100 value: 45.806999999999995 - type: recall_at_1000 value: 73.06899999999999 - type: recall_at_3 value: 18.041 - type: recall_at_5 value: 21.121000000000002 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.675999999999999 - type: map_at_10 value: 13.600000000000001 - type: map_at_100 value: 15.287999999999998 - type: map_at_1000 value: 15.508 - type: map_at_3 value: 11.256 - type: map_at_5 value: 12.312 - type: mrr_at_1 value: 17.459 - type: mrr_at_10 value: 27.166 - type: mrr_at_100 value: 28.406 - type: mrr_at_1000 value: 28.464 - type: mrr_at_3 value: 23.931 - type: mrr_at_5 value: 25.66 - type: ndcg_at_1 value: 17.459 - type: ndcg_at_10 value: 20.146 - type: ndcg_at_100 value: 27.625 - type: ndcg_at_1000 value: 31.819999999999997 - type: ndcg_at_3 value: 15.870999999999999 - type: ndcg_at_5 value: 17.158 - type: precision_at_1 value: 17.459 - type: precision_at_10 value: 6.638 - type: precision_at_100 value: 1.4569999999999999 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 12.074 - type: precision_at_5 value: 9.407 - type: recall_at_1 value: 7.675999999999999 - type: recall_at_10 value: 25.267 - type: recall_at_100 value: 51.69200000000001 - type: recall_at_1000 value: 75.58 - type: recall_at_3 value: 14.901 - type: recall_at_5 value: 18.543000000000003 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.424 - type: map_at_10 value: 10.278 - type: map_at_100 value: 14.516000000000002 - type: map_at_1000 value: 15.584999999999999 - type: map_at_3 value: 7.179 - type: map_at_5 value: 8.556 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 52.653000000000006 - type: mrr_at_100 value: 53.33599999999999 - type: mrr_at_1000 value: 53.364999999999995 - type: mrr_at_3 value: 50.542 - type: mrr_at_5 value: 51.803999999999995 - type: ndcg_at_1 value: 31.624999999999996 - type: ndcg_at_10 value: 25.167 - type: ndcg_at_100 value: 28.766000000000002 - type: ndcg_at_1000 value: 35.959 - type: ndcg_at_3 value: 27.807 - type: ndcg_at_5 value: 26.569 - type: precision_at_1 value: 42.0 - type: precision_at_10 value: 22.5 - type: precision_at_100 value: 7.295 - type: precision_at_1000 value: 1.543 - type: precision_at_3 value: 33.5 - type: precision_at_5 value: 29.099999999999998 - type: recall_at_1 value: 4.424 - type: recall_at_10 value: 15.359 - type: recall_at_100 value: 35.99 - type: recall_at_1000 value: 60.707 - type: recall_at_3 value: 8.803999999999998 - type: recall_at_5 value: 11.349 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.99 - type: f1 value: 44.72383731785718 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.753 - type: map_at_10 value: 26.762999999999998 - type: map_at_100 value: 27.773999999999997 - type: map_at_1000 value: 27.845 - type: map_at_3 value: 24.096 - type: map_at_5 value: 25.6 - type: mrr_at_1 value: 19.022 - type: mrr_at_10 value: 28.46 - type: mrr_at_100 value: 29.462 - type: mrr_at_1000 value: 29.520999999999997 - type: mrr_at_3 value: 25.679999999999996 - type: mrr_at_5 value: 27.272999999999996 - type: ndcg_at_1 value: 19.022 - type: ndcg_at_10 value: 32.063 - type: ndcg_at_100 value: 37.169999999999995 - type: ndcg_at_1000 value: 39.048 - type: ndcg_at_3 value: 26.558999999999997 - type: ndcg_at_5 value: 29.266 - type: precision_at_1 value: 19.022 - type: precision_at_10 value: 5.119 - type: precision_at_100 value: 0.786 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 11.571 - type: precision_at_5 value: 8.368 - type: recall_at_1 value: 17.753 - type: recall_at_10 value: 47.061 - type: recall_at_100 value: 70.75200000000001 - type: recall_at_1000 value: 85.134 - type: recall_at_3 value: 32.049 - type: recall_at_5 value: 38.556000000000004 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 6.845 - type: map_at_10 value: 11.806999999999999 - type: map_at_100 value: 13.104 - type: map_at_1000 value: 13.317 - type: map_at_3 value: 9.746 - type: map_at_5 value: 10.806000000000001 - type: mrr_at_1 value: 13.889000000000001 - type: mrr_at_10 value: 20.456 - type: mrr_at_100 value: 21.572 - type: mrr_at_1000 value: 21.666 - type: mrr_at_3 value: 18.184 - type: mrr_at_5 value: 19.387999999999998 - type: ndcg_at_1 value: 13.889000000000001 - type: ndcg_at_10 value: 16.552 - type: ndcg_at_100 value: 22.817999999999998 - type: ndcg_at_1000 value: 27.401999999999997 - type: ndcg_at_3 value: 13.527000000000001 - type: ndcg_at_5 value: 14.6 - type: precision_at_1 value: 13.889000000000001 - type: precision_at_10 value: 4.984999999999999 - type: precision_at_100 value: 1.097 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 9.208 - type: precision_at_5 value: 7.13 - type: recall_at_1 value: 6.845 - type: recall_at_10 value: 22.012999999999998 - type: recall_at_100 value: 46.75 - type: recall_at_1000 value: 74.945 - type: recall_at_3 value: 12.352 - type: recall_at_5 value: 16.217000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 18.886 - type: map_at_10 value: 26.729999999999997 - type: map_at_100 value: 27.732 - type: map_at_1000 value: 27.85 - type: map_at_3 value: 24.57 - type: map_at_5 value: 25.774 - type: mrr_at_1 value: 37.772 - type: mrr_at_10 value: 45.239000000000004 - type: mrr_at_100 value: 45.972 - type: mrr_at_1000 value: 46.027 - type: mrr_at_3 value: 43.279 - type: mrr_at_5 value: 44.397 - type: ndcg_at_1 value: 37.772 - type: ndcg_at_10 value: 33.973 - type: ndcg_at_100 value: 38.456 - type: ndcg_at_1000 value: 41.178 - type: ndcg_at_3 value: 29.988999999999997 - type: ndcg_at_5 value: 31.935999999999996 - type: precision_at_1 value: 37.772 - type: precision_at_10 value: 7.465 - type: precision_at_100 value: 1.1039999999999999 - type: precision_at_1000 value: 0.147 - type: precision_at_3 value: 18.902 - type: precision_at_5 value: 12.883 - type: recall_at_1 value: 18.886 - type: recall_at_10 value: 37.326 - type: recall_at_100 value: 55.186 - type: recall_at_1000 value: 73.309 - type: recall_at_3 value: 28.352 - type: recall_at_5 value: 32.208 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.6808 - type: ap value: 64.78268083902698 - type: f1 value: 70.48410216634053 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.300999999999999 - type: map_at_10 value: 11.068999999999999 - type: map_at_100 value: 12.0 - type: map_at_1000 value: 12.113999999999999 - type: map_at_3 value: 9.381 - type: map_at_5 value: 10.265 - type: mrr_at_1 value: 6.476 - type: mrr_at_10 value: 11.357000000000001 - type: mrr_at_100 value: 12.293 - type: mrr_at_1000 value: 12.403 - type: mrr_at_3 value: 9.62 - type: mrr_at_5 value: 10.544 - type: ndcg_at_1 value: 6.461 - type: ndcg_at_10 value: 14.058000000000002 - type: ndcg_at_100 value: 19.156000000000002 - type: ndcg_at_1000 value: 22.570999999999998 - type: ndcg_at_3 value: 10.475 - type: ndcg_at_5 value: 12.092 - type: precision_at_1 value: 6.461 - type: precision_at_10 value: 2.431 - type: precision_at_100 value: 0.508 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 4.6080000000000005 - type: precision_at_5 value: 3.5900000000000003 - type: recall_at_1 value: 6.300999999999999 - type: recall_at_10 value: 23.378 - type: recall_at_100 value: 48.258 - type: recall_at_1000 value: 75.652 - type: recall_at_3 value: 13.422 - type: recall_at_5 value: 17.316000000000003 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.34290925672595 - type: f1 value: 90.45651851550997 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 63.57045143638851 - type: f1 value: 44.02606500037181 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.63214525891057 - type: f1 value: 63.33629303043603 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.65635507733691 - type: f1 value: 71.52506282204605 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.593804768886113 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.41249151566158 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.0983658178549 - type: mrr value: 32.18857446274346 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.79 - type: map_at_10 value: 9.095 - type: map_at_100 value: 11.738999999999999 - type: map_at_1000 value: 13.203000000000001 - type: map_at_3 value: 6.68 - type: map_at_5 value: 7.924 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 46.283 - type: mrr_at_100 value: 46.983999999999995 - type: mrr_at_1000 value: 47.046 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 45.268 - type: ndcg_at_1 value: 35.604 - type: ndcg_at_10 value: 27.249000000000002 - type: ndcg_at_100 value: 26.215 - type: ndcg_at_1000 value: 35.867 - type: ndcg_at_3 value: 30.330000000000002 - type: ndcg_at_5 value: 29.574 - type: precision_at_1 value: 37.152 - type: precision_at_10 value: 20.031 - type: precision_at_100 value: 7.217 - type: precision_at_1000 value: 2.072 - type: precision_at_3 value: 27.761000000000003 - type: precision_at_5 value: 25.448999999999998 - type: recall_at_1 value: 4.79 - type: recall_at_10 value: 13.197000000000001 - type: recall_at_100 value: 28.816999999999997 - type: recall_at_1000 value: 63.010999999999996 - type: recall_at_3 value: 7.53 - type: recall_at_5 value: 10.234 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.345 - type: map_at_10 value: 16.655 - type: map_at_100 value: 17.991 - type: map_at_1000 value: 18.093999999999998 - type: map_at_3 value: 13.825000000000001 - type: map_at_5 value: 15.445 - type: mrr_at_1 value: 10.834000000000001 - type: mrr_at_10 value: 18.533 - type: mrr_at_100 value: 19.750999999999998 - type: mrr_at_1000 value: 19.837 - type: mrr_at_3 value: 15.623999999999999 - type: mrr_at_5 value: 17.307 - type: ndcg_at_1 value: 10.834000000000001 - type: ndcg_at_10 value: 21.503 - type: ndcg_at_100 value: 28.141 - type: ndcg_at_1000 value: 30.951 - type: ndcg_at_3 value: 15.7 - type: ndcg_at_5 value: 18.608 - type: precision_at_1 value: 10.834000000000001 - type: precision_at_10 value: 4.09 - type: precision_at_100 value: 0.782 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.474 - type: precision_at_5 value: 6.089 - type: recall_at_1 value: 9.345 - type: recall_at_10 value: 34.760000000000005 - type: recall_at_100 value: 65.455 - type: recall_at_1000 value: 87.008 - type: recall_at_3 value: 19.397000000000002 - type: recall_at_5 value: 26.205000000000002 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.864 - type: map_at_10 value: 76.823 - type: map_at_100 value: 77.58699999999999 - type: map_at_1000 value: 77.619 - type: map_at_3 value: 73.834 - type: map_at_5 value: 75.703 - type: mrr_at_1 value: 73.55000000000001 - type: mrr_at_10 value: 81.077 - type: mrr_at_100 value: 81.296 - type: mrr_at_1000 value: 81.3 - type: mrr_at_3 value: 79.647 - type: mrr_at_5 value: 80.601 - type: ndcg_at_1 value: 73.63 - type: ndcg_at_10 value: 81.526 - type: ndcg_at_100 value: 83.544 - type: ndcg_at_1000 value: 83.86200000000001 - type: ndcg_at_3 value: 77.96300000000001 - type: ndcg_at_5 value: 79.888 - type: precision_at_1 value: 73.63 - type: precision_at_10 value: 12.325 - type: precision_at_100 value: 1.468 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.857 - type: precision_at_5 value: 22.428 - type: recall_at_1 value: 63.864 - type: recall_at_10 value: 90.537 - type: recall_at_100 value: 97.985 - type: recall_at_1000 value: 99.679 - type: recall_at_3 value: 80.351 - type: recall_at_5 value: 85.697 - type: map_at_1 value: 2.8979999999999997 - type: map_at_10 value: 7.376 - type: map_at_100 value: 8.902000000000001 - type: map_at_1000 value: 9.174 - type: map_at_3 value: 5.47 - type: map_at_5 value: 6.432 - type: mrr_at_1 value: 14.2 - type: mrr_at_10 value: 22.966 - type: mrr_at_100 value: 24.117 - type: mrr_at_1000 value: 24.209 - type: mrr_at_3 value: 20.033 - type: mrr_at_5 value: 21.532999999999998 - type: ndcg_at_1 value: 14.2 - type: ndcg_at_10 value: 13.016 - type: ndcg_at_100 value: 19.804 - 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type: ndcg_at_10 value: 44.658 - type: ndcg_at_100 value: 33.977000000000004 - type: ndcg_at_1000 value: 30.621 - type: ndcg_at_3 value: 48.939 - type: ndcg_at_5 value: 45.396 - type: precision_at_1 value: 57.99999999999999 - type: precision_at_10 value: 47.4 - type: precision_at_100 value: 35.82 - type: precision_at_1000 value: 14.876000000000001 - type: precision_at_3 value: 50.0 - type: precision_at_5 value: 45.6 - type: recall_at_1 value: 0.155 - type: recall_at_10 value: 1.0670000000000002 - type: recall_at_100 value: 7.651 - type: recall_at_1000 value: 29.537000000000003 - type: recall_at_3 value: 0.35500000000000004 - type: recall_at_5 value: 0.518 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 44.25321653229755 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - 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type: cos_sim_pearson value: 77.42103332836258 - type: cos_sim_spearman value: 78.48875534932043 - type: euclidean_pearson value: 78.1930584097837 - type: euclidean_spearman value: 78.48879315793262 - type: manhattan_pearson value: 75.7705791679418 - type: manhattan_spearman value: 76.01194506942352 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 78.58218995046416 - type: cos_sim_spearman value: 75.61279190671051 - type: euclidean_pearson value: 77.58820759180631 - type: euclidean_spearman value: 75.61278221440635 - type: manhattan_pearson value: 76.12440001778819 - type: manhattan_spearman value: 74.4269498969252 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.2891641302121 - type: cos_sim_spearman value: 82.73098262647434 - type: euclidean_pearson value: 82.5188483930312 - 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type: manhattan_spearman value: 86.76481453820853 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.80818689506919 - type: cos_sim_spearman value: 59.903534431363916 - type: euclidean_pearson value: 60.967975393911466 - type: euclidean_spearman value: 59.903534431363916 - type: manhattan_pearson value: 59.348745545947104 - type: manhattan_spearman value: 58.506942610232116 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.61911955925173 - type: cos_sim_spearman value: 79.18748066540941 - type: euclidean_pearson value: 80.06976231938555 - type: euclidean_spearman value: 79.18749912961366 - type: manhattan_pearson value: 78.3922696264025 - type: manhattan_spearman value: 77.68224365306664 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.36063395414824 - type: mrr value: 91.81411453470277 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.139 - type: map_at_10 value: 47.508 - type: map_at_100 value: 48.631 - type: map_at_1000 value: 48.691 - type: map_at_3 value: 44.926 - type: map_at_5 value: 46.093 - type: mrr_at_1 value: 41.333 - type: mrr_at_10 value: 49.289 - type: mrr_at_100 value: 50.209 - type: mrr_at_1000 value: 50.261 - type: mrr_at_3 value: 46.944 - type: mrr_at_5 value: 47.978 - type: ndcg_at_1 value: 41.333 - type: ndcg_at_10 value: 52.306 - type: ndcg_at_100 value: 57.403999999999996 - type: ndcg_at_1000 value: 58.733999999999995 - type: ndcg_at_3 value: 47.113 - type: ndcg_at_5 value: 48.966 - type: precision_at_1 value: 41.333 - type: precision_at_10 value: 7.167 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.0 - type: recall_at_1 value: 39.139 - type: recall_at_10 value: 65.84400000000001 - type: recall_at_100 value: 88.94999999999999 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 51.222 - type: recall_at_5 value: 55.72200000000001 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66831683168317 - type: cos_sim_ap value: 89.56758407722171 - type: cos_sim_f1 value: 82.97029702970298 - type: cos_sim_precision value: 82.15686274509804 - type: cos_sim_recall value: 83.8 - type: dot_accuracy value: 99.66831683168317 - type: dot_ap value: 89.56758407722171 - type: dot_f1 value: 82.97029702970298 - type: dot_precision value: 82.15686274509804 - type: dot_recall value: 83.8 - type: euclidean_accuracy value: 99.66831683168317 - type: euclidean_ap value: 89.56758407722171 - type: euclidean_f1 value: 82.97029702970298 - type: euclidean_precision value: 82.15686274509804 - type: euclidean_recall value: 83.8 - type: manhattan_accuracy value: 99.65445544554456 - type: manhattan_ap value: 88.81637821295462 - type: manhattan_f1 value: 81.9047619047619 - type: manhattan_precision value: 82.1105527638191 - type: manhattan_recall value: 81.69999999999999 - type: max_accuracy value: 99.66831683168317 - type: max_ap value: 89.56758407722171 - type: max_f1 value: 82.97029702970298 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 47.34055809539011 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 29.658298502445096 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.989840235812004 - type: mrr value: 44.506899350649356 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.29667664654959 - type: cos_sim_spearman value: 29.818596667773882 - type: dot_pearson value: 31.296676647072026 - type: dot_spearman value: 29.779857187330265 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.426 - type: map_at_10 value: 8.38 - type: map_at_100 value: 14.308000000000002 - type: map_at_1000 value: 15.956000000000001 - type: map_at_3 value: 4.596 - type: map_at_5 value: 6.1339999999999995 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 44.577 - type: mrr_at_100 value: 45.754 - type: mrr_at_1000 value: 45.754 - type: mrr_at_3 value: 41.156 - type: mrr_at_5 value: 43.401 - type: ndcg_at_1 value: 28.571 - type: ndcg_at_10 value: 21.116 - type: ndcg_at_100 value: 35.193000000000005 - type: ndcg_at_1000 value: 46.989 - type: ndcg_at_3 value: 24.708 - type: ndcg_at_5 value: 23.594 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 19.592000000000002 - type: precision_at_100 value: 8.265 - type: precision_at_1000 value: 1.5939999999999999 - type: precision_at_3 value: 27.211000000000002 - type: precision_at_5 value: 25.306 - type: recall_at_1 value: 2.426 - type: recall_at_10 value: 13.691 - type: recall_at_100 value: 49.446 - type: recall_at_1000 value: 86.124 - type: recall_at_3 value: 5.67 - type: recall_at_5 value: 8.506 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 79.2904 - type: ap value: 19.73734798884487 - type: f1 value: 61.89018130098357 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.97906055461234 - type: f1 value: 61.25225658586279 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.859245341604115 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.4714192048638 - type: cos_sim_ap value: 64.50474781834589 - type: cos_sim_f1 value: 60.58070866141732 - type: cos_sim_precision value: 56.75426463808206 - type: cos_sim_recall value: 64.96042216358839 - type: dot_accuracy value: 83.4714192048638 - type: dot_ap value: 64.50474781834589 - type: dot_f1 value: 60.58070866141732 - type: dot_precision value: 56.75426463808206 - type: dot_recall value: 64.96042216358839 - type: euclidean_accuracy value: 83.4714192048638 - type: euclidean_ap value: 64.50474781834589 - type: euclidean_f1 value: 60.58070866141732 - type: euclidean_precision value: 56.75426463808206 - type: euclidean_recall value: 64.96042216358839 - type: manhattan_accuracy value: 83.48334028729809 - type: manhattan_ap value: 64.6227449383717 - type: manhattan_f1 value: 60.88942307692308 - type: manhattan_precision value: 55.916114790286976 - type: manhattan_recall value: 66.83377308707124 - type: max_accuracy value: 83.48334028729809 - type: max_ap value: 64.6227449383717 - type: max_f1 value: 60.88942307692308 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.58295494236815 - type: cos_sim_ap value: 83.22579648788027 - type: cos_sim_f1 value: 75.27357054859048 - type: cos_sim_precision value: 71.09514031485284 - type: cos_sim_recall value: 79.97382198952879 - type: dot_accuracy value: 87.58295494236815 - type: dot_ap value: 83.22579579937255 - type: dot_f1 value: 75.27357054859048 - type: dot_precision value: 71.09514031485284 - type: dot_recall value: 79.97382198952879 - type: euclidean_accuracy value: 87.58295494236815 - type: euclidean_ap value: 83.22580643443949 - type: euclidean_f1 value: 75.27357054859048 - type: euclidean_precision value: 71.09514031485284 - type: euclidean_recall value: 79.97382198952879 - type: manhattan_accuracy value: 87.57325260992742 - type: manhattan_ap value: 83.05240665725778 - type: manhattan_f1 value: 75.09726237641432 - type: manhattan_precision value: 69.99800385920554 - type: manhattan_recall value: 80.99784416384355 - type: max_accuracy value: 87.58295494236815 - type: max_ap value: 83.22580643443949 - type: max_f1 value: 75.27357054859048 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/GTE256_sw

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:24:56

2025-01-09T11:25:04

0

--- tags: - mteb model-index: - name: gte-base-en-v1.5_embs_nofiltering_sortlenTrue_phrase2sent_15epoch_15epoch__adam0.001_accum1_best_epoch_3863037_bs128_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.71641791044777 - type: ap value: 35.599140186230734 - type: f1 value: 66.72372354326045 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 72.7823 - type: ap value: 66.88980427652794 - type: f1 value: 72.60105018624591 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.483999999999995 - type: f1 value: 37.867826932045745 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.195 - type: map_at_10 value: 35.876999999999995 - type: map_at_100 value: 37.147999999999996 - type: map_at_1000 value: 37.165 - type: map_at_3 value: 31.342 - type: map_at_5 value: 33.764 - type: mrr_at_1 value: 21.906 - type: mrr_at_10 value: 36.128 - type: mrr_at_100 value: 37.397999999999996 - type: mrr_at_1000 value: 37.416 - type: mrr_at_3 value: 31.555 - type: mrr_at_5 value: 34.001999999999995 - type: ndcg_at_1 value: 21.195 - type: ndcg_at_10 value: 44.207 - type: ndcg_at_100 value: 49.88 - type: ndcg_at_1000 value: 50.298 - type: ndcg_at_3 value: 34.755 - type: ndcg_at_5 value: 39.135 - type: precision_at_1 value: 21.195 - type: precision_at_10 value: 7.091 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 14.889 - type: precision_at_5 value: 11.067 - type: recall_at_1 value: 21.195 - type: recall_at_10 value: 70.91 - type: recall_at_100 value: 96.30199999999999 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 44.666 - type: recall_at_5 value: 55.334 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 38.289190023047105 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 28.15017802770073 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.677327183831046 - type: mrr value: 68.2003253748406 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 81.79485923763309 - type: cos_sim_spearman value: 79.71265968052003 - type: euclidean_pearson value: 80.78575386279923 - type: euclidean_spearman value: 79.71265968052003 - type: manhattan_pearson value: 81.12300703450198 - type: manhattan_spearman value: 81.23377867759768 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.04870129870129 - type: f1 value: 74.29090714638184 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.18202629628483 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.419352316577875 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.305 - type: map_at_10 value: 28.046 - type: map_at_100 value: 29.174 - type: map_at_1000 value: 29.326999999999998 - type: map_at_3 value: 25.464 - type: map_at_5 value: 26.874 - type: mrr_at_1 value: 26.179999999999996 - type: mrr_at_10 value: 34.0 - type: mrr_at_100 value: 34.797 - type: mrr_at_1000 value: 34.864 - type: mrr_at_3 value: 31.784000000000002 - type: mrr_at_5 value: 32.992 - type: ndcg_at_1 value: 26.179999999999996 - type: ndcg_at_10 value: 33.46 - type: ndcg_at_100 value: 38.539 - type: ndcg_at_1000 value: 41.619 - type: ndcg_at_3 value: 29.471000000000004 - type: ndcg_at_5 value: 31.169999999999998 - type: precision_at_1 value: 26.179999999999996 - type: precision_at_10 value: 6.680999999999999 - type: precision_at_100 value: 1.15 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 14.591999999999999 - type: precision_at_5 value: 10.73 - type: recall_at_1 value: 20.305 - type: recall_at_10 value: 43.199 - type: recall_at_100 value: 66.46 - type: recall_at_1000 value: 87.469 - type: recall_at_3 value: 30.94 - type: recall_at_5 value: 35.927 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 18.265 - type: map_at_10 value: 24.661 - type: map_at_100 value: 25.739 - type: map_at_1000 value: 25.86 - type: map_at_3 value: 22.775000000000002 - type: map_at_5 value: 23.814 - type: mrr_at_1 value: 23.185 - type: mrr_at_10 value: 29.067 - type: mrr_at_100 value: 29.939 - type: mrr_at_1000 value: 30.007 - type: mrr_at_3 value: 27.197 - type: mrr_at_5 value: 28.248 - type: ndcg_at_1 value: 23.185 - type: ndcg_at_10 value: 28.638 - type: ndcg_at_100 value: 33.341 - type: ndcg_at_1000 value: 36.11 - type: ndcg_at_3 value: 25.599 - type: ndcg_at_5 value: 26.901999999999997 - type: precision_at_1 value: 23.185 - type: precision_at_10 value: 5.306 - type: precision_at_100 value: 0.959 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.442 - type: precision_at_5 value: 8.764 - type: recall_at_1 value: 18.265 - type: recall_at_10 value: 36.055 - type: recall_at_100 value: 56.419 - type: recall_at_1000 value: 75.25500000000001 - type: recall_at_3 value: 26.906999999999996 - type: recall_at_5 value: 30.637999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 27.065 - type: map_at_10 value: 35.952 - type: map_at_100 value: 37.092999999999996 - type: map_at_1000 value: 37.19 - type: map_at_3 value: 33.410000000000004 - type: map_at_5 value: 34.743 - type: mrr_at_1 value: 31.223 - type: mrr_at_10 value: 39.174 - type: mrr_at_100 value: 40.091 - type: mrr_at_1000 value: 40.152 - type: mrr_at_3 value: 37.011 - type: mrr_at_5 value: 38.115 - type: ndcg_at_1 value: 31.223 - type: ndcg_at_10 value: 40.871 - type: ndcg_at_100 value: 46.068 - type: ndcg_at_1000 value: 48.295 - type: ndcg_at_3 value: 36.285000000000004 - type: ndcg_at_5 value: 38.25 - type: precision_at_1 value: 31.223 - type: precision_at_10 value: 6.639 - type: precision_at_100 value: 1.014 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 16.092000000000002 - type: precision_at_5 value: 11.047 - type: recall_at_1 value: 27.065 - type: recall_at_10 value: 52.605000000000004 - type: recall_at_100 value: 75.653 - type: recall_at_1000 value: 91.724 - type: recall_at_3 value: 40.150999999999996 - type: recall_at_5 value: 44.979 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 12.692999999999998 - type: map_at_10 value: 17.427 - type: map_at_100 value: 18.235 - type: map_at_1000 value: 18.355 - type: map_at_3 value: 16.144 - type: map_at_5 value: 16.81 - type: mrr_at_1 value: 13.672 - type: mrr_at_10 value: 18.633 - type: mrr_at_100 value: 19.447 - type: mrr_at_1000 value: 19.554 - type: mrr_at_3 value: 17.401 - type: mrr_at_5 value: 17.983 - type: ndcg_at_1 value: 13.672 - type: ndcg_at_10 value: 20.212 - type: ndcg_at_100 value: 24.66 - type: ndcg_at_1000 value: 28.265 - type: ndcg_at_3 value: 17.625 - type: ndcg_at_5 value: 18.728 - type: precision_at_1 value: 13.672 - type: precision_at_10 value: 3.141 - type: precision_at_100 value: 0.569 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 7.5329999999999995 - type: precision_at_5 value: 5.220000000000001 - type: recall_at_1 value: 12.692999999999998 - type: recall_at_10 value: 27.656 - type: recall_at_100 value: 48.927 - type: recall_at_1000 value: 77.113 - type: recall_at_3 value: 20.54 - type: recall_at_5 value: 23.177 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.814 - type: map_at_10 value: 11.472 - type: map_at_100 value: 12.283 - type: map_at_1000 value: 12.407 - type: map_at_3 value: 9.892 - type: map_at_5 value: 10.525 - type: mrr_at_1 value: 9.950000000000001 - type: mrr_at_10 value: 13.947999999999999 - type: mrr_at_100 value: 14.790000000000001 - type: mrr_at_1000 value: 14.893999999999998 - type: mrr_at_3 value: 12.189 - type: mrr_at_5 value: 12.91 - type: ndcg_at_1 value: 9.950000000000001 - type: ndcg_at_10 value: 14.481 - type: ndcg_at_100 value: 18.999 - type: ndcg_at_1000 value: 22.519 - type: ndcg_at_3 value: 11.212 - type: ndcg_at_5 value: 12.238 - type: precision_at_1 value: 9.950000000000001 - type: precision_at_10 value: 2.861 - type: precision_at_100 value: 0.607 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 5.224 - type: precision_at_5 value: 3.856 - type: recall_at_1 value: 7.814 - type: recall_at_10 value: 21.507 - type: recall_at_100 value: 42.067 - type: recall_at_1000 value: 68.059 - type: recall_at_3 value: 12.489 - type: recall_at_5 value: 14.973 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.572 - type: map_at_10 value: 24.854000000000003 - type: map_at_100 value: 26.029000000000003 - type: map_at_1000 value: 26.177 - type: map_at_3 value: 22.417 - type: map_at_5 value: 23.612 - type: mrr_at_1 value: 22.907 - type: mrr_at_10 value: 29.643000000000004 - type: mrr_at_100 value: 30.499 - type: mrr_at_1000 value: 30.586999999999996 - type: mrr_at_3 value: 27.108999999999998 - type: mrr_at_5 value: 28.355999999999998 - type: ndcg_at_1 value: 22.907 - type: ndcg_at_10 value: 29.601 - type: ndcg_at_100 value: 35.11 - type: ndcg_at_1000 value: 38.433 - type: ndcg_at_3 value: 25.068 - type: ndcg_at_5 value: 26.828000000000003 - type: precision_at_1 value: 22.907 - type: precision_at_10 value: 5.525 - type: precision_at_100 value: 1.002 - type: precision_at_1000 value: 0.149 - type: precision_at_3 value: 11.389000000000001 - type: precision_at_5 value: 8.354000000000001 - type: recall_at_1 value: 18.572 - type: recall_at_10 value: 39.499 - type: recall_at_100 value: 63.46000000000001 - type: recall_at_1000 value: 86.52499999999999 - type: recall_at_3 value: 26.699 - type: recall_at_5 value: 31.175000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.918000000000001 - type: map_at_10 value: 20.223 - type: map_at_100 value: 21.429000000000002 - type: map_at_1000 value: 21.578 - type: map_at_3 value: 18.278 - type: map_at_5 value: 19.312 - type: mrr_at_1 value: 18.037 - type: mrr_at_10 value: 23.75 - type: mrr_at_100 value: 24.804000000000002 - type: mrr_at_1000 value: 24.898 - type: mrr_at_3 value: 21.842 - type: mrr_at_5 value: 22.755 - type: ndcg_at_1 value: 18.037 - type: ndcg_at_10 value: 23.907 - type: ndcg_at_100 value: 29.663 - type: ndcg_at_1000 value: 33.245000000000005 - type: ndcg_at_3 value: 20.379 - type: ndcg_at_5 value: 21.799 - type: precision_at_1 value: 18.037 - type: precision_at_10 value: 4.452 - type: precision_at_100 value: 0.881 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 9.513 - type: precision_at_5 value: 6.895 - type: recall_at_1 value: 14.918000000000001 - type: recall_at_10 value: 31.503999999999998 - type: recall_at_100 value: 56.354000000000006 - type: recall_at_1000 value: 81.774 - type: recall_at_3 value: 21.819 - type: recall_at_5 value: 25.459 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 14.668083333333332 - type: map_at_10 value: 20.24666666666667 - type: map_at_100 value: 21.21025 - type: map_at_1000 value: 21.340666666666664 - type: map_at_3 value: 18.417083333333334 - type: map_at_5 value: 19.366833333333332 - type: mrr_at_1 value: 17.777833333333334 - type: mrr_at_10 value: 23.403333333333336 - type: mrr_at_100 value: 24.25408333333333 - type: mrr_at_1000 value: 24.34333333333333 - type: mrr_at_3 value: 21.6155 - type: mrr_at_5 value: 22.521 - type: ndcg_at_1 value: 17.777833333333334 - type: ndcg_at_10 value: 23.933500000000002 - type: ndcg_at_100 value: 28.714749999999995 - type: ndcg_at_1000 value: 31.968833333333336 - type: ndcg_at_3 value: 20.60758333333333 - type: ndcg_at_5 value: 21.982416666666666 - type: precision_at_1 value: 17.777833333333334 - type: precision_at_10 value: 4.3180000000000005 - type: precision_at_100 value: 0.8045833333333333 - type: precision_at_1000 value: 0.12691666666666668 - type: precision_at_3 value: 9.535000000000002 - type: precision_at_5 value: 6.825916666666666 - type: recall_at_1 value: 14.668083333333332 - type: recall_at_10 value: 31.930916666666665 - type: recall_at_100 value: 53.753249999999994 - type: recall_at_1000 value: 77.43366666666667 - type: recall_at_3 value: 22.524250000000002 - type: recall_at_5 value: 26.094916666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 10.096 - type: map_at_10 value: 15.190999999999999 - type: map_at_100 value: 15.922 - type: map_at_1000 value: 16.017 - type: map_at_3 value: 13.664000000000001 - type: map_at_5 value: 14.446 - type: mrr_at_1 value: 12.117 - type: mrr_at_10 value: 17.294 - type: mrr_at_100 value: 18.074 - type: mrr_at_1000 value: 18.153 - type: mrr_at_3 value: 15.823 - type: mrr_at_5 value: 16.59 - type: ndcg_at_1 value: 12.117 - type: ndcg_at_10 value: 18.248 - type: ndcg_at_100 value: 22.418 - type: ndcg_at_1000 value: 25.271 - type: ndcg_at_3 value: 15.368 - type: ndcg_at_5 value: 16.614 - type: precision_at_1 value: 12.117 - type: precision_at_10 value: 3.206 - type: precision_at_100 value: 0.583 - type: precision_at_1000 value: 0.09 - type: precision_at_3 value: 7.106 - type: precision_at_5 value: 5.061 - type: recall_at_1 value: 10.096 - type: recall_at_10 value: 25.624000000000002 - type: recall_at_100 value: 45.49 - type: recall_at_1000 value: 67.392 - type: recall_at_3 value: 17.68 - type: recall_at_5 value: 20.823 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.7780000000000005 - type: map_at_10 value: 11.493 - type: map_at_100 value: 12.200999999999999 - type: map_at_1000 value: 12.324 - type: map_at_3 value: 10.244 - type: map_at_5 value: 10.899000000000001 - type: mrr_at_1 value: 9.876 - type: mrr_at_10 value: 14.001 - type: mrr_at_100 value: 14.701 - type: mrr_at_1000 value: 14.799999999999999 - type: mrr_at_3 value: 12.583 - type: mrr_at_5 value: 13.325000000000001 - type: ndcg_at_1 value: 9.876 - type: ndcg_at_10 value: 14.158000000000001 - type: ndcg_at_100 value: 18.038999999999998 - type: ndcg_at_1000 value: 21.58 - type: ndcg_at_3 value: 11.722000000000001 - type: ndcg_at_5 value: 12.769 - type: precision_at_1 value: 9.876 - type: precision_at_10 value: 2.705 - type: precision_at_100 value: 0.555 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 5.666 - type: precision_at_5 value: 4.178 - type: recall_at_1 value: 7.7780000000000005 - type: recall_at_10 value: 19.86 - type: recall_at_100 value: 38.0 - type: recall_at_1000 value: 64.331 - type: recall_at_3 value: 13.117999999999999 - type: recall_at_5 value: 15.783 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.088 - type: map_at_10 value: 17.759 - type: map_at_100 value: 18.597 - type: map_at_1000 value: 18.718 - type: map_at_3 value: 16.232 - type: map_at_5 value: 17.129 - type: mrr_at_1 value: 15.672 - type: mrr_at_10 value: 20.676 - type: mrr_at_100 value: 21.505 - type: mrr_at_1000 value: 21.605 - type: mrr_at_3 value: 18.999 - type: mrr_at_5 value: 19.932 - type: ndcg_at_1 value: 15.672 - type: ndcg_at_10 value: 21.035 - type: ndcg_at_100 value: 25.52 - type: ndcg_at_1000 value: 28.875 - type: ndcg_at_3 value: 18.015 - type: ndcg_at_5 value: 19.476 - type: precision_at_1 value: 15.672 - type: precision_at_10 value: 3.535 - type: precision_at_100 value: 0.652 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.24 - type: precision_at_5 value: 5.821 - type: recall_at_1 value: 13.088 - type: recall_at_10 value: 28.414 - type: recall_at_100 value: 48.949999999999996 - type: recall_at_1000 value: 73.67399999999999 - type: recall_at_3 value: 19.893 - type: recall_at_5 value: 23.718 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.433 - type: map_at_10 value: 19.926 - type: map_at_100 value: 21.11 - type: map_at_1000 value: 21.302 - type: map_at_3 value: 17.991 - type: map_at_5 value: 19.078999999999997 - type: mrr_at_1 value: 17.391000000000002 - type: mrr_at_10 value: 23.433999999999997 - type: mrr_at_100 value: 24.41 - type: mrr_at_1000 value: 24.501 - type: mrr_at_3 value: 21.706 - type: mrr_at_5 value: 22.684 - type: ndcg_at_1 value: 17.391000000000002 - type: ndcg_at_10 value: 24.11 - type: ndcg_at_100 value: 29.500999999999998 - type: ndcg_at_1000 value: 33.093 - type: ndcg_at_3 value: 21.037 - type: ndcg_at_5 value: 22.439 - type: precision_at_1 value: 17.391000000000002 - type: precision_at_10 value: 4.881 - type: precision_at_100 value: 1.138 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 10.277 - type: precision_at_5 value: 7.549 - type: recall_at_1 value: 13.433 - type: recall_at_10 value: 32.029 - type: recall_at_100 value: 57.727 - type: recall_at_1000 value: 82.536 - type: recall_at_3 value: 22.914 - type: recall_at_5 value: 26.844 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.99 - type: map_at_10 value: 15.956000000000001 - type: map_at_100 value: 16.711000000000002 - type: map_at_1000 value: 16.833000000000002 - type: map_at_3 value: 14.494000000000002 - type: map_at_5 value: 15.159 - type: mrr_at_1 value: 13.123999999999999 - type: mrr_at_10 value: 17.22 - type: mrr_at_100 value: 17.992 - type: mrr_at_1000 value: 18.105 - type: mrr_at_3 value: 15.742 - type: mrr_at_5 value: 16.362 - type: ndcg_at_1 value: 13.123999999999999 - type: ndcg_at_10 value: 18.481 - type: ndcg_at_100 value: 22.719 - type: ndcg_at_1000 value: 26.321 - type: ndcg_at_3 value: 15.509999999999998 - type: ndcg_at_5 value: 16.576 - type: precision_at_1 value: 13.123999999999999 - type: precision_at_10 value: 2.884 - type: precision_at_100 value: 0.545 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.436 - type: recall_at_1 value: 11.99 - type: recall_at_10 value: 25.219 - type: recall_at_100 value: 45.532000000000004 - type: recall_at_1000 value: 73.35199999999999 - type: recall_at_3 value: 17.141000000000002 - type: recall_at_5 value: 19.643 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 7.6240000000000006 - type: map_at_10 value: 13.211999999999998 - type: map_at_100 value: 14.82 - type: map_at_1000 value: 15.039 - type: map_at_3 value: 10.793999999999999 - type: map_at_5 value: 12.035 - type: mrr_at_1 value: 17.459 - type: mrr_at_10 value: 26.590000000000003 - type: mrr_at_100 value: 27.792 - type: mrr_at_1000 value: 27.851 - type: mrr_at_3 value: 23.268 - type: mrr_at_5 value: 25.192999999999998 - type: ndcg_at_1 value: 17.459 - type: ndcg_at_10 value: 19.606 - type: ndcg_at_100 value: 26.87 - type: ndcg_at_1000 value: 31.080000000000002 - type: ndcg_at_3 value: 15.190000000000001 - type: ndcg_at_5 value: 16.85 - type: precision_at_1 value: 17.459 - type: precision_at_10 value: 6.45 - type: precision_at_100 value: 1.421 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 11.488 - type: precision_at_5 value: 9.316 - type: recall_at_1 value: 7.6240000000000006 - type: recall_at_10 value: 24.593 - type: recall_at_100 value: 50.300999999999995 - type: recall_at_1000 value: 74.439 - type: recall_at_3 value: 14.097000000000001 - type: recall_at_5 value: 18.362000000000002 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.456 - type: map_at_10 value: 9.995 - type: map_at_100 value: 14.196 - type: map_at_1000 value: 15.284 - type: map_at_3 value: 7.02 - type: map_at_5 value: 8.341 - type: mrr_at_1 value: 43.25 - type: mrr_at_10 value: 52.626 - type: mrr_at_100 value: 53.361000000000004 - type: mrr_at_1000 value: 53.396 - type: mrr_at_3 value: 50.208 - type: mrr_at_5 value: 51.696 - type: ndcg_at_1 value: 31.75 - type: ndcg_at_10 value: 24.557000000000002 - type: ndcg_at_100 value: 28.179 - type: ndcg_at_1000 value: 35.42 - type: ndcg_at_3 value: 27.05 - type: ndcg_at_5 value: 25.938 - type: precision_at_1 value: 43.25 - type: precision_at_10 value: 21.95 - type: precision_at_100 value: 7.21 - type: precision_at_1000 value: 1.5310000000000001 - type: precision_at_3 value: 32.25 - type: precision_at_5 value: 28.050000000000004 - type: recall_at_1 value: 4.456 - type: recall_at_10 value: 14.808 - type: recall_at_100 value: 35.062 - type: recall_at_1000 value: 60.111000000000004 - type: recall_at_3 value: 8.333 - type: recall_at_5 value: 10.847999999999999 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 48.275 - type: f1 value: 44.11697299626323 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.512 - type: map_at_10 value: 25.102000000000004 - type: map_at_100 value: 26.14 - type: map_at_1000 value: 26.212000000000003 - type: map_at_3 value: 22.531000000000002 - type: map_at_5 value: 23.959 - type: mrr_at_1 value: 17.642 - type: mrr_at_10 value: 26.665 - type: mrr_at_100 value: 27.700000000000003 - type: mrr_at_1000 value: 27.762999999999998 - type: mrr_at_3 value: 24.03 - type: mrr_at_5 value: 25.501 - type: ndcg_at_1 value: 17.642 - type: ndcg_at_10 value: 30.162 - type: ndcg_at_100 value: 35.393 - type: ndcg_at_1000 value: 37.370999999999995 - type: ndcg_at_3 value: 24.878 - type: ndcg_at_5 value: 27.426000000000002 - type: precision_at_1 value: 17.642 - type: precision_at_10 value: 4.845 - type: precision_at_100 value: 0.765 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 10.876 - type: precision_at_5 value: 7.864 - type: recall_at_1 value: 16.512 - type: recall_at_10 value: 44.528 - type: recall_at_100 value: 68.794 - type: recall_at_1000 value: 84.055 - type: recall_at_3 value: 30.151 - type: recall_at_5 value: 36.244 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 6.548 - type: map_at_10 value: 11.365 - type: map_at_100 value: 12.659 - type: map_at_1000 value: 12.870999999999999 - type: map_at_3 value: 9.238 - type: map_at_5 value: 10.295 - type: mrr_at_1 value: 13.735 - type: mrr_at_10 value: 19.666 - type: mrr_at_100 value: 20.848 - type: mrr_at_1000 value: 20.951 - type: mrr_at_3 value: 17.335 - type: mrr_at_5 value: 18.616 - type: ndcg_at_1 value: 13.735 - type: ndcg_at_10 value: 15.923000000000002 - type: ndcg_at_100 value: 22.23 - type: ndcg_at_1000 value: 26.893 - type: ndcg_at_3 value: 12.756 - type: ndcg_at_5 value: 13.883999999999999 - type: precision_at_1 value: 13.735 - type: precision_at_10 value: 4.7379999999999995 - type: precision_at_100 value: 1.086 - type: precision_at_1000 value: 0.19 - type: precision_at_3 value: 8.436 - type: precision_at_5 value: 6.7589999999999995 - type: recall_at_1 value: 6.548 - type: recall_at_10 value: 21.267 - type: recall_at_100 value: 46.07 - type: recall_at_1000 value: 74.868 - type: recall_at_3 value: 11.611 - type: recall_at_5 value: 15.284 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 17.387 - type: map_at_10 value: 24.564 - type: map_at_100 value: 25.503999999999998 - type: map_at_1000 value: 25.619999999999997 - type: map_at_3 value: 22.496 - type: map_at_5 value: 23.646 - type: mrr_at_1 value: 34.774 - type: mrr_at_10 value: 41.935 - type: mrr_at_100 value: 42.679 - type: mrr_at_1000 value: 42.737 - type: mrr_at_3 value: 39.883 - type: mrr_at_5 value: 41.063 - type: ndcg_at_1 value: 34.774 - type: ndcg_at_10 value: 31.456 - type: ndcg_at_100 value: 35.827 - type: ndcg_at_1000 value: 38.627 - type: ndcg_at_3 value: 27.534999999999997 - type: ndcg_at_5 value: 29.452 - type: precision_at_1 value: 34.774 - type: precision_at_10 value: 6.97 - type: precision_at_100 value: 1.048 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 17.349 - type: precision_at_5 value: 11.924 - type: recall_at_1 value: 17.387 - type: recall_at_10 value: 34.848 - type: recall_at_100 value: 52.384 - type: recall_at_1000 value: 71.134 - type: recall_at_3 value: 26.023000000000003 - type: recall_at_5 value: 29.811 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 69.98119999999999 - type: ap value: 64.17725086855937 - type: f1 value: 69.78928359055172 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.372999999999999 - type: map_at_10 value: 11.145 - type: map_at_100 value: 12.055 - type: map_at_1000 value: 12.17 - type: map_at_3 value: 9.391 - type: map_at_5 value: 10.270999999999999 - type: mrr_at_1 value: 6.561999999999999 - type: mrr_at_10 value: 11.446000000000002 - type: mrr_at_100 value: 12.359 - type: mrr_at_1000 value: 12.47 - type: mrr_at_3 value: 9.654 - type: mrr_at_5 value: 10.566 - type: ndcg_at_1 value: 6.5329999999999995 - type: ndcg_at_10 value: 14.174000000000001 - type: ndcg_at_100 value: 19.168 - type: ndcg_at_1000 value: 22.579 - type: ndcg_at_3 value: 10.465 - type: ndcg_at_5 value: 12.057 - type: precision_at_1 value: 6.5329999999999995 - type: precision_at_10 value: 2.451 - type: precision_at_100 value: 0.506 - type: precision_at_1000 value: 0.08 - type: precision_at_3 value: 4.58 - type: precision_at_5 value: 3.553 - type: recall_at_1 value: 6.372999999999999 - type: recall_at_10 value: 23.639 - type: recall_at_100 value: 48.012 - type: recall_at_1000 value: 75.368 - type: recall_at_3 value: 13.333 - type: recall_at_5 value: 17.147000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.3953488372093 - type: f1 value: 90.47618297254341 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.249430004559954 - type: f1 value: 42.242289025471344 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.15131136516476 - type: f1 value: 62.8508450491576 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 71.9670477471419 - type: f1 value: 70.83719077833712 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.27049656570754 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.8992311215977 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.216583547389536 - type: mrr value: 32.31147129597184 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.651000000000001 - type: map_at_10 value: 8.924999999999999 - type: map_at_100 value: 11.43 - type: map_at_1000 value: 12.879999999999999 - type: map_at_3 value: 6.718 - type: map_at_5 value: 7.727 - type: mrr_at_1 value: 37.461 - type: mrr_at_10 value: 46.018 - type: mrr_at_100 value: 46.649 - type: mrr_at_1000 value: 46.713 - type: mrr_at_3 value: 43.55 - type: mrr_at_5 value: 44.928000000000004 - type: ndcg_at_1 value: 36.378 - type: ndcg_at_10 value: 27.193 - type: ndcg_at_100 value: 25.840000000000003 - type: ndcg_at_1000 value: 35.382999999999996 - type: ndcg_at_3 value: 31.054 - type: ndcg_at_5 value: 29.523 - type: precision_at_1 value: 37.461 - type: precision_at_10 value: 19.875999999999998 - type: precision_at_100 value: 7.198 - type: precision_at_1000 value: 2.069 - type: precision_at_3 value: 28.38 - type: precision_at_5 value: 25.386999999999997 - type: recall_at_1 value: 4.651000000000001 - type: recall_at_10 value: 13.517999999999999 - type: recall_at_100 value: 28.475 - type: recall_at_1000 value: 61.861999999999995 - type: recall_at_3 value: 7.657 - type: recall_at_5 value: 9.76 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 9.456000000000001 - type: map_at_10 value: 16.392 - type: map_at_100 value: 17.730999999999998 - type: map_at_1000 value: 17.835 - type: map_at_3 value: 13.743 - type: map_at_5 value: 15.262999999999998 - type: mrr_at_1 value: 10.776 - type: mrr_at_10 value: 18.163999999999998 - type: mrr_at_100 value: 19.403000000000002 - type: mrr_at_1000 value: 19.489 - type: mrr_at_3 value: 15.464 - type: mrr_at_5 value: 17.035 - type: ndcg_at_1 value: 10.776 - type: ndcg_at_10 value: 20.959 - type: ndcg_at_100 value: 27.589000000000002 - type: ndcg_at_1000 value: 30.416999999999998 - type: ndcg_at_3 value: 15.552 - type: ndcg_at_5 value: 18.275 - type: precision_at_1 value: 10.776 - type: precision_at_10 value: 3.94 - type: precision_at_100 value: 0.763 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 7.396999999999999 - type: precision_at_5 value: 5.933 - type: recall_at_1 value: 9.456000000000001 - type: recall_at_10 value: 33.394 - type: recall_at_100 value: 63.915 - type: recall_at_1000 value: 85.598 - type: recall_at_3 value: 19.098000000000003 - type: recall_at_5 value: 25.466 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 64.744 - type: map_at_10 value: 77.86 - type: map_at_100 value: 78.58800000000001 - type: map_at_1000 value: 78.617 - type: map_at_3 value: 74.788 - type: map_at_5 value: 76.716 - type: mrr_at_1 value: 74.49 - type: mrr_at_10 value: 81.843 - type: mrr_at_100 value: 82.035 - type: mrr_at_1000 value: 82.038 - type: mrr_at_3 value: 80.39 - type: mrr_at_5 value: 81.372 - type: ndcg_at_1 value: 74.59 - type: ndcg_at_10 value: 82.459 - type: ndcg_at_100 value: 84.34899999999999 - type: ndcg_at_1000 value: 84.626 - type: ndcg_at_3 value: 78.821 - type: ndcg_at_5 value: 80.83500000000001 - type: precision_at_1 value: 74.59 - type: precision_at_10 value: 12.494 - type: precision_at_100 value: 1.477 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.233000000000004 - type: precision_at_5 value: 22.747999999999998 - type: recall_at_1 value: 64.744 - type: recall_at_10 value: 91.355 - type: recall_at_100 value: 98.30799999999999 - type: recall_at_1000 value: 99.766 - type: recall_at_3 value: 81.109 - type: recall_at_5 value: 86.572 - type: map_at_1 value: 2.868 - type: map_at_10 value: 7.155 - type: map_at_100 value: 8.651 - type: map_at_1000 value: 8.921 - type: map_at_3 value: 5.197 - type: map_at_5 value: 6.168 - type: mrr_at_1 value: 14.099999999999998 - type: mrr_at_10 value: 22.528000000000002 - type: mrr_at_100 value: 23.730999999999998 - type: mrr_at_1000 value: 23.827 - type: mrr_at_3 value: 19.683 - type: mrr_at_5 value: 21.233 - type: ndcg_at_1 value: 14.099999999999998 - type: ndcg_at_10 value: 12.756 - type: ndcg_at_100 value: 19.49 - type: ndcg_at_1000 value: 24.942 - type: ndcg_at_3 value: 11.905000000000001 - type: ndcg_at_5 value: 10.474 - type: precision_at_1 value: 14.099999999999998 - type: precision_at_10 value: 6.7299999999999995 - type: precision_at_100 value: 1.657 - type: precision_at_1000 value: 0.297 - type: precision_at_3 value: 11.200000000000001 - type: precision_at_5 value: 9.3 - type: recall_at_1 value: 2.868 - type: recall_at_10 value: 13.613 - type: recall_at_100 value: 33.645 - type: recall_at_1000 value: 60.372 - type: recall_at_3 value: 6.808 - type: recall_at_5 value: 9.418 - type: map_at_1 value: 0.157 - type: map_at_10 value: 0.989 - type: map_at_100 value: 5.3580000000000005 - type: map_at_1000 value: 13.614999999999998 - type: map_at_3 value: 0.391 - type: map_at_5 value: 0.557 - type: mrr_at_1 value: 57.99999999999999 - type: mrr_at_10 value: 69.039 - type: mrr_at_100 value: 69.618 - type: mrr_at_1000 value: 69.618 - type: mrr_at_3 value: 67.667 - type: mrr_at_5 value: 68.56700000000001 - type: ndcg_at_1 value: 55.00000000000001 - type: ndcg_at_10 value: 48.394 - type: ndcg_at_100 value: 37.158 - type: ndcg_at_1000 value: 34.204 - type: ndcg_at_3 value: 53.754000000000005 - type: ndcg_at_5 value: 50.712999999999994 - type: precision_at_1 value: 57.99999999999999 - type: precision_at_10 value: 51.800000000000004 - type: precision_at_100 value: 39.26 - type: precision_at_1000 value: 16.503999999999998 - type: precision_at_3 value: 57.333 - type: precision_at_5 value: 52.800000000000004 - type: recall_at_1 value: 0.157 - type: recall_at_10 value: 1.238 - type: recall_at_100 value: 8.674 - type: recall_at_1000 value: 33.222 - type: recall_at_3 value: 0.436 - type: recall_at_5 value: 0.643 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 46.254882422464526 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 50.94989318333412 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.98965504956827 - type: cos_sim_spearman value: 68.28460263921258 - type: euclidean_pearson value: 73.50270698016448 - type: euclidean_spearman value: 68.28468403646217 - type: manhattan_pearson value: 72.8261914195885 - type: manhattan_spearman value: 67.86873546122553 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 77.14830947681742 - type: cos_sim_spearman value: 68.60266030636393 - type: euclidean_pearson value: 72.88451477994006 - type: euclidean_spearman value: 68.60389167221209 - type: manhattan_pearson value: 71.89880964464528 - type: manhattan_spearman value: 68.11051648970675 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.72037928360238 - type: cos_sim_spearman value: 79.74389537608737 - type: euclidean_pearson value: 79.39980926218213 - type: euclidean_spearman value: 79.74393317465844 - type: manhattan_pearson value: 78.7481714360194 - type: manhattan_spearman value: 79.05784658583435 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.20839429694983 - type: cos_sim_spearman value: 75.75758249233702 - type: euclidean_pearson value: 78.2593144118954 - type: euclidean_spearman value: 75.7575727998599 - type: manhattan_pearson value: 77.98797449902915 - type: manhattan_spearman value: 75.58570762607603 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.38797626966284 - type: cos_sim_spearman value: 83.0821006142509 - type: euclidean_pearson value: 82.89995084283936 - type: euclidean_spearman value: 83.08209908184749 - type: manhattan_pearson value: 82.6019409098804 - type: manhattan_spearman value: 82.76534947735776 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.80219740466768 - type: cos_sim_spearman value: 79.07336247296158 - type: euclidean_pearson value: 78.34175159212086 - type: euclidean_spearman value: 79.07335507859334 - type: manhattan_pearson value: 78.146156004842 - type: manhattan_spearman value: 78.85783029933849 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.74773705987958 - type: cos_sim_spearman value: 85.73402749289298 - type: euclidean_pearson value: 85.18510280404286 - type: euclidean_spearman value: 85.73490066116952 - type: manhattan_pearson value: 84.93638596678905 - type: manhattan_spearman value: 85.5315548466084 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.17015437324628 - type: cos_sim_spearman value: 59.75467857816752 - type: euclidean_pearson value: 60.812443155269534 - type: euclidean_spearman value: 59.75467857816752 - type: manhattan_pearson value: 59.950493146979255 - type: manhattan_spearman value: 58.932105528273645 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.46948132600193 - type: cos_sim_spearman value: 79.10069645170242 - type: euclidean_pearson value: 80.31463403998292 - type: euclidean_spearman value: 79.10071491600597 - type: manhattan_pearson value: 80.01917165738134 - type: manhattan_spearman value: 78.86150076844012 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 74.0368453616957 - type: mrr value: 91.42105987694224 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 37.083 - type: map_at_10 value: 45.626 - type: map_at_100 value: 46.741 - type: map_at_1000 value: 46.796 - type: map_at_3 value: 43.397999999999996 - type: map_at_5 value: 44.098 - type: mrr_at_1 value: 39.333 - type: mrr_at_10 value: 47.424 - type: mrr_at_100 value: 48.365 - type: mrr_at_1000 value: 48.413000000000004 - type: mrr_at_3 value: 45.444 - type: mrr_at_5 value: 46.011 - type: ndcg_at_1 value: 39.333 - type: ndcg_at_10 value: 50.324999999999996 - type: ndcg_at_100 value: 55.74400000000001 - type: ndcg_at_1000 value: 57.092 - type: ndcg_at_3 value: 45.805 - type: ndcg_at_5 value: 46.826 - type: precision_at_1 value: 39.333 - type: precision_at_10 value: 6.9 - type: precision_at_100 value: 0.993 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.111 - type: precision_at_5 value: 11.466999999999999 - type: recall_at_1 value: 37.083 - type: recall_at_10 value: 63.444 - type: recall_at_100 value: 88.617 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 50.556 - type: recall_at_5 value: 53.056000000000004 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.66039603960397 - type: cos_sim_ap value: 89.16346887114837 - type: cos_sim_f1 value: 83.18072289156628 - type: cos_sim_precision value: 80.27906976744185 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.66039603960397 - type: dot_ap value: 89.16346887114837 - type: dot_f1 value: 83.18072289156628 - type: dot_precision value: 80.27906976744185 - type: dot_recall value: 86.3 - type: euclidean_accuracy value: 99.66039603960397 - type: euclidean_ap value: 89.16346887114837 - type: euclidean_f1 value: 83.18072289156628 - type: euclidean_precision value: 80.27906976744185 - type: euclidean_recall value: 86.3 - type: manhattan_accuracy value: 99.66930693069307 - type: manhattan_ap value: 89.13276894140405 - type: manhattan_f1 value: 83.46534653465346 - type: manhattan_precision value: 82.6470588235294 - type: manhattan_recall value: 84.3 - type: max_accuracy value: 99.66930693069307 - type: max_ap value: 89.16346887114837 - type: max_f1 value: 83.46534653465346 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 49.394155025012324 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.32321222461949 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.523517787741575 - type: mrr value: 44.07447638146168 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.28542082978425 - type: cos_sim_spearman value: 30.039804865964005 - type: dot_pearson value: 31.28542082880828 - type: dot_spearman value: 30.051397798547818 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.385 - type: map_at_10 value: 7.414 - type: map_at_100 value: 13.084999999999999 - type: map_at_1000 value: 14.765 - type: map_at_3 value: 3.5909999999999997 - type: map_at_5 value: 5.402 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 37.669000000000004 - type: mrr_at_100 value: 38.823 - type: mrr_at_1000 value: 38.823 - type: mrr_at_3 value: 33.672999999999995 - type: mrr_at_5 value: 35.612 - type: ndcg_at_1 value: 19.387999999999998 - type: ndcg_at_10 value: 19.288 - type: ndcg_at_100 value: 33.376 - type: ndcg_at_1000 value: 45.28 - type: ndcg_at_3 value: 20.511 - type: ndcg_at_5 value: 21.182000000000002 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 18.776 - type: precision_at_100 value: 8.061 - type: precision_at_1000 value: 1.5779999999999998 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 23.673 - type: recall_at_1 value: 1.385 - type: recall_at_10 value: 13.113 - type: recall_at_100 value: 48.345 - type: recall_at_1000 value: 85.087 - type: recall_at_3 value: 4.932 - type: recall_at_5 value: 8.4 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 79.2658 - type: ap value: 19.45051650328674 - type: f1 value: 61.721255030714005 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.19524617996604 - type: f1 value: 60.47726202926952 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.20230019842334 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 83.32240567443525 - type: cos_sim_ap value: 63.95052535841297 - type: cos_sim_f1 value: 60.78094302554028 - type: cos_sim_precision value: 56.844281120808446 - type: cos_sim_recall value: 65.30343007915567 - type: dot_accuracy value: 83.32240567443525 - type: dot_ap value: 63.95052535841297 - type: dot_f1 value: 60.78094302554028 - type: dot_precision value: 56.844281120808446 - type: dot_recall value: 65.30343007915567 - type: euclidean_accuracy value: 83.32240567443525 - type: euclidean_ap value: 63.95052535841297 - type: euclidean_f1 value: 60.78094302554028 - type: euclidean_precision value: 56.844281120808446 - type: euclidean_recall value: 65.30343007915567 - type: manhattan_accuracy value: 83.30452405078381 - type: manhattan_ap value: 63.82521079916541 - type: manhattan_f1 value: 60.567750833237554 - type: manhattan_precision value: 53.65506006923234 - type: manhattan_recall value: 69.52506596306068 - type: max_accuracy value: 83.32240567443525 - type: max_ap value: 63.95052535841297 - type: max_f1 value: 60.78094302554028 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.51309814879497 - type: cos_sim_ap value: 83.04143677647984 - type: cos_sim_f1 value: 75.14412661109682 - type: cos_sim_precision value: 71.82871182871183 - type: cos_sim_recall value: 78.78041268863566 - type: dot_accuracy value: 87.51309814879497 - type: dot_ap value: 83.0414382592019 - type: dot_f1 value: 75.14412661109682 - type: dot_precision value: 71.82871182871183 - type: dot_recall value: 78.78041268863566 - type: euclidean_accuracy value: 87.51309814879497 - type: euclidean_ap value: 83.04144849399968 - type: euclidean_f1 value: 75.14412661109682 - type: euclidean_precision value: 71.82871182871183 - type: euclidean_recall value: 78.78041268863566 - type: manhattan_accuracy value: 87.50921721581868 - type: manhattan_ap value: 82.97187030449552 - type: manhattan_f1 value: 74.93584260051325 - type: manhattan_precision value: 72.48003453485863 - type: manhattan_recall value: 77.56390514320911 - type: max_accuracy value: 87.51309814879497 - type: max_ap value: 83.04144849399968 - type: max_f1 value: 75.14412661109682 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/mv_sw

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:25:19

2025-01-09T11:25:26

0

--- tags: - mteb model-index: - name: model2vec_result_fixed results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.7910447761194 - type: ap value: 33.038020188116036 - type: f1 value: 65.03799728338926 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 72.47644999999999 - type: ap value: 66.91002822830875 - type: f1 value: 72.2600863044581 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 36.012 - type: f1 value: 35.38209336470206 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 21.124000000000002 - type: map_at_10 value: 34.329 - type: map_at_100 value: 35.612 - type: map_at_1000 value: 35.647 - type: map_at_3 value: 30.263 - type: map_at_5 value: 32.358 - type: mrr_at_1 value: 21.764 - type: mrr_at_10 value: 34.558 - type: mrr_at_100 value: 35.848 - type: mrr_at_1000 value: 35.882999999999996 - type: mrr_at_3 value: 30.441000000000003 - type: mrr_at_5 value: 32.621 - type: ndcg_at_1 value: 21.124000000000002 - type: ndcg_at_10 value: 41.961 - type: ndcg_at_100 value: 47.746 - type: ndcg_at_1000 value: 48.63 - type: ndcg_at_3 value: 33.469 - type: ndcg_at_5 value: 37.261 - type: precision_at_1 value: 21.124000000000002 - type: precision_at_10 value: 6.643000000000001 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.272000000000002 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.124000000000002 - type: recall_at_10 value: 66.43 - type: recall_at_100 value: 92.461 - type: recall_at_1000 value: 99.289 - type: recall_at_3 value: 42.817 - type: recall_at_5 value: 52.063 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 35.422522812555265 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 25.271555965391595 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 54.11180788298141 - type: mrr value: 68.73587477465594 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.11612347924923 - type: cos_sim_spearman value: 75.85775256673794 - type: euclidean_pearson value: 77.46080567383865 - type: euclidean_spearman value: 75.85775256673794 - type: manhattan_pearson value: 77.7319143671074 - type: manhattan_spearman value: 75.98908086034702 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 72.63636363636363 - type: f1 value: 71.69751597573539 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 30.861094091770546 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 20.222365644637257 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 19.939 - type: map_at_10 value: 26.924 - type: map_at_100 value: 28.16 - type: map_at_1000 value: 28.316999999999997 - type: map_at_3 value: 24.45 - type: map_at_5 value: 25.751 - type: mrr_at_1 value: 25.894000000000002 - type: mrr_at_10 value: 32.652 - type: mrr_at_100 value: 33.584 - type: mrr_at_1000 value: 33.664 - type: mrr_at_3 value: 30.520000000000003 - type: mrr_at_5 value: 31.671 - type: ndcg_at_1 value: 25.894000000000002 - type: ndcg_at_10 value: 31.835 - type: ndcg_at_100 value: 37.325 - type: ndcg_at_1000 value: 40.586 - type: ndcg_at_3 value: 28.143 - type: ndcg_at_5 value: 29.648999999999997 - type: precision_at_1 value: 25.894000000000002 - type: precision_at_10 value: 6.194999999999999 - type: precision_at_100 value: 1.126 - type: precision_at_1000 value: 0.173 - type: precision_at_3 value: 13.543 - type: precision_at_5 value: 9.757 - type: recall_at_1 value: 19.939 - type: recall_at_10 value: 40.537 - type: recall_at_100 value: 64.717 - type: recall_at_1000 value: 87.01299999999999 - type: recall_at_3 value: 29.301 - type: recall_at_5 value: 33.918 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.601 - type: map_at_10 value: 22.07 - type: map_at_100 value: 22.958000000000002 - type: map_at_1000 value: 23.074 - type: map_at_3 value: 20.137 - type: map_at_5 value: 21.315 - type: mrr_at_1 value: 20.382 - type: mrr_at_10 value: 25.954 - type: mrr_at_100 value: 26.723000000000003 - type: mrr_at_1000 value: 26.791999999999998 - type: mrr_at_3 value: 24.098 - type: mrr_at_5 value: 25.27 - type: ndcg_at_1 value: 20.382 - type: ndcg_at_10 value: 25.734 - type: ndcg_at_100 value: 29.952 - type: ndcg_at_1000 value: 32.618 - type: ndcg_at_3 value: 22.445999999999998 - type: ndcg_at_5 value: 24.162 - type: precision_at_1 value: 20.382 - type: precision_at_10 value: 4.662 - type: precision_at_100 value: 0.8580000000000001 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 10.446 - type: precision_at_5 value: 7.682 - type: recall_at_1 value: 16.601 - type: recall_at_10 value: 32.882 - type: recall_at_100 value: 51.273 - type: recall_at_1000 value: 69.33200000000001 - type: recall_at_3 value: 23.54 - type: recall_at_5 value: 28.054000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.386999999999997 - type: map_at_10 value: 34.183 - type: map_at_100 value: 35.198 - type: map_at_1000 value: 35.292 - type: map_at_3 value: 31.466 - type: map_at_5 value: 33.037 - type: mrr_at_1 value: 29.404000000000003 - type: mrr_at_10 value: 37.519000000000005 - type: mrr_at_100 value: 38.305 - type: mrr_at_1000 value: 38.365 - type: mrr_at_3 value: 35.152 - type: mrr_at_5 value: 36.531000000000006 - type: ndcg_at_1 value: 29.404000000000003 - type: ndcg_at_10 value: 39.235 - type: ndcg_at_100 value: 44.072 - type: ndcg_at_1000 value: 46.272999999999996 - type: ndcg_at_3 value: 34.292 - type: ndcg_at_5 value: 36.735 - type: precision_at_1 value: 29.404000000000003 - type: precision_at_10 value: 6.539000000000001 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 15.423 - type: precision_at_5 value: 10.984 - type: recall_at_1 value: 25.386999999999997 - type: recall_at_10 value: 51.256 - type: recall_at_100 value: 73.53699999999999 - type: recall_at_1000 value: 89.522 - type: recall_at_3 value: 37.830999999999996 - type: recall_at_5 value: 43.811 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 10.832 - type: map_at_10 value: 16.154 - type: map_at_100 value: 16.863 - type: map_at_1000 value: 16.979 - type: map_at_3 value: 14.654 - type: map_at_5 value: 15.634 - type: mrr_at_1 value: 11.751000000000001 - type: mrr_at_10 value: 17.286 - type: mrr_at_100 value: 18.019 - type: mrr_at_1000 value: 18.122 - type: mrr_at_3 value: 15.706000000000001 - type: mrr_at_5 value: 16.774 - type: ndcg_at_1 value: 11.751000000000001 - type: ndcg_at_10 value: 19.197 - type: ndcg_at_100 value: 23.159 - type: ndcg_at_1000 value: 26.453 - type: ndcg_at_3 value: 16.186 - type: ndcg_at_5 value: 17.936 - type: precision_at_1 value: 11.751000000000001 - type: precision_at_10 value: 3.1189999999999998 - type: precision_at_100 value: 0.54 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 7.194000000000001 - type: precision_at_5 value: 5.311 - type: recall_at_1 value: 10.832 - type: recall_at_10 value: 27.472 - type: recall_at_100 value: 46.471000000000004 - type: recall_at_1000 value: 71.91199999999999 - type: recall_at_3 value: 19.417 - type: recall_at_5 value: 23.577 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 6.019 - type: map_at_10 value: 9.584 - type: map_at_100 value: 10.433 - type: map_at_1000 value: 10.562000000000001 - type: map_at_3 value: 8.351 - type: map_at_5 value: 9.005 - type: mrr_at_1 value: 7.2139999999999995 - type: mrr_at_10 value: 11.62 - type: mrr_at_100 value: 12.469 - type: mrr_at_1000 value: 12.577 - type: mrr_at_3 value: 10.158000000000001 - type: mrr_at_5 value: 10.898 - type: ndcg_at_1 value: 7.2139999999999995 - type: ndcg_at_10 value: 12.145 - type: ndcg_at_100 value: 16.672 - type: ndcg_at_1000 value: 20.342 - type: ndcg_at_3 value: 9.607000000000001 - type: ndcg_at_5 value: 10.712000000000002 - type: precision_at_1 value: 7.2139999999999995 - type: precision_at_10 value: 2.338 - type: precision_at_100 value: 0.5459999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 4.726 - type: precision_at_5 value: 3.5319999999999996 - type: recall_at_1 value: 6.019 - type: recall_at_10 value: 18.102999999999998 - type: recall_at_100 value: 38.482 - type: recall_at_1000 value: 65.436 - type: recall_at_3 value: 11.178 - type: recall_at_5 value: 13.877 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 16.822 - type: map_at_10 value: 22.476 - type: map_at_100 value: 23.69 - type: map_at_1000 value: 23.827 - type: map_at_3 value: 20.441000000000003 - type: map_at_5 value: 21.512 - type: mrr_at_1 value: 20.788999999999998 - type: mrr_at_10 value: 26.674 - type: mrr_at_100 value: 27.675 - type: mrr_at_1000 value: 27.753 - type: mrr_at_3 value: 24.495 - type: mrr_at_5 value: 25.629999999999995 - type: ndcg_at_1 value: 20.788999999999998 - type: ndcg_at_10 value: 26.667999999999996 - type: ndcg_at_100 value: 32.565 - type: ndcg_at_1000 value: 35.634 - type: ndcg_at_3 value: 22.942 - type: ndcg_at_5 value: 24.514 - type: precision_at_1 value: 20.788999999999998 - type: precision_at_10 value: 4.947 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.748000000000001 - type: precision_at_5 value: 7.68 - type: recall_at_1 value: 16.822 - type: recall_at_10 value: 35.237 - type: recall_at_100 value: 61.219 - type: recall_at_1000 value: 82.499 - type: recall_at_3 value: 24.524 - type: recall_at_5 value: 28.787000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 12.416 - type: map_at_10 value: 17.684 - type: map_at_100 value: 18.851000000000003 - type: map_at_1000 value: 18.991 - type: map_at_3 value: 15.770999999999999 - type: map_at_5 value: 16.606 - type: mrr_at_1 value: 15.068000000000001 - type: mrr_at_10 value: 21.288 - type: mrr_at_100 value: 22.306 - type: mrr_at_1000 value: 22.396 - type: mrr_at_3 value: 19.273 - type: mrr_at_5 value: 20.398 - type: ndcg_at_1 value: 15.068000000000001 - type: ndcg_at_10 value: 21.66 - type: ndcg_at_100 value: 27.245 - type: ndcg_at_1000 value: 30.591 - type: ndcg_at_3 value: 17.968999999999998 - type: ndcg_at_5 value: 19.352 - type: precision_at_1 value: 15.068000000000001 - type: precision_at_10 value: 4.326 - type: precision_at_100 value: 0.855 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 8.713999999999999 - type: precision_at_5 value: 6.3469999999999995 - type: recall_at_1 value: 12.416 - type: recall_at_10 value: 30.008000000000003 - type: recall_at_100 value: 54.498999999999995 - type: recall_at_1000 value: 78.32000000000001 - type: recall_at_3 value: 19.79 - type: recall_at_5 value: 23.376 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.36133333333333 - type: map_at_10 value: 18.6895 - type: map_at_100 value: 19.62275 - type: map_at_1000 value: 19.748833333333334 - type: map_at_3 value: 16.8815 - type: map_at_5 value: 17.84133333333334 - type: mrr_at_1 value: 16.093083333333336 - type: mrr_at_10 value: 21.63225 - type: mrr_at_100 value: 22.477333333333334 - type: mrr_at_1000 value: 22.563166666666664 - type: mrr_at_3 value: 19.83 - type: mrr_at_5 value: 20.799166666666668 - type: ndcg_at_1 value: 16.093083333333336 - type: ndcg_at_10 value: 22.30233333333333 - type: ndcg_at_100 value: 27.000333333333337 - type: ndcg_at_1000 value: 30.14883333333333 - type: ndcg_at_3 value: 18.966499999999996 - type: ndcg_at_5 value: 20.425916666666666 - type: precision_at_1 value: 16.093083333333336 - type: precision_at_10 value: 4.062916666666667 - type: precision_at_100 value: 0.7655833333333333 - type: precision_at_1000 value: 0.12208333333333334 - type: precision_at_3 value: 8.848666666666666 - type: precision_at_5 value: 6.400833333333333 - type: recall_at_1 value: 13.36133333333333 - type: recall_at_10 value: 30.32383333333334 - type: recall_at_100 value: 51.808 - type: recall_at_1000 value: 74.64483333333332 - type: recall_at_3 value: 20.884249999999994 - type: recall_at_5 value: 24.67641666666667 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 9.722999999999999 - type: map_at_10 value: 14.280999999999999 - type: map_at_100 value: 15.065000000000001 - type: map_at_1000 value: 15.154 - type: map_at_3 value: 13.004 - type: map_at_5 value: 13.626 - type: mrr_at_1 value: 11.81 - type: mrr_at_10 value: 16.384 - type: mrr_at_100 value: 17.189 - type: mrr_at_1000 value: 17.269000000000002 - type: mrr_at_3 value: 15.082 - type: mrr_at_5 value: 15.711 - type: ndcg_at_1 value: 11.81 - type: ndcg_at_10 value: 17.253 - type: ndcg_at_100 value: 21.404 - type: ndcg_at_1000 value: 24.09 - type: ndcg_at_3 value: 14.716999999999999 - type: ndcg_at_5 value: 15.706000000000001 - type: precision_at_1 value: 11.81 - type: precision_at_10 value: 2.9749999999999996 - type: precision_at_100 value: 0.543 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 6.902 - type: precision_at_5 value: 4.816 - type: recall_at_1 value: 9.722999999999999 - type: recall_at_10 value: 24.569 - type: recall_at_100 value: 43.997 - type: recall_at_1000 value: 64.44 - type: recall_at_3 value: 17.134 - type: recall_at_5 value: 19.72 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 7.497 - type: map_at_10 value: 10.846 - type: map_at_100 value: 11.498999999999999 - type: map_at_1000 value: 11.618 - type: map_at_3 value: 9.658999999999999 - type: map_at_5 value: 10.298 - type: mrr_at_1 value: 9.119 - type: mrr_at_10 value: 12.992999999999999 - type: mrr_at_100 value: 13.700999999999999 - type: mrr_at_1000 value: 13.797999999999998 - type: mrr_at_3 value: 11.666 - type: mrr_at_5 value: 12.362 - type: ndcg_at_1 value: 9.119 - type: ndcg_at_10 value: 13.308 - type: ndcg_at_100 value: 16.98 - type: ndcg_at_1000 value: 20.488 - type: ndcg_at_3 value: 10.982 - type: ndcg_at_5 value: 12.003 - type: precision_at_1 value: 9.119 - type: precision_at_10 value: 2.4979999999999998 - type: precision_at_100 value: 0.519 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 5.288 - type: precision_at_5 value: 3.8890000000000002 - type: recall_at_1 value: 7.497 - type: recall_at_10 value: 18.817999999999998 - type: recall_at_100 value: 35.893 - type: recall_at_1000 value: 61.966 - type: recall_at_3 value: 12.199 - type: recall_at_5 value: 14.87 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 11.856 - type: map_at_10 value: 16.685 - type: map_at_100 value: 17.433 - type: map_at_1000 value: 17.558 - type: map_at_3 value: 15.021 - type: map_at_5 value: 15.931999999999999 - type: mrr_at_1 value: 14.179 - type: mrr_at_10 value: 19.398 - type: mrr_at_100 value: 20.153 - type: mrr_at_1000 value: 20.251 - type: mrr_at_3 value: 17.631 - type: mrr_at_5 value: 18.517 - type: ndcg_at_1 value: 14.179 - type: ndcg_at_10 value: 20.061999999999998 - type: ndcg_at_100 value: 24.149 - type: ndcg_at_1000 value: 27.644999999999996 - type: ndcg_at_3 value: 16.794 - type: ndcg_at_5 value: 18.224 - type: precision_at_1 value: 14.179 - type: precision_at_10 value: 3.582 - type: precision_at_100 value: 0.623 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.774 - type: precision_at_5 value: 5.5969999999999995 - type: recall_at_1 value: 11.856 - type: recall_at_10 value: 27.778999999999996 - type: recall_at_100 value: 46.733000000000004 - type: recall_at_1000 value: 72.481 - type: recall_at_3 value: 18.859 - type: recall_at_5 value: 22.435 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 13.164000000000001 - type: map_at_10 value: 19.317999999999998 - type: map_at_100 value: 20.463 - type: map_at_1000 value: 20.646 - type: map_at_3 value: 17.126 - type: map_at_5 value: 18.056 - type: mrr_at_1 value: 16.601 - type: mrr_at_10 value: 22.62 - type: mrr_at_100 value: 23.601 - type: mrr_at_1000 value: 23.676 - type: mrr_at_3 value: 20.685000000000002 - type: mrr_at_5 value: 21.465999999999998 - type: ndcg_at_1 value: 16.601 - type: ndcg_at_10 value: 23.735999999999997 - type: ndcg_at_100 value: 29.047 - type: ndcg_at_1000 value: 32.323 - type: ndcg_at_3 value: 20.013 - type: ndcg_at_5 value: 21.165 - type: precision_at_1 value: 16.601 - type: precision_at_10 value: 4.7829999999999995 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 9.881 - type: precision_at_5 value: 7.074999999999999 - type: recall_at_1 value: 13.164000000000001 - type: recall_at_10 value: 33.041 - type: recall_at_100 value: 57.907 - type: recall_at_1000 value: 79.887 - type: recall_at_3 value: 21.397 - type: recall_at_5 value: 24.863 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 10.08 - type: map_at_10 value: 14.069 - type: map_at_100 value: 14.860000000000001 - type: map_at_1000 value: 14.968 - type: map_at_3 value: 12.498 - type: map_at_5 value: 13.324 - type: mrr_at_1 value: 10.906 - type: mrr_at_10 value: 15.198999999999998 - type: mrr_at_100 value: 16.003 - type: mrr_at_1000 value: 16.095000000000002 - type: mrr_at_3 value: 13.494 - type: mrr_at_5 value: 14.362 - type: ndcg_at_1 value: 10.906 - type: ndcg_at_10 value: 16.794999999999998 - type: ndcg_at_100 value: 21.434 - type: ndcg_at_1000 value: 24.743000000000002 - type: ndcg_at_3 value: 13.507 - type: ndcg_at_5 value: 14.953 - type: precision_at_1 value: 10.906 - type: precision_at_10 value: 2.791 - type: precision_at_100 value: 0.5559999999999999 - type: precision_at_1000 value: 0.091 - type: precision_at_3 value: 5.545 - type: precision_at_5 value: 4.14 - type: recall_at_1 value: 10.08 - type: recall_at_10 value: 24.184 - type: recall_at_100 value: 46.967999999999996 - type: recall_at_1000 value: 72.92999999999999 - type: recall_at_3 value: 15.440999999999999 - type: recall_at_5 value: 18.829 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.537 - type: map_at_10 value: 11.465 - type: map_at_100 value: 12.851 - type: map_at_1000 value: 13.045000000000002 - type: map_at_3 value: 9.369 - type: map_at_5 value: 10.331 - type: mrr_at_1 value: 15.244 - type: mrr_at_10 value: 23.593 - type: mrr_at_100 value: 24.772 - type: mrr_at_1000 value: 24.839 - type: mrr_at_3 value: 20.467 - type: mrr_at_5 value: 22.027 - type: ndcg_at_1 value: 15.244 - type: ndcg_at_10 value: 17.288999999999998 - type: ndcg_at_100 value: 23.757 - type: ndcg_at_1000 value: 27.725 - type: ndcg_at_3 value: 13.245000000000001 - type: ndcg_at_5 value: 14.485000000000001 - type: precision_at_1 value: 15.244 - type: precision_at_10 value: 5.733 - type: precision_at_100 value: 1.264 - type: precision_at_1000 value: 0.199 - type: precision_at_3 value: 10.054 - type: precision_at_5 value: 7.9350000000000005 - type: recall_at_1 value: 6.537 - type: recall_at_10 value: 22.046 - type: recall_at_100 value: 44.818000000000005 - type: recall_at_1000 value: 67.676 - type: recall_at_3 value: 12.232 - type: recall_at_5 value: 15.540999999999999 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.304 - type: map_at_10 value: 9.944 - type: map_at_100 value: 14.113000000000001 - type: map_at_1000 value: 15.085 - type: map_at_3 value: 7.228999999999999 - type: map_at_5 value: 8.368 - type: mrr_at_1 value: 43.0 - type: mrr_at_10 value: 53.303999999999995 - type: mrr_at_100 value: 53.979 - type: mrr_at_1000 value: 54.005 - type: mrr_at_3 value: 50.542 - type: mrr_at_5 value: 52.154 - type: ndcg_at_1 value: 31.5 - type: ndcg_at_10 value: 24.235 - type: ndcg_at_100 value: 28.01 - type: ndcg_at_1000 value: 34.724 - type: ndcg_at_3 value: 26.682 - type: ndcg_at_5 value: 25.249 - type: precision_at_1 value: 43.0 - type: precision_at_10 value: 21.65 - type: precision_at_100 value: 6.97 - type: precision_at_1000 value: 1.4449999999999998 - type: precision_at_3 value: 32.25 - type: precision_at_5 value: 27.250000000000004 - type: recall_at_1 value: 4.304 - type: recall_at_10 value: 15.014 - type: recall_at_100 value: 35.115 - type: recall_at_1000 value: 58.52 - type: recall_at_3 value: 8.698 - type: recall_at_5 value: 11.052 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 45.09 - type: f1 value: 41.3731018097549 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.349 - type: map_at_10 value: 24.917 - type: map_at_100 value: 26.003 - type: map_at_1000 value: 26.072 - type: map_at_3 value: 22.067999999999998 - type: map_at_5 value: 23.610999999999997 - type: mrr_at_1 value: 17.416999999999998 - type: mrr_at_10 value: 26.44 - type: mrr_at_100 value: 27.509 - type: mrr_at_1000 value: 27.57 - type: mrr_at_3 value: 23.422 - type: mrr_at_5 value: 25.063999999999997 - type: ndcg_at_1 value: 17.416999999999998 - type: ndcg_at_10 value: 30.267 - type: ndcg_at_100 value: 35.650999999999996 - type: ndcg_at_1000 value: 37.57 - type: ndcg_at_3 value: 24.303 - type: ndcg_at_5 value: 27.099 - type: precision_at_1 value: 17.416999999999998 - type: precision_at_10 value: 4.9590000000000005 - type: precision_at_100 value: 0.7799999999999999 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 10.536 - type: precision_at_5 value: 7.807 - type: recall_at_1 value: 16.349 - type: recall_at_10 value: 45.678999999999995 - type: recall_at_100 value: 70.541 - type: recall_at_1000 value: 85.36500000000001 - type: recall_at_3 value: 29.42 - type: recall_at_5 value: 36.112 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.478999999999999 - type: map_at_10 value: 11.933 - type: map_at_100 value: 13.078000000000001 - type: map_at_1000 value: 13.267999999999999 - type: map_at_3 value: 9.975000000000001 - type: map_at_5 value: 10.928 - type: mrr_at_1 value: 14.66 - type: mrr_at_10 value: 20.737 - type: mrr_at_100 value: 21.719 - type: mrr_at_1000 value: 21.809 - type: mrr_at_3 value: 18.57 - type: mrr_at_5 value: 19.558 - type: ndcg_at_1 value: 14.66 - type: ndcg_at_10 value: 16.619 - type: ndcg_at_100 value: 22.467000000000002 - type: ndcg_at_1000 value: 26.745 - type: ndcg_at_3 value: 13.547 - type: ndcg_at_5 value: 14.466999999999999 - type: precision_at_1 value: 14.66 - type: precision_at_10 value: 4.8149999999999995 - type: precision_at_100 value: 1.0619999999999998 - type: precision_at_1000 value: 0.182 - type: precision_at_3 value: 9.002 - type: precision_at_5 value: 6.79 - type: recall_at_1 value: 7.478999999999999 - type: recall_at_10 value: 21.884 - type: recall_at_100 value: 45.545 - type: recall_at_1000 value: 71.887 - type: recall_at_3 value: 12.485 - type: recall_at_5 value: 15.862000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.628 - type: map_at_10 value: 28.559 - type: map_at_100 value: 29.5 - type: map_at_1000 value: 29.601 - type: map_at_3 value: 26.429000000000002 - type: map_at_5 value: 27.589000000000002 - type: mrr_at_1 value: 41.256 - type: mrr_at_10 value: 48.842999999999996 - type: mrr_at_100 value: 49.523 - type: mrr_at_1000 value: 49.57 - type: mrr_at_3 value: 46.894000000000005 - type: mrr_at_5 value: 48.024 - type: ndcg_at_1 value: 41.256 - type: ndcg_at_10 value: 36.217 - type: ndcg_at_100 value: 40.422000000000004 - type: ndcg_at_1000 value: 42.762 - type: ndcg_at_3 value: 32.275999999999996 - type: ndcg_at_5 value: 34.184 - type: precision_at_1 value: 41.256 - type: precision_at_10 value: 7.838000000000001 - type: precision_at_100 value: 1.119 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 20.207 - type: precision_at_5 value: 13.636999999999999 - type: recall_at_1 value: 20.628 - type: recall_at_10 value: 39.190000000000005 - type: recall_at_100 value: 55.962 - type: recall_at_1000 value: 71.56700000000001 - type: recall_at_3 value: 30.311 - type: recall_at_5 value: 34.092 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.78 - type: ap value: 65.09281598781793 - type: f1 value: 70.56498155979408 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.149 - type: map_at_10 value: 12.494 - type: map_at_100 value: 13.438 - type: map_at_1000 value: 13.544 - type: map_at_3 value: 10.58 - type: map_at_5 value: 11.623 - type: mrr_at_1 value: 7.364 - type: mrr_at_10 value: 12.817 - type: mrr_at_100 value: 13.758000000000001 - type: mrr_at_1000 value: 13.861 - type: mrr_at_3 value: 10.879 - type: mrr_at_5 value: 11.942 - type: ndcg_at_1 value: 7.364 - type: ndcg_at_10 value: 15.787999999999998 - type: ndcg_at_100 value: 20.973 - type: ndcg_at_1000 value: 24.156 - type: ndcg_at_3 value: 11.782 - type: ndcg_at_5 value: 13.675 - type: precision_at_1 value: 7.364 - type: precision_at_10 value: 2.702 - type: precision_at_100 value: 0.539 - type: precision_at_1000 value: 0.08099999999999999 - type: precision_at_3 value: 5.148 - type: precision_at_5 value: 4.043 - type: recall_at_1 value: 7.149 - type: recall_at_10 value: 26.039 - type: recall_at_100 value: 51.405 - type: recall_at_1000 value: 76.97500000000001 - type: recall_at_3 value: 14.979000000000001 - type: recall_at_5 value: 19.553 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 88.95576835385319 - type: f1 value: 88.06364678376042 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.99726402188783 - type: f1 value: 38.19916053247397 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.79287155346336 - type: f1 value: 61.634629394462934 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 70.30934767989241 - type: f1 value: 68.77914761769519 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 27.617349409076375 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 23.802943866708315 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 29.431263837648547 - type: mrr value: 30.205900793315156 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 3.479 - type: map_at_10 value: 7.603 - type: map_at_100 value: 9.725999999999999 - type: map_at_1000 value: 10.84 - type: map_at_3 value: 5.844 - type: map_at_5 value: 6.732 - type: mrr_at_1 value: 33.745999999999995 - type: mrr_at_10 value: 43.516 - type: mrr_at_100 value: 44.190000000000005 - type: mrr_at_1000 value: 44.248 - type: mrr_at_3 value: 41.744 - type: mrr_at_5 value: 42.828 - type: ndcg_at_1 value: 31.424000000000003 - type: ndcg_at_10 value: 24.267 - type: ndcg_at_100 value: 22.416 - type: ndcg_at_1000 value: 31.165 - type: ndcg_at_3 value: 28.349999999999998 - type: ndcg_at_5 value: 26.596999999999998 - type: precision_at_1 value: 33.745999999999995 - type: precision_at_10 value: 18.173000000000002 - type: precision_at_100 value: 6.142 - type: precision_at_1000 value: 1.856 - type: precision_at_3 value: 27.141 - type: precision_at_5 value: 22.91 - type: recall_at_1 value: 3.479 - type: recall_at_10 value: 10.838000000000001 - type: recall_at_100 value: 23.817 - type: recall_at_1000 value: 54.910000000000004 - type: recall_at_3 value: 7.236 - type: recall_at_5 value: 9.003 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 8.413 - type: map_at_10 value: 15.137 - type: map_at_100 value: 16.393 - type: map_at_1000 value: 16.492 - type: map_at_3 value: 12.584999999999999 - type: map_at_5 value: 13.963000000000001 - type: mrr_at_1 value: 9.762 - type: mrr_at_10 value: 16.813 - type: mrr_at_100 value: 17.98 - type: mrr_at_1000 value: 18.064 - type: mrr_at_3 value: 14.257 - type: mrr_at_5 value: 15.651000000000002 - type: ndcg_at_1 value: 9.733 - type: ndcg_at_10 value: 19.543 - type: ndcg_at_100 value: 25.965 - type: ndcg_at_1000 value: 28.663 - type: ndcg_at_3 value: 14.308000000000002 - type: ndcg_at_5 value: 16.771 - type: precision_at_1 value: 9.733 - type: precision_at_10 value: 3.7249999999999996 - type: precision_at_100 value: 0.739 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 6.856 - type: precision_at_5 value: 5.475 - type: recall_at_1 value: 8.413 - type: recall_at_10 value: 31.668000000000003 - type: recall_at_100 value: 61.551 - type: recall_at_1000 value: 82.228 - type: recall_at_3 value: 17.669 - type: recall_at_5 value: 23.488999999999997 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.522 - type: map_at_10 value: 76.068 - type: map_at_100 value: 76.858 - type: map_at_1000 value: 76.89099999999999 - type: map_at_3 value: 73.07000000000001 - type: map_at_5 value: 74.883 - type: mrr_at_1 value: 73.11 - type: mrr_at_10 value: 80.134 - type: mrr_at_100 value: 80.403 - type: mrr_at_1000 value: 80.411 - type: mrr_at_3 value: 78.728 - type: mrr_at_5 value: 79.60000000000001 - type: ndcg_at_1 value: 73.1 - type: ndcg_at_10 value: 80.595 - type: ndcg_at_100 value: 82.749 - type: ndcg_at_1000 value: 83.14099999999999 - type: ndcg_at_3 value: 77.021 - type: ndcg_at_5 value: 78.846 - type: precision_at_1 value: 73.1 - type: precision_at_10 value: 12.206999999999999 - type: precision_at_100 value: 1.459 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 33.36 - type: precision_at_5 value: 22.09 - type: recall_at_1 value: 63.522 - type: recall_at_10 value: 89.32600000000001 - type: recall_at_100 value: 97.35000000000001 - type: recall_at_1000 value: 99.613 - type: recall_at_3 value: 79.074 - type: recall_at_5 value: 84.143 - type: map_at_1 value: 3.053 - type: map_at_10 value: 6.912999999999999 - type: map_at_100 value: 8.261000000000001 - type: map_at_1000 value: 8.530999999999999 - type: map_at_3 value: 5.094 - type: map_at_5 value: 5.997 - type: mrr_at_1 value: 15.0 - type: mrr_at_10 value: 22.795 - type: mrr_at_100 value: 24.008 - type: mrr_at_1000 value: 24.099999999999998 - type: mrr_at_3 value: 20.1 - type: mrr_at_5 value: 21.685 - type: ndcg_at_1 value: 15.0 - type: ndcg_at_10 value: 12.386999999999999 - type: ndcg_at_100 value: 18.533 - type: ndcg_at_1000 value: 23.955000000000002 - type: ndcg_at_3 value: 11.75 - type: ndcg_at_5 value: 10.285 - type: precision_at_1 value: 15.0 - type: precision_at_10 value: 6.36 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.28300000000000003 - type: precision_at_3 value: 10.767 - type: precision_at_5 value: 8.9 - type: recall_at_1 value: 3.053 - type: recall_at_10 value: 12.873000000000001 - type: recall_at_100 value: 30.982 - type: recall_at_1000 value: 57.489999999999995 - type: recall_at_3 value: 6.553000000000001 - type: recall_at_5 value: 9.013 - type: map_at_1 value: 0.148 - type: map_at_10 value: 0.971 - type: map_at_100 value: 4.65 - type: map_at_1000 value: 11.509 - type: map_at_3 value: 0.366 - type: map_at_5 value: 0.5599999999999999 - type: mrr_at_1 value: 62.0 - type: mrr_at_10 value: 70.069 - type: mrr_at_100 value: 70.455 - type: mrr_at_1000 value: 70.455 - type: mrr_at_3 value: 68.0 - type: mrr_at_5 value: 69.19999999999999 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 45.729 - type: ndcg_at_100 value: 32.757 - type: ndcg_at_1000 value: 29.631999999999998 - type: ndcg_at_3 value: 50.407999999999994 - type: ndcg_at_5 value: 48.208 - type: precision_at_1 value: 62.0 - type: precision_at_10 value: 47.8 - type: precision_at_100 value: 33.72 - type: precision_at_1000 value: 14.238000000000001 - type: precision_at_3 value: 53.333 - type: precision_at_5 value: 50.8 - type: recall_at_1 value: 0.148 - type: recall_at_10 value: 1.143 - type: recall_at_100 value: 7.219 - type: recall_at_1000 value: 28.294999999999998 - type: recall_at_3 value: 0.392 - type: recall_at_5 value: 0.628 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 39.546512756347916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 47.07923662495948 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 75.6733681207629 - type: cos_sim_spearman value: 64.67529822790183 - type: euclidean_pearson value: 69.13481548437119 - type: euclidean_spearman value: 64.67521597440148 - type: manhattan_pearson value: 69.01619022585454 - type: manhattan_spearman value: 64.8728374071917 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.06681953798454 - type: cos_sim_spearman value: 62.247506425866405 - type: euclidean_pearson value: 68.05816014766324 - type: euclidean_spearman value: 62.24902354181767 - type: manhattan_pearson value: 66.68543187933726 - type: manhattan_spearman value: 61.438544148098664 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 76.53983672284885 - type: cos_sim_spearman value: 77.2760080817994 - type: euclidean_pearson value: 76.7796065728204 - type: euclidean_spearman value: 77.27600787572996 - type: manhattan_pearson value: 76.37651419577129 - type: manhattan_spearman value: 76.85568457177312 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 76.2085441120845 - type: cos_sim_spearman value: 71.91409062241355 - type: euclidean_pearson value: 74.52730472762947 - type: euclidean_spearman value: 71.91409512725335 - type: manhattan_pearson value: 74.53275469819042 - type: manhattan_spearman value: 71.9720930787841 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.2427339046162 - type: cos_sim_spearman value: 79.75345017876988 - type: euclidean_pearson value: 79.31395774152486 - type: euclidean_spearman value: 79.75345672749796 - type: manhattan_pearson value: 79.24199253925532 - type: manhattan_spearman value: 79.64057053536243 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.64452384480809 - type: cos_sim_spearman value: 76.26343905510407 - type: euclidean_pearson value: 75.64112078051633 - type: euclidean_spearman value: 76.26343823222666 - type: manhattan_pearson value: 75.32718790811802 - type: manhattan_spearman value: 75.9420892784719 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 84.67406953406964 - type: cos_sim_spearman value: 85.96709815630739 - type: euclidean_pearson value: 84.71863724469544 - type: euclidean_spearman value: 85.96709815630739 - type: manhattan_pearson value: 85.07894738833434 - type: manhattan_spearman value: 86.57110045700985 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 59.318066667301615 - type: cos_sim_spearman value: 63.07956002739231 - type: euclidean_pearson value: 62.464248268498814 - type: euclidean_spearman value: 63.07956002739231 - type: manhattan_pearson value: 62.04813588964373 - type: manhattan_spearman value: 61.83898606879604 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 77.25982574948274 - type: cos_sim_spearman value: 75.4051305973876 - type: euclidean_pearson value: 77.1987828515963 - type: euclidean_spearman value: 75.40516069202422 - type: manhattan_pearson value: 77.04099633595793 - type: manhattan_spearman value: 75.32222510947251 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 72.10127087089839 - type: mrr value: 90.62288020621355 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 35.5 - type: map_at_10 value: 45.238 - type: map_at_100 value: 46.135999999999996 - type: map_at_1000 value: 46.181 - type: map_at_3 value: 42.329 - type: map_at_5 value: 44.054 - type: mrr_at_1 value: 37.667 - type: mrr_at_10 value: 46.661 - type: mrr_at_100 value: 47.378 - type: mrr_at_1000 value: 47.418 - type: mrr_at_3 value: 43.944 - type: mrr_at_5 value: 45.528 - type: ndcg_at_1 value: 37.667 - type: ndcg_at_10 value: 50.63999999999999 - type: ndcg_at_100 value: 54.885 - type: ndcg_at_1000 value: 56.274 - type: ndcg_at_3 value: 44.891999999999996 - type: ndcg_at_5 value: 47.788000000000004 - type: precision_at_1 value: 37.667 - type: precision_at_10 value: 7.3 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 12.6 - type: recall_at_1 value: 35.5 - type: recall_at_10 value: 66.178 - type: recall_at_100 value: 85.9 - type: recall_at_1000 value: 97.1 - type: recall_at_3 value: 50.306 - type: recall_at_5 value: 57.443999999999996 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.71386138613862 - type: cos_sim_ap value: 90.20131932554314 - type: cos_sim_f1 value: 84.7749114820435 - type: cos_sim_precision value: 85.7727737973388 - type: cos_sim_recall value: 83.8 - type: dot_accuracy value: 99.71386138613862 - type: dot_ap value: 90.20131927652947 - type: dot_f1 value: 84.7749114820435 - type: dot_precision value: 85.7727737973388 - type: dot_recall value: 83.8 - type: euclidean_accuracy value: 99.71386138613862 - type: euclidean_ap value: 90.20131927652946 - type: euclidean_f1 value: 84.7749114820435 - type: euclidean_precision value: 85.7727737973388 - type: euclidean_recall value: 83.8 - type: manhattan_accuracy value: 99.7059405940594 - type: manhattan_ap value: 90.00682250828238 - type: manhattan_f1 value: 84.44211629125196 - type: manhattan_precision value: 88.66886688668868 - type: manhattan_recall value: 80.60000000000001 - type: max_accuracy value: 99.71386138613862 - type: max_ap value: 90.20131932554314 - type: max_f1 value: 84.7749114820435 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 48.18939518021159 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.748387331082416 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.24644967679195 - type: mrr value: 43.66944126135303 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.88359913790285 - type: cos_sim_spearman value: 29.20319307230353 - type: dot_pearson value: 29.883592420103206 - type: dot_spearman value: 29.228231500970136 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 1.22 - type: map_at_10 value: 6.635000000000001 - type: map_at_100 value: 10.873 - type: map_at_1000 value: 12.415 - type: map_at_3 value: 2.8240000000000003 - type: map_at_5 value: 4.111 - type: mrr_at_1 value: 14.285999999999998 - type: mrr_at_10 value: 31.857999999999997 - type: mrr_at_100 value: 33.049 - type: mrr_at_1000 value: 33.049 - type: mrr_at_3 value: 25.85 - type: mrr_at_5 value: 29.218 - type: ndcg_at_1 value: 12.245000000000001 - type: ndcg_at_10 value: 18.618000000000002 - type: ndcg_at_100 value: 28.488000000000003 - type: ndcg_at_1000 value: 41.208 - type: ndcg_at_3 value: 15.045 - type: ndcg_at_5 value: 16.359 - type: precision_at_1 value: 14.285999999999998 - type: precision_at_10 value: 19.796 - type: precision_at_100 value: 6.5920000000000005 - type: precision_at_1000 value: 1.471 - type: precision_at_3 value: 18.367 - type: precision_at_5 value: 18.776 - type: recall_at_1 value: 1.22 - type: recall_at_10 value: 13.763 - type: recall_at_100 value: 40.107 - type: recall_at_1000 value: 79.06800000000001 - type: recall_at_3 value: 4.2540000000000004 - type: recall_at_5 value: 7.142999999999999 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.82600000000001 - type: ap value: 14.59656193783295 - type: f1 value: 55.237720537754875 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 55.387662705149964 - type: f1 value: 55.62292803889264 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.53590896395144 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 81.57000655659535 - type: cos_sim_ap value: 57.187256107173354 - type: cos_sim_f1 value: 54.94480738905159 - type: cos_sim_precision value: 47.93632075471698 - type: cos_sim_recall value: 64.35356200527704 - type: dot_accuracy value: 81.57000655659535 - type: dot_ap value: 57.187234074371496 - type: dot_f1 value: 54.94480738905159 - type: dot_precision value: 47.93632075471698 - type: dot_recall value: 64.35356200527704 - type: euclidean_accuracy value: 81.57000655659535 - type: euclidean_ap value: 57.18724422350816 - type: euclidean_f1 value: 54.94480738905159 - type: euclidean_precision value: 47.93632075471698 - type: euclidean_recall value: 64.35356200527704 - type: manhattan_accuracy value: 81.71902008702389 - type: manhattan_ap value: 57.51605309414705 - type: manhattan_f1 value: 55.16339869281046 - type: manhattan_precision value: 50.18378378378379 - type: manhattan_recall value: 61.24010554089709 - type: max_accuracy value: 81.71902008702389 - type: max_ap value: 57.51605309414705 - type: max_f1 value: 55.16339869281046 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.09977878682035 - type: cos_sim_ap value: 81.948747937846 - type: cos_sim_f1 value: 74.04089724292375 - type: cos_sim_precision value: 70.7599466704091 - type: cos_sim_recall value: 77.64089929165382 - type: dot_accuracy value: 87.09977878682035 - type: dot_ap value: 81.94874861792225 - type: dot_f1 value: 74.04089724292375 - type: dot_precision value: 70.7599466704091 - type: dot_recall value: 77.64089929165382 - type: euclidean_accuracy value: 87.09977878682035 - type: euclidean_ap value: 81.94875280390386 - type: euclidean_f1 value: 74.04089724292375 - type: euclidean_precision value: 70.7599466704091 - type: euclidean_recall value: 77.64089929165382 - type: manhattan_accuracy value: 87.19292117825125 - type: manhattan_ap value: 82.13752985145429 - type: manhattan_f1 value: 74.36426623424485 - type: manhattan_precision value: 71.32051463311183 - type: manhattan_recall value: 77.6793963658762 - type: max_accuracy value: 87.19292117825125 - type: max_ap value: 82.13752985145429 - type: max_f1 value: 74.36426623424485 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/l3_wl

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:25:40

2025-01-09T11:25:45

0

--- tags: - mteb model-index: - name: l3_wordllama_fixed results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 67.40298507462687 - type: ap value: 28.677454675181384 - type: f1 value: 60.58324071299079 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.75847499999999 - type: ap value: 59.00482910406265 - type: f1 value: 63.59920748914567 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 32.09 - type: f1 value: 31.527306414565835 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 20.413 - type: map_at_10 value: 35.176 - type: map_at_100 value: 36.489 - type: map_at_1000 value: 36.507 - type: map_at_3 value: 30.69 - type: map_at_5 value: 32.859 - type: mrr_at_1 value: 21.124000000000002 - type: mrr_at_10 value: 35.44 - type: mrr_at_100 value: 36.753 - type: mrr_at_1000 value: 36.77 - type: mrr_at_3 value: 30.915 - type: mrr_at_5 value: 33.113 - type: ndcg_at_1 value: 20.413 - type: ndcg_at_10 value: 43.565 - type: ndcg_at_100 value: 49.329 - type: ndcg_at_1000 value: 49.757 - type: ndcg_at_3 value: 34.143 - type: ndcg_at_5 value: 38.046 - type: precision_at_1 value: 20.413 - type: precision_at_10 value: 7.048 - type: precision_at_100 value: 0.9610000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.723 - type: precision_at_5 value: 10.725 - type: recall_at_1 value: 20.413 - type: recall_at_10 value: 70.48400000000001 - type: recall_at_100 value: 96.088 - type: recall_at_1000 value: 99.36 - type: recall_at_3 value: 44.168 - type: recall_at_5 value: 53.627 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.885229242790935 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.49720713710708 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.61953366105678 - type: mrr value: 70.12344457635315 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 76.26075421266883 - type: cos_sim_spearman value: 71.32873370732024 - type: euclidean_pearson value: 74.59312194402976 - type: euclidean_spearman value: 71.32873370732024 - type: manhattan_pearson value: 74.5892678336525 - type: manhattan_spearman value: 71.02450990790472 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.68506493506494 - type: f1 value: 72.88555102531198 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.29089107203252 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.19965378718348 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.508 - type: map_at_10 value: 29.088 - type: map_at_100 value: 30.279 - type: map_at_1000 value: 30.445 - type: map_at_3 value: 26.552999999999997 - type: map_at_5 value: 27.939000000000004 - type: mrr_at_1 value: 26.466 - type: mrr_at_10 value: 34.171 - type: mrr_at_100 value: 35.059000000000005 - type: mrr_at_1000 value: 35.137 - type: mrr_at_3 value: 31.855 - type: mrr_at_5 value: 33.093 - type: ndcg_at_1 value: 26.466 - type: ndcg_at_10 value: 34.097 - type: ndcg_at_100 value: 39.612 - type: ndcg_at_1000 value: 42.819 - type: ndcg_at_3 value: 29.918 - type: ndcg_at_5 value: 31.683 - type: precision_at_1 value: 26.466 - type: precision_at_10 value: 6.422999999999999 - type: precision_at_100 value: 1.15 - type: precision_at_1000 value: 0.17700000000000002 - type: precision_at_3 value: 13.972000000000001 - type: precision_at_5 value: 10.129000000000001 - type: recall_at_1 value: 21.508 - type: recall_at_10 value: 43.699 - type: recall_at_100 value: 68.404 - type: recall_at_1000 value: 89.687 - type: recall_at_3 value: 31.773 - type: recall_at_5 value: 36.687 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.865 - type: map_at_10 value: 23.164 - type: map_at_100 value: 24.15 - type: map_at_1000 value: 24.288 - type: map_at_3 value: 20.97 - type: map_at_5 value: 22.277 - type: mrr_at_1 value: 21.401 - type: mrr_at_10 value: 27.614 - type: mrr_at_100 value: 28.395 - type: mrr_at_1000 value: 28.469 - type: mrr_at_3 value: 25.594 - type: mrr_at_5 value: 26.735 - type: ndcg_at_1 value: 21.401 - type: ndcg_at_10 value: 27.343 - type: ndcg_at_100 value: 31.726 - type: ndcg_at_1000 value: 34.586 - type: ndcg_at_3 value: 23.723 - type: ndcg_at_5 value: 25.524 - type: precision_at_1 value: 21.401 - type: precision_at_10 value: 5.236 - type: precision_at_100 value: 0.9650000000000001 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 11.444 - type: precision_at_5 value: 8.497 - type: recall_at_1 value: 16.865 - type: recall_at_10 value: 35.209 - type: recall_at_100 value: 54.371 - type: recall_at_1000 value: 73.651 - type: recall_at_3 value: 24.943 - type: recall_at_5 value: 29.634 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 25.424000000000003 - type: map_at_10 value: 34.318 - type: map_at_100 value: 35.461999999999996 - type: map_at_1000 value: 35.551 - type: map_at_3 value: 31.694 - type: map_at_5 value: 33.111000000000004 - type: mrr_at_1 value: 29.215999999999998 - type: mrr_at_10 value: 37.333 - type: mrr_at_100 value: 38.223 - type: mrr_at_1000 value: 38.282 - type: mrr_at_3 value: 35.004999999999995 - type: mrr_at_5 value: 36.272 - type: ndcg_at_1 value: 29.215999999999998 - type: ndcg_at_10 value: 39.309 - type: ndcg_at_100 value: 44.718999999999994 - type: ndcg_at_1000 value: 46.877 - type: ndcg_at_3 value: 34.449999999999996 - type: ndcg_at_5 value: 36.675999999999995 - type: precision_at_1 value: 29.215999999999998 - type: precision_at_10 value: 6.483 - type: precision_at_100 value: 1.0330000000000001 - type: precision_at_1000 value: 0.128 - type: precision_at_3 value: 15.298 - type: precision_at_5 value: 10.734 - type: recall_at_1 value: 25.424000000000003 - type: recall_at_10 value: 51.464 - type: recall_at_100 value: 75.87 - type: recall_at_1000 value: 91.77300000000001 - type: recall_at_3 value: 38.396 - type: recall_at_5 value: 43.759 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 14.674000000000001 - type: map_at_10 value: 18.984 - type: map_at_100 value: 19.867 - type: map_at_1000 value: 19.975 - type: map_at_3 value: 17.488999999999997 - type: map_at_5 value: 18.412 - type: mrr_at_1 value: 15.818999999999999 - type: mrr_at_10 value: 20.472 - type: mrr_at_100 value: 21.342 - type: mrr_at_1000 value: 21.431 - type: mrr_at_3 value: 18.908 - type: mrr_at_5 value: 19.811999999999998 - type: ndcg_at_1 value: 15.818999999999999 - type: ndcg_at_10 value: 21.823 - type: ndcg_at_100 value: 27.0 - type: ndcg_at_1000 value: 30.064999999999998 - type: ndcg_at_3 value: 18.776 - type: ndcg_at_5 value: 20.395 - type: precision_at_1 value: 15.818999999999999 - type: precision_at_10 value: 3.367 - type: precision_at_100 value: 0.649 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.797 - type: precision_at_5 value: 5.582 - type: recall_at_1 value: 14.674000000000001 - type: recall_at_10 value: 29.087000000000003 - type: recall_at_100 value: 54.52 - type: recall_at_1000 value: 78.27 - type: recall_at_3 value: 21.075 - type: recall_at_5 value: 24.92 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 8.123 - type: map_at_10 value: 13.483 - type: map_at_100 value: 14.457999999999998 - type: map_at_1000 value: 14.579 - type: map_at_3 value: 11.271 - type: map_at_5 value: 12.418 - type: mrr_at_1 value: 10.323 - type: mrr_at_10 value: 16.244 - type: mrr_at_100 value: 17.186 - type: mrr_at_1000 value: 17.27 - type: mrr_at_3 value: 13.91 - type: mrr_at_5 value: 15.116 - type: ndcg_at_1 value: 10.323 - type: ndcg_at_10 value: 17.366999999999997 - type: ndcg_at_100 value: 22.553 - type: ndcg_at_1000 value: 25.817 - type: ndcg_at_3 value: 12.895000000000001 - type: ndcg_at_5 value: 14.856 - type: precision_at_1 value: 10.323 - type: precision_at_10 value: 3.5069999999999997 - type: precision_at_100 value: 0.711 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 6.3020000000000005 - type: precision_at_5 value: 5.0 - type: recall_at_1 value: 8.123 - type: recall_at_10 value: 26.889000000000003 - type: recall_at_100 value: 50.397999999999996 - type: recall_at_1000 value: 74.244 - type: recall_at_3 value: 14.691 - type: recall_at_5 value: 19.503 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.607000000000003 - type: map_at_10 value: 25.596000000000004 - type: map_at_100 value: 26.984 - type: map_at_1000 value: 27.125 - type: map_at_3 value: 22.917 - type: map_at_5 value: 24.201 - type: mrr_at_1 value: 22.907 - type: mrr_at_10 value: 30.384 - type: mrr_at_100 value: 31.432 - type: mrr_at_1000 value: 31.5 - type: mrr_at_3 value: 27.703 - type: mrr_at_5 value: 29.137 - type: ndcg_at_1 value: 22.907 - type: ndcg_at_10 value: 30.824 - type: ndcg_at_100 value: 37.265 - type: ndcg_at_1000 value: 40.191 - type: ndcg_at_3 value: 25.913000000000004 - type: ndcg_at_5 value: 27.849 - type: precision_at_1 value: 22.907 - type: precision_at_10 value: 5.9479999999999995 - type: precision_at_100 value: 1.094 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 12.384 - type: precision_at_5 value: 9.009 - type: recall_at_1 value: 18.607000000000003 - type: recall_at_10 value: 42.082 - type: recall_at_100 value: 70.018 - type: recall_at_1000 value: 90.003 - type: recall_at_3 value: 27.932000000000002 - type: recall_at_5 value: 32.975 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.52 - type: map_at_10 value: 21.61 - type: map_at_100 value: 22.827 - type: map_at_1000 value: 22.964000000000002 - type: map_at_3 value: 19.500999999999998 - type: map_at_5 value: 20.798 - type: mrr_at_1 value: 17.694 - type: mrr_at_10 value: 25.161 - type: mrr_at_100 value: 26.180999999999997 - type: mrr_at_1000 value: 26.269 - type: mrr_at_3 value: 23.116 - type: mrr_at_5 value: 24.412 - type: ndcg_at_1 value: 17.694 - type: ndcg_at_10 value: 25.924000000000003 - type: ndcg_at_100 value: 31.615 - type: ndcg_at_1000 value: 34.955000000000005 - type: ndcg_at_3 value: 22.161 - type: ndcg_at_5 value: 24.16 - type: precision_at_1 value: 17.694 - type: precision_at_10 value: 4.874 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 10.731 - type: precision_at_5 value: 8.014000000000001 - type: recall_at_1 value: 14.52 - type: recall_at_10 value: 35.369 - type: recall_at_100 value: 60.0 - type: recall_at_1000 value: 83.66799999999999 - type: recall_at_3 value: 25.058999999999997 - type: recall_at_5 value: 30.131999999999998 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.35675 - type: map_at_10 value: 21.087750000000007 - type: map_at_100 value: 22.12925 - type: map_at_1000 value: 22.262 - type: map_at_3 value: 19.156249999999996 - type: map_at_5 value: 20.202916666666663 - type: mrr_at_1 value: 18.301583333333333 - type: mrr_at_10 value: 24.283083333333334 - type: mrr_at_100 value: 25.176583333333337 - type: mrr_at_1000 value: 25.262083333333337 - type: mrr_at_3 value: 22.38533333333333 - type: mrr_at_5 value: 23.408 - type: ndcg_at_1 value: 18.301583333333333 - type: ndcg_at_10 value: 24.931416666666667 - type: ndcg_at_100 value: 30.107249999999997 - type: ndcg_at_1000 value: 33.292500000000004 - type: ndcg_at_3 value: 21.380833333333335 - type: ndcg_at_5 value: 22.965416666666663 - type: precision_at_1 value: 18.301583333333333 - type: precision_at_10 value: 4.475583333333334 - type: precision_at_100 value: 0.84875 - type: precision_at_1000 value: 0.13066666666666668 - type: precision_at_3 value: 9.858500000000001 - type: precision_at_5 value: 7.125333333333334 - type: recall_at_1 value: 15.35675 - type: recall_at_10 value: 33.385666666666665 - type: recall_at_100 value: 57.03541666666667 - type: recall_at_1000 value: 80.00874999999999 - type: recall_at_3 value: 23.440833333333337 - type: recall_at_5 value: 27.48841666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 13.023000000000001 - type: map_at_10 value: 17.116999999999997 - type: map_at_100 value: 18.016 - type: map_at_1000 value: 18.124000000000002 - type: map_at_3 value: 15.654000000000002 - type: map_at_5 value: 16.494 - type: mrr_at_1 value: 14.877 - type: mrr_at_10 value: 19.061 - type: mrr_at_100 value: 19.933 - type: mrr_at_1000 value: 20.027 - type: mrr_at_3 value: 17.740000000000002 - type: mrr_at_5 value: 18.384 - type: ndcg_at_1 value: 14.877 - type: ndcg_at_10 value: 19.991999999999997 - type: ndcg_at_100 value: 24.836 - type: ndcg_at_1000 value: 27.922000000000004 - type: ndcg_at_3 value: 17.221 - type: ndcg_at_5 value: 18.496000000000002 - type: precision_at_1 value: 14.877 - type: precision_at_10 value: 3.298 - type: precision_at_100 value: 0.629 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 7.617999999999999 - type: precision_at_5 value: 5.428999999999999 - type: recall_at_1 value: 13.023000000000001 - type: recall_at_10 value: 27.064 - type: recall_at_100 value: 49.971 - type: recall_at_1000 value: 73.195 - type: recall_at_3 value: 19.273 - type: recall_at_5 value: 22.465 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.86 - type: map_at_10 value: 12.806999999999999 - type: map_at_100 value: 13.55 - type: map_at_1000 value: 13.684 - type: map_at_3 value: 11.368 - type: map_at_5 value: 12.106 - type: mrr_at_1 value: 10.943 - type: mrr_at_10 value: 15.397 - type: mrr_at_100 value: 16.139 - type: mrr_at_1000 value: 16.242 - type: mrr_at_3 value: 13.805 - type: mrr_at_5 value: 14.601 - type: ndcg_at_1 value: 10.943 - type: ndcg_at_10 value: 15.693999999999999 - type: ndcg_at_100 value: 19.869 - type: ndcg_at_1000 value: 23.579 - type: ndcg_at_3 value: 12.920000000000002 - type: ndcg_at_5 value: 14.054 - type: precision_at_1 value: 10.943 - type: precision_at_10 value: 2.97 - type: precision_at_100 value: 0.609 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 6.148 - type: precision_at_5 value: 4.529 - type: recall_at_1 value: 8.86 - type: recall_at_10 value: 22.041 - type: recall_at_100 value: 41.528 - type: recall_at_1000 value: 68.917 - type: recall_at_3 value: 14.257 - type: recall_at_5 value: 17.191000000000003 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 14.508 - type: map_at_10 value: 19.814999999999998 - type: map_at_100 value: 20.761 - type: map_at_1000 value: 20.899 - type: map_at_3 value: 17.959 - type: map_at_5 value: 18.877 - type: mrr_at_1 value: 17.444000000000003 - type: mrr_at_10 value: 23.067 - type: mrr_at_100 value: 23.906 - type: mrr_at_1000 value: 24.015 - type: mrr_at_3 value: 21.191 - type: mrr_at_5 value: 22.124 - type: ndcg_at_1 value: 17.444000000000003 - type: ndcg_at_10 value: 23.519000000000002 - type: ndcg_at_100 value: 28.546 - type: ndcg_at_1000 value: 32.243 - type: ndcg_at_3 value: 19.958000000000002 - type: ndcg_at_5 value: 21.391 - type: precision_at_1 value: 17.444000000000003 - type: precision_at_10 value: 4.104 - type: precision_at_100 value: 0.758 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 9.142 - type: precision_at_5 value: 6.474 - type: recall_at_1 value: 14.508 - type: recall_at_10 value: 31.788 - type: recall_at_100 value: 55.047999999999995 - type: recall_at_1000 value: 82.155 - type: recall_at_3 value: 21.857 - type: recall_at_5 value: 25.549 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 16.733 - type: map_at_10 value: 21.721 - type: map_at_100 value: 22.986 - type: map_at_1000 value: 23.198 - type: map_at_3 value: 20.229 - type: map_at_5 value: 21.066 - type: mrr_at_1 value: 19.96 - type: mrr_at_10 value: 25.683 - type: mrr_at_100 value: 26.662000000000003 - type: mrr_at_1000 value: 26.749000000000002 - type: mrr_at_3 value: 24.209 - type: mrr_at_5 value: 25.049 - type: ndcg_at_1 value: 19.96 - type: ndcg_at_10 value: 25.413999999999998 - type: ndcg_at_100 value: 30.916 - type: ndcg_at_1000 value: 34.678 - type: ndcg_at_3 value: 23.138 - type: ndcg_at_5 value: 24.169 - type: precision_at_1 value: 19.96 - type: precision_at_10 value: 4.743 - type: precision_at_100 value: 1.126 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 10.935 - type: precision_at_5 value: 7.707999999999999 - type: recall_at_1 value: 16.733 - type: recall_at_10 value: 31.512 - type: recall_at_100 value: 57.079 - type: recall_at_1000 value: 82.661 - type: recall_at_3 value: 24.252000000000002 - type: recall_at_5 value: 27.317000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 11.436 - type: map_at_10 value: 15.35 - type: map_at_100 value: 16.211000000000002 - type: map_at_1000 value: 16.311999999999998 - type: map_at_3 value: 14.27 - type: map_at_5 value: 14.735999999999999 - type: mrr_at_1 value: 12.568999999999999 - type: mrr_at_10 value: 16.81 - type: mrr_at_100 value: 17.660999999999998 - type: mrr_at_1000 value: 17.754 - type: mrr_at_3 value: 15.588 - type: mrr_at_5 value: 16.161 - type: ndcg_at_1 value: 12.568999999999999 - type: ndcg_at_10 value: 17.871000000000002 - type: ndcg_at_100 value: 22.63 - type: ndcg_at_1000 value: 25.778000000000002 - type: ndcg_at_3 value: 15.497 - type: ndcg_at_5 value: 16.332 - type: precision_at_1 value: 12.568999999999999 - type: precision_at_10 value: 2.754 - type: precision_at_100 value: 0.551 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 6.531000000000001 - type: precision_at_5 value: 4.399 - type: recall_at_1 value: 11.436 - type: recall_at_10 value: 24.424 - type: recall_at_100 value: 47.217999999999996 - type: recall_at_1000 value: 71.881 - type: recall_at_3 value: 17.782 - type: recall_at_5 value: 19.729 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.822 - type: map_at_10 value: 12.872 - type: map_at_100 value: 14.504 - type: map_at_1000 value: 14.712 - type: map_at_3 value: 10.357 - type: map_at_5 value: 11.700000000000001 - type: mrr_at_1 value: 15.895999999999999 - type: mrr_at_10 value: 26.407999999999998 - type: mrr_at_100 value: 27.528999999999996 - type: mrr_at_1000 value: 27.586 - type: mrr_at_3 value: 22.714000000000002 - type: mrr_at_5 value: 24.762999999999998 - type: ndcg_at_1 value: 15.895999999999999 - type: ndcg_at_10 value: 19.643 - type: ndcg_at_100 value: 26.863999999999997 - type: ndcg_at_1000 value: 30.804 - type: ndcg_at_3 value: 14.914 - type: ndcg_at_5 value: 16.723 - type: precision_at_1 value: 15.895999999999999 - type: precision_at_10 value: 6.612 - type: precision_at_100 value: 1.434 - type: precision_at_1000 value: 0.216 - type: precision_at_3 value: 11.488 - type: precision_at_5 value: 9.354999999999999 - type: recall_at_1 value: 6.822 - type: recall_at_10 value: 25.478 - type: recall_at_100 value: 50.94 - type: recall_at_1000 value: 73.264 - type: recall_at_3 value: 14.228 - type: recall_at_5 value: 18.91 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.601999999999999 - type: map_at_10 value: 9.225999999999999 - type: map_at_100 value: 12.692 - type: map_at_1000 value: 13.65 - type: map_at_3 value: 6.883 - type: map_at_5 value: 7.904 - type: mrr_at_1 value: 34.0 - type: mrr_at_10 value: 45.83 - type: mrr_at_100 value: 46.608 - type: mrr_at_1000 value: 46.635 - type: mrr_at_3 value: 42.583 - type: mrr_at_5 value: 44.721 - type: ndcg_at_1 value: 24.75 - type: ndcg_at_10 value: 21.092 - type: ndcg_at_100 value: 25.288 - type: ndcg_at_1000 value: 32.550000000000004 - type: ndcg_at_3 value: 22.808999999999997 - type: ndcg_at_5 value: 21.931 - type: precision_at_1 value: 34.0 - type: precision_at_10 value: 18.525 - type: precision_at_100 value: 6.265 - type: precision_at_1000 value: 1.395 - type: precision_at_3 value: 27.500000000000004 - type: precision_at_5 value: 23.799999999999997 - type: recall_at_1 value: 4.601999999999999 - type: recall_at_10 value: 13.578000000000001 - type: recall_at_100 value: 32.438 - type: recall_at_1000 value: 57.067 - type: recall_at_3 value: 8.013 - type: recall_at_5 value: 10.057 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 39.0 - type: f1 value: 35.038106148143335 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.577 - type: map_at_10 value: 25.397 - type: map_at_100 value: 26.493 - type: map_at_1000 value: 26.56 - type: map_at_3 value: 22.523 - type: map_at_5 value: 24.102 - type: mrr_at_1 value: 17.717 - type: mrr_at_10 value: 26.999000000000002 - type: mrr_at_100 value: 28.084999999999997 - type: mrr_at_1000 value: 28.144999999999996 - type: mrr_at_3 value: 24.01 - type: mrr_at_5 value: 25.669999999999998 - type: ndcg_at_1 value: 17.717 - type: ndcg_at_10 value: 30.836999999999996 - type: ndcg_at_100 value: 36.278 - type: ndcg_at_1000 value: 38.139 - type: ndcg_at_3 value: 24.868000000000002 - type: ndcg_at_5 value: 27.701999999999998 - type: precision_at_1 value: 17.717 - type: precision_at_10 value: 5.0569999999999995 - type: precision_at_100 value: 0.791 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 10.850999999999999 - type: precision_at_5 value: 8.004999999999999 - type: recall_at_1 value: 16.577 - type: recall_at_10 value: 46.451 - type: recall_at_100 value: 71.61800000000001 - type: recall_at_1000 value: 85.902 - type: recall_at_3 value: 30.130000000000003 - type: recall_at_5 value: 36.902 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.0680000000000005 - type: map_at_10 value: 12.424 - type: map_at_100 value: 13.750000000000002 - type: map_at_1000 value: 13.963999999999999 - type: map_at_3 value: 10.41 - type: map_at_5 value: 11.459999999999999 - type: mrr_at_1 value: 14.506 - type: mrr_at_10 value: 21.644 - type: mrr_at_100 value: 22.708000000000002 - type: mrr_at_1000 value: 22.811 - type: mrr_at_3 value: 19.084 - type: mrr_at_5 value: 20.543 - type: ndcg_at_1 value: 14.506 - type: ndcg_at_10 value: 17.485 - type: ndcg_at_100 value: 23.565 - type: ndcg_at_1000 value: 28.177000000000003 - type: ndcg_at_3 value: 14.423 - type: ndcg_at_5 value: 15.536 - type: precision_at_1 value: 14.506 - type: precision_at_10 value: 5.122999999999999 - type: precision_at_100 value: 1.13 - type: precision_at_1000 value: 0.193 - type: precision_at_3 value: 9.722 - type: precision_at_5 value: 7.623 - type: recall_at_1 value: 7.0680000000000005 - type: recall_at_10 value: 23.423 - type: recall_at_100 value: 46.682 - type: recall_at_1000 value: 75.22999999999999 - type: recall_at_3 value: 13.544999999999998 - type: recall_at_5 value: 17.448 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 21.837 - type: map_at_10 value: 30.614 - type: map_at_100 value: 31.6 - type: map_at_1000 value: 31.71 - type: map_at_3 value: 28.219 - type: map_at_5 value: 29.598000000000003 - type: mrr_at_1 value: 43.673 - type: mrr_at_10 value: 51.627 - type: mrr_at_100 value: 52.323 - type: mrr_at_1000 value: 52.364 - type: mrr_at_3 value: 49.527 - type: mrr_at_5 value: 50.76500000000001 - type: ndcg_at_1 value: 43.673 - type: ndcg_at_10 value: 38.696000000000005 - type: ndcg_at_100 value: 43.124 - type: ndcg_at_1000 value: 45.552 - type: ndcg_at_3 value: 34.338 - type: ndcg_at_5 value: 36.553000000000004 - type: precision_at_1 value: 43.673 - type: precision_at_10 value: 8.432 - type: precision_at_100 value: 1.198 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 21.58 - type: precision_at_5 value: 14.706 - type: recall_at_1 value: 21.837 - type: recall_at_10 value: 42.161 - type: recall_at_100 value: 59.899 - type: recall_at_1000 value: 76.036 - type: recall_at_3 value: 32.37 - type: recall_at_5 value: 36.766 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 65.0232 - type: ap value: 59.81346113056583 - type: f1 value: 64.78827292080608 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.614000000000001 - type: map_at_10 value: 11.733 - type: map_at_100 value: 12.757 - type: map_at_1000 value: 12.873999999999999 - type: map_at_3 value: 9.783999999999999 - type: map_at_5 value: 10.807 - type: mrr_at_1 value: 6.834 - type: mrr_at_10 value: 12.074 - type: mrr_at_100 value: 13.099 - type: mrr_at_1000 value: 13.211 - type: mrr_at_3 value: 10.098 - type: mrr_at_5 value: 11.132 - type: ndcg_at_1 value: 6.834 - type: ndcg_at_10 value: 15.046000000000001 - type: ndcg_at_100 value: 20.657 - type: ndcg_at_1000 value: 24.112000000000002 - type: ndcg_at_3 value: 10.95 - type: ndcg_at_5 value: 12.796 - type: precision_at_1 value: 6.834 - type: precision_at_10 value: 2.633 - type: precision_at_100 value: 0.555 - type: precision_at_1000 value: 0.08499999999999999 - type: precision_at_3 value: 4.842 - type: precision_at_5 value: 3.8249999999999997 - type: recall_at_1 value: 6.614000000000001 - type: recall_at_10 value: 25.39 - type: recall_at_100 value: 52.793 - type: recall_at_1000 value: 80.415 - type: recall_at_3 value: 14.033000000000001 - type: recall_at_5 value: 18.496000000000002 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 85.58139534883719 - type: f1 value: 84.72133199480218 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 56.2608299133607 - type: f1 value: 36.74698315617003 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.993947545393404 - type: f1 value: 59.68762807193991 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 68.49361129791525 - type: f1 value: 67.16568787114376 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.675655693797378 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.87954369022046 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.47254787311633 - type: mrr value: 31.476216991631862 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 2.703 - type: map_at_10 value: 6.99 - type: map_at_100 value: 9.191 - type: map_at_1000 value: 10.385 - type: map_at_3 value: 5.015 - type: map_at_5 value: 5.904 - type: mrr_at_1 value: 30.031000000000002 - type: mrr_at_10 value: 40.001 - type: mrr_at_100 value: 40.724 - type: mrr_at_1000 value: 40.778 - type: mrr_at_3 value: 37.358000000000004 - type: mrr_at_5 value: 38.426 - type: ndcg_at_1 value: 28.483000000000004 - type: ndcg_at_10 value: 23.229 - type: ndcg_at_100 value: 22.115000000000002 - type: ndcg_at_1000 value: 31.263 - type: ndcg_at_3 value: 26.432 - type: ndcg_at_5 value: 25.074999999999996 - type: precision_at_1 value: 30.031000000000002 - type: precision_at_10 value: 17.957 - type: precision_at_100 value: 6.3 - type: precision_at_1000 value: 1.909 - type: precision_at_3 value: 26.006 - type: precision_at_5 value: 22.786 - type: recall_at_1 value: 2.703 - type: recall_at_10 value: 11.333 - type: recall_at_100 value: 24.629 - type: recall_at_1000 value: 57.162 - type: recall_at_3 value: 6.148 - type: recall_at_5 value: 7.902000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.904 - type: map_at_10 value: 18.551000000000002 - type: map_at_100 value: 19.913 - type: map_at_1000 value: 20.008 - type: map_at_3 value: 15.8 - type: map_at_5 value: 17.261000000000003 - type: mrr_at_1 value: 12.457 - type: mrr_at_10 value: 20.319000000000003 - type: mrr_at_100 value: 21.532999999999998 - type: mrr_at_1000 value: 21.61 - type: mrr_at_3 value: 17.449 - type: mrr_at_5 value: 19.023 - type: ndcg_at_1 value: 12.457 - type: ndcg_at_10 value: 23.488999999999997 - type: ndcg_at_100 value: 30.109 - type: ndcg_at_1000 value: 32.725 - type: ndcg_at_3 value: 17.73 - type: ndcg_at_5 value: 20.387 - type: precision_at_1 value: 12.457 - type: precision_at_10 value: 4.3709999999999996 - type: precision_at_100 value: 0.8109999999999999 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.333 - type: precision_at_5 value: 6.489000000000001 - type: recall_at_1 value: 10.904 - type: recall_at_10 value: 37.143 - type: recall_at_100 value: 67.432 - type: recall_at_1000 value: 87.59400000000001 - type: recall_at_3 value: 21.734 - type: recall_at_5 value: 27.927999999999997 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.499 - type: map_at_10 value: 77.088 - type: map_at_100 value: 77.91 - type: map_at_1000 value: 77.935 - type: map_at_3 value: 73.88900000000001 - type: map_at_5 value: 75.797 - type: mrr_at_1 value: 73.2 - type: mrr_at_10 value: 80.927 - type: mrr_at_100 value: 81.146 - type: mrr_at_1000 value: 81.148 - type: mrr_at_3 value: 79.427 - type: mrr_at_5 value: 80.363 - type: ndcg_at_1 value: 73.22999999999999 - type: ndcg_at_10 value: 81.926 - type: ndcg_at_100 value: 83.929 - type: ndcg_at_1000 value: 84.127 - type: ndcg_at_3 value: 78.071 - type: ndcg_at_5 value: 80.015 - type: precision_at_1 value: 73.22999999999999 - type: precision_at_10 value: 12.639 - type: precision_at_100 value: 1.5110000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.217 - type: precision_at_5 value: 22.722 - type: recall_at_1 value: 63.499 - type: recall_at_10 value: 91.646 - type: recall_at_100 value: 98.92999999999999 - type: recall_at_1000 value: 99.914 - type: recall_at_3 value: 80.703 - type: recall_at_5 value: 86.048 - type: map_at_1 value: 3.773 - type: map_at_10 value: 9.305 - type: map_at_100 value: 11.469 - type: map_at_1000 value: 11.828 - type: map_at_3 value: 6.675000000000001 - type: map_at_5 value: 7.965 - type: mrr_at_1 value: 18.6 - type: mrr_at_10 value: 28.392 - type: mrr_at_100 value: 29.664 - type: mrr_at_1000 value: 29.724 - type: mrr_at_3 value: 25.183 - type: mrr_at_5 value: 26.893 - type: ndcg_at_1 value: 18.6 - type: ndcg_at_10 value: 16.292 - type: ndcg_at_100 value: 25.0 - type: ndcg_at_1000 value: 31.136000000000003 - type: ndcg_at_3 value: 15.212 - type: ndcg_at_5 value: 13.354 - type: precision_at_1 value: 18.6 - type: precision_at_10 value: 8.57 - type: precision_at_100 value: 2.122 - type: precision_at_1000 value: 0.359 - type: precision_at_3 value: 14.267 - type: precision_at_5 value: 11.799999999999999 - type: recall_at_1 value: 3.773 - type: recall_at_10 value: 17.352999999999998 - type: recall_at_100 value: 43.062 - type: recall_at_1000 value: 72.775 - type: recall_at_3 value: 8.677999999999999 - type: recall_at_5 value: 11.958 - type: map_at_1 value: 0.186 - type: map_at_10 value: 1.304 - type: map_at_100 value: 6.688 - type: map_at_1000 value: 15.162 - type: map_at_3 value: 0.46499999999999997 - type: map_at_5 value: 0.7100000000000001 - type: mrr_at_1 value: 72.0 - type: mrr_at_10 value: 79.51899999999999 - type: mrr_at_100 value: 79.673 - type: mrr_at_1000 value: 79.673 - type: mrr_at_3 value: 77.667 - type: mrr_at_5 value: 78.567 - type: ndcg_at_1 value: 66.0 - type: ndcg_at_10 value: 58.172000000000004 - type: ndcg_at_100 value: 41.583999999999996 - type: ndcg_at_1000 value: 34.916000000000004 - type: ndcg_at_3 value: 62.0 - type: ndcg_at_5 value: 60.104 - type: precision_at_1 value: 72.0 - type: precision_at_10 value: 62.0 - type: precision_at_100 value: 43.32 - type: precision_at_1000 value: 15.962000000000002 - type: precision_at_3 value: 65.333 - type: precision_at_5 value: 63.6 - type: recall_at_1 value: 0.186 - type: recall_at_10 value: 1.525 - type: recall_at_100 value: 9.600999999999999 - type: recall_at_1000 value: 32.72 - type: recall_at_3 value: 0.492 - type: recall_at_5 value: 0.782 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.80644854168368 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 48.708800185514974 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 77.70369570699015 - type: cos_sim_spearman value: 67.00421728409633 - type: euclidean_pearson value: 71.7303217538682 - type: euclidean_spearman value: 67.00421728409633 - type: manhattan_pearson value: 71.62358736603595 - type: manhattan_spearman value: 66.93696271331966 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 72.3464707081196 - type: cos_sim_spearman value: 63.91086584602619 - type: euclidean_pearson value: 68.22390430027092 - type: euclidean_spearman value: 63.91086584602619 - type: manhattan_pearson value: 68.14984324829423 - type: manhattan_spearman value: 63.86219497566778 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 72.80276772789091 - type: cos_sim_spearman value: 73.34700075766551 - type: euclidean_pearson value: 72.88415583236083 - type: euclidean_spearman value: 73.34700075766551 - type: manhattan_pearson value: 72.71141307415924 - type: manhattan_spearman value: 73.10626124984765 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 74.00122656955553 - type: cos_sim_spearman value: 69.07090069837032 - type: euclidean_pearson value: 71.79931055857548 - type: euclidean_spearman value: 69.07090069837032 - type: manhattan_pearson value: 71.71577354549707 - type: manhattan_spearman value: 69.0177557195104 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 81.17450916936498 - type: cos_sim_spearman value: 81.53568053124042 - type: euclidean_pearson value: 81.04779414575466 - type: euclidean_spearman value: 81.53568053124042 - type: manhattan_pearson value: 80.95262960295437 - type: manhattan_spearman value: 81.43365291054681 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 75.7401837172966 - type: cos_sim_spearman value: 76.13099867057305 - type: euclidean_pearson value: 75.56851096153042 - type: euclidean_spearman value: 76.13099867057305 - type: manhattan_pearson value: 75.4483276223799 - type: manhattan_spearman value: 75.96804558062843 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 85.0294369233462 - type: cos_sim_spearman value: 85.17543345937065 - type: euclidean_pearson value: 84.55546274084796 - type: euclidean_spearman value: 85.17543345937065 - type: manhattan_pearson value: 84.48053547013386 - type: manhattan_spearman value: 85.1543300887167 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 61.225097153955176 - type: cos_sim_spearman value: 60.16234340521003 - type: euclidean_pearson value: 62.59204214787284 - type: euclidean_spearman value: 60.16234340521003 - type: manhattan_pearson value: 62.17494761193987 - type: manhattan_spearman value: 59.80098747946264 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 78.76720129845401 - type: cos_sim_spearman value: 77.01581381977705 - type: euclidean_pearson value: 78.25405293225397 - type: euclidean_spearman value: 77.01581381977705 - type: manhattan_pearson value: 78.1737464440924 - type: manhattan_spearman value: 76.98020258619971 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 83.38429389881968 - type: mrr value: 94.92898441427853 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.306000000000004 - type: map_at_10 value: 49.913000000000004 - type: map_at_100 value: 50.965 - type: map_at_1000 value: 51.022999999999996 - type: map_at_3 value: 47.398 - type: map_at_5 value: 48.962 - type: mrr_at_1 value: 41.0 - type: mrr_at_10 value: 51.147 - type: mrr_at_100 value: 52.022 - type: mrr_at_1000 value: 52.073 - type: mrr_at_3 value: 48.888999999999996 - type: mrr_at_5 value: 50.239 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 55.033 - type: ndcg_at_100 value: 59.364 - type: ndcg_at_1000 value: 60.849 - type: ndcg_at_3 value: 50.159 - type: ndcg_at_5 value: 52.788999999999994 - type: precision_at_1 value: 41.0 - type: precision_at_10 value: 7.632999999999999 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 20.222 - type: precision_at_5 value: 13.667000000000002 - type: recall_at_1 value: 39.306000000000004 - type: recall_at_10 value: 69.45599999999999 - type: recall_at_100 value: 88.022 - type: recall_at_1000 value: 99.6 - type: recall_at_3 value: 56.27799999999999 - type: recall_at_5 value: 62.639 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.73960396039604 - type: cos_sim_ap value: 91.77061379171414 - type: cos_sim_f1 value: 86.49746192893402 - type: cos_sim_precision value: 87.83505154639175 - type: cos_sim_recall value: 85.2 - type: dot_accuracy value: 99.73960396039604 - type: dot_ap value: 91.77061379171414 - type: dot_f1 value: 86.49746192893402 - type: dot_precision value: 87.83505154639175 - type: dot_recall value: 85.2 - type: euclidean_accuracy value: 99.73960396039604 - type: euclidean_ap value: 91.77061379171414 - type: euclidean_f1 value: 86.49746192893402 - type: euclidean_precision value: 87.83505154639175 - type: euclidean_recall value: 85.2 - type: manhattan_accuracy value: 99.73861386138614 - type: manhattan_ap value: 91.73584684604442 - type: manhattan_f1 value: 86.41722193746797 - type: manhattan_precision value: 88.64353312302839 - type: manhattan_recall value: 84.3 - type: max_accuracy value: 99.73960396039604 - type: max_ap value: 91.77061379171414 - type: max_f1 value: 86.49746192893402 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 53.7931704300123 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.48651577951652 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 41.818447756127505 - type: mrr value: 42.1808155080214 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.799110359832028 - type: cos_sim_spearman value: 30.826213689865888 - type: dot_pearson value: 29.79911097173556 - type: dot_spearman value: 30.8964325010969 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.6100000000000003 - type: map_at_10 value: 10.317 - type: map_at_100 value: 16.651 - type: map_at_1000 value: 18.4 - type: map_at_3 value: 4.952999999999999 - type: map_at_5 value: 7.037 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 51.351 - type: mrr_at_100 value: 51.912000000000006 - type: mrr_at_1000 value: 51.912000000000006 - type: mrr_at_3 value: 46.599000000000004 - type: mrr_at_5 value: 49.762 - type: ndcg_at_1 value: 31.633 - type: ndcg_at_10 value: 27.601 - type: ndcg_at_100 value: 39.080999999999996 - type: ndcg_at_1000 value: 50.308 - type: ndcg_at_3 value: 30.020000000000003 - type: ndcg_at_5 value: 29.465999999999998 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 26.122 - type: precision_at_100 value: 8.530999999999999 - type: precision_at_1000 value: 1.5650000000000002 - type: precision_at_3 value: 31.973000000000003 - type: precision_at_5 value: 31.019999999999996 - type: recall_at_1 value: 2.6100000000000003 - type: recall_at_10 value: 17.166 - type: recall_at_100 value: 50.480999999999995 - type: recall_at_1000 value: 84.87599999999999 - type: recall_at_3 value: 6.026 - type: recall_at_5 value: 10.165000000000001 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 66.8218 - type: ap value: 11.906071313412117 - type: f1 value: 50.99103419180737 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 50.1188455008489 - type: f1 value: 50.19144196024773 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 33.550995025713995 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 79.29307981164689 - type: cos_sim_ap value: 48.474835734978406 - type: cos_sim_f1 value: 48.95389383959706 - type: cos_sim_precision value: 38.674625038261404 - type: cos_sim_recall value: 66.6754617414248 - type: dot_accuracy value: 79.29307981164689 - type: dot_ap value: 48.4748345893063 - type: dot_f1 value: 48.95389383959706 - type: dot_precision value: 38.674625038261404 - type: dot_recall value: 66.6754617414248 - type: euclidean_accuracy value: 79.29307981164689 - type: euclidean_ap value: 48.47484295524529 - type: euclidean_f1 value: 48.95389383959706 - type: euclidean_precision value: 38.674625038261404 - type: euclidean_recall value: 66.6754617414248 - type: manhattan_accuracy value: 79.34672468260118 - type: manhattan_ap value: 48.423218655778356 - type: manhattan_f1 value: 48.93181153058239 - type: manhattan_precision value: 38.81752050766135 - type: manhattan_recall value: 66.17414248021109 - type: max_accuracy value: 79.34672468260118 - type: max_ap value: 48.47484295524529 - type: max_f1 value: 48.95389383959706 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 86.65541196103544 - type: cos_sim_ap value: 80.9065470343605 - type: cos_sim_f1 value: 73.7394283267316 - type: cos_sim_precision value: 68.7196541403392 - type: cos_sim_recall value: 79.55035417308285 - type: dot_accuracy value: 86.65541196103544 - type: dot_ap value: 80.90654522467446 - type: dot_f1 value: 73.7394283267316 - type: dot_precision value: 68.7196541403392 - type: dot_recall value: 79.55035417308285 - type: euclidean_accuracy value: 86.65541196103544 - type: euclidean_ap value: 80.90654748736512 - type: euclidean_f1 value: 73.7394283267316 - type: euclidean_precision value: 68.7196541403392 - type: euclidean_recall value: 79.55035417308285 - type: manhattan_accuracy value: 86.61272169829627 - type: manhattan_ap value: 80.85801370403492 - type: manhattan_f1 value: 73.63878299647558 - type: manhattan_precision value: 69.0916452962613 - type: manhattan_recall value: 78.8266091777025 - type: max_accuracy value: 86.65541196103544 - type: max_ap value: 80.90654748736512 - type: max_f1 value: 73.7394283267316 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/gte_wl

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:26:07

2025-01-09T11:26:12

0

--- tags: - mteb model-index: - name: gte_wordllama_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 72.07462686567165 - type: ap value: 34.03639155919273 - type: f1 value: 65.69832537072352 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 69.453025 - type: ap value: 63.87884877644433 - type: f1 value: 69.23150048939367 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 36.364 - type: f1 value: 35.72067919658383 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 22.546 - type: map_at_10 value: 37.411 - type: map_at_100 value: 38.582 - type: map_at_1000 value: 38.597 - type: map_at_3 value: 32.492 - type: map_at_5 value: 35.141 - type: mrr_at_1 value: 23.186 - type: mrr_at_10 value: 37.651 - type: mrr_at_100 value: 38.822 - type: mrr_at_1000 value: 38.836999999999996 - type: mrr_at_3 value: 32.741 - type: mrr_at_5 value: 35.408 - type: ndcg_at_1 value: 22.546 - type: ndcg_at_10 value: 46.012 - type: ndcg_at_100 value: 51.197 - type: ndcg_at_1000 value: 51.547 - type: ndcg_at_3 value: 35.762 - type: ndcg_at_5 value: 40.567 - type: precision_at_1 value: 22.546 - type: precision_at_10 value: 7.367999999999999 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 15.078 - type: precision_at_5 value: 11.394 - type: recall_at_1 value: 22.546 - type: recall_at_10 value: 73.68400000000001 - type: recall_at_100 value: 96.799 - type: recall_at_1000 value: 99.431 - type: recall_at_3 value: 45.235 - type: recall_at_5 value: 56.97 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.643731613769525 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.63510872385387 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.581954717688454 - type: mrr value: 69.65857626522447 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.65184787408168 - type: cos_sim_spearman value: 76.59391391898701 - type: euclidean_pearson value: 78.27369147487082 - type: euclidean_spearman value: 76.59391391898701 - type: manhattan_pearson value: 78.35436546555296 - type: manhattan_spearman value: 76.41258448606804 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.67532467532469 - type: f1 value: 74.96407787263568 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.80818669258118 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 27.110794795227715 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 22.831000000000003 - type: map_at_10 value: 30.358 - type: map_at_100 value: 31.708 - type: map_at_1000 value: 31.857999999999997 - type: map_at_3 value: 27.721 - type: map_at_5 value: 29.054000000000002 - type: mrr_at_1 value: 29.041 - type: mrr_at_10 value: 36.405 - type: mrr_at_100 value: 37.358000000000004 - type: mrr_at_1000 value: 37.419999999999995 - type: mrr_at_3 value: 34.335 - type: mrr_at_5 value: 35.365 - type: ndcg_at_1 value: 29.041 - type: ndcg_at_10 value: 35.673 - type: ndcg_at_100 value: 41.432 - type: ndcg_at_1000 value: 44.372 - type: ndcg_at_3 value: 31.707 - type: ndcg_at_5 value: 33.147999999999996 - type: precision_at_1 value: 29.041 - type: precision_at_10 value: 6.895999999999999 - type: precision_at_100 value: 1.237 - type: precision_at_1000 value: 0.181 - type: precision_at_3 value: 15.212 - type: precision_at_5 value: 10.901 - type: recall_at_1 value: 22.831000000000003 - type: recall_at_10 value: 45.234 - type: recall_at_100 value: 70.658 - type: recall_at_1000 value: 90.70700000000001 - type: recall_at_3 value: 32.729 - type: recall_at_5 value: 37.242 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 18.834 - type: map_at_10 value: 25.796999999999997 - type: map_at_100 value: 26.881 - type: map_at_1000 value: 27.004 - type: map_at_3 value: 23.857999999999997 - type: map_at_5 value: 24.89 - type: mrr_at_1 value: 24.204 - type: mrr_at_10 value: 30.529 - type: mrr_at_100 value: 31.386999999999997 - type: mrr_at_1000 value: 31.456 - type: mrr_at_3 value: 28.715000000000003 - type: mrr_at_5 value: 29.658 - type: ndcg_at_1 value: 24.204 - type: ndcg_at_10 value: 30.053 - type: ndcg_at_100 value: 34.826 - type: ndcg_at_1000 value: 37.557 - type: ndcg_at_3 value: 26.927 - type: ndcg_at_5 value: 28.205999999999996 - type: precision_at_1 value: 24.204 - type: precision_at_10 value: 5.561 - type: precision_at_100 value: 1.011 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 12.994 - type: precision_at_5 value: 9.107999999999999 - type: recall_at_1 value: 18.834 - type: recall_at_10 value: 38.022 - type: recall_at_100 value: 58.587 - type: recall_at_1000 value: 76.953 - type: recall_at_3 value: 28.777 - type: recall_at_5 value: 32.372 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 28.138999999999996 - type: map_at_10 value: 37.378 - type: map_at_100 value: 38.576 - type: map_at_1000 value: 38.673 - type: map_at_3 value: 34.733000000000004 - type: map_at_5 value: 36.083999999999996 - type: mrr_at_1 value: 32.414 - type: mrr_at_10 value: 40.589999999999996 - type: mrr_at_100 value: 41.519 - type: mrr_at_1000 value: 41.577999999999996 - type: mrr_at_3 value: 38.213 - type: mrr_at_5 value: 39.428999999999995 - type: ndcg_at_1 value: 32.414 - type: ndcg_at_10 value: 42.501 - type: ndcg_at_100 value: 47.715 - type: ndcg_at_1000 value: 49.899 - type: ndcg_at_3 value: 37.595 - type: ndcg_at_5 value: 39.653 - type: precision_at_1 value: 32.414 - type: precision_at_10 value: 6.978 - type: precision_at_100 value: 1.054 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 16.761 - type: precision_at_5 value: 11.498 - type: recall_at_1 value: 28.138999999999996 - type: recall_at_10 value: 54.803999999999995 - type: recall_at_100 value: 77.648 - type: recall_at_1000 value: 93.545 - type: recall_at_3 value: 41.323 - type: recall_at_5 value: 46.489999999999995 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.864 - type: map_at_10 value: 18.775 - type: map_at_100 value: 19.706000000000003 - type: map_at_1000 value: 19.822 - type: map_at_3 value: 17.314 - type: map_at_5 value: 18.028 - type: mrr_at_1 value: 14.915000000000001 - type: mrr_at_10 value: 20.095 - type: mrr_at_100 value: 20.992 - type: mrr_at_1000 value: 21.092 - type: mrr_at_3 value: 18.587999999999997 - type: mrr_at_5 value: 19.271 - type: ndcg_at_1 value: 14.915000000000001 - type: ndcg_at_10 value: 21.811 - type: ndcg_at_100 value: 26.656000000000002 - type: ndcg_at_1000 value: 30.009000000000004 - type: ndcg_at_3 value: 18.790000000000003 - type: ndcg_at_5 value: 20.009 - type: precision_at_1 value: 14.915000000000001 - type: precision_at_10 value: 3.401 - type: precision_at_100 value: 0.623 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.06 - type: precision_at_5 value: 5.537 - type: recall_at_1 value: 13.864 - type: recall_at_10 value: 29.914 - type: recall_at_100 value: 52.580000000000005 - type: recall_at_1000 value: 78.648 - type: recall_at_3 value: 21.586 - type: recall_at_5 value: 24.58 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.223 - type: map_at_10 value: 12.272 - type: map_at_100 value: 13.252 - type: map_at_1000 value: 13.381000000000002 - type: map_at_3 value: 10.610999999999999 - type: map_at_5 value: 11.505 - type: mrr_at_1 value: 9.203999999999999 - type: mrr_at_10 value: 14.639 - type: mrr_at_100 value: 15.629000000000001 - type: mrr_at_1000 value: 15.733 - type: mrr_at_3 value: 12.852 - type: mrr_at_5 value: 13.797999999999998 - type: ndcg_at_1 value: 9.203999999999999 - type: ndcg_at_10 value: 15.543999999999999 - type: ndcg_at_100 value: 20.89 - type: ndcg_at_1000 value: 24.547 - type: ndcg_at_3 value: 12.264 - type: ndcg_at_5 value: 13.748 - type: precision_at_1 value: 9.203999999999999 - type: precision_at_10 value: 3.085 - type: precision_at_100 value: 0.688 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 6.095 - type: precision_at_5 value: 4.677 - type: recall_at_1 value: 7.223 - type: recall_at_10 value: 23.268 - type: recall_at_100 value: 47.452 - type: recall_at_1000 value: 74.69200000000001 - type: recall_at_3 value: 14.437 - type: recall_at_5 value: 18.007 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 19.661 - type: map_at_10 value: 26.145000000000003 - type: map_at_100 value: 27.477 - type: map_at_1000 value: 27.622999999999998 - type: map_at_3 value: 23.315 - type: map_at_5 value: 24.87 - type: mrr_at_1 value: 24.157999999999998 - type: mrr_at_10 value: 31.035 - type: mrr_at_100 value: 32.011 - type: mrr_at_1000 value: 32.086999999999996 - type: mrr_at_3 value: 28.199999999999996 - type: mrr_at_5 value: 29.769000000000002 - type: ndcg_at_1 value: 24.157999999999998 - type: ndcg_at_10 value: 31.249 - type: ndcg_at_100 value: 37.319 - type: ndcg_at_1000 value: 40.394999999999996 - type: ndcg_at_3 value: 26.184 - type: ndcg_at_5 value: 28.518 - type: precision_at_1 value: 24.157999999999998 - type: precision_at_10 value: 5.9479999999999995 - type: precision_at_100 value: 1.077 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 12.191 - type: precision_at_5 value: 9.142999999999999 - type: recall_at_1 value: 19.661 - type: recall_at_10 value: 41.959 - type: recall_at_100 value: 68.22399999999999 - type: recall_at_1000 value: 89.071 - type: recall_at_3 value: 27.617000000000004 - type: recall_at_5 value: 33.693 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.714 - type: map_at_10 value: 21.786 - type: map_at_100 value: 23.052 - type: map_at_1000 value: 23.186999999999998 - type: map_at_3 value: 19.286 - type: map_at_5 value: 20.699 - type: mrr_at_1 value: 19.064 - type: mrr_at_10 value: 25.576 - type: mrr_at_100 value: 26.613 - type: mrr_at_1000 value: 26.697 - type: mrr_at_3 value: 23.212 - type: mrr_at_5 value: 24.553 - type: ndcg_at_1 value: 19.064 - type: ndcg_at_10 value: 26.19 - type: ndcg_at_100 value: 32.019 - type: ndcg_at_1000 value: 35.323 - type: ndcg_at_3 value: 21.609 - type: ndcg_at_5 value: 23.747 - type: precision_at_1 value: 19.064 - type: precision_at_10 value: 5.045999999999999 - type: precision_at_100 value: 0.947 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.16 - type: precision_at_5 value: 7.693999999999999 - type: recall_at_1 value: 15.714 - type: recall_at_10 value: 35.846000000000004 - type: recall_at_100 value: 60.885 - type: recall_at_1000 value: 84.437 - type: recall_at_3 value: 23.357 - type: recall_at_5 value: 28.698 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.797416666666667 - type: map_at_10 value: 21.674916666666668 - type: map_at_100 value: 22.73633333333333 - type: map_at_1000 value: 22.868583333333333 - type: map_at_3 value: 19.66508333333333 - type: map_at_5 value: 20.75133333333333 - type: mrr_at_1 value: 19.052333333333333 - type: mrr_at_10 value: 24.958083333333335 - type: mrr_at_100 value: 25.862666666666666 - type: mrr_at_1000 value: 25.95 - type: mrr_at_3 value: 23.02525 - type: mrr_at_5 value: 24.053166666666666 - type: ndcg_at_1 value: 19.052333333333333 - type: ndcg_at_10 value: 25.618249999999996 - type: ndcg_at_100 value: 30.751666666666665 - type: ndcg_at_1000 value: 33.93783333333333 - type: ndcg_at_3 value: 21.966166666666666 - type: ndcg_at_5 value: 23.569333333333333 - type: precision_at_1 value: 19.052333333333333 - type: precision_at_10 value: 4.6321666666666665 - type: precision_at_100 value: 0.8673333333333333 - type: precision_at_1000 value: 0.13283333333333333 - type: precision_at_3 value: 10.15075 - type: precision_at_5 value: 7.330416666666667 - type: recall_at_1 value: 15.797416666666667 - type: recall_at_10 value: 34.28000000000001 - type: recall_at_100 value: 57.498416666666664 - type: recall_at_1000 value: 80.52425000000001 - type: recall_at_3 value: 23.929416666666665 - type: recall_at_5 value: 28.09466666666667 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.323 - type: map_at_10 value: 17.07 - type: map_at_100 value: 17.849999999999998 - type: map_at_1000 value: 17.957 - type: map_at_3 value: 15.414 - type: map_at_5 value: 16.431 - type: mrr_at_1 value: 13.497 - type: mrr_at_10 value: 19.188 - type: mrr_at_100 value: 19.978 - type: mrr_at_1000 value: 20.071 - type: mrr_at_3 value: 17.663999999999998 - type: mrr_at_5 value: 18.538 - type: ndcg_at_1 value: 13.497 - type: ndcg_at_10 value: 20.485999999999997 - type: ndcg_at_100 value: 24.855 - type: ndcg_at_1000 value: 27.773999999999997 - type: ndcg_at_3 value: 17.399 - type: ndcg_at_5 value: 18.988 - type: precision_at_1 value: 13.497 - type: precision_at_10 value: 3.5740000000000003 - type: precision_at_100 value: 0.63 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 8.129 - type: precision_at_5 value: 5.92 - type: recall_at_1 value: 11.323 - type: recall_at_10 value: 28.92 - type: recall_at_100 value: 49.75 - type: recall_at_1000 value: 71.492 - type: recall_at_3 value: 20.452 - type: recall_at_5 value: 24.346 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.625 - type: map_at_10 value: 12.41 - type: map_at_100 value: 13.200999999999999 - type: map_at_1000 value: 13.333999999999998 - type: map_at_3 value: 11.141 - type: map_at_5 value: 11.776 - type: mrr_at_1 value: 10.805 - type: mrr_at_10 value: 14.979999999999999 - type: mrr_at_100 value: 15.759 - type: mrr_at_1000 value: 15.867 - type: mrr_at_3 value: 13.569999999999999 - type: mrr_at_5 value: 14.316 - type: ndcg_at_1 value: 10.805 - type: ndcg_at_10 value: 15.129999999999999 - type: ndcg_at_100 value: 19.339000000000002 - type: ndcg_at_1000 value: 23.034 - type: ndcg_at_3 value: 12.661 - type: ndcg_at_5 value: 13.664000000000001 - type: precision_at_1 value: 10.805 - type: precision_at_10 value: 2.88 - type: precision_at_100 value: 0.5950000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.091 - type: precision_at_5 value: 4.4319999999999995 - type: recall_at_1 value: 8.625 - type: recall_at_10 value: 20.924 - type: recall_at_100 value: 40.343 - type: recall_at_1000 value: 67.60199999999999 - type: recall_at_3 value: 13.963000000000001 - type: recall_at_5 value: 16.558999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 15.116999999999999 - type: map_at_10 value: 20.283 - type: map_at_100 value: 21.181 - type: map_at_1000 value: 21.318 - type: map_at_3 value: 18.528 - type: map_at_5 value: 19.506 - type: mrr_at_1 value: 17.91 - type: mrr_at_10 value: 23.399 - type: mrr_at_100 value: 24.254 - type: mrr_at_1000 value: 24.36 - type: mrr_at_3 value: 21.502 - type: mrr_at_5 value: 22.617 - type: ndcg_at_1 value: 17.91 - type: ndcg_at_10 value: 23.848 - type: ndcg_at_100 value: 28.63 - type: ndcg_at_1000 value: 32.236 - type: ndcg_at_3 value: 20.351 - type: ndcg_at_5 value: 21.992 - type: precision_at_1 value: 17.91 - type: precision_at_10 value: 4.011 - type: precision_at_100 value: 0.722 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 9.049 - type: precision_at_5 value: 6.455 - type: recall_at_1 value: 15.116999999999999 - type: recall_at_10 value: 31.911 - type: recall_at_100 value: 53.791999999999994 - type: recall_at_1000 value: 79.997 - type: recall_at_3 value: 22.229 - type: recall_at_5 value: 26.366 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.415999999999999 - type: map_at_10 value: 21.364 - type: map_at_100 value: 22.631 - type: map_at_1000 value: 22.832 - type: map_at_3 value: 19.139999999999997 - type: map_at_5 value: 20.549 - type: mrr_at_1 value: 19.368 - type: mrr_at_10 value: 25.218 - type: mrr_at_100 value: 26.135 - type: mrr_at_1000 value: 26.218999999999998 - type: mrr_at_3 value: 23.155 - type: mrr_at_5 value: 24.371000000000002 - type: ndcg_at_1 value: 19.368 - type: ndcg_at_10 value: 25.715 - type: ndcg_at_100 value: 31.291999999999998 - type: ndcg_at_1000 value: 34.757 - type: ndcg_at_3 value: 22.131999999999998 - type: ndcg_at_5 value: 24.018 - type: precision_at_1 value: 19.368 - type: precision_at_10 value: 5.138 - type: precision_at_100 value: 1.229 - type: precision_at_1000 value: 0.209 - type: precision_at_3 value: 10.474 - type: precision_at_5 value: 7.904999999999999 - type: recall_at_1 value: 15.415999999999999 - type: recall_at_10 value: 33.83 - type: recall_at_100 value: 60.19799999999999 - type: recall_at_1000 value: 83.88600000000001 - type: recall_at_3 value: 23.018 - type: recall_at_5 value: 28.37 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.822 - type: map_at_10 value: 16.461000000000002 - type: map_at_100 value: 17.321 - type: map_at_1000 value: 17.434 - type: map_at_3 value: 14.92 - type: map_at_5 value: 15.623999999999999 - type: mrr_at_1 value: 14.048 - type: mrr_at_10 value: 17.843 - type: mrr_at_100 value: 18.717 - type: mrr_at_1000 value: 18.82 - type: mrr_at_3 value: 16.297 - type: mrr_at_5 value: 16.953 - type: ndcg_at_1 value: 14.048 - type: ndcg_at_10 value: 19.219 - type: ndcg_at_100 value: 24.047 - type: ndcg_at_1000 value: 27.351 - type: ndcg_at_3 value: 15.975 - type: ndcg_at_5 value: 17.141000000000002 - type: precision_at_1 value: 14.048 - type: precision_at_10 value: 3.068 - type: precision_at_100 value: 0.5950000000000001 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 6.593 - type: precision_at_5 value: 4.695 - type: recall_at_1 value: 12.822 - type: recall_at_10 value: 26.728 - type: recall_at_100 value: 49.864000000000004 - type: recall_at_1000 value: 75.261 - type: recall_at_3 value: 17.665 - type: recall_at_5 value: 20.413 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 8.301 - type: map_at_10 value: 14.709 - type: map_at_100 value: 16.396 - type: map_at_1000 value: 16.606 - type: map_at_3 value: 11.987 - type: map_at_5 value: 13.401 - type: mrr_at_1 value: 19.088 - type: mrr_at_10 value: 29.421999999999997 - type: mrr_at_100 value: 30.517 - type: mrr_at_1000 value: 30.568 - type: mrr_at_3 value: 25.646 - type: mrr_at_5 value: 27.897 - type: ndcg_at_1 value: 19.088 - type: ndcg_at_10 value: 21.851000000000003 - type: ndcg_at_100 value: 29.093999999999998 - type: ndcg_at_1000 value: 33.101 - type: ndcg_at_3 value: 16.862 - type: ndcg_at_5 value: 18.790000000000003 - type: precision_at_1 value: 19.088 - type: precision_at_10 value: 7.244000000000001 - type: precision_at_100 value: 1.496 - type: precision_at_1000 value: 0.22300000000000003 - type: precision_at_3 value: 12.812000000000001 - type: precision_at_5 value: 10.41 - type: recall_at_1 value: 8.301 - type: recall_at_10 value: 27.49 - type: recall_at_100 value: 52.937999999999995 - type: recall_at_1000 value: 75.79599999999999 - type: recall_at_3 value: 15.603 - type: recall_at_5 value: 20.612 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.576 - type: map_at_10 value: 11.394 - type: map_at_100 value: 16.276 - type: map_at_1000 value: 17.459 - type: map_at_3 value: 8.269 - type: map_at_5 value: 9.711 - type: mrr_at_1 value: 47.25 - type: mrr_at_10 value: 57.201 - type: mrr_at_100 value: 57.727 - type: mrr_at_1000 value: 57.751 - type: mrr_at_3 value: 54.458 - type: mrr_at_5 value: 56.421 - type: ndcg_at_1 value: 35.25 - type: ndcg_at_10 value: 26.617 - type: ndcg_at_100 value: 30.952 - type: ndcg_at_1000 value: 38.287 - type: ndcg_at_3 value: 29.814 - type: ndcg_at_5 value: 28.436 - type: precision_at_1 value: 47.25 - type: precision_at_10 value: 23.175 - type: precision_at_100 value: 7.6450000000000005 - type: precision_at_1000 value: 1.624 - type: precision_at_3 value: 35.667 - type: precision_at_5 value: 30.65 - type: recall_at_1 value: 5.576 - type: recall_at_10 value: 15.804000000000002 - type: recall_at_100 value: 38.086 - type: recall_at_1000 value: 63.034 - type: recall_at_3 value: 9.407 - type: recall_at_5 value: 12.247 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.21 - type: f1 value: 43.021356364911156 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 17.775 - type: map_at_10 value: 27.131 - type: map_at_100 value: 28.186 - type: map_at_1000 value: 28.255999999999997 - type: map_at_3 value: 24.198 - type: map_at_5 value: 25.907000000000004 - type: mrr_at_1 value: 19.006999999999998 - type: mrr_at_10 value: 28.769 - type: mrr_at_100 value: 29.809 - type: mrr_at_1000 value: 29.866 - type: mrr_at_3 value: 25.773000000000003 - type: mrr_at_5 value: 27.51 - type: ndcg_at_1 value: 19.006999999999998 - type: ndcg_at_10 value: 32.698 - type: ndcg_at_100 value: 37.891999999999996 - type: ndcg_at_1000 value: 39.728 - type: ndcg_at_3 value: 26.680999999999997 - type: ndcg_at_5 value: 29.73 - type: precision_at_1 value: 19.006999999999998 - type: precision_at_10 value: 5.2909999999999995 - type: precision_at_100 value: 0.8049999999999999 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 11.616 - type: precision_at_5 value: 8.554 - type: recall_at_1 value: 17.775 - type: recall_at_10 value: 48.603 - type: recall_at_100 value: 72.465 - type: recall_at_1000 value: 86.509 - type: recall_at_3 value: 32.26 - type: recall_at_5 value: 39.589999999999996 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 8.584 - type: map_at_10 value: 13.774000000000001 - type: map_at_100 value: 15.247 - type: map_at_1000 value: 15.468000000000002 - type: map_at_3 value: 11.779 - type: map_at_5 value: 12.732 - type: mrr_at_1 value: 16.512 - type: mrr_at_10 value: 23.016000000000002 - type: mrr_at_100 value: 24.276 - type: mrr_at_1000 value: 24.362000000000002 - type: mrr_at_3 value: 20.756 - type: mrr_at_5 value: 21.852 - type: ndcg_at_1 value: 16.512 - type: ndcg_at_10 value: 18.604000000000003 - type: ndcg_at_100 value: 25.298 - type: ndcg_at_1000 value: 29.803 - type: ndcg_at_3 value: 15.790000000000001 - type: ndcg_at_5 value: 16.614 - type: precision_at_1 value: 16.512 - type: precision_at_10 value: 5.293 - type: precision_at_100 value: 1.17 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 10.237 - type: precision_at_5 value: 7.7780000000000005 - type: recall_at_1 value: 8.584 - type: recall_at_10 value: 23.685000000000002 - type: recall_at_100 value: 49.461 - type: recall_at_1000 value: 76.972 - type: recall_at_3 value: 14.657 - type: recall_at_5 value: 17.861 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 19.662 - type: map_at_10 value: 28.195999999999998 - type: map_at_100 value: 29.21 - type: map_at_1000 value: 29.322 - type: map_at_3 value: 25.852999999999998 - type: map_at_5 value: 27.121000000000002 - type: mrr_at_1 value: 39.324999999999996 - type: mrr_at_10 value: 47.083999999999996 - type: mrr_at_100 value: 47.805 - type: mrr_at_1000 value: 47.853 - type: mrr_at_3 value: 44.913 - type: mrr_at_5 value: 46.132 - type: ndcg_at_1 value: 39.324999999999996 - type: ndcg_at_10 value: 35.766999999999996 - type: ndcg_at_100 value: 40.306 - type: ndcg_at_1000 value: 42.870000000000005 - type: ndcg_at_3 value: 31.395 - type: ndcg_at_5 value: 33.469 - type: precision_at_1 value: 39.324999999999996 - type: precision_at_10 value: 7.933999999999999 - type: precision_at_100 value: 1.157 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 19.855999999999998 - type: precision_at_5 value: 13.556000000000001 - type: recall_at_1 value: 19.662 - type: recall_at_10 value: 39.669 - type: recall_at_100 value: 57.833 - type: recall_at_1000 value: 74.929 - type: recall_at_3 value: 29.784 - type: recall_at_5 value: 33.889 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 68.03079999999999 - type: ap value: 62.45465282637356 - type: f1 value: 67.82133366706746 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.297 - type: map_at_10 value: 12.847 - type: map_at_100 value: 13.872000000000002 - type: map_at_1000 value: 13.987 - type: map_at_3 value: 10.741 - type: map_at_5 value: 11.838999999999999 - type: mrr_at_1 value: 7.536 - type: mrr_at_10 value: 13.157 - type: mrr_at_100 value: 14.184 - type: mrr_at_1000 value: 14.295 - type: mrr_at_3 value: 11.020000000000001 - type: mrr_at_5 value: 12.133 - type: ndcg_at_1 value: 7.507 - type: ndcg_at_10 value: 16.374 - type: ndcg_at_100 value: 22.039 - type: ndcg_at_1000 value: 25.380999999999997 - type: ndcg_at_3 value: 11.935 - type: ndcg_at_5 value: 13.919999999999998 - type: precision_at_1 value: 7.507 - type: precision_at_10 value: 2.8449999999999998 - type: precision_at_100 value: 0.581 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 5.191 - type: precision_at_5 value: 4.112 - type: recall_at_1 value: 7.297 - type: recall_at_10 value: 27.450999999999997 - type: recall_at_100 value: 55.215 - type: recall_at_1000 value: 81.878 - type: recall_at_3 value: 15.143 - type: recall_at_5 value: 19.922 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.23347013223893 - type: f1 value: 90.37745005574784 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 60.43775649794802 - type: f1 value: 41.826394298669705 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 65.53799596503026 - type: f1 value: 63.37514998537075 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.92535305985206 - type: f1 value: 72.01043365342854 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.093053205851135 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.838169401102558 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.012335830272843 - type: mrr value: 32.04656357642063 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.865 - type: map_at_10 value: 9.599 - type: map_at_100 value: 12.466000000000001 - type: map_at_1000 value: 13.935 - type: map_at_3 value: 7.260999999999999 - type: map_at_5 value: 8.526 - type: mrr_at_1 value: 38.080000000000005 - type: mrr_at_10 value: 47.695 - type: mrr_at_100 value: 48.304 - type: mrr_at_1000 value: 48.351 - type: mrr_at_3 value: 45.098 - type: mrr_at_5 value: 46.569 - type: ndcg_at_1 value: 36.223 - type: ndcg_at_10 value: 28.582 - type: ndcg_at_100 value: 27.229 - type: ndcg_at_1000 value: 36.643 - type: ndcg_at_3 value: 32.653 - type: ndcg_at_5 value: 31.215 - type: precision_at_1 value: 38.080000000000005 - type: precision_at_10 value: 21.207 - type: precision_at_100 value: 7.498 - type: precision_at_1000 value: 2.104 - type: precision_at_3 value: 30.65 - type: precision_at_5 value: 27.059 - type: recall_at_1 value: 4.865 - type: recall_at_10 value: 13.614 - type: recall_at_100 value: 29.659999999999997 - type: recall_at_1000 value: 63.172 - type: recall_at_3 value: 8.248 - type: recall_at_5 value: 10.684000000000001 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.581 - type: map_at_10 value: 18.221 - type: map_at_100 value: 19.637999999999998 - type: map_at_1000 value: 19.737 - type: map_at_3 value: 15.341 - type: map_at_5 value: 16.943 - type: mrr_at_1 value: 12.051 - type: mrr_at_10 value: 20.102 - type: mrr_at_100 value: 21.385 - type: mrr_at_1000 value: 21.465 - type: mrr_at_3 value: 17.159 - type: mrr_at_5 value: 18.851000000000003 - type: ndcg_at_1 value: 12.051 - type: ndcg_at_10 value: 23.267 - type: ndcg_at_100 value: 30.211 - type: ndcg_at_1000 value: 32.878 - type: ndcg_at_3 value: 17.354 - type: ndcg_at_5 value: 20.247999999999998 - type: precision_at_1 value: 12.051 - type: precision_at_10 value: 4.356999999999999 - type: precision_at_100 value: 0.827 - type: precision_at_1000 value: 0.108 - type: precision_at_3 value: 8.266 - type: precision_at_5 value: 6.553000000000001 - type: recall_at_1 value: 10.581 - type: recall_at_10 value: 37.119 - type: recall_at_100 value: 68.89699999999999 - type: recall_at_1000 value: 89.354 - type: recall_at_3 value: 21.404999999999998 - type: recall_at_5 value: 28.194000000000003 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 66.119 - type: map_at_10 value: 79.611 - type: map_at_100 value: 80.354 - type: map_at_1000 value: 80.38 - type: map_at_3 value: 76.606 - type: map_at_5 value: 78.485 - type: mrr_at_1 value: 76.12 - type: mrr_at_10 value: 83.328 - type: mrr_at_100 value: 83.499 - type: mrr_at_1000 value: 83.502 - type: mrr_at_3 value: 82.00699999999999 - type: mrr_at_5 value: 82.89699999999999 - type: ndcg_at_1 value: 76.22 - type: ndcg_at_10 value: 84.051 - type: ndcg_at_100 value: 85.797 - type: ndcg_at_1000 value: 86.007 - type: ndcg_at_3 value: 80.646 - type: ndcg_at_5 value: 82.50800000000001 - type: precision_at_1 value: 76.22 - type: precision_at_10 value: 12.76 - type: precision_at_100 value: 1.5010000000000001 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.160000000000004 - type: precision_at_5 value: 23.264000000000003 - type: recall_at_1 value: 66.119 - type: recall_at_10 value: 92.664 - type: recall_at_100 value: 98.863 - type: recall_at_1000 value: 99.91 - type: recall_at_3 value: 82.994 - type: recall_at_5 value: 88.119 - type: map_at_1 value: 3.2680000000000002 - type: map_at_10 value: 8.579 - type: map_at_100 value: 10.421999999999999 - type: map_at_1000 value: 10.737 - type: map_at_3 value: 6.0040000000000004 - type: map_at_5 value: 7.26 - type: mrr_at_1 value: 16.0 - type: mrr_at_10 value: 26.185000000000002 - type: mrr_at_100 value: 27.439000000000004 - type: mrr_at_1000 value: 27.511999999999997 - type: mrr_at_3 value: 22.917 - type: mrr_at_5 value: 24.642 - type: ndcg_at_1 value: 16.0 - type: ndcg_at_10 value: 15.232000000000001 - type: ndcg_at_100 value: 23.047 - type: ndcg_at_1000 value: 28.774 - type: ndcg_at_3 value: 13.834 - type: ndcg_at_5 value: 12.304 - type: precision_at_1 value: 16.0 - type: precision_at_10 value: 8.19 - type: precision_at_100 value: 1.958 - type: precision_at_1000 value: 0.333 - type: precision_at_3 value: 13.167000000000002 - type: precision_at_5 value: 11.06 - type: recall_at_1 value: 3.2680000000000002 - type: recall_at_10 value: 16.563 - type: recall_at_100 value: 39.708 - type: recall_at_1000 value: 67.60199999999999 - type: recall_at_3 value: 8.018 - type: recall_at_5 value: 11.193 - type: map_at_1 value: 0.161 - type: map_at_10 value: 1.171 - type: map_at_100 value: 6.306000000000001 - type: map_at_1000 value: 16.732 - type: map_at_3 value: 0.432 - type: map_at_5 value: 0.645 - type: mrr_at_1 value: 57.99999999999999 - type: mrr_at_10 value: 72.32499999999999 - type: mrr_at_100 value: 72.458 - type: mrr_at_1000 value: 72.458 - type: mrr_at_3 value: 69.667 - type: mrr_at_5 value: 71.56700000000001 - type: ndcg_at_1 value: 53.0 - type: ndcg_at_10 value: 52.207 - type: ndcg_at_100 value: 40.717 - type: ndcg_at_1000 value: 38.254 - type: ndcg_at_3 value: 57.553 - type: ndcg_at_5 value: 53.795 - type: precision_at_1 value: 60.0 - type: precision_at_10 value: 56.599999999999994 - type: precision_at_100 value: 42.84 - type: precision_at_1000 value: 18.386 - type: precision_at_3 value: 63.333 - type: precision_at_5 value: 57.99999999999999 - type: recall_at_1 value: 0.161 - type: recall_at_10 value: 1.434 - type: recall_at_100 value: 9.454 - type: recall_at_1000 value: 37.175000000000004 - type: recall_at_3 value: 0.477 - type: recall_at_5 value: 0.735 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 43.342566470284666 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.11469484366251 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 78.76771912274579 - type: cos_sim_spearman value: 68.21965433585433 - type: euclidean_pearson value: 73.41725536408647 - type: euclidean_spearman value: 68.21970849513703 - type: manhattan_pearson value: 73.07310010299138 - type: manhattan_spearman value: 68.02842343011922 - task: type: STS dataset: name: MTEB STS12 type: None config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 77.24856339472711 - type: cos_sim_spearman value: 68.13233535199409 - type: euclidean_pearson value: 72.83173400932682 - type: euclidean_spearman value: 68.13353961544857 - type: manhattan_pearson value: 72.364020033214 - type: manhattan_spearman value: 67.96817473009628 - task: type: STS dataset: name: MTEB STS13 type: None config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 78.11822706559114 - type: cos_sim_spearman value: 78.82692788488787 - type: euclidean_pearson value: 78.42176146428962 - type: euclidean_spearman value: 78.82696569079468 - type: manhattan_pearson value: 77.94207608371939 - type: manhattan_spearman value: 78.30672557882981 - task: type: STS dataset: name: MTEB STS14 type: None config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 79.37520382719511 - type: cos_sim_spearman value: 75.09236770903914 - type: euclidean_pearson value: 77.94076407783429 - type: euclidean_spearman value: 75.0923580173567 - type: manhattan_pearson value: 77.739191296084 - type: manhattan_spearman value: 74.9480210937594 - task: type: STS dataset: name: MTEB STS15 type: None config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 82.9584878497231 - type: cos_sim_spearman value: 83.58865804953194 - type: euclidean_pearson value: 83.32064366874845 - type: euclidean_spearman value: 83.58865650778534 - type: manhattan_pearson value: 83.17898835151296 - type: manhattan_spearman value: 83.45146824277634 - task: type: STS dataset: name: MTEB STS16 type: None config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.40206220271352 - type: cos_sim_spearman value: 78.18587292841029 - type: euclidean_pearson value: 77.63109474603048 - type: euclidean_spearman value: 78.18586561703366 - type: manhattan_pearson value: 77.56336963431791 - type: manhattan_spearman value: 78.13426002359485 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.28987235462407 - type: cos_sim_spearman value: 86.91762382232156 - type: euclidean_pearson value: 86.05340443036164 - type: euclidean_spearman value: 86.91849630883524 - type: manhattan_pearson value: 85.98189959096196 - type: manhattan_spearman value: 86.94471215865201 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 61.248533592592025 - type: cos_sim_spearman value: 61.25674726411208 - type: euclidean_pearson value: 62.668232482670724 - type: euclidean_spearman value: 61.25674726411208 - type: manhattan_pearson value: 62.217580952381915 - type: manhattan_spearman value: 60.77021894786932 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.84077621570408 - type: cos_sim_spearman value: 79.26302777438052 - type: euclidean_pearson value: 80.5028036765331 - type: euclidean_spearman value: 79.26304623849835 - type: manhattan_pearson value: 80.45325721545979 - type: manhattan_spearman value: 79.22021810584245 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.71971528163719 - type: mrr value: 94.15308003543299 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 36.611 - type: map_at_10 value: 46.424 - type: map_at_100 value: 47.347 - type: map_at_1000 value: 47.404 - type: map_at_3 value: 43.153000000000006 - type: map_at_5 value: 45.024 - type: mrr_at_1 value: 39.0 - type: mrr_at_10 value: 48.423 - type: mrr_at_100 value: 49.126 - type: mrr_at_1000 value: 49.179 - type: mrr_at_3 value: 45.389 - type: mrr_at_5 value: 47.221999999999994 - type: ndcg_at_1 value: 39.0 - type: ndcg_at_10 value: 52.142999999999994 - type: ndcg_at_100 value: 56.606 - type: ndcg_at_1000 value: 57.894 - type: ndcg_at_3 value: 45.611000000000004 - type: ndcg_at_5 value: 48.85 - type: precision_at_1 value: 39.0 - type: precision_at_10 value: 7.467 - type: precision_at_100 value: 1.0030000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 18.111 - type: precision_at_5 value: 12.6 - type: recall_at_1 value: 36.611 - type: recall_at_10 value: 68.289 - type: recall_at_100 value: 89.267 - type: recall_at_1000 value: 98.867 - type: recall_at_3 value: 50.471999999999994 - type: recall_at_5 value: 58.289 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72475247524753 - type: cos_sim_ap value: 92.0612887387195 - type: cos_sim_f1 value: 85.78528827037775 - type: cos_sim_precision value: 85.27667984189723 - type: cos_sim_recall value: 86.3 - type: dot_accuracy value: 99.72475247524753 - type: dot_ap value: 92.0612887387195 - type: dot_f1 value: 85.78528827037775 - type: dot_precision value: 85.27667984189723 - type: dot_recall value: 86.3 - type: euclidean_accuracy value: 99.72475247524753 - type: euclidean_ap value: 92.0612887387195 - type: euclidean_f1 value: 85.78528827037775 - type: euclidean_precision value: 85.27667984189723 - type: euclidean_recall value: 86.3 - type: manhattan_accuracy value: 99.72475247524753 - type: manhattan_ap value: 92.11384029855155 - type: manhattan_f1 value: 85.75595527467186 - type: manhattan_precision value: 83.44370860927152 - type: manhattan_recall value: 88.2 - type: max_accuracy value: 99.72475247524753 - type: max_ap value: 92.11384029855155 - type: max_f1 value: 85.78528827037775 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 51.43694167734459 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.99750013836291 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 44.11670648850121 - type: mrr value: 44.651265809354044 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.82538139718491 - type: cos_sim_spearman value: 30.223708279486612 - type: dot_pearson value: 29.8253813971849 - type: dot_spearman value: 30.26388644272319 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.144 - type: map_at_10 value: 8.538 - type: map_at_100 value: 14.526 - type: map_at_1000 value: 16.253 - type: map_at_3 value: 3.721 - type: map_at_5 value: 5.979 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 41.553000000000004 - type: mrr_at_100 value: 42.672 - type: mrr_at_1000 value: 42.672 - type: mrr_at_3 value: 35.714 - type: mrr_at_5 value: 40.306 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 21.421 - type: ndcg_at_100 value: 35.417 - type: ndcg_at_1000 value: 47.281 - type: ndcg_at_3 value: 20.107 - type: ndcg_at_5 value: 23.012 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 21.02 - type: precision_at_100 value: 8.245 - type: precision_at_1000 value: 1.608 - type: precision_at_3 value: 22.448999999999998 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.144 - type: recall_at_10 value: 15.318999999999999 - type: recall_at_100 value: 50.608 - type: recall_at_1000 value: 86.652 - type: recall_at_3 value: 4.65 - type: recall_at_5 value: 9.286 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 76.1994 - type: ap value: 17.166874536029024 - type: f1 value: 58.91563395048056 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.56140350877194 - type: f1 value: 59.83462102375279 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 42.717753205468256 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 82.51177206890385 - type: cos_sim_ap value: 61.880585258206324 - type: cos_sim_f1 value: 59.29389759176994 - type: cos_sim_precision value: 53.232577665827044 - type: cos_sim_recall value: 66.91292875989447 - type: dot_accuracy value: 82.51177206890385 - type: dot_ap value: 61.880585258206324 - type: dot_f1 value: 59.29389759176994 - type: dot_precision value: 53.232577665827044 - type: dot_recall value: 66.91292875989447 - type: euclidean_accuracy value: 82.51177206890385 - type: euclidean_ap value: 61.880585258206324 - type: euclidean_f1 value: 59.29389759176994 - type: euclidean_precision value: 53.232577665827044 - type: euclidean_recall value: 66.91292875989447 - type: manhattan_accuracy value: 82.41044286821243 - type: manhattan_ap value: 61.69366003781778 - type: manhattan_f1 value: 59.267976933035186 - type: manhattan_precision value: 53.494794986190776 - type: manhattan_recall value: 66.43799472295514 - type: max_accuracy value: 82.51177206890385 - type: max_ap value: 61.880585258206324 - type: max_f1 value: 59.29389759176994 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.58683587534443 - type: cos_sim_ap value: 83.41906537158532 - type: cos_sim_f1 value: 75.80436150912658 - type: cos_sim_precision value: 73.01191070537052 - type: cos_sim_recall value: 78.81890976285803 - type: dot_accuracy value: 87.58683587534443 - type: dot_ap value: 83.41906537158532 - type: dot_f1 value: 75.80436150912658 - type: dot_precision value: 73.01191070537052 - type: dot_recall value: 78.81890976285803 - type: euclidean_accuracy value: 87.58683587534443 - type: euclidean_ap value: 83.41906537158532 - type: euclidean_f1 value: 75.80436150912658 - type: euclidean_precision value: 73.01191070537052 - type: euclidean_recall value: 78.81890976285803 - type: manhattan_accuracy value: 87.55190747855785 - type: manhattan_ap value: 83.37075875688966 - type: manhattan_f1 value: 75.71862755868028 - type: manhattan_precision value: 72.19467914251798 - type: manhattan_recall value: 79.60425007699415 - type: max_accuracy value: 87.58683587534443 - type: max_ap value: 83.41906537158532 - type: max_f1 value: 75.80436150912658 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/GTE_wl_mv

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:26:26

2025-01-09T11:26:30

0

--- tags: - mteb model-index: - name: gte_WORDLLAMA_MODEL2VEC_result results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.13432835820896 - type: ap value: 35.167459200441506 - type: f1 value: 66.74544259725131 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 71.5158 - type: ap value: 65.87290139797425 - type: f1 value: 71.31117308043078 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 37.032 - type: f1 value: 36.34554421029957 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 23.541999999999998 - type: map_at_10 value: 38.172 - type: map_at_100 value: 39.339 - type: map_at_1000 value: 39.353 - type: map_at_3 value: 33.286 - type: map_at_5 value: 35.942 - type: mrr_at_1 value: 24.253 - type: mrr_at_10 value: 38.423 - type: mrr_at_100 value: 39.589 - type: mrr_at_1000 value: 39.604 - type: mrr_at_3 value: 33.559 - type: mrr_at_5 value: 36.169000000000004 - type: ndcg_at_1 value: 23.541999999999998 - type: ndcg_at_10 value: 46.660000000000004 - type: ndcg_at_100 value: 51.800999999999995 - type: ndcg_at_1000 value: 52.147 - type: ndcg_at_3 value: 36.498000000000005 - type: ndcg_at_5 value: 41.309000000000005 - type: precision_at_1 value: 23.541999999999998 - type: precision_at_10 value: 7.396999999999999 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.268 - type: precision_at_5 value: 11.508000000000001 - type: recall_at_1 value: 23.541999999999998 - type: recall_at_10 value: 73.969 - type: recall_at_100 value: 96.871 - type: recall_at_1000 value: 99.502 - type: recall_at_3 value: 45.804 - type: recall_at_5 value: 57.538999999999994 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.8392617925804 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.39147233524174 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.43457632808065 - type: mrr value: 69.7011168271556 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 79.40924171268267 - type: cos_sim_spearman value: 76.48728498335026 - type: euclidean_pearson value: 78.11322656013188 - type: euclidean_spearman value: 76.48728498335026 - type: manhattan_pearson value: 78.39882365124392 - type: manhattan_spearman value: 76.55837094044142 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.63311688311688 - type: f1 value: 74.89031278068427 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 34.47759744268641 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 26.72176842867392 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 21.918000000000003 - type: map_at_10 value: 29.912 - type: map_at_100 value: 31.205 - type: map_at_1000 value: 31.357000000000003 - type: map_at_3 value: 27.206000000000003 - type: map_at_5 value: 28.613 - type: mrr_at_1 value: 27.897 - type: mrr_at_10 value: 35.921 - type: mrr_at_100 value: 36.825 - type: mrr_at_1000 value: 36.894 - type: mrr_at_3 value: 33.858 - type: mrr_at_5 value: 34.881 - type: ndcg_at_1 value: 27.897 - type: ndcg_at_10 value: 35.306 - type: ndcg_at_100 value: 40.955999999999996 - type: ndcg_at_1000 value: 43.909 - type: ndcg_at_3 value: 31.422 - type: ndcg_at_5 value: 32.89 - type: precision_at_1 value: 27.897 - type: precision_at_10 value: 6.9239999999999995 - type: precision_at_100 value: 1.233 - type: precision_at_1000 value: 0.18 - type: precision_at_3 value: 15.451 - type: precision_at_5 value: 11.044 - type: recall_at_1 value: 21.918000000000003 - type: recall_at_10 value: 45.171 - type: recall_at_100 value: 70.226 - type: recall_at_1000 value: 90.279 - type: recall_at_3 value: 32.657000000000004 - type: recall_at_5 value: 37.372 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 20.456 - type: map_at_10 value: 26.596999999999998 - type: map_at_100 value: 27.639999999999997 - type: map_at_1000 value: 27.766000000000002 - type: map_at_3 value: 24.487000000000002 - type: map_at_5 value: 25.683 - type: mrr_at_1 value: 25.605 - type: mrr_at_10 value: 31.326999999999998 - type: mrr_at_100 value: 32.133 - type: mrr_at_1000 value: 32.198 - type: mrr_at_3 value: 29.310000000000002 - type: mrr_at_5 value: 30.431 - type: ndcg_at_1 value: 25.605 - type: ndcg_at_10 value: 30.728 - type: ndcg_at_100 value: 35.318 - type: ndcg_at_1000 value: 38.082 - type: ndcg_at_3 value: 27.226 - type: ndcg_at_5 value: 28.828 - type: precision_at_1 value: 25.605 - type: precision_at_10 value: 5.561 - type: precision_at_100 value: 1.001 - type: precision_at_1000 value: 0.15 - type: precision_at_3 value: 12.717999999999998 - type: precision_at_5 value: 9.134 - type: recall_at_1 value: 20.456 - type: recall_at_10 value: 38.476 - type: recall_at_100 value: 58.120000000000005 - type: recall_at_1000 value: 76.793 - type: recall_at_3 value: 28.232000000000003 - type: recall_at_5 value: 32.53 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 28.088 - type: map_at_10 value: 37.584 - type: map_at_100 value: 38.75 - type: map_at_1000 value: 38.842999999999996 - type: map_at_3 value: 34.839999999999996 - type: map_at_5 value: 36.352000000000004 - type: mrr_at_1 value: 32.476 - type: mrr_at_10 value: 40.892 - type: mrr_at_100 value: 41.792 - type: mrr_at_1000 value: 41.845 - type: mrr_at_3 value: 38.474000000000004 - type: mrr_at_5 value: 39.818999999999996 - type: ndcg_at_1 value: 32.476 - type: ndcg_at_10 value: 42.811 - type: ndcg_at_100 value: 48.045 - type: ndcg_at_1000 value: 50.09400000000001 - type: ndcg_at_3 value: 37.830000000000005 - type: ndcg_at_5 value: 40.168 - type: precision_at_1 value: 32.476 - type: precision_at_10 value: 7.034 - type: precision_at_100 value: 1.061 - type: precision_at_1000 value: 0.131 - type: precision_at_3 value: 16.949 - type: precision_at_5 value: 11.799 - type: recall_at_1 value: 28.088 - type: recall_at_10 value: 55.318 - type: recall_at_100 value: 78.66499999999999 - type: recall_at_1000 value: 93.415 - type: recall_at_3 value: 41.865 - type: recall_at_5 value: 47.675 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 13.13 - type: map_at_10 value: 18.506 - type: map_at_100 value: 19.405 - type: map_at_1000 value: 19.516 - type: map_at_3 value: 16.821 - type: map_at_5 value: 17.782 - type: mrr_at_1 value: 14.124 - type: mrr_at_10 value: 19.767000000000003 - type: mrr_at_100 value: 20.66 - type: mrr_at_1000 value: 20.755000000000003 - type: mrr_at_3 value: 18.023 - type: mrr_at_5 value: 19.0 - type: ndcg_at_1 value: 14.124 - type: ndcg_at_10 value: 21.728 - type: ndcg_at_100 value: 26.422 - type: ndcg_at_1000 value: 29.73 - type: ndcg_at_3 value: 18.312 - type: ndcg_at_5 value: 19.993 - type: precision_at_1 value: 14.124 - type: precision_at_10 value: 3.4459999999999997 - type: precision_at_100 value: 0.617 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.91 - type: precision_at_5 value: 5.695 - type: recall_at_1 value: 13.13 - type: recall_at_10 value: 30.470000000000002 - type: recall_at_100 value: 52.449 - type: recall_at_1000 value: 78.25 - type: recall_at_3 value: 21.209 - type: recall_at_5 value: 25.281 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 7.7 - type: map_at_10 value: 12.333 - type: map_at_100 value: 13.367999999999999 - type: map_at_1000 value: 13.492 - type: map_at_3 value: 10.747 - type: map_at_5 value: 11.645999999999999 - type: mrr_at_1 value: 9.826 - type: mrr_at_10 value: 14.81 - type: mrr_at_100 value: 15.854 - type: mrr_at_1000 value: 15.953000000000001 - type: mrr_at_3 value: 13.039000000000001 - type: mrr_at_5 value: 14.046 - type: ndcg_at_1 value: 9.826 - type: ndcg_at_10 value: 15.437000000000001 - type: ndcg_at_100 value: 21.009 - type: ndcg_at_1000 value: 24.515 - type: ndcg_at_3 value: 12.349 - type: ndcg_at_5 value: 13.850000000000001 - type: precision_at_1 value: 9.826 - type: precision_at_10 value: 3.01 - type: precision_at_100 value: 0.692 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 6.053 - type: precision_at_5 value: 4.577 - type: recall_at_1 value: 7.7 - type: recall_at_10 value: 22.546 - type: recall_at_100 value: 47.648 - type: recall_at_1000 value: 73.655 - type: recall_at_3 value: 14.289 - type: recall_at_5 value: 17.994 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 19.886 - type: map_at_10 value: 26.63 - type: map_at_100 value: 27.944999999999997 - type: map_at_1000 value: 28.097 - type: map_at_3 value: 24.077 - type: map_at_5 value: 25.378 - type: mrr_at_1 value: 24.254 - type: mrr_at_10 value: 31.416 - type: mrr_at_100 value: 32.425 - type: mrr_at_1000 value: 32.501999999999995 - type: mrr_at_3 value: 28.793999999999997 - type: mrr_at_5 value: 30.237000000000002 - type: ndcg_at_1 value: 24.254 - type: ndcg_at_10 value: 31.524 - type: ndcg_at_100 value: 37.658 - type: ndcg_at_1000 value: 40.722 - type: ndcg_at_3 value: 26.953 - type: ndcg_at_5 value: 28.919 - type: precision_at_1 value: 24.254 - type: precision_at_10 value: 5.881 - type: precision_at_100 value: 1.072 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 12.479999999999999 - type: precision_at_5 value: 9.105 - type: recall_at_1 value: 19.886 - type: recall_at_10 value: 41.593 - type: recall_at_100 value: 68.43599999999999 - type: recall_at_1000 value: 89.041 - type: recall_at_3 value: 28.723 - type: recall_at_5 value: 33.804 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 15.821 - type: map_at_10 value: 21.898999999999997 - type: map_at_100 value: 23.189 - type: map_at_1000 value: 23.323 - type: map_at_3 value: 19.634999999999998 - type: map_at_5 value: 20.848 - type: mrr_at_1 value: 19.064 - type: mrr_at_10 value: 25.784000000000002 - type: mrr_at_100 value: 26.828999999999997 - type: mrr_at_1000 value: 26.904 - type: mrr_at_3 value: 23.573 - type: mrr_at_5 value: 24.812 - type: ndcg_at_1 value: 19.064 - type: ndcg_at_10 value: 26.229999999999997 - type: ndcg_at_100 value: 32.326 - type: ndcg_at_1000 value: 35.435 - type: ndcg_at_3 value: 22.070999999999998 - type: ndcg_at_5 value: 23.93 - type: precision_at_1 value: 19.064 - type: precision_at_10 value: 4.966 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 10.54 - type: precision_at_5 value: 7.785 - type: recall_at_1 value: 15.821 - type: recall_at_10 value: 35.516 - type: recall_at_100 value: 61.971 - type: recall_at_1000 value: 83.848 - type: recall_at_3 value: 23.97 - type: recall_at_5 value: 28.662 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.921916666666666 - type: map_at_10 value: 21.780166666666666 - type: map_at_100 value: 22.84433333333333 - type: map_at_1000 value: 22.975916666666667 - type: map_at_3 value: 19.735916666666665 - type: map_at_5 value: 20.860416666666666 - type: mrr_at_1 value: 19.054249999999996 - type: mrr_at_10 value: 25.021333333333335 - type: mrr_at_100 value: 25.93491666666667 - type: mrr_at_1000 value: 26.019166666666667 - type: mrr_at_3 value: 23.03583333333333 - type: mrr_at_5 value: 24.140000000000004 - type: ndcg_at_1 value: 19.054249999999996 - type: ndcg_at_10 value: 25.70233333333334 - type: ndcg_at_100 value: 30.890500000000003 - type: ndcg_at_1000 value: 34.02575 - type: ndcg_at_3 value: 22.017666666666663 - type: ndcg_at_5 value: 23.718666666666664 - type: precision_at_1 value: 19.054249999999996 - type: precision_at_10 value: 4.622083333333333 - type: precision_at_100 value: 0.86825 - type: precision_at_1000 value: 0.13258333333333333 - type: precision_at_3 value: 10.176166666666669 - type: precision_at_5 value: 7.382749999999999 - type: recall_at_1 value: 15.921916666666666 - type: recall_at_10 value: 34.314833333333326 - type: recall_at_100 value: 57.83341666666667 - type: recall_at_1000 value: 80.45625000000001 - type: recall_at_3 value: 23.967166666666667 - type: recall_at_5 value: 28.36841666666666 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 12.857 - type: map_at_10 value: 17.826 - type: map_at_100 value: 18.677 - type: map_at_1000 value: 18.775 - type: map_at_3 value: 16.227 - type: map_at_5 value: 17.168 - type: mrr_at_1 value: 14.877 - type: mrr_at_10 value: 19.784 - type: mrr_at_100 value: 20.662 - type: mrr_at_1000 value: 20.746000000000002 - type: mrr_at_3 value: 18.175 - type: mrr_at_5 value: 19.08 - type: ndcg_at_1 value: 14.877 - type: ndcg_at_10 value: 20.987000000000002 - type: ndcg_at_100 value: 25.654 - type: ndcg_at_1000 value: 28.360000000000003 - type: ndcg_at_3 value: 17.919 - type: ndcg_at_5 value: 19.404 - type: precision_at_1 value: 14.877 - type: precision_at_10 value: 3.528 - type: precision_at_100 value: 0.641 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.129 - type: precision_at_5 value: 5.798 - type: recall_at_1 value: 12.857 - type: recall_at_10 value: 28.864 - type: recall_at_100 value: 50.943000000000005 - type: recall_at_1000 value: 71.158 - type: recall_at_3 value: 20.330000000000002 - type: recall_at_5 value: 24.03 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.823 - type: map_at_10 value: 12.664 - type: map_at_100 value: 13.447000000000001 - type: map_at_1000 value: 13.58 - type: map_at_3 value: 11.372 - type: map_at_5 value: 12.052 - type: mrr_at_1 value: 10.84 - type: mrr_at_10 value: 15.135000000000002 - type: mrr_at_100 value: 15.919 - type: mrr_at_1000 value: 16.026 - type: mrr_at_3 value: 13.702 - type: mrr_at_5 value: 14.496 - type: ndcg_at_1 value: 10.84 - type: ndcg_at_10 value: 15.375 - type: ndcg_at_100 value: 19.612 - type: ndcg_at_1000 value: 23.305 - type: ndcg_at_3 value: 12.879999999999999 - type: ndcg_at_5 value: 13.980999999999998 - type: precision_at_1 value: 10.84 - type: precision_at_10 value: 2.887 - type: precision_at_100 value: 0.599 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.171 - type: precision_at_5 value: 4.522 - type: recall_at_1 value: 8.823 - type: recall_at_10 value: 21.19 - type: recall_at_100 value: 40.843 - type: recall_at_1000 value: 68.118 - type: recall_at_3 value: 14.219000000000001 - type: recall_at_5 value: 17.061 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 14.841999999999999 - type: map_at_10 value: 19.807 - type: map_at_100 value: 20.646 - type: map_at_1000 value: 20.782 - type: map_at_3 value: 17.881 - type: map_at_5 value: 18.94 - type: mrr_at_1 value: 17.631 - type: mrr_at_10 value: 22.949 - type: mrr_at_100 value: 23.727 - type: mrr_at_1000 value: 23.829 - type: mrr_at_3 value: 20.896 - type: mrr_at_5 value: 21.964 - type: ndcg_at_1 value: 17.631 - type: ndcg_at_10 value: 23.544999999999998 - type: ndcg_at_100 value: 28.042 - type: ndcg_at_1000 value: 31.66 - type: ndcg_at_3 value: 19.697 - type: ndcg_at_5 value: 21.467 - type: precision_at_1 value: 17.631 - type: precision_at_10 value: 4.039000000000001 - type: precision_at_100 value: 0.7080000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 8.831 - type: precision_at_5 value: 6.381 - type: recall_at_1 value: 14.841999999999999 - type: recall_at_10 value: 32.144 - type: recall_at_100 value: 52.896 - type: recall_at_1000 value: 79.3 - type: recall_at_3 value: 21.64 - type: recall_at_5 value: 26.127 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 15.182 - type: map_at_10 value: 21.423000000000002 - type: map_at_100 value: 22.766000000000002 - type: map_at_1000 value: 22.966 - type: map_at_3 value: 19.096 - type: map_at_5 value: 20.514 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.834999999999997 - type: mrr_at_100 value: 25.818 - type: mrr_at_1000 value: 25.893 - type: mrr_at_3 value: 22.628 - type: mrr_at_5 value: 24.032 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 25.766 - type: ndcg_at_100 value: 31.677 - type: ndcg_at_1000 value: 35.024 - type: ndcg_at_3 value: 22.027 - type: ndcg_at_5 value: 24.046 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 5.158 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.211 - type: precision_at_3 value: 10.474 - type: precision_at_5 value: 7.983999999999999 - type: recall_at_1 value: 15.182 - type: recall_at_10 value: 34.008 - type: recall_at_100 value: 61.882000000000005 - type: recall_at_1000 value: 84.635 - type: recall_at_3 value: 23.3 - type: recall_at_5 value: 28.732999999999997 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.36 - type: map_at_10 value: 16.181 - type: map_at_100 value: 17.094 - type: map_at_1000 value: 17.214 - type: map_at_3 value: 14.442 - type: map_at_5 value: 15.348999999999998 - type: mrr_at_1 value: 13.678 - type: mrr_at_10 value: 17.636 - type: mrr_at_100 value: 18.575 - type: mrr_at_1000 value: 18.685 - type: mrr_at_3 value: 15.958 - type: mrr_at_5 value: 16.882 - type: ndcg_at_1 value: 13.678 - type: ndcg_at_10 value: 18.991 - type: ndcg_at_100 value: 23.967 - type: ndcg_at_1000 value: 27.473 - type: ndcg_at_3 value: 15.526000000000002 - type: ndcg_at_5 value: 17.148 - type: precision_at_1 value: 13.678 - type: precision_at_10 value: 3.031 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 6.4079999999999995 - type: precision_at_5 value: 4.769 - type: recall_at_1 value: 12.36 - type: recall_at_10 value: 26.482 - type: recall_at_100 value: 49.922 - type: recall_at_1000 value: 76.983 - type: recall_at_3 value: 17.172 - type: recall_at_5 value: 21.152 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 8.464 - type: map_at_10 value: 14.78 - type: map_at_100 value: 16.436999999999998 - type: map_at_1000 value: 16.650000000000002 - type: map_at_3 value: 12.027000000000001 - type: map_at_5 value: 13.428999999999998 - type: mrr_at_1 value: 19.544 - type: mrr_at_10 value: 29.537999999999997 - type: mrr_at_100 value: 30.653000000000002 - type: mrr_at_1000 value: 30.708000000000002 - type: mrr_at_3 value: 25.798 - type: mrr_at_5 value: 28.072000000000003 - type: ndcg_at_1 value: 19.544 - type: ndcg_at_10 value: 21.953 - type: ndcg_at_100 value: 29.188 - type: ndcg_at_1000 value: 33.222 - type: ndcg_at_3 value: 16.89 - type: ndcg_at_5 value: 18.825 - type: precision_at_1 value: 19.544 - type: precision_at_10 value: 7.277 - type: precision_at_100 value: 1.506 - type: precision_at_1000 value: 0.22399999999999998 - type: precision_at_3 value: 12.834000000000001 - type: precision_at_5 value: 10.488999999999999 - type: recall_at_1 value: 8.464 - type: recall_at_10 value: 27.762999999999998 - type: recall_at_100 value: 53.147999999999996 - type: recall_at_1000 value: 76.183 - type: recall_at_3 value: 15.642 - type: recall_at_5 value: 20.593 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 5.676 - type: map_at_10 value: 11.847000000000001 - type: map_at_100 value: 16.875999999999998 - type: map_at_1000 value: 18.081 - type: map_at_3 value: 8.512 - type: map_at_5 value: 9.956 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 57.928000000000004 - type: mrr_at_100 value: 58.52 - type: mrr_at_1000 value: 58.544 - type: mrr_at_3 value: 55.333 - type: mrr_at_5 value: 56.958 - type: ndcg_at_1 value: 35.875 - type: ndcg_at_10 value: 27.221 - type: ndcg_at_100 value: 31.808999999999997 - type: ndcg_at_1000 value: 39.199 - type: ndcg_at_3 value: 30.274 - type: ndcg_at_5 value: 28.785 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 23.65 - type: precision_at_100 value: 7.818 - type: precision_at_1000 value: 1.651 - type: precision_at_3 value: 35.833 - type: precision_at_5 value: 31.0 - type: recall_at_1 value: 5.676 - type: recall_at_10 value: 16.619 - type: recall_at_100 value: 39.422000000000004 - type: recall_at_1000 value: 64.095 - type: recall_at_3 value: 9.608 - type: recall_at_5 value: 12.277000000000001 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.185 - type: f1 value: 44.87033813298503 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 18.904 - type: map_at_10 value: 28.435 - type: map_at_100 value: 29.498 - type: map_at_1000 value: 29.567 - type: map_at_3 value: 25.319000000000003 - type: map_at_5 value: 27.13 - type: mrr_at_1 value: 20.116999999999997 - type: mrr_at_10 value: 30.112 - type: mrr_at_100 value: 31.155 - type: mrr_at_1000 value: 31.213 - type: mrr_at_3 value: 26.895000000000003 - type: mrr_at_5 value: 28.793000000000003 - type: ndcg_at_1 value: 20.116999999999997 - type: ndcg_at_10 value: 34.244 - type: ndcg_at_100 value: 39.409 - type: ndcg_at_1000 value: 41.195 - type: ndcg_at_3 value: 27.872000000000003 - type: ndcg_at_5 value: 31.128 - type: precision_at_1 value: 20.116999999999997 - type: precision_at_10 value: 5.534 - type: precision_at_100 value: 0.828 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 12.076 - type: precision_at_5 value: 8.965 - type: recall_at_1 value: 18.904 - type: recall_at_10 value: 50.858000000000004 - type: recall_at_100 value: 74.42 - type: recall_at_1000 value: 88.023 - type: recall_at_3 value: 33.675 - type: recall_at_5 value: 41.449999999999996 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 8.892 - type: map_at_10 value: 14.363000000000001 - type: map_at_100 value: 15.75 - type: map_at_1000 value: 15.959000000000001 - type: map_at_3 value: 12.25 - type: map_at_5 value: 13.286999999999999 - type: mrr_at_1 value: 16.821 - type: mrr_at_10 value: 23.425 - type: mrr_at_100 value: 24.556 - type: mrr_at_1000 value: 24.637 - type: mrr_at_3 value: 20.885 - type: mrr_at_5 value: 22.127 - type: ndcg_at_1 value: 16.821 - type: ndcg_at_10 value: 19.412 - type: ndcg_at_100 value: 25.836 - type: ndcg_at_1000 value: 30.131000000000004 - type: ndcg_at_3 value: 16.198 - type: ndcg_at_5 value: 17.185 - type: precision_at_1 value: 16.821 - type: precision_at_10 value: 5.556 - type: precision_at_100 value: 1.1820000000000002 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 10.545 - type: precision_at_5 value: 8.056000000000001 - type: recall_at_1 value: 8.892 - type: recall_at_10 value: 25.249 - type: recall_at_100 value: 50.263000000000005 - type: recall_at_1000 value: 76.43299999999999 - type: recall_at_3 value: 15.094 - type: recall_at_5 value: 18.673000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.831 - type: map_at_10 value: 29.959999999999997 - type: map_at_100 value: 30.959999999999997 - type: map_at_1000 value: 31.069000000000003 - type: map_at_3 value: 27.453 - type: map_at_5 value: 28.838 - type: mrr_at_1 value: 41.661 - type: mrr_at_10 value: 49.647999999999996 - type: mrr_at_100 value: 50.304 - type: mrr_at_1000 value: 50.352 - type: mrr_at_3 value: 47.403 - type: mrr_at_5 value: 48.657000000000004 - type: ndcg_at_1 value: 41.661 - type: ndcg_at_10 value: 37.854 - type: ndcg_at_100 value: 42.248999999999995 - type: ndcg_at_1000 value: 44.756 - type: ndcg_at_3 value: 33.243 - type: ndcg_at_5 value: 35.467 - type: precision_at_1 value: 41.661 - type: precision_at_10 value: 8.386000000000001 - type: precision_at_100 value: 1.1900000000000002 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 21.022 - type: precision_at_5 value: 14.377 - type: recall_at_1 value: 20.831 - type: recall_at_10 value: 41.931000000000004 - type: recall_at_100 value: 59.507 - type: recall_at_1000 value: 76.232 - type: recall_at_3 value: 31.533 - type: recall_at_5 value: 35.942 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 70.2136 - type: ap value: 64.38274263735502 - type: f1 value: 70.02577813394484 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 7.542999999999999 - type: map_at_10 value: 13.229 - type: map_at_100 value: 14.283999999999999 - type: map_at_1000 value: 14.396 - type: map_at_3 value: 11.139000000000001 - type: map_at_5 value: 12.259 - type: mrr_at_1 value: 7.808 - type: mrr_at_10 value: 13.577 - type: mrr_at_100 value: 14.625 - type: mrr_at_1000 value: 14.732000000000001 - type: mrr_at_3 value: 11.464 - type: mrr_at_5 value: 12.584999999999999 - type: ndcg_at_1 value: 7.779 - type: ndcg_at_10 value: 16.793 - type: ndcg_at_100 value: 22.564 - type: ndcg_at_1000 value: 25.799 - type: ndcg_at_3 value: 12.431000000000001 - type: ndcg_at_5 value: 14.442 - type: precision_at_1 value: 7.779 - type: precision_at_10 value: 2.894 - type: precision_at_100 value: 0.59 - type: precision_at_1000 value: 0.087 - type: precision_at_3 value: 5.454 - type: precision_at_5 value: 4.278 - type: recall_at_1 value: 7.542999999999999 - type: recall_at_10 value: 27.907 - type: recall_at_100 value: 56.13399999999999 - type: recall_at_1000 value: 81.877 - type: recall_at_3 value: 15.878999999999998 - type: recall_at_5 value: 20.726 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 91.68490652074783 - type: f1 value: 90.90009716586837 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 61.33150934792522 - type: f1 value: 42.414995407585955 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 66.29455279085406 - type: f1 value: 64.0154454215856 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 73.91055817081372 - type: f1 value: 72.79505573377739 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.478611587568 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.395691978780366 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.75504868917307 - type: mrr value: 31.723412508217553 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 4.739 - type: map_at_10 value: 9.419 - type: map_at_100 value: 12.209 - type: map_at_1000 value: 13.653 - type: map_at_3 value: 7.292999999999999 - type: map_at_5 value: 8.291 - type: mrr_at_1 value: 38.7 - type: mrr_at_10 value: 47.934 - type: mrr_at_100 value: 48.605 - type: mrr_at_1000 value: 48.646 - type: mrr_at_3 value: 45.717 - type: mrr_at_5 value: 47.157 - type: ndcg_at_1 value: 36.842000000000006 - type: ndcg_at_10 value: 28.077 - type: ndcg_at_100 value: 26.83 - type: ndcg_at_1000 value: 36.272 - type: ndcg_at_3 value: 32.429 - type: ndcg_at_5 value: 30.823 - type: precision_at_1 value: 38.7 - type: precision_at_10 value: 20.774 - type: precision_at_100 value: 7.331 - type: precision_at_1000 value: 2.085 - type: precision_at_3 value: 30.341 - type: precision_at_5 value: 26.502 - type: recall_at_1 value: 4.739 - type: recall_at_10 value: 13.065999999999999 - type: recall_at_100 value: 28.875 - type: recall_at_1000 value: 62.751000000000005 - type: recall_at_3 value: 8.338 - type: recall_at_5 value: 10.211 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.764 - type: map_at_10 value: 18.582 - type: map_at_100 value: 19.953000000000003 - type: map_at_1000 value: 20.049 - type: map_at_3 value: 15.551 - type: map_at_5 value: 17.143 - type: mrr_at_1 value: 12.283 - type: mrr_at_10 value: 20.507 - type: mrr_at_100 value: 21.724 - type: mrr_at_1000 value: 21.801000000000002 - type: mrr_at_3 value: 17.434 - type: mrr_at_5 value: 19.097 - type: ndcg_at_1 value: 12.254 - type: ndcg_at_10 value: 23.818 - type: ndcg_at_100 value: 30.652 - type: ndcg_at_1000 value: 33.25 - type: ndcg_at_3 value: 17.577 - type: ndcg_at_5 value: 20.43 - type: precision_at_1 value: 12.254 - type: precision_at_10 value: 4.492999999999999 - type: precision_at_100 value: 0.8370000000000001 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 8.333 - type: precision_at_5 value: 6.593 - type: recall_at_1 value: 10.764 - type: recall_at_10 value: 38.279999999999994 - type: recall_at_100 value: 69.77600000000001 - type: recall_at_1000 value: 89.75 - type: recall_at_3 value: 21.608 - type: recall_at_5 value: 28.247 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 66.238 - type: map_at_10 value: 79.61 - type: map_at_100 value: 80.339 - type: map_at_1000 value: 80.366 - type: map_at_3 value: 76.572 - type: map_at_5 value: 78.45100000000001 - type: mrr_at_1 value: 76.18 - type: mrr_at_10 value: 83.319 - type: mrr_at_100 value: 83.492 - type: mrr_at_1000 value: 83.49499999999999 - type: mrr_at_3 value: 82.002 - type: mrr_at_5 value: 82.88 - type: ndcg_at_1 value: 76.24 - type: ndcg_at_10 value: 84.048 - type: ndcg_at_100 value: 85.76700000000001 - type: ndcg_at_1000 value: 85.989 - type: ndcg_at_3 value: 80.608 - type: ndcg_at_5 value: 82.45 - type: precision_at_1 value: 76.24 - type: precision_at_10 value: 12.775 - type: precision_at_100 value: 1.498 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.107 - type: precision_at_5 value: 23.198 - type: recall_at_1 value: 66.238 - type: recall_at_10 value: 92.655 - type: recall_at_100 value: 98.79599999999999 - type: recall_at_1000 value: 99.914 - type: recall_at_3 value: 82.818 - type: recall_at_5 value: 87.985 - type: map_at_1 value: 3.3029999999999995 - type: map_at_10 value: 8.534 - type: map_at_100 value: 10.269 - type: map_at_1000 value: 10.569 - type: map_at_3 value: 6.02 - type: map_at_5 value: 7.3 - type: mrr_at_1 value: 16.2 - type: mrr_at_10 value: 26.048 - type: mrr_at_100 value: 27.229 - type: mrr_at_1000 value: 27.307 - type: mrr_at_3 value: 22.8 - type: mrr_at_5 value: 24.555 - type: ndcg_at_1 value: 16.2 - type: ndcg_at_10 value: 15.152 - type: ndcg_at_100 value: 22.692999999999998 - type: ndcg_at_1000 value: 28.283 - type: ndcg_at_3 value: 13.831 - type: ndcg_at_5 value: 12.383 - type: precision_at_1 value: 16.2 - type: precision_at_10 value: 8.15 - type: precision_at_100 value: 1.921 - type: precision_at_1000 value: 0.326 - type: precision_at_3 value: 13.167000000000002 - type: precision_at_5 value: 11.200000000000001 - type: recall_at_1 value: 3.3029999999999995 - type: recall_at_10 value: 16.463 - type: recall_at_100 value: 38.968 - type: recall_at_1000 value: 66.208 - type: recall_at_3 value: 8.023 - type: recall_at_5 value: 11.338 - type: map_at_1 value: 0.154 - type: map_at_10 value: 1.216 - type: map_at_100 value: 6.401 - type: map_at_1000 value: 16.882 - type: map_at_3 value: 0.418 - type: map_at_5 value: 0.7040000000000001 - type: mrr_at_1 value: 62.0 - type: mrr_at_10 value: 75.319 - type: mrr_at_100 value: 75.435 - type: mrr_at_1000 value: 75.435 - type: mrr_at_3 value: 73.333 - type: mrr_at_5 value: 75.033 - type: ndcg_at_1 value: 56.00000000000001 - type: ndcg_at_10 value: 54.176 - type: ndcg_at_100 value: 40.741 - type: ndcg_at_1000 value: 38.385000000000005 - type: ndcg_at_3 value: 57.676 - type: ndcg_at_5 value: 57.867000000000004 - type: precision_at_1 value: 62.0 - type: precision_at_10 value: 57.8 - type: precision_at_100 value: 42.68 - type: precision_at_1000 value: 18.478 - type: precision_at_3 value: 61.333000000000006 - type: precision_at_5 value: 63.6 - type: recall_at_1 value: 0.154 - type: recall_at_10 value: 1.468 - type: recall_at_100 value: 9.541 - type: recall_at_1000 value: 37.218 - type: recall_at_3 value: 0.46299999999999997 - type: recall_at_5 value: 0.8340000000000001 - task: type: Clustering dataset: name: MTEB RedditClustering type: None config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 45.96790773164943 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: None config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 51.114201492992976 - task: type: STS dataset: name: MTEB SICK-R type: None config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 78.21858054391086 - type: cos_sim_spearman value: 67.3365618536054 - 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type: cos_sim_pearson value: 77.784876359328 - type: cos_sim_spearman value: 78.360543979936 - type: euclidean_pearson value: 77.73937696752135 - type: euclidean_spearman value: 78.36053665222538 - type: manhattan_pearson value: 77.56126269274264 - type: manhattan_spearman value: 78.18717393504727 - task: type: STS dataset: name: MTEB STS17 (en-en) type: None config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.63171981287952 - type: cos_sim_spearman value: 87.49687143000429 - type: euclidean_pearson value: 86.37853734517222 - type: euclidean_spearman value: 87.4977435828658 - type: manhattan_pearson value: 86.40342805532555 - type: manhattan_spearman value: 87.57812091712806 - task: type: STS dataset: name: MTEB STS22 (en) type: None config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 60.00736823914696 - type: cos_sim_spearman value: 60.59580774316736 - type: euclidean_pearson value: 61.893600849213094 - type: euclidean_spearman value: 60.59580774316736 - type: manhattan_pearson value: 61.43013801720455 - type: manhattan_spearman value: 59.92526461879062 - task: type: STS dataset: name: MTEB STSBenchmark type: None config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 80.58292387813594 - type: cos_sim_spearman value: 78.85975762418589 - type: euclidean_pearson value: 80.28122335716425 - type: euclidean_spearman value: 78.85977608876506 - type: manhattan_pearson value: 80.20419882971093 - type: manhattan_spearman value: 78.79811621332709 - task: type: Reranking dataset: name: MTEB SciDocsRR type: None config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.54383068715617 - type: mrr value: 93.62365031482678 - task: type: Retrieval dataset: name: MTEB SciFact type: None config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics: - type: map_at_1 value: 39.111000000000004 - 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type: recall_at_5 value: 57.99999999999999 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.72772277227723 - type: cos_sim_ap value: 91.98542118937158 - type: cos_sim_f1 value: 85.91691995947316 - type: cos_sim_precision value: 87.06365503080082 - type: cos_sim_recall value: 84.8 - type: dot_accuracy value: 99.72772277227723 - type: dot_ap value: 91.98542118937158 - type: dot_f1 value: 85.91691995947316 - type: dot_precision value: 87.06365503080082 - type: dot_recall value: 84.8 - type: euclidean_accuracy value: 99.72772277227723 - type: euclidean_ap value: 91.98542118937158 - type: euclidean_f1 value: 85.91691995947316 - type: euclidean_precision value: 87.06365503080082 - type: euclidean_recall value: 84.8 - type: manhattan_accuracy value: 99.72574257425742 - type: manhattan_ap value: 91.96773898408213 - type: manhattan_f1 value: 85.8601327207759 - type: manhattan_precision value: 87.69551616266945 - type: manhattan_recall value: 84.1 - type: max_accuracy value: 99.72772277227723 - type: max_ap value: 91.98542118937158 - type: max_f1 value: 85.91691995947316 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: None config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 50.974351388709024 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: None config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 30.94724711190474 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: None config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 43.618618519378074 - type: mrr value: 44.19061942959002 - task: type: Summarization dataset: name: MTEB SummEval type: None config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 29.75942900919329 - type: cos_sim_spearman value: 30.265779375382486 - type: dot_pearson value: 29.759429009193283 - type: dot_spearman value: 30.216316271647514 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.144 - type: map_at_10 value: 8.38 - type: map_at_100 value: 14.482000000000001 - type: map_at_1000 value: 16.179 - type: map_at_3 value: 3.821 - type: map_at_5 value: 5.96 - type: mrr_at_1 value: 26.531 - type: mrr_at_10 value: 41.501 - type: mrr_at_100 value: 42.575 - type: mrr_at_1000 value: 42.575 - type: mrr_at_3 value: 36.054 - type: mrr_at_5 value: 40.238 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 21.644 - type: ndcg_at_100 value: 35.427 - type: ndcg_at_1000 value: 47.116 - type: ndcg_at_3 value: 20.814 - type: ndcg_at_5 value: 22.783 - type: precision_at_1 value: 26.531 - type: precision_at_10 value: 21.224 - type: precision_at_100 value: 8.265 - type: precision_at_1000 value: 1.5959999999999999 - type: precision_at_3 value: 23.810000000000002 - type: precision_at_5 value: 26.122 - type: recall_at_1 value: 2.144 - type: recall_at_10 value: 15.278 - type: recall_at_100 value: 50.541000000000004 - type: recall_at_1000 value: 86.144 - type: recall_at_3 value: 5.056 - type: recall_at_5 value: 9.203 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: None config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 75.88100000000001 - type: ap value: 17.210410808772743 - type: f1 value: 58.7851360197636 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: None config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.68024900962084 - type: f1 value: 59.95386992880734 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: None config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.55446050017461 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: None config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 82.32699529117244 - type: cos_sim_ap value: 61.49148139881723 - type: cos_sim_f1 value: 59.31940298507462 - type: cos_sim_precision value: 54.17666303162486 - type: cos_sim_recall value: 65.54089709762533 - type: dot_accuracy value: 82.32699529117244 - type: dot_ap value: 61.49148139881723 - type: dot_f1 value: 59.31940298507462 - type: dot_precision value: 54.17666303162486 - type: dot_recall value: 65.54089709762533 - type: euclidean_accuracy value: 82.32699529117244 - type: euclidean_ap value: 61.49148139881723 - type: euclidean_f1 value: 59.31940298507462 - type: euclidean_precision value: 54.17666303162486 - type: euclidean_recall value: 65.54089709762533 - type: manhattan_accuracy value: 82.44024557429815 - type: manhattan_ap value: 61.57050440663527 - type: manhattan_f1 value: 59.36456916800594 - type: manhattan_precision value: 55.8501977204001 - type: manhattan_recall value: 63.35092348284961 - type: max_accuracy value: 82.44024557429815 - type: max_ap value: 61.57050440663527 - type: max_f1 value: 59.36456916800594 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: None config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.70714479760935 - type: cos_sim_ap value: 83.52059059692118 - type: cos_sim_f1 value: 75.8043805261034 - type: cos_sim_precision value: 72.40171000070083 - type: cos_sim_recall value: 79.54265475823837 - type: dot_accuracy value: 87.70714479760935 - type: dot_ap value: 83.52059016767844 - type: dot_f1 value: 75.8043805261034 - type: dot_precision value: 72.40171000070083 - type: dot_recall value: 79.54265475823837 - type: euclidean_accuracy value: 87.70714479760935 - type: euclidean_ap value: 83.52059046795347 - type: euclidean_f1 value: 75.8043805261034 - type: euclidean_precision value: 72.40171000070083 - type: euclidean_recall value: 79.54265475823837 - type: manhattan_accuracy value: 87.7187875965382 - type: manhattan_ap value: 83.5377383098018 - type: manhattan_f1 value: 75.87021520062012 - type: manhattan_precision value: 72.87102035028008 - type: manhattan_recall value: 79.12688635663689 - type: max_accuracy value: 87.7187875965382 - type: max_ap value: 83.5377383098018 - type: max_f1 value: 75.87021520062012 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

twadada/wl_sw_256

twadada

null

[ "mteb", "model-index", "region:us" ]

2025-01-09T11:43:11

2025-01-09T11:43:20

0

--- tags: - mteb model-index: - name: l3_wordllama_256 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: None config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 65.97014925373134 - type: ap value: 27.33017285839569 - type: f1 value: 59.04330619047924 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: None config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 63.248250000000006 - type: ap value: 58.695642654646576 - type: f1 value: 62.98826255412888 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: None config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 31.689999999999998 - type: f1 value: 31.106666192619258 - task: type: Retrieval dataset: name: MTEB ArguAna type: None config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 19.986 - type: map_at_10 value: 34.634 - type: map_at_100 value: 35.937000000000005 - type: map_at_1000 value: 35.954 - type: map_at_3 value: 29.742 - type: map_at_5 value: 32.444 - type: mrr_at_1 value: 20.341 - type: mrr_at_10 value: 34.763 - type: mrr_at_100 value: 36.065999999999995 - type: mrr_at_1000 value: 36.083 - type: mrr_at_3 value: 29.872 - type: mrr_at_5 value: 32.574999999999996 - type: ndcg_at_1 value: 19.986 - type: ndcg_at_10 value: 43.074 - type: ndcg_at_100 value: 48.819 - type: ndcg_at_1000 value: 49.26 - type: ndcg_at_3 value: 32.934000000000005 - type: ndcg_at_5 value: 37.830999999999996 - type: precision_at_1 value: 19.986 - type: precision_at_10 value: 7.02 - type: precision_at_100 value: 0.958 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 14.059 - type: precision_at_5 value: 10.825 - type: recall_at_1 value: 19.986 - type: recall_at_10 value: 70.199 - type: recall_at_100 value: 95.804 - type: recall_at_1000 value: 99.21799999999999 - type: recall_at_3 value: 42.176 - type: recall_at_5 value: 54.125 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: None config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 39.64176717184799 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: None config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 29.06122250673383 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: None config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 55.808484614132844 - type: mrr value: 71.09121487930351 - task: type: STS dataset: name: MTEB BIOSSES type: None config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 74.96889982129713 - type: cos_sim_spearman value: 70.34256665852179 - type: euclidean_pearson value: 73.59375229907496 - type: euclidean_spearman value: 70.34256665852179 - type: manhattan_pearson value: 72.38820178677287 - type: manhattan_spearman value: 69.3919425882689 - task: type: Classification dataset: name: MTEB Banking77Classification type: None config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 73.56818181818181 - type: f1 value: 72.78107232170503 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: None config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 33.10380086081637 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: None config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 25.238238325966222 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: None config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 20.294999999999998 - type: map_at_10 value: 27.535999999999998 - type: map_at_100 value: 28.803 - type: map_at_1000 value: 28.971000000000004 - type: map_at_3 value: 25.029 - type: map_at_5 value: 26.526 - type: mrr_at_1 value: 24.893 - type: mrr_at_10 value: 32.554 - type: mrr_at_100 value: 33.504 - type: mrr_at_1000 value: 33.583 - type: mrr_at_3 value: 30.091 - type: mrr_at_5 value: 31.535999999999998 - type: ndcg_at_1 value: 24.893 - type: ndcg_at_10 value: 32.495000000000005 - type: ndcg_at_100 value: 38.288 - type: ndcg_at_1000 value: 41.559000000000005 - type: ndcg_at_3 value: 28.321 - type: ndcg_at_5 value: 30.401 - type: precision_at_1 value: 24.893 - type: precision_at_10 value: 6.109 - type: precision_at_100 value: 1.142 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 13.447999999999999 - type: precision_at_5 value: 9.927999999999999 - type: recall_at_1 value: 20.294999999999998 - type: recall_at_10 value: 42.129 - type: recall_at_100 value: 67.709 - type: recall_at_1000 value: 89.534 - type: recall_at_3 value: 30.148999999999997 - type: recall_at_5 value: 35.804 - task: type: Retrieval dataset: name: MTEB CQADupstackEnglishRetrieval type: None config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 16.426 - type: map_at_10 value: 22.461000000000002 - type: map_at_100 value: 23.424 - type: map_at_1000 value: 23.559 - type: map_at_3 value: 20.643 - type: map_at_5 value: 21.602 - type: mrr_at_1 value: 20.701 - type: mrr_at_10 value: 26.734 - type: mrr_at_100 value: 27.516000000000002 - type: mrr_at_1000 value: 27.594 - type: mrr_at_3 value: 24.936 - type: mrr_at_5 value: 25.901000000000003 - type: ndcg_at_1 value: 20.701 - type: ndcg_at_10 value: 26.381 - type: ndcg_at_100 value: 30.731 - type: ndcg_at_1000 value: 33.603 - type: ndcg_at_3 value: 23.336000000000002 - type: ndcg_at_5 value: 24.644 - type: precision_at_1 value: 20.701 - type: precision_at_10 value: 5.006 - type: precision_at_100 value: 0.9339999999999999 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 11.315999999999999 - type: precision_at_5 value: 8.14 - type: recall_at_1 value: 16.426 - type: recall_at_10 value: 33.593 - type: recall_at_100 value: 52.746 - type: recall_at_1000 value: 72.15899999999999 - type: recall_at_3 value: 24.712 - type: recall_at_5 value: 28.233000000000004 - task: type: Retrieval dataset: name: MTEB CQADupstackGamingRetrieval type: None config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 24.46 - type: map_at_10 value: 33.292 - type: map_at_100 value: 34.437 - type: map_at_1000 value: 34.534 - type: map_at_3 value: 30.567 - type: map_at_5 value: 32.202 - type: mrr_at_1 value: 28.276 - type: mrr_at_10 value: 36.235 - type: mrr_at_100 value: 37.173 - type: mrr_at_1000 value: 37.234 - type: mrr_at_3 value: 33.783 - type: mrr_at_5 value: 35.237 - type: ndcg_at_1 value: 28.276 - type: ndcg_at_10 value: 38.202000000000005 - type: ndcg_at_100 value: 43.634 - type: ndcg_at_1000 value: 45.894 - type: ndcg_at_3 value: 33.19 - type: ndcg_at_5 value: 35.798 - type: precision_at_1 value: 28.276 - type: precision_at_10 value: 6.332 - type: precision_at_100 value: 1.008 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 14.671000000000001 - type: precision_at_5 value: 10.571 - type: recall_at_1 value: 24.46 - type: recall_at_10 value: 50.156 - type: recall_at_100 value: 74.648 - type: recall_at_1000 value: 91.269 - type: recall_at_3 value: 36.937999999999995 - type: recall_at_5 value: 43.15 - task: type: Retrieval dataset: name: MTEB CQADupstackGisRetrieval type: None config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 14.052999999999999 - type: map_at_10 value: 18.287 - type: map_at_100 value: 19.137 - type: map_at_1000 value: 19.258 - type: map_at_3 value: 16.79 - type: map_at_5 value: 17.618000000000002 - type: mrr_at_1 value: 15.254000000000001 - type: mrr_at_10 value: 19.88 - type: mrr_at_100 value: 20.71 - type: mrr_at_1000 value: 20.812 - type: mrr_at_3 value: 18.23 - type: mrr_at_5 value: 19.185 - type: ndcg_at_1 value: 15.254000000000001 - type: ndcg_at_10 value: 21.183 - type: ndcg_at_100 value: 25.972 - type: ndcg_at_1000 value: 29.271 - type: ndcg_at_3 value: 18.046 - type: ndcg_at_5 value: 19.570999999999998 - type: precision_at_1 value: 15.254000000000001 - type: precision_at_10 value: 3.288 - type: precision_at_100 value: 0.614 - type: precision_at_1000 value: 0.094 - type: precision_at_3 value: 7.5329999999999995 - type: precision_at_5 value: 5.379 - type: recall_at_1 value: 14.052999999999999 - type: recall_at_10 value: 28.599999999999998 - type: recall_at_100 value: 51.815 - type: recall_at_1000 value: 77.04299999999999 - type: recall_at_3 value: 20.238999999999997 - type: recall_at_5 value: 23.837 - task: type: Retrieval dataset: name: MTEB CQADupstackMathematicaRetrieval type: None config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 8.475000000000001 - type: map_at_10 value: 12.898000000000001 - type: map_at_100 value: 13.950000000000001 - type: map_at_1000 value: 14.063999999999998 - type: map_at_3 value: 10.965 - type: map_at_5 value: 11.905000000000001 - type: mrr_at_1 value: 10.323 - type: mrr_at_10 value: 15.431000000000001 - type: mrr_at_100 value: 16.442 - type: mrr_at_1000 value: 16.526 - type: mrr_at_3 value: 13.288 - type: mrr_at_5 value: 14.382 - type: ndcg_at_1 value: 10.323 - type: ndcg_at_10 value: 16.325 - type: ndcg_at_100 value: 21.831999999999997 - type: ndcg_at_1000 value: 25.079 - type: ndcg_at_3 value: 12.372 - type: ndcg_at_5 value: 14.011999999999999 - type: precision_at_1 value: 10.323 - type: precision_at_10 value: 3.197 - type: precision_at_100 value: 0.6930000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 5.970000000000001 - type: precision_at_5 value: 4.627 - type: recall_at_1 value: 8.475000000000001 - type: recall_at_10 value: 24.651999999999997 - type: recall_at_100 value: 49.63 - type: recall_at_1000 value: 73.35000000000001 - type: recall_at_3 value: 13.852 - type: recall_at_5 value: 17.813000000000002 - task: type: Retrieval dataset: name: MTEB CQADupstackPhysicsRetrieval type: None config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 18.278 - type: map_at_10 value: 24.852 - type: map_at_100 value: 26.308999999999997 - type: map_at_1000 value: 26.450000000000003 - type: map_at_3 value: 22.183 - type: map_at_5 value: 23.493 - type: mrr_at_1 value: 22.522000000000002 - type: mrr_at_10 value: 29.554000000000002 - type: mrr_at_100 value: 30.705 - type: mrr_at_1000 value: 30.774 - type: mrr_at_3 value: 26.821 - type: mrr_at_5 value: 28.288000000000004 - type: ndcg_at_1 value: 22.522000000000002 - type: ndcg_at_10 value: 29.79 - type: ndcg_at_100 value: 36.473 - type: ndcg_at_1000 value: 39.440999999999995 - type: ndcg_at_3 value: 24.915000000000003 - type: ndcg_at_5 value: 26.941 - type: precision_at_1 value: 22.522000000000002 - type: precision_at_10 value: 5.707 - type: precision_at_100 value: 1.076 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 11.645999999999999 - type: precision_at_5 value: 8.584999999999999 - type: recall_at_1 value: 18.278 - type: recall_at_10 value: 40.150999999999996 - type: recall_at_100 value: 68.978 - type: recall_at_1000 value: 89.295 - type: recall_at_3 value: 26.548 - type: recall_at_5 value: 31.772 - task: type: Retrieval dataset: name: MTEB CQADupstackProgrammersRetrieval type: None config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 14.634 - type: map_at_10 value: 21.377 - type: map_at_100 value: 22.522000000000002 - type: map_at_1000 value: 22.657 - type: map_at_3 value: 19.292 - type: map_at_5 value: 20.278 - type: mrr_at_1 value: 18.151 - type: mrr_at_10 value: 25.263999999999996 - type: mrr_at_100 value: 26.156000000000002 - type: mrr_at_1000 value: 26.247 - type: mrr_at_3 value: 23.154 - type: mrr_at_5 value: 24.188000000000002 - type: ndcg_at_1 value: 18.151 - type: ndcg_at_10 value: 25.773000000000003 - type: ndcg_at_100 value: 31.130999999999997 - type: ndcg_at_1000 value: 34.452 - type: ndcg_at_3 value: 21.975 - type: ndcg_at_5 value: 23.36 - type: precision_at_1 value: 18.151 - type: precision_at_10 value: 4.829 - type: precision_at_100 value: 0.894 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 10.693 - type: precision_at_5 value: 7.648000000000001 - type: recall_at_1 value: 14.634 - type: recall_at_10 value: 35.433 - type: recall_at_100 value: 58.617 - type: recall_at_1000 value: 82.364 - type: recall_at_3 value: 24.59 - type: recall_at_5 value: 28.217 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: mteb/cqadupstack config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.736583333333334 - type: map_at_10 value: 20.393 - type: map_at_100 value: 21.42775 - type: map_at_1000 value: 21.560666666666666 - type: map_at_3 value: 18.52958333333333 - type: map_at_5 value: 19.509249999999998 - type: mrr_at_1 value: 17.61366666666667 - type: mrr_at_10 value: 23.522250000000003 - type: mrr_at_100 value: 24.424166666666668 - type: mrr_at_1000 value: 24.512166666666666 - type: mrr_at_3 value: 21.64875 - type: mrr_at_5 value: 22.648916666666665 - type: ndcg_at_1 value: 17.61366666666667 - type: ndcg_at_10 value: 24.16458333333333 - type: ndcg_at_100 value: 29.305916666666672 - type: ndcg_at_1000 value: 32.52291666666667 - type: ndcg_at_3 value: 20.732 - type: ndcg_at_5 value: 22.223333333333333 - type: precision_at_1 value: 17.61366666666667 - type: precision_at_10 value: 4.33925 - type: precision_at_100 value: 0.8296666666666666 - type: precision_at_1000 value: 0.12933333333333333 - type: precision_at_3 value: 9.6265 - type: precision_at_5 value: 6.921666666666666 - type: recall_at_1 value: 14.736583333333334 - type: recall_at_10 value: 32.46958333333333 - type: recall_at_100 value: 55.94050000000001 - type: recall_at_1000 value: 79.17466666666667 - type: recall_at_3 value: 22.765749999999997 - type: recall_at_5 value: 26.614583333333336 - task: type: Retrieval dataset: name: MTEB CQADupstackStatsRetrieval type: None config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 11.152 - type: map_at_10 value: 16.052 - type: map_at_100 value: 16.892 - type: map_at_1000 value: 17.0 - type: map_at_3 value: 14.677999999999999 - type: map_at_5 value: 15.424 - type: mrr_at_1 value: 12.883 - type: mrr_at_10 value: 17.871000000000002 - type: mrr_at_100 value: 18.694 - type: mrr_at_1000 value: 18.793000000000003 - type: mrr_at_3 value: 16.641000000000002 - type: mrr_at_5 value: 17.262 - type: ndcg_at_1 value: 12.883 - type: ndcg_at_10 value: 18.981 - type: ndcg_at_100 value: 23.704 - type: ndcg_at_1000 value: 26.810000000000002 - type: ndcg_at_3 value: 16.361 - type: ndcg_at_5 value: 17.507 - type: precision_at_1 value: 12.883 - type: precision_at_10 value: 3.221 - type: precision_at_100 value: 0.612 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 7.4639999999999995 - type: precision_at_5 value: 5.244999999999999 - type: recall_at_1 value: 11.152 - type: recall_at_10 value: 26.22 - type: recall_at_100 value: 48.870000000000005 - type: recall_at_1000 value: 72.328 - type: recall_at_3 value: 18.838 - type: recall_at_5 value: 21.693 - task: type: Retrieval dataset: name: MTEB CQADupstackTexRetrieval type: None config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 8.338 - type: map_at_10 value: 12.315 - type: map_at_100 value: 13.086 - type: map_at_1000 value: 13.214 - type: map_at_3 value: 11.032 - type: map_at_5 value: 11.691 - type: mrr_at_1 value: 10.255 - type: mrr_at_10 value: 14.723 - type: mrr_at_100 value: 15.528 - type: mrr_at_1000 value: 15.626000000000001 - type: mrr_at_3 value: 13.289000000000001 - type: mrr_at_5 value: 14.047 - type: ndcg_at_1 value: 10.255 - type: ndcg_at_10 value: 15.058 - type: ndcg_at_100 value: 19.326 - type: ndcg_at_1000 value: 22.972 - type: ndcg_at_3 value: 12.565999999999999 - type: ndcg_at_5 value: 13.603000000000002 - type: precision_at_1 value: 10.255 - type: precision_at_10 value: 2.815 - type: precision_at_100 value: 0.597 - type: precision_at_1000 value: 0.109 - type: precision_at_3 value: 6.045 - type: precision_at_5 value: 4.405 - type: recall_at_1 value: 8.338 - type: recall_at_10 value: 21.125 - type: recall_at_100 value: 40.936 - type: recall_at_1000 value: 67.984 - type: recall_at_3 value: 14.018 - type: recall_at_5 value: 16.725 - task: type: Retrieval dataset: name: MTEB CQADupstackUnixRetrieval type: None config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 13.575000000000001 - type: map_at_10 value: 18.967 - type: map_at_100 value: 19.924 - type: map_at_1000 value: 20.06 - type: map_at_3 value: 17.101 - type: map_at_5 value: 18.142 - type: mrr_at_1 value: 16.418 - type: mrr_at_10 value: 22.131 - type: mrr_at_100 value: 22.993 - type: mrr_at_1000 value: 23.101 - type: mrr_at_3 value: 20.288999999999998 - type: mrr_at_5 value: 21.282999999999998 - type: ndcg_at_1 value: 16.418 - type: ndcg_at_10 value: 22.625 - type: ndcg_at_100 value: 27.676000000000002 - type: ndcg_at_1000 value: 31.41 - type: ndcg_at_3 value: 19.136 - type: ndcg_at_5 value: 20.748 - type: precision_at_1 value: 16.418 - type: precision_at_10 value: 3.9739999999999998 - type: precision_at_100 value: 0.743 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 8.924 - type: precision_at_5 value: 6.381 - type: recall_at_1 value: 13.575000000000001 - type: recall_at_10 value: 30.794 - type: recall_at_100 value: 54.02400000000001 - type: recall_at_1000 value: 81.634 - type: recall_at_3 value: 21.095 - type: recall_at_5 value: 25.25 - task: type: Retrieval dataset: name: MTEB CQADupstackWebmastersRetrieval type: None config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 14.915999999999999 - type: map_at_10 value: 20.976 - type: map_at_100 value: 22.127 - type: map_at_1000 value: 22.329 - type: map_at_3 value: 19.62 - type: map_at_5 value: 20.247999999999998 - type: mrr_at_1 value: 18.379 - type: mrr_at_10 value: 24.822 - type: mrr_at_100 value: 25.765 - type: mrr_at_1000 value: 25.852000000000004 - type: mrr_at_3 value: 23.551 - type: mrr_at_5 value: 24.193 - type: ndcg_at_1 value: 18.379 - type: ndcg_at_10 value: 24.956999999999997 - type: ndcg_at_100 value: 30.224 - type: ndcg_at_1000 value: 33.883 - type: ndcg_at_3 value: 23.094 - type: ndcg_at_5 value: 23.659 - type: precision_at_1 value: 18.379 - type: precision_at_10 value: 4.802 - type: precision_at_100 value: 1.105 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 11.462 - type: precision_at_5 value: 7.826 - type: recall_at_1 value: 14.915999999999999 - type: recall_at_10 value: 31.902 - type: recall_at_100 value: 57.296 - type: recall_at_1000 value: 82.107 - type: recall_at_3 value: 25.013 - type: recall_at_5 value: 27.281 - task: type: Retrieval dataset: name: MTEB CQADupstackWordpressRetrieval type: None config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 12.237 - type: map_at_10 value: 15.703 - type: map_at_100 value: 16.522000000000002 - type: map_at_1000 value: 16.631999999999998 - type: map_at_3 value: 14.455000000000002 - type: map_at_5 value: 14.982000000000001 - type: mrr_at_1 value: 13.309000000000001 - type: mrr_at_10 value: 17.068 - type: mrr_at_100 value: 17.904 - type: mrr_at_1000 value: 18.004 - type: mrr_at_3 value: 15.712000000000002 - type: mrr_at_5 value: 16.285 - type: ndcg_at_1 value: 13.309000000000001 - type: ndcg_at_10 value: 18.205 - type: ndcg_at_100 value: 22.68 - type: ndcg_at_1000 value: 25.901000000000003 - type: ndcg_at_3 value: 15.472 - type: ndcg_at_5 value: 16.436 - type: precision_at_1 value: 13.309000000000001 - type: precision_at_10 value: 2.791 - type: precision_at_100 value: 0.538 - type: precision_at_1000 value: 0.086 - type: precision_at_3 value: 6.346 - type: precision_at_5 value: 4.324999999999999 - type: recall_at_1 value: 12.237 - type: recall_at_10 value: 24.88 - type: recall_at_100 value: 46.017 - type: recall_at_1000 value: 71.029 - type: recall_at_3 value: 17.197000000000003 - type: recall_at_5 value: 19.6 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: None config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 6.732 - type: map_at_10 value: 12.674 - type: map_at_100 value: 14.257 - type: map_at_1000 value: 14.463999999999999 - type: map_at_3 value: 10.355 - type: map_at_5 value: 11.524 - type: mrr_at_1 value: 15.831000000000001 - type: mrr_at_10 value: 25.972 - type: mrr_at_100 value: 27.107999999999997 - type: mrr_at_1000 value: 27.167 - type: mrr_at_3 value: 22.637999999999998 - type: mrr_at_5 value: 24.319 - type: ndcg_at_1 value: 15.831000000000001 - type: ndcg_at_10 value: 19.244 - type: ndcg_at_100 value: 26.329 - type: ndcg_at_1000 value: 30.270999999999997 - type: ndcg_at_3 value: 14.966 - type: ndcg_at_5 value: 16.377 - type: precision_at_1 value: 15.831000000000001 - type: precision_at_10 value: 6.404 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.212 - type: precision_at_3 value: 11.64 - type: precision_at_5 value: 9.134 - type: recall_at_1 value: 6.732 - type: recall_at_10 value: 24.855 - type: recall_at_100 value: 49.730000000000004 - type: recall_at_1000 value: 72.214 - type: recall_at_3 value: 14.299000000000001 - type: recall_at_5 value: 18.363 - task: type: Retrieval dataset: name: MTEB DBPedia type: None config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 4.529 - type: map_at_10 value: 9.075999999999999 - type: map_at_100 value: 12.394 - type: map_at_1000 value: 13.272999999999998 - type: map_at_3 value: 6.688 - type: map_at_5 value: 7.803 - type: mrr_at_1 value: 36.25 - type: mrr_at_10 value: 46.867 - type: mrr_at_100 value: 47.654 - type: mrr_at_1000 value: 47.679 - type: mrr_at_3 value: 43.791999999999994 - type: mrr_at_5 value: 45.742 - type: ndcg_at_1 value: 26.75 - type: ndcg_at_10 value: 21.146 - type: ndcg_at_100 value: 25.113999999999997 - type: ndcg_at_1000 value: 31.873 - type: ndcg_at_3 value: 23.142 - type: ndcg_at_5 value: 22.273 - type: precision_at_1 value: 36.25 - type: precision_at_10 value: 18.25 - type: precision_at_100 value: 6.16 - type: precision_at_1000 value: 1.34 - type: precision_at_3 value: 27.250000000000004 - type: precision_at_5 value: 23.75 - type: recall_at_1 value: 4.529 - type: recall_at_10 value: 13.442000000000002 - type: recall_at_100 value: 32.534 - type: recall_at_1000 value: 55.346 - type: recall_at_3 value: 7.771999999999999 - type: recall_at_5 value: 10.061 - task: type: Classification dataset: name: MTEB EmotionClassification type: None config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 37.89000000000001 - type: f1 value: 34.12692942265391 - task: type: Retrieval dataset: name: MTEB FEVER type: None config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 16.28 - type: map_at_10 value: 24.729 - type: map_at_100 value: 25.785999999999998 - type: map_at_1000 value: 25.855 - type: map_at_3 value: 22.083 - type: map_at_5 value: 23.534 - type: mrr_at_1 value: 17.462 - type: mrr_at_10 value: 26.358999999999998 - type: mrr_at_100 value: 27.412 - type: mrr_at_1000 value: 27.473 - type: mrr_at_3 value: 23.615 - type: mrr_at_5 value: 25.115 - type: ndcg_at_1 value: 17.462 - type: ndcg_at_10 value: 29.885 - type: ndcg_at_100 value: 35.268 - type: ndcg_at_1000 value: 37.203 - type: ndcg_at_3 value: 24.397 - type: ndcg_at_5 value: 26.995 - type: precision_at_1 value: 17.462 - type: precision_at_10 value: 4.851 - type: precision_at_100 value: 0.77 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 10.666 - type: precision_at_5 value: 7.762 - type: recall_at_1 value: 16.28 - type: recall_at_10 value: 44.554 - type: recall_at_100 value: 69.736 - type: recall_at_1000 value: 84.654 - type: recall_at_3 value: 29.529 - type: recall_at_5 value: 35.789 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: None config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 7.406 - type: map_at_10 value: 12.162 - type: map_at_100 value: 13.501 - type: map_at_1000 value: 13.700000000000001 - type: map_at_3 value: 10.282 - type: map_at_5 value: 11.182 - type: mrr_at_1 value: 14.969 - type: mrr_at_10 value: 21.453 - type: mrr_at_100 value: 22.579 - type: mrr_at_1000 value: 22.665 - type: mrr_at_3 value: 19.084 - type: mrr_at_5 value: 20.233999999999998 - type: ndcg_at_1 value: 14.969 - type: ndcg_at_10 value: 17.022000000000002 - type: ndcg_at_100 value: 23.415 - type: ndcg_at_1000 value: 27.811000000000003 - type: ndcg_at_3 value: 14.191999999999998 - type: ndcg_at_5 value: 15.026 - type: precision_at_1 value: 14.969 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 1.133 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 9.516 - type: precision_at_5 value: 7.191 - type: recall_at_1 value: 7.406 - type: recall_at_10 value: 22.404 - type: recall_at_100 value: 47.351 - type: recall_at_1000 value: 74.701 - type: recall_at_3 value: 13.108 - type: recall_at_5 value: 16.531000000000002 - task: type: Retrieval dataset: name: MTEB HotpotQA type: None config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 20.662 - type: map_at_10 value: 28.956 - type: map_at_100 value: 29.942999999999998 - type: map_at_1000 value: 30.052 - type: map_at_3 value: 26.767999999999997 - type: map_at_5 value: 28.011000000000003 - type: mrr_at_1 value: 41.323 - type: mrr_at_10 value: 49.242999999999995 - type: mrr_at_100 value: 49.97 - type: mrr_at_1000 value: 50.016000000000005 - type: mrr_at_3 value: 47.207 - type: mrr_at_5 value: 48.364000000000004 - type: ndcg_at_1 value: 41.323 - type: ndcg_at_10 value: 36.756 - type: ndcg_at_100 value: 41.189 - type: ndcg_at_1000 value: 43.667 - type: ndcg_at_3 value: 32.690999999999995 - type: ndcg_at_5 value: 34.703 - type: precision_at_1 value: 41.323 - type: precision_at_10 value: 8.015 - type: precision_at_100 value: 1.155 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 20.612 - type: precision_at_5 value: 13.961000000000002 - type: recall_at_1 value: 20.662 - type: recall_at_10 value: 40.074 - type: recall_at_100 value: 57.745000000000005 - type: recall_at_1000 value: 74.24 - type: recall_at_3 value: 30.918 - type: recall_at_5 value: 34.902 - task: type: Classification dataset: name: MTEB ImdbClassification type: None config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 64.62239999999998 - type: ap value: 59.505106899987936 - type: f1 value: 64.39587267286105 - task: type: Retrieval dataset: name: MTEB MSMARCO type: None config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 6.507000000000001 - type: map_at_10 value: 11.542 - type: map_at_100 value: 12.542 - type: map_at_1000 value: 12.658 - type: map_at_3 value: 9.67 - type: map_at_5 value: 10.631 - type: mrr_at_1 value: 6.705 - type: mrr_at_10 value: 11.857 - type: mrr_at_100 value: 12.863 - type: mrr_at_1000 value: 12.974 - type: mrr_at_3 value: 9.957 - type: mrr_at_5 value: 10.933 - type: ndcg_at_1 value: 6.705 - type: ndcg_at_10 value: 14.764 - type: ndcg_at_100 value: 20.258000000000003 - type: ndcg_at_1000 value: 23.685000000000002 - type: ndcg_at_3 value: 10.809000000000001 - type: ndcg_at_5 value: 12.543000000000001 - type: precision_at_1 value: 6.705 - type: precision_at_10 value: 2.579 - type: precision_at_100 value: 0.543 - type: precision_at_1000 value: 0.084 - type: precision_at_3 value: 4.771 - type: precision_at_5 value: 3.734 - type: recall_at_1 value: 6.507000000000001 - type: recall_at_10 value: 24.842 - type: recall_at_100 value: 51.697 - type: recall_at_1000 value: 79.081 - type: recall_at_3 value: 13.828 - type: recall_at_5 value: 18.009 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: None config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 84.40264477884178 - type: f1 value: 83.43871348215795 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: None config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 54.90196078431372 - type: f1 value: 35.66115135754105 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: None config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.371889710827176 - type: f1 value: 58.91304009131599 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: None config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 67.52185608607937 - type: f1 value: 66.27921261407421 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: None config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 30.40912967319626 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: None config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 26.77476593032722 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: None config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.522211560565317 - type: mrr value: 31.540554976019745 - task: type: Retrieval dataset: name: MTEB NFCorpus type: None config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 2.871 - type: map_at_10 value: 6.643000000000001 - type: map_at_100 value: 8.801 - type: map_at_1000 value: 9.961 - type: map_at_3 value: 4.862 - type: map_at_5 value: 5.704 - type: mrr_at_1 value: 29.102 - type: mrr_at_10 value: 38.79 - type: mrr_at_100 value: 39.616 - type: mrr_at_1000 value: 39.659 - type: mrr_at_3 value: 35.913000000000004 - type: mrr_at_5 value: 37.74 - type: ndcg_at_1 value: 27.554000000000002 - type: ndcg_at_10 value: 22.215 - type: ndcg_at_100 value: 21.386 - type: ndcg_at_1000 value: 30.615 - type: ndcg_at_3 value: 25.546000000000003 - type: ndcg_at_5 value: 24.425 - type: precision_at_1 value: 29.102 - type: precision_at_10 value: 17.121 - type: precision_at_100 value: 6.146 - type: precision_at_1000 value: 1.9029999999999998 - type: precision_at_3 value: 24.871 - type: precision_at_5 value: 22.291 - type: recall_at_1 value: 2.871 - type: recall_at_10 value: 10.184999999999999 - type: recall_at_100 value: 24.057000000000002 - type: recall_at_1000 value: 56.788000000000004 - type: recall_at_3 value: 5.606 - type: recall_at_5 value: 7.353 - task: type: Retrieval dataset: name: MTEB NQ type: None config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 10.455 - type: map_at_10 value: 17.904999999999998 - type: map_at_100 value: 19.215 - type: map_at_1000 value: 19.314 - type: map_at_3 value: 15.133 - type: map_at_5 value: 16.624 - type: mrr_at_1 value: 11.906 - type: mrr_at_10 value: 19.595000000000002 - type: mrr_at_100 value: 20.765 - type: mrr_at_1000 value: 20.845 - type: mrr_at_3 value: 16.7 - type: mrr_at_5 value: 18.314 - type: ndcg_at_1 value: 11.906 - type: ndcg_at_10 value: 22.733999999999998 - type: ndcg_at_100 value: 29.179 - type: ndcg_at_1000 value: 31.848 - type: ndcg_at_3 value: 16.98 - type: ndcg_at_5 value: 19.695 - type: precision_at_1 value: 11.906 - type: precision_at_10 value: 4.234999999999999 - type: precision_at_100 value: 0.79 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 7.976 - type: precision_at_5 value: 6.286 - type: recall_at_1 value: 10.455 - type: recall_at_10 value: 36.114000000000004 - type: recall_at_100 value: 65.742 - type: recall_at_1000 value: 86.22800000000001 - type: recall_at_3 value: 20.826 - type: recall_at_5 value: 27.165 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: None config: default split: test revision: None metrics: - type: map_at_1 value: 63.336000000000006 - type: map_at_10 value: 76.859 - type: map_at_100 value: 77.679 - type: map_at_1000 value: 77.705 - type: map_at_3 value: 73.681 - type: map_at_5 value: 75.558 - type: mrr_at_1 value: 73.13 - type: mrr_at_10 value: 80.757 - type: mrr_at_100 value: 80.99300000000001 - type: mrr_at_1000 value: 80.99499999999999 - type: mrr_at_3 value: 79.267 - type: mrr_at_5 value: 80.209 - type: ndcg_at_1 value: 73.15 - type: ndcg_at_10 value: 81.693 - type: ndcg_at_100 value: 83.733 - type: ndcg_at_1000 value: 83.943 - type: ndcg_at_3 value: 77.866 - type: ndcg_at_5 value: 79.779 - type: precision_at_1 value: 73.15 - type: precision_at_10 value: 12.603 - type: precision_at_100 value: 1.51 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 34.123 - type: precision_at_5 value: 22.636 - type: recall_at_1 value: 63.336000000000006 - type: recall_at_10 value: 91.36999999999999 - type: recall_at_100 value: 98.831 - type: recall_at_1000 value: 99.901 - type: recall_at_3 value: 80.495 - type: recall_at_5 value: 85.799 - type: map_at_1 value: 3.5479999999999996 - type: map_at_10 value: 8.923 - type: map_at_100 value: 11.038 - type: map_at_1000 value: 11.384 - type: map_at_3 value: 6.387 - type: map_at_5 value: 7.646999999999999 - type: mrr_at_1 value: 17.5 - type: mrr_at_10 value: 27.71 - type: mrr_at_100 value: 28.898000000000003 - type: mrr_at_1000 value: 28.96 - type: mrr_at_3 value: 24.282999999999998 - type: mrr_at_5 value: 26.123 - type: ndcg_at_1 value: 17.5 - type: ndcg_at_10 value: 15.831999999999999 - type: ndcg_at_100 value: 24.478 - type: ndcg_at_1000 value: 30.548 - type: ndcg_at_3 value: 14.66 - type: ndcg_at_5 value: 12.969 - type: precision_at_1 value: 17.5 - type: precision_at_10 value: 8.38 - type: precision_at_100 value: 2.103 - type: precision_at_1000 value: 0.356 - type: precision_at_3 value: 13.866999999999999 - type: precision_at_5 value: 11.58 - type: recall_at_1 value: 3.5479999999999996 - type: recall_at_10 value: 16.958000000000002 - type: recall_at_100 value: 42.687999999999995 - type: recall_at_1000 value: 72.173 - type: recall_at_3 value: 8.437999999999999 - type: recall_at_5 value: 11.738 - type: map_at_1 value: 0.186 - type: map_at_10 value: 1.2149999999999999 - type: map_at_100 value: 6.516 - type: map_at_1000 value: 14.704999999999998 - type: map_at_3 value: 0.469 - type: map_at_5 value: 0.701 - type: mrr_at_1 value: 72.0 - type: mrr_at_10 value: 80.238 - type: mrr_at_100 value: 80.622 - type: mrr_at_1000 value: 80.622 - type: mrr_at_3 value: 79.667 - type: mrr_at_5 value: 79.667 - type: ndcg_at_1 value: 64.0 - type: ndcg_at_10 value: 57.147000000000006 - type: ndcg_at_100 value: 40.5 - type: ndcg_at_1000 value: 33.954 - type: ndcg_at_3 value: 62.754 - type: ndcg_at_5 value: 59.933 - type: precision_at_1 value: 72.0 - type: precision_at_10 value: 60.6 - 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type: recall_at_1000 value: 98.667 - type: recall_at_3 value: 54.193999999999996 - type: recall_at_5 value: 61.138999999999996 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: None config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.73762376237623 - type: cos_sim_ap value: 91.61413659372461 - type: cos_sim_f1 value: 86.34046890927624 - type: cos_sim_precision value: 88.04573804573805 - type: cos_sim_recall value: 84.7 - type: dot_accuracy value: 99.73762376237623 - type: dot_ap value: 91.61413659372461 - type: dot_f1 value: 86.34046890927624 - type: dot_precision value: 88.04573804573805 - type: dot_recall value: 84.7 - type: euclidean_accuracy value: 99.73762376237623 - type: euclidean_ap value: 91.61413659372461 - type: euclidean_f1 value: 86.34046890927624 - type: euclidean_precision value: 88.04573804573805 - type: euclidean_recall value: 84.7 - type: manhattan_accuracy value: 99.74059405940594 - 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type: cos_sim_pearson value: 29.69015090602311 - type: cos_sim_spearman value: 30.124791626004075 - type: dot_pearson value: 29.69015070868056 - type: dot_spearman value: 30.09621990241238 - task: type: Retrieval dataset: name: MTEB Touche2020 type: None config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics: - type: map_at_1 value: 2.0660000000000003 - type: map_at_10 value: 9.783999999999999 - type: map_at_100 value: 16.005 - type: map_at_1000 value: 17.694 - type: map_at_3 value: 4.524 - type: map_at_5 value: 6.651 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 49.26 - type: mrr_at_100 value: 49.791000000000004 - type: mrr_at_1000 value: 49.791000000000004 - type: mrr_at_3 value: 45.238 - type: mrr_at_5 value: 47.177 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.35 - type: ndcg_at_100 value: 38.078 - type: ndcg_at_1000 value: 49.222 - type: ndcg_at_3 value: 28.749000000000002 - type: ndcg_at_5 value: 28.156 - type: precision_at_1 value: 32.653 - 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type: euclidean_precision value: 69.10146492945385 - type: euclidean_recall value: 78.81121034801355 - type: manhattan_accuracy value: 86.47882951061435 - type: manhattan_ap value: 80.56146544234434 - type: manhattan_f1 value: 73.43608995415659 - type: manhattan_precision value: 69.1267414203194 - type: manhattan_recall value: 78.31844779796735 - type: max_accuracy value: 86.58167423448597 - type: max_ap value: 80.70277058558774 - type: max_f1 value: 73.6376389338513 ---

[ "SUMMARIZATION" ]

[ "BIOSSES", "SCIFACT" ]

NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-gooaq-en

NickyNicky

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:3012496", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "es", "dataset:sentence-transformers/gooaq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2025-01-16T00:58:56

2025-01-22T03:45:27

0

2

--- datasets: - sentence-transformers/gooaq language: - en - es library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:3012496 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en  ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(256000, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-gooaq-en") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0007 | 1 | 48.9183 | - | | 0.0682 | 100 | 24.7453 | 3.5934 | | 0.1363 | 200 | 8.3975 | 2.4385 | | 0.2045 | 300 | 6.3171 | 1.9962 | | 0.2727 | 400 | 5.3817 | 1.7536 | | 0.3408 | 500 | 4.8295 | 1.6392 | | 0.4090 | 600 | 4.4745 | 1.5070 | | 0.4772 | 700 | 4.1783 | 1.4406 | | 0.5453 | 800 | 3.952 | 1.3655 | | 0.6135 | 900 | 3.7352 | 1.3114 | | 0.6817 | 1000 | 3.6185 | 1.2551 | | 0.7498 | 1100 | 3.4514 | 1.2143 | | 0.8180 | 1200 | 3.3535 | 1.1816 | | 0.8862 | 1300 | 3.2741 | 1.1527 | | 0.9543 | 1400 | 3.1862 | 1.1411 | ### Framework Versions - Python: 3.11.11 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## NanoBEIREvaluator > 0.8 ``` { "NanoDBPedia_cosine_accuracy@3": 0.86, "NanoDBPedia_cosine_accuracy@5": 0.92, "NanoDBPedia_cosine_accuracy@10": 0.96, "NanoFEVER_cosine_accuracy@3": 0.86, "NanoFEVER_cosine_accuracy@5": 0.92, "NanoFEVER_cosine_accuracy@10": 0.96, "NanoHotpotQA_cosine_accuracy@3": 0.82, "NanoHotpotQA_cosine_accuracy@5": 0.84, "NanoHotpotQA_cosine_accuracy@10": 0.88, "NanoQuoraRetrieval_cosine_accuracy@1": 0.88, "NanoQuoraRetrieval_cosine_accuracy@3": 0.96, "NanoQuoraRetrieval_cosine_accuracy@5": 1.0, "NanoQuoraRetrieval_cosine_accuracy@10": 1.0, "NanoSCIDOCS_cosine_accuracy@5": 0.82, "NanoSCIDOCS_cosine_accuracy@10": 0.92, "NanoArguAna_cosine_accuracy@10": 0.92, "NanoSciFact_cosine_accuracy@10": 0.88, "NanoTouche2020_cosine_accuracy@3": 0.8367346938775511, "NanoTouche2020_cosine_accuracy@5": 0.9183673469387755, "NanoTouche2020_cosine_accuracy@10": 0.9387755102040817, "NanoBEIR_mean_cosine_accuracy@10": 0.8583673469387756 } ```` ## All NanoBEIREvaluator ```bibtext {'NanoClimateFEVER_cosine_accuracy@1': 0.28, 'NanoClimateFEVER_cosine_accuracy@3': 0.44, 'NanoClimateFEVER_cosine_accuracy@5': 0.54, 'NanoClimateFEVER_cosine_accuracy@10': 0.72, 'NanoClimateFEVER_cosine_precision@1': 0.28, 'NanoClimateFEVER_cosine_precision@3': 0.15333333333333332, 'NanoClimateFEVER_cosine_precision@5': 0.124, 'NanoClimateFEVER_cosine_precision@10': 0.08999999999999998, 'NanoClimateFEVER_cosine_recall@1': 0.145, 'NanoClimateFEVER_cosine_recall@3': 0.205, 'NanoClimateFEVER_cosine_recall@5': 0.264, 'NanoClimateFEVER_cosine_recall@10': 0.36200000000000004, 'NanoClimateFEVER_cosine_ndcg@10': 0.2957527689242254, 'NanoClimateFEVER_cosine_mrr@10': 0.3996666666666668, 'NanoClimateFEVER_cosine_map@100': 0.23258384801937396, 'NanoDBPedia_cosine_accuracy@1': 0.68, 'NanoDBPedia_cosine_accuracy@3': 0.86, 'NanoDBPedia_cosine_accuracy@5': 0.92, 'NanoDBPedia_cosine_accuracy@10': 0.96, 'NanoDBPedia_cosine_precision@1': 0.68, 'NanoDBPedia_cosine_precision@3': 0.56, 'NanoDBPedia_cosine_precision@5': 0.5120000000000001, 'NanoDBPedia_cosine_precision@10': 0.43800000000000006, 'NanoDBPedia_cosine_recall@1': 0.07601531530835434, 'NanoDBPedia_cosine_recall@3': 0.1438904710839341, 'NanoDBPedia_cosine_recall@5': 0.20681359525684506, 'NanoDBPedia_cosine_recall@10': 0.319966975132044, 'NanoDBPedia_cosine_ndcg@10': 0.5501100350453579, 'NanoDBPedia_cosine_mrr@10': 0.7855000000000001, 'NanoDBPedia_cosine_map@100': 0.39476156890024533, 'NanoFEVER_cosine_accuracy@1': 0.68, 'NanoFEVER_cosine_accuracy@3': 0.86, 'NanoFEVER_cosine_accuracy@5': 0.92, 'NanoFEVER_cosine_accuracy@10': 0.96, 'NanoFEVER_cosine_precision@1': 0.68, 'NanoFEVER_cosine_precision@3': 0.29333333333333333, 'NanoFEVER_cosine_precision@5': 0.19199999999999995, 'NanoFEVER_cosine_precision@10': 0.10199999999999998, 'NanoFEVER_cosine_recall@1': 0.6266666666666666, 'NanoFEVER_cosine_recall@3': 0.8133333333333332, 'NanoFEVER_cosine_recall@5': 0.8833333333333333, 'NanoFEVER_cosine_recall@10': 0.9233333333333333, 'NanoFEVER_cosine_ndcg@10': 0.7933479848498471, 'NanoFEVER_cosine_mrr@10': 0.7780793650793651, 'NanoFEVER_cosine_map@100': 0.7406571665049926, 'NanoFiQA2018_cosine_accuracy@1': 0.46, 'NanoFiQA2018_cosine_accuracy@3': 0.64, 'NanoFiQA2018_cosine_accuracy@5': 0.7, 'NanoFiQA2018_cosine_accuracy@10': 0.72, 'NanoFiQA2018_cosine_precision@1': 0.46, 'NanoFiQA2018_cosine_precision@3': 0.2866666666666666, 'NanoFiQA2018_cosine_precision@5': 0.22399999999999998, 'NanoFiQA2018_cosine_precision@10': 0.12999999999999998, 'NanoFiQA2018_cosine_recall@1': 0.23924603174603173, 'NanoFiQA2018_cosine_recall@3': 0.4251031746031746, 'NanoFiQA2018_cosine_recall@5': 0.5099603174603174, 'NanoFiQA2018_cosine_recall@10': 0.566015873015873, 'NanoFiQA2018_cosine_ndcg@10': 0.4774545077577204, 'NanoFiQA2018_cosine_mrr@10': 0.5475555555555556, 'NanoFiQA2018_cosine_map@100': 0.4125452702654584, 'NanoHotpotQA_cosine_accuracy@1': 0.64, 'NanoHotpotQA_cosine_accuracy@3': 0.82, 'NanoHotpotQA_cosine_accuracy@5': 0.84, 'NanoHotpotQA_cosine_accuracy@10': 0.88, 'NanoHotpotQA_cosine_precision@1': 0.64, 'NanoHotpotQA_cosine_precision@3': 0.3533333333333333, 'NanoHotpotQA_cosine_precision@5': 0.23599999999999993, 'NanoHotpotQA_cosine_precision@10': 0.128, 'NanoHotpotQA_cosine_recall@1': 0.32, 'NanoHotpotQA_cosine_recall@3': 0.53, 'NanoHotpotQA_cosine_recall@5': 0.59, 'NanoHotpotQA_cosine_recall@10': 0.64, 'NanoHotpotQA_cosine_ndcg@10': 0.5959681682828366, 'NanoHotpotQA_cosine_mrr@10': 0.723888888888889, 'NanoHotpotQA_cosine_map@100': 0.5262469568756968, 'NanoMSMARCO_cosine_accuracy@1': 0.36, 'NanoMSMARCO_cosine_accuracy@3': 0.52, 'NanoMSMARCO_cosine_accuracy@5': 0.58, 'NanoMSMARCO_cosine_accuracy@10': 0.8, 'NanoMSMARCO_cosine_precision@1': 0.36, 'NanoMSMARCO_cosine_precision@3': 0.1733333333333333, 'NanoMSMARCO_cosine_precision@5': 0.11599999999999999, 'NanoMSMARCO_cosine_precision@10': 0.08, 'NanoMSMARCO_cosine_recall@1': 0.36, 'NanoMSMARCO_cosine_recall@3': 0.52, 'NanoMSMARCO_cosine_recall@5': 0.58, 'NanoMSMARCO_cosine_recall@10': 0.8, 'NanoMSMARCO_cosine_ndcg@10': 0.5539831330912274, 'NanoMSMARCO_cosine_mrr@10': 0.47960317460317464, 'NanoMSMARCO_cosine_map@100': 0.4907628900864195, 'NanoNFCorpus_cosine_accuracy@1': 0.42, 'NanoNFCorpus_cosine_accuracy@3': 0.56, 'NanoNFCorpus_cosine_accuracy@5': 0.6, 'NanoNFCorpus_cosine_accuracy@10': 0.7, 'NanoNFCorpus_cosine_precision@1': 0.42, 'NanoNFCorpus_cosine_precision@3': 0.3466666666666666, 'NanoNFCorpus_cosine_precision@5': 0.32800000000000007, 'NanoNFCorpus_cosine_precision@10': 0.286, 'NanoNFCorpus_cosine_recall@1': 0.03391318439564492, 'NanoNFCorpus_cosine_recall@3': 0.06311668492872162, 'NanoNFCorpus_cosine_recall@5': 0.08191277059586696, 'NanoNFCorpus_cosine_recall@10': 0.13476845853527392, 'NanoNFCorpus_cosine_ndcg@10': 0.3322933792371396, 'NanoNFCorpus_cosine_mrr@10': 0.4983333333333333, 'NanoNFCorpus_cosine_map@100': 0.13985354018581944, 'NanoNQ_cosine_accuracy@1': 0.44, 'NanoNQ_cosine_accuracy@3': 0.64, 'NanoNQ_cosine_accuracy@5': 0.66, 'NanoNQ_cosine_accuracy@10': 0.76, 'NanoNQ_cosine_precision@1': 0.44, 'NanoNQ_cosine_precision@3': 0.22, 'NanoNQ_cosine_precision@5': 0.14, 'NanoNQ_cosine_precision@10': 0.08199999999999999, 'NanoNQ_cosine_recall@1': 0.42, 'NanoNQ_cosine_recall@3': 0.62, 'NanoNQ_cosine_recall@5': 0.64, 'NanoNQ_cosine_recall@10': 0.75, 'NanoNQ_cosine_ndcg@10': 0.5903874296113161, 'NanoNQ_cosine_mrr@10': 0.5456349206349206, 'NanoNQ_cosine_map@100': 0.5437440035864959, 'NanoQuoraRetrieval_cosine_accuracy@1': 0.88, 'NanoQuoraRetrieval_cosine_accuracy@3': 0.96, 'NanoQuoraRetrieval_cosine_accuracy@5': 1.0, 'NanoQuoraRetrieval_cosine_accuracy@10': 1.0, 'NanoQuoraRetrieval_cosine_precision@1': 0.88, 'NanoQuoraRetrieval_cosine_precision@3': 0.3933333333333333, 'NanoQuoraRetrieval_cosine_precision@5': 0.256, 'NanoQuoraRetrieval_cosine_precision@10': 0.13599999999999998, 'NanoQuoraRetrieval_cosine_recall@1': 0.784, 'NanoQuoraRetrieval_cosine_recall@3': 0.9186666666666667, 'NanoQuoraRetrieval_cosine_recall@5': 0.976, 'NanoQuoraRetrieval_cosine_recall@10': 0.9933333333333334, 'NanoQuoraRetrieval_cosine_ndcg@10': 0.9367841595958026, 'NanoQuoraRetrieval_cosine_mrr@10': 0.9246666666666666, 'NanoQuoraRetrieval_cosine_map@100': 0.913554834054834, 'NanoSCIDOCS_cosine_accuracy@1': 0.52, 'NanoSCIDOCS_cosine_accuracy@3': 0.68, 'NanoSCIDOCS_cosine_accuracy@5': 0.82, 'NanoSCIDOCS_cosine_accuracy@10': 0.92, 'NanoSCIDOCS_cosine_precision@1': 0.52, 'NanoSCIDOCS_cosine_precision@3': 0.3933333333333333, 'NanoSCIDOCS_cosine_precision@5': 0.33599999999999997, 'NanoSCIDOCS_cosine_precision@10': 0.21600000000000003, 'NanoSCIDOCS_cosine_recall@1': 0.10966666666666666, 'NanoSCIDOCS_cosine_recall@3': 0.24466666666666664, 'NanoSCIDOCS_cosine_recall@5': 0.34566666666666657, 'NanoSCIDOCS_cosine_recall@10': 0.44266666666666665, 'NanoSCIDOCS_cosine_ndcg@10': 0.4328110226758414, 'NanoSCIDOCS_cosine_mrr@10': 0.6317222222222222, 'NanoSCIDOCS_cosine_map@100': 0.34997841607847063, 'NanoArguAna_cosine_accuracy@1': 0.2, 'NanoArguAna_cosine_accuracy@3': 0.56, 'NanoArguAna_cosine_accuracy@5': 0.76, 'NanoArguAna_cosine_accuracy@10': 0.92, 'NanoArguAna_cosine_precision@1': 0.2, 'NanoArguAna_cosine_precision@3': 0.18666666666666668, 'NanoArguAna_cosine_precision@5': 0.15200000000000002, 'NanoArguAna_cosine_precision@10': 0.092, 'NanoArguAna_cosine_recall@1': 0.2, 'NanoArguAna_cosine_recall@3': 0.56, 'NanoArguAna_cosine_recall@5': 0.76, 'NanoArguAna_cosine_recall@10': 0.92, 'NanoArguAna_cosine_ndcg@10': 0.5499071039525992, 'NanoArguAna_cosine_mrr@10': 0.43229365079365073, 'NanoArguAna_cosine_map@100': 0.43523820792684886, 'NanoSciFact_cosine_accuracy@1': 0.6, 'NanoSciFact_cosine_accuracy@3': 0.72, 'NanoSciFact_cosine_accuracy@5': 0.8, 'NanoSciFact_cosine_accuracy@10': 0.88, 'NanoSciFact_cosine_precision@1': 0.6, 'NanoSciFact_cosine_precision@3': 0.25333333333333335, 'NanoSciFact_cosine_precision@5': 0.18, 'NanoSciFact_cosine_precision@10': 0.09799999999999999, 'NanoSciFact_cosine_recall@1': 0.58, 'NanoSciFact_cosine_recall@3': 0.7, 'NanoSciFact_cosine_recall@5': 0.8, 'NanoSciFact_cosine_recall@10': 0.87, 'NanoSciFact_cosine_ndcg@10': 0.7265348054031264, 'NanoSciFact_cosine_mrr@10': 0.6841031746031746, 'NanoSciFact_cosine_map@100': 0.6810233866101422, 'NanoTouche2020_cosine_accuracy@1': 0.5102040816326531, 'NanoTouche2020_cosine_accuracy@3': 0.8367346938775511, 'NanoTouche2020_cosine_accuracy@5': 0.9183673469387755, 'NanoTouche2020_cosine_accuracy@10': 0.9387755102040817, 'NanoTouche2020_cosine_precision@1': 0.5102040816326531, 'NanoTouche2020_cosine_precision@3': 0.5374149659863945, 'NanoTouche2020_cosine_precision@5': 0.5061224489795918, 'NanoTouche2020_cosine_precision@10': 0.43265306122448977, 'NanoTouche2020_cosine_recall@1': 0.03546508562664911, 'NanoTouche2020_cosine_recall@3': 0.11189238805791148, 'NanoTouche2020_cosine_recall@5': 0.1673503566176574, 'NanoTouche2020_cosine_recall@10': 0.2818808841266296, 'NanoTouche2020_cosine_ndcg@10': 0.47479704449085264, 'NanoTouche2020_cosine_mrr@10': 0.6714285714285714, 'NanoTouche2020_cosine_map@100': 0.3438320372291555, 'NanoBEIR_mean_cosine_accuracy@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_accuracy@3': 0.6997488226059654, 'NanoBEIR_mean_cosine_accuracy@5': 0.7737205651491367, 'NanoBEIR_mean_cosine_accuracy@10': 0.8583673469387756, 'NanoBEIR_mean_cosine_precision@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_precision@3': 0.31928833071690216, 'NanoBEIR_mean_cosine_precision@5': 0.2540094191522763, 'NanoBEIR_mean_cosine_precision@10': 0.1777425431711146, 'NanoBEIR_mean_cosine_recall@1': 0.302305611570001, 'NanoBEIR_mean_cosine_recall@3': 0.4504361065646467, 'NanoBEIR_mean_cosine_recall@5': 0.5234643876869758, 'NanoBEIR_mean_cosine_recall@10': 0.6156896557033196, 'NanoBEIR_mean_cosine_ndcg@10': 0.5623178109936842, 'NanoBEIR_mean_cosine_mrr@10': 0.6232673992673993, 'NanoBEIR_mean_cosine_map@100': 0.47729093279415025} ``` ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-en-es

NickyNicky

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:4322286", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2025-01-16T04:07:42

2025-01-22T03:44:46

0

2

--- library_name: sentence-transformers license: apache-2.0 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4322286 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - '[''From the Home screen, swipe left to Apps.'', ''Tap Transfer my Data.'', ''Tap Menu (...).'', ''Tap Export to SD card.'']' - Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg of THC. ... This is a perfect product for both cannabis and chocolate lovers, who appreciate a little twist. - source_sentence: how to delete vdom in fortigate? sentences: - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - 'Both combination birth control pills and progestin-only pills may cause headaches as a side effect. Additional side effects of birth control pills may include: breast tenderness. nausea.' - White cheese tends to show imperfections more readily and as consumers got more used to yellow-orange cheese, it became an expected option. Today, many cheddars are yellow. While most cheesemakers use annatto, some use an artificial coloring agent instead, according to Sachs. - source_sentence: where are earthquakes most likely to occur on earth? sentences: - Zelle in the Bank of the America app is a fast, safe, and easy way to send and receive money with family and friends who have a bank account in the U.S., all with no fees. Money moves in minutes directly between accounts that are already enrolled with Zelle. - It takes about 3 days for a spacecraft to reach the Moon. During that time a spacecraft travels at least 240,000 miles (386,400 kilometers) which is the distance between Earth and the Moon. - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - source_sentence: fix iphone is disabled connect to itunes without itunes? sentences: - To fix a disabled iPhone or iPad without iTunes, you have to erase your device. Click on the "Erase iPhone" option and confirm your selection. Wait for a while as the "Find My iPhone" feature will remotely erase your iOS device. Needless to say, it will also disable its lock. - How Māui brought fire to the world. One evening, after eating a hearty meal, Māui lay beside his fire staring into the flames. ... In the middle of the night, while everyone was sleeping, Māui went from village to village and extinguished all the fires until not a single fire burned in the world. - Angry Orchard makes a variety of year-round craft cider styles, including Angry Orchard Crisp Apple, a fruit-forward hard cider that balances the sweetness of culinary apples with dryness and bright acidity of bittersweet apples for a complex, refreshing taste. - source_sentence: how to reverse a video on tiktok that's not yours? sentences: - '[''Tap "Effects" at the bottom of your screen — it\''s an icon that looks like a clock. Open the Effects menu. ... '', ''At the end of the new list that appears, tap "Time." Select "Time" at the end. ... '', ''Select "Reverse" — you\''ll then see a preview of your new, reversed video appear on the screen.'']' - Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a total initial investment range of $157,800 to $438,000. The initial cost of a franchise includes several fees -- Unlock this franchise to better understand the costs such as training and territory fees. - Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating. ---  # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity    ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(256000, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("NickyNicky/StaticEmbedding-MatryoshkaLoss-gemma-2-2b-en-es") # Run inference sentences = [ "how to reverse a video on tiktok that's not yours?", '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']', 'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 4,322,286 training samples english and spanish [dataset news, QA, summary,news cryptocurrency]. * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 10,005 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 768, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `learning_rate`: 0.2 - `warmup_ratio`: 0.1 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 2048 - `per_device_eval_batch_size`: 2048 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0005 | 1 | 49.8746 | - | | 0.0474 | 100 | 35.8567 | 7.1776 | | 0.0947 | 200 | 13.988 | 3.2848 | | 0.1421 | 300 | 8.0009 | 2.3610 | | 0.1895 | 400 | 6.3293 | 2.0293 | | 0.2369 | 500 | 5.6296 | 1.8849 | | 0.2842 | 600 | 5.238 | 1.7495 | | 0.3316 | 700 | 4.9115 | 1.6694 | | 0.3790 | 800 | 4.5779 | 1.5583 | | 0.4263 | 900 | 4.2608 | 1.4784 | | 0.4737 | 1000 | 4.0893 | 1.4020 | | 0.5211 | 1100 | 3.8669 | 1.3426 | | 0.5685 | 1200 | 3.7505 | 1.3160 | | 0.6158 | 1300 | 3.6529 | 1.2822 | | 0.6632 | 1400 | 3.5203 | 1.2612 | | 0.7106 | 1500 | 5.1906 | 1.4469 | | 0.7579 | 1600 | 4.0273 | 1.6219 | | 0.8053 | 1700 | 4.8308 | 3.1338 | | 0.8527 | 1800 | 0.5336 | 3.2854 | | 0.9000 | 1900 | 0.3 | 3.3757 | | 0.9474 | 2000 | 0.0886 | 3.3620 | | 0.9948 | 2100 | 0.0817 | 3.3510 | | 1.0417 | 2200 | 4.0692 | 1.3638 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.5.1+cu121 - Accelerate: 1.2.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## NanoBEIREvaluator > 0.8 ``` { "NanoDBPedia_cosine_accuracy@3": 0.86, "NanoDBPedia_cosine_accuracy@5": 0.92, "NanoDBPedia_cosine_accuracy@10": 0.96, "NanoFEVER_cosine_accuracy@3": 0.86, "NanoFEVER_cosine_accuracy@5": 0.92, "NanoFEVER_cosine_accuracy@10": 0.96, "NanoQuoraRetrieval_cosine_accuracy@1": 0.88, "NanoQuoraRetrieval_cosine_accuracy@3": 0.96, "NanoQuoraRetrieval_cosine_accuracy@5": 1.0, "NanoQuoraRetrieval_cosine_accuracy@10": 1.0, "NanoSCIDOCS_cosine_accuracy@5": 0.82, "NanoSCIDOCS_cosine_accuracy@10": 0.92, "NanoArguAna_cosine_accuracy@10": 0.92, "NanoSciFact_cosine_accuracy@10": 0.88, "NanoHotpotQA_cosine_accuracy@10": 0.88, "NanoTouche2020_cosine_accuracy@5": 0.9183673469387755, "NanoTouche2020_cosine_accuracy@10": 0.9387755102040817, "NanoBEIR_mean_cosine_accuracy@10": 0.8583673469387756 } ``` ## All NanoBEIREvaluator ``` {'NanoClimateFEVER_cosine_accuracy@1': 0.28, 'NanoClimateFEVER_cosine_accuracy@3': 0.44, 'NanoClimateFEVER_cosine_accuracy@5': 0.54, 'NanoClimateFEVER_cosine_accuracy@10': 0.72, 'NanoClimateFEVER_cosine_precision@1': 0.28, 'NanoClimateFEVER_cosine_precision@3': 0.15333333333333332, 'NanoClimateFEVER_cosine_precision@5': 0.124, 'NanoClimateFEVER_cosine_precision@10': 0.08999999999999998, 'NanoClimateFEVER_cosine_recall@1': 0.145, 'NanoClimateFEVER_cosine_recall@3': 0.205, 'NanoClimateFEVER_cosine_recall@5': 0.264, 'NanoClimateFEVER_cosine_recall@10': 0.36200000000000004, 'NanoClimateFEVER_cosine_ndcg@10': 0.2957527689242254, 'NanoClimateFEVER_cosine_mrr@10': 0.3996666666666668, 'NanoClimateFEVER_cosine_map@100': 0.23258384801937396, 'NanoDBPedia_cosine_accuracy@1': 0.68, 'NanoDBPedia_cosine_accuracy@3': 0.86, 'NanoDBPedia_cosine_accuracy@5': 0.92, 'NanoDBPedia_cosine_accuracy@10': 0.96, 'NanoDBPedia_cosine_precision@1': 0.68, 'NanoDBPedia_cosine_precision@3': 0.56, 'NanoDBPedia_cosine_precision@5': 0.5120000000000001, 'NanoDBPedia_cosine_precision@10': 0.43800000000000006, 'NanoDBPedia_cosine_recall@1': 0.07601531530835434, 'NanoDBPedia_cosine_recall@3': 0.1438904710839341, 'NanoDBPedia_cosine_recall@5': 0.20681359525684506, 'NanoDBPedia_cosine_recall@10': 0.319966975132044, 'NanoDBPedia_cosine_ndcg@10': 0.5501100350453579, 'NanoDBPedia_cosine_mrr@10': 0.7855000000000001, 'NanoDBPedia_cosine_map@100': 0.39476156890024533, 'NanoFEVER_cosine_accuracy@1': 0.68, 'NanoFEVER_cosine_accuracy@3': 0.86, 'NanoFEVER_cosine_accuracy@5': 0.92, 'NanoFEVER_cosine_accuracy@10': 0.96, 'NanoFEVER_cosine_precision@1': 0.68, 'NanoFEVER_cosine_precision@3': 0.29333333333333333, 'NanoFEVER_cosine_precision@5': 0.19199999999999995, 'NanoFEVER_cosine_precision@10': 0.10199999999999998, 'NanoFEVER_cosine_recall@1': 0.6266666666666666, 'NanoFEVER_cosine_recall@3': 0.8133333333333332, 'NanoFEVER_cosine_recall@5': 0.8833333333333333, 'NanoFEVER_cosine_recall@10': 0.9233333333333333, 'NanoFEVER_cosine_ndcg@10': 0.7933479848498471, 'NanoFEVER_cosine_mrr@10': 0.7780793650793651, 'NanoFEVER_cosine_map@100': 0.7406571665049926, 'NanoFiQA2018_cosine_accuracy@1': 0.46, 'NanoFiQA2018_cosine_accuracy@3': 0.64, 'NanoFiQA2018_cosine_accuracy@5': 0.7, 'NanoFiQA2018_cosine_accuracy@10': 0.72, 'NanoFiQA2018_cosine_precision@1': 0.46, 'NanoFiQA2018_cosine_precision@3': 0.2866666666666666, 'NanoFiQA2018_cosine_precision@5': 0.22399999999999998, 'NanoFiQA2018_cosine_precision@10': 0.12999999999999998, 'NanoFiQA2018_cosine_recall@1': 0.23924603174603173, 'NanoFiQA2018_cosine_recall@3': 0.4251031746031746, 'NanoFiQA2018_cosine_recall@5': 0.5099603174603174, 'NanoFiQA2018_cosine_recall@10': 0.566015873015873, 'NanoFiQA2018_cosine_ndcg@10': 0.4774545077577204, 'NanoFiQA2018_cosine_mrr@10': 0.5475555555555556, 'NanoFiQA2018_cosine_map@100': 0.4125452702654584, 'NanoHotpotQA_cosine_accuracy@1': 0.64, 'NanoHotpotQA_cosine_accuracy@3': 0.82, 'NanoHotpotQA_cosine_accuracy@5': 0.84, 'NanoHotpotQA_cosine_accuracy@10': 0.88, 'NanoHotpotQA_cosine_precision@1': 0.64, 'NanoHotpotQA_cosine_precision@3': 0.3533333333333333, 'NanoHotpotQA_cosine_precision@5': 0.23599999999999993, 'NanoHotpotQA_cosine_precision@10': 0.128, 'NanoHotpotQA_cosine_recall@1': 0.32, 'NanoHotpotQA_cosine_recall@3': 0.53, 'NanoHotpotQA_cosine_recall@5': 0.59, 'NanoHotpotQA_cosine_recall@10': 0.64, 'NanoHotpotQA_cosine_ndcg@10': 0.5959681682828366, 'NanoHotpotQA_cosine_mrr@10': 0.723888888888889, 'NanoHotpotQA_cosine_map@100': 0.5262469568756968, 'NanoMSMARCO_cosine_accuracy@1': 0.36, 'NanoMSMARCO_cosine_accuracy@3': 0.52, 'NanoMSMARCO_cosine_accuracy@5': 0.58, 'NanoMSMARCO_cosine_accuracy@10': 0.8, 'NanoMSMARCO_cosine_precision@1': 0.36, 'NanoMSMARCO_cosine_precision@3': 0.1733333333333333, 'NanoMSMARCO_cosine_precision@5': 0.11599999999999999, 'NanoMSMARCO_cosine_precision@10': 0.08, 'NanoMSMARCO_cosine_recall@1': 0.36, 'NanoMSMARCO_cosine_recall@3': 0.52, 'NanoMSMARCO_cosine_recall@5': 0.58, 'NanoMSMARCO_cosine_recall@10': 0.8, 'NanoMSMARCO_cosine_ndcg@10': 0.5539831330912274, 'NanoMSMARCO_cosine_mrr@10': 0.47960317460317464, 'NanoMSMARCO_cosine_map@100': 0.4907628900864195, 'NanoNFCorpus_cosine_accuracy@1': 0.42, 'NanoNFCorpus_cosine_accuracy@3': 0.56, 'NanoNFCorpus_cosine_accuracy@5': 0.6, 'NanoNFCorpus_cosine_accuracy@10': 0.7, 'NanoNFCorpus_cosine_precision@1': 0.42, 'NanoNFCorpus_cosine_precision@3': 0.3466666666666666, 'NanoNFCorpus_cosine_precision@5': 0.32800000000000007, 'NanoNFCorpus_cosine_precision@10': 0.286, 'NanoNFCorpus_cosine_recall@1': 0.03391318439564492, 'NanoNFCorpus_cosine_recall@3': 0.06311668492872162, 'NanoNFCorpus_cosine_recall@5': 0.08191277059586696, 'NanoNFCorpus_cosine_recall@10': 0.13476845853527392, 'NanoNFCorpus_cosine_ndcg@10': 0.3322933792371396, 'NanoNFCorpus_cosine_mrr@10': 0.4983333333333333, 'NanoNFCorpus_cosine_map@100': 0.13985354018581944, 'NanoNQ_cosine_accuracy@1': 0.44, 'NanoNQ_cosine_accuracy@3': 0.64, 'NanoNQ_cosine_accuracy@5': 0.66, 'NanoNQ_cosine_accuracy@10': 0.76, 'NanoNQ_cosine_precision@1': 0.44, 'NanoNQ_cosine_precision@3': 0.22, 'NanoNQ_cosine_precision@5': 0.14, 'NanoNQ_cosine_precision@10': 0.08199999999999999, 'NanoNQ_cosine_recall@1': 0.42, 'NanoNQ_cosine_recall@3': 0.62, 'NanoNQ_cosine_recall@5': 0.64, 'NanoNQ_cosine_recall@10': 0.75, 'NanoNQ_cosine_ndcg@10': 0.5903874296113161, 'NanoNQ_cosine_mrr@10': 0.5456349206349206, 'NanoNQ_cosine_map@100': 0.5437440035864959, 'NanoQuoraRetrieval_cosine_accuracy@1': 0.88, 'NanoQuoraRetrieval_cosine_accuracy@3': 0.96, 'NanoQuoraRetrieval_cosine_accuracy@5': 1.0, 'NanoQuoraRetrieval_cosine_accuracy@10': 1.0, 'NanoQuoraRetrieval_cosine_precision@1': 0.88, 'NanoQuoraRetrieval_cosine_precision@3': 0.3933333333333333, 'NanoQuoraRetrieval_cosine_precision@5': 0.256, 'NanoQuoraRetrieval_cosine_precision@10': 0.13599999999999998, 'NanoQuoraRetrieval_cosine_recall@1': 0.784, 'NanoQuoraRetrieval_cosine_recall@3': 0.9186666666666667, 'NanoQuoraRetrieval_cosine_recall@5': 0.976, 'NanoQuoraRetrieval_cosine_recall@10': 0.9933333333333334, 'NanoQuoraRetrieval_cosine_ndcg@10': 0.9367841595958026, 'NanoQuoraRetrieval_cosine_mrr@10': 0.9246666666666666, 'NanoQuoraRetrieval_cosine_map@100': 0.913554834054834, 'NanoSCIDOCS_cosine_accuracy@1': 0.52, 'NanoSCIDOCS_cosine_accuracy@3': 0.68, 'NanoSCIDOCS_cosine_accuracy@5': 0.82, 'NanoSCIDOCS_cosine_accuracy@10': 0.92, 'NanoSCIDOCS_cosine_precision@1': 0.52, 'NanoSCIDOCS_cosine_precision@3': 0.3933333333333333, 'NanoSCIDOCS_cosine_precision@5': 0.33599999999999997, 'NanoSCIDOCS_cosine_precision@10': 0.21600000000000003, 'NanoSCIDOCS_cosine_recall@1': 0.10966666666666666, 'NanoSCIDOCS_cosine_recall@3': 0.24466666666666664, 'NanoSCIDOCS_cosine_recall@5': 0.34566666666666657, 'NanoSCIDOCS_cosine_recall@10': 0.44266666666666665, 'NanoSCIDOCS_cosine_ndcg@10': 0.4328110226758414, 'NanoSCIDOCS_cosine_mrr@10': 0.6317222222222222, 'NanoSCIDOCS_cosine_map@100': 0.34997841607847063, 'NanoArguAna_cosine_accuracy@1': 0.2, 'NanoArguAna_cosine_accuracy@3': 0.56, 'NanoArguAna_cosine_accuracy@5': 0.76, 'NanoArguAna_cosine_accuracy@10': 0.92, 'NanoArguAna_cosine_precision@1': 0.2, 'NanoArguAna_cosine_precision@3': 0.18666666666666668, 'NanoArguAna_cosine_precision@5': 0.15200000000000002, 'NanoArguAna_cosine_precision@10': 0.092, 'NanoArguAna_cosine_recall@1': 0.2, 'NanoArguAna_cosine_recall@3': 0.56, 'NanoArguAna_cosine_recall@5': 0.76, 'NanoArguAna_cosine_recall@10': 0.92, 'NanoArguAna_cosine_ndcg@10': 0.5499071039525992, 'NanoArguAna_cosine_mrr@10': 0.43229365079365073, 'NanoArguAna_cosine_map@100': 0.43523820792684886, 'NanoSciFact_cosine_accuracy@1': 0.6, 'NanoSciFact_cosine_accuracy@3': 0.72, 'NanoSciFact_cosine_accuracy@5': 0.8, 'NanoSciFact_cosine_accuracy@10': 0.88, 'NanoSciFact_cosine_precision@1': 0.6, 'NanoSciFact_cosine_precision@3': 0.25333333333333335, 'NanoSciFact_cosine_precision@5': 0.18, 'NanoSciFact_cosine_precision@10': 0.09799999999999999, 'NanoSciFact_cosine_recall@1': 0.58, 'NanoSciFact_cosine_recall@3': 0.7, 'NanoSciFact_cosine_recall@5': 0.8, 'NanoSciFact_cosine_recall@10': 0.87, 'NanoSciFact_cosine_ndcg@10': 0.7265348054031264, 'NanoSciFact_cosine_mrr@10': 0.6841031746031746, 'NanoSciFact_cosine_map@100': 0.6810233866101422, 'NanoTouche2020_cosine_accuracy@1': 0.5102040816326531, 'NanoTouche2020_cosine_accuracy@3': 0.8367346938775511, 'NanoTouche2020_cosine_accuracy@5': 0.9183673469387755, 'NanoTouche2020_cosine_accuracy@10': 0.9387755102040817, 'NanoTouche2020_cosine_precision@1': 0.5102040816326531, 'NanoTouche2020_cosine_precision@3': 0.5374149659863945, 'NanoTouche2020_cosine_precision@5': 0.5061224489795918, 'NanoTouche2020_cosine_precision@10': 0.43265306122448977, 'NanoTouche2020_cosine_recall@1': 0.03546508562664911, 'NanoTouche2020_cosine_recall@3': 0.11189238805791148, 'NanoTouche2020_cosine_recall@5': 0.1673503566176574, 'NanoTouche2020_cosine_recall@10': 0.2818808841266296, 'NanoTouche2020_cosine_ndcg@10': 0.47479704449085264, 'NanoTouche2020_cosine_mrr@10': 0.6714285714285714, 'NanoTouche2020_cosine_map@100': 0.3438320372291555, 'NanoBEIR_mean_cosine_accuracy@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_accuracy@3': 0.6997488226059654, 'NanoBEIR_mean_cosine_accuracy@5': 0.7737205651491367, 'NanoBEIR_mean_cosine_accuracy@10': 0.8583673469387756, 'NanoBEIR_mean_cosine_precision@1': 0.5130926216640502, 'NanoBEIR_mean_cosine_precision@3': 0.31928833071690216, 'NanoBEIR_mean_cosine_precision@5': 0.2540094191522763, 'NanoBEIR_mean_cosine_precision@10': 0.1777425431711146, 'NanoBEIR_mean_cosine_recall@1': 0.302305611570001, 'NanoBEIR_mean_cosine_recall@3': 0.4504361065646467, 'NanoBEIR_mean_cosine_recall@5': 0.5234643876869758, 'NanoBEIR_mean_cosine_recall@10': 0.6156896557033196, 'NanoBEIR_mean_cosine_ndcg@10': 0.5623178109936842, 'NanoBEIR_mean_cosine_mrr@10': 0.6232673992673993, 'NanoBEIR_mean_cosine_map@100': 0.47729093279415025} ``` ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

carlfeynman/reproduce-static-retrieval-mrl-en-v1

carlfeynman

sentence-similarity

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:68534726", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:sentence-transformers/gooaq", "dataset:sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1", "dataset:sentence-transformers/s2orc", "dataset:sentence-transformers/all-nli", "dataset:sentence-transformers/paq", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

2025-01-17T11:22:01

2025-01-17T11:22:11

0

--- datasets: - sentence-transformers/gooaq - sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1 - sentence-transformers/s2orc - sentence-transformers/all-nli - sentence-transformers/paq language: - en library_name: sentence-transformers license: apache-2.0 metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:68534726 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to sign legal documents as power of attorney? sentences: - 'After the principal''s name, write “by” and then sign your own name. Under or after the signature line, indicate your status as POA by including any of the following identifiers: as POA, as Agent, as Attorney in Fact or as Power of Attorney.' - Most earthquakes occur along the edge of the oceanic and continental plates. The earth's crust (the outer layer of the planet) is made up of several pieces, called plates. The plates under the oceans are called oceanic plates and the rest are continental plates. - Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now successfully removed from the configuration. - source_sentence: what is upwork sentences: - Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork has nine million registered freelancers and four million registered clients. Three million jobs are posted annually, worth a total of $1 billion USD, making it the world's largest freelancer marketplace. - Upwork, formerly Elance-oDesk, is a global freelancing platform where businesses and independent professionals connect and collaborate remotely.In 2015, Elance-oDesk was rebranded as Upwork. It is based out of Mountain View and San Francisco, California.pwork has nine million registered freelancers and four million registered clients. Three million jobs are posted annually, worth a total of $1 billion USD, making it the world's largest freelancer marketplace. - 'That is, while fructose consumption may increase uric acid levels, to actually precipitate a gout attack, you need to deviate from the narrow band of normal blood pH range: 7.35 to 7.45. Ideally you wanna be at 7.45 or slightly above.' - source_sentence: how many km is a mile sentences: - Periodontal disease is a bacterial infection of the gums and bone that if not treated, can cause you to lose your teeth. Medical research is now showing that these bacteria in your mouth can also travel through your bloodstream into other organs in the body. - Master the formula for converting kilometers to miles. 1 kilometer is equal to 0.621371 miles (often shortened to .62).1 mile is equal to 1.609344 kilometers. Thus, to convert kilometers to miles, simply multiply the number of kilometers by 0.62137. For example, let's say you start with 5 kilometers. People are often interested in this conversion because they want to know how many miles are in a 5K run. The formula is 5 X 0.62137= 3.1 miles. - To find out how many kilometers in miles, multiply by this factor or simply use the converter below. 1 Mile = 1.609344 Kilometers. Mile is an imperial and US customary length unit and equals to 5280 feet. The abbreviation is mi. Kilometer is a metric length unit and equals to 1000 meters. - source_sentence: A group of children walking on a trail. sentences: - The man is performing. - Children are walking. - The people are adults. - source_sentence: A boy with a basketballs glowers at the camera. sentences: - The boy is smiling - The boy scowls - Surfer in red catches a wave. model-index: - name: '[REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets' results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.82 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.152 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15666666666666665 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.31633333333333336 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.44133333333333336 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.35027529831718174 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4537698412698412 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2754610667422747 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.88 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.92 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.6066666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5479999999999999 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.45399999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.05820050708225643 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1660478879214754 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2233296888728599 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.32642161484749216 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5611886908023029 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7551904761904763 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42159733554382045 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.54 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.54 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2733333333333334 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09999999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.5066666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7566666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8033333333333332 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9033333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7223300246075101 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6857460317460319 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6591296848555135 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.22 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.22 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09799999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12688888888888888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.29007936507936505 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3347460317460317 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.453015873015873 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33206103177846985 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.34974603174603175 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2723064374777477 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.66 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.66 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.35999999999999993 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.264 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14799999999999996 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.33 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.54 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.66 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6507660730204244 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.746690476190476 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5743825107321581 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.16 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.66 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.066 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.44 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.54 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.66 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4069260774532657 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3269126984126984 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.34104660879940385 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.34666666666666673 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.24400000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06140064224956239 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.09381944627241434 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.11465220470723159 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.13758064454249494 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3251344168353932 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.49083333333333345 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.15346080343511273 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.44 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.55 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.67 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4284752232212853 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3555714285714285 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.35954687250943856 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.8 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.92 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.96 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.8 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.35999999999999993 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23999999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.12799999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7106666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8653333333333333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9226666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9593333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.874423773707081 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8666666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8354028527028526 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.62 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.184 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.14 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.059666666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.1416666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18966666666666665 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2886666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2657817193581118 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4188571428571429 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.20270708890067454 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15999999999999998 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.48 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4064179360568565 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.31785714285714284 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33454708384798976 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.64 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.485 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.61 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.655 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.72 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6053823991819648 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5862222222222221 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5721097562068183 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5918367346938775 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9183673469387755 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9795918367346939 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5918367346938775 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5850340136054422 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.6000000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.5204081632653061 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0405610423291237 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12039267252775386 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.20296687044371778 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3313283589291373 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5594653746925154 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.749514091350826 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4414984325557448 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.41937205651491377 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6475667189952904 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7168916797488225 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8030769230769231 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.41937205651491377 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2942333856619571 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23784615384615387 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17172370486656197 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.23120905747819215 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.399538926035975 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4702072919822955 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5623856275385894 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4991252337717202 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5464290448780245 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.41870742571611924 name: Cosine Map@100 --- # [REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets This is a [sentence-transformers](https://www.SBERT.net) model trained on the [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc), [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) and [paq](https://huggingface.co/datasets/sentence-transformers/paq) datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer  - **Maximum Sequence Length:** inf tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) - [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) - [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) - [paq](https://huggingface.co/datasets/sentence-transformers/paq) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): StaticEmbedding( (embedding): EmbeddingBag(30522, 1024, mode='mean') ) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("carlfeynman/reproduce-static-retrieval-mrl-en-v1") # Run inference sentences = [ 'A boy with a basketballs glowers at the camera.', 'The boy scowls', 'The boy is smiling', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.32 | 0.64 | 0.54 | 0.22 | 0.66 | 0.16 | 0.4 | 0.2 | 0.8 | 0.28 | 0.12 | 0.52 | 0.5918 | | cosine_accuracy@3 | 0.54 | 0.88 | 0.82 | 0.44 | 0.82 | 0.44 | 0.54 | 0.46 | 0.92 | 0.52 | 0.48 | 0.64 | 0.9184 | | cosine_accuracy@5 | 0.64 | 0.92 | 0.84 | 0.5 | 0.86 | 0.54 | 0.6 | 0.58 | 0.96 | 0.62 | 0.6 | 0.68 | 0.9796 | | cosine_accuracy@10 | 0.82 | 0.94 | 0.94 | 0.64 | 0.94 | 0.66 | 0.7 | 0.68 | 0.98 | 0.72 | 0.68 | 0.74 | 1.0 | | cosine_precision@1 | 0.32 | 0.64 | 0.54 | 0.22 | 0.66 | 0.16 | 0.4 | 0.2 | 0.8 | 0.28 | 0.12 | 0.52 | 0.5918 | | cosine_precision@3 | 0.2 | 0.6067 | 0.2733 | 0.1867 | 0.36 | 0.1467 | 0.3467 | 0.1533 | 0.36 | 0.2267 | 0.16 | 0.22 | 0.585 | | cosine_precision@5 | 0.152 | 0.548 | 0.18 | 0.132 | 0.264 | 0.108 | 0.3 | 0.12 | 0.24 | 0.184 | 0.12 | 0.144 | 0.6 | | cosine_precision@10 | 0.112 | 0.454 | 0.1 | 0.098 | 0.148 | 0.066 | 0.244 | 0.074 | 0.128 | 0.14 | 0.068 | 0.08 | 0.5204 | | cosine_recall@1 | 0.1567 | 0.0582 | 0.5067 | 0.1269 | 0.33 | 0.16 | 0.0614 | 0.19 | 0.7107 | 0.0597 | 0.12 | 0.485 | 0.0406 | | cosine_recall@3 | 0.25 | 0.166 | 0.7567 | 0.2901 | 0.54 | 0.44 | 0.0938 | 0.44 | 0.8653 | 0.1417 | 0.48 | 0.61 | 0.1204 | | cosine_recall@5 | 0.3163 | 0.2233 | 0.8033 | 0.3347 | 0.66 | 0.54 | 0.1147 | 0.55 | 0.9227 | 0.1897 | 0.6 | 0.655 | 0.203 | | cosine_recall@10 | 0.4413 | 0.3264 | 0.9033 | 0.453 | 0.74 | 0.66 | 0.1376 | 0.67 | 0.9593 | 0.2887 | 0.68 | 0.72 | 0.3313 | | **cosine_ndcg@10** | **0.3503** | **0.5612** | **0.7223** | **0.3321** | **0.6508** | **0.4069** | **0.3251** | **0.4285** | **0.8744** | **0.2658** | **0.4064** | **0.6054** | **0.5595** | | cosine_mrr@10 | 0.4538 | 0.7552 | 0.6857 | 0.3497 | 0.7467 | 0.3269 | 0.4908 | 0.3556 | 0.8667 | 0.4189 | 0.3179 | 0.5862 | 0.7495 | | cosine_map@100 | 0.2755 | 0.4216 | 0.6591 | 0.2723 | 0.5744 | 0.341 | 0.1535 | 0.3595 | 0.8354 | 0.2027 | 0.3345 | 0.5721 | 0.4415 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4194 | | cosine_accuracy@3 | 0.6476 | | cosine_accuracy@5 | 0.7169 | | cosine_accuracy@10 | 0.8031 | | cosine_precision@1 | 0.4194 | | cosine_precision@3 | 0.2942 | | cosine_precision@5 | 0.2378 | | cosine_precision@10 | 0.1717 | | cosine_recall@1 | 0.2312 | | cosine_recall@3 | 0.3995 | | cosine_recall@5 | 0.4702 | | cosine_recall@10 | 0.5624 | | **cosine_ndcg@10** | **0.4991** | | cosine_mrr@10 | 0.5464 | | cosine_map@100 | 0.4187 |   ## Training Details ### Training Datasets #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 training samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.23 characters</li><li>max: 96 characters</li></ul> | <ul><li>min: 55 characters</li><li>mean: 253.36 characters</li><li>max: 371 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>what is the difference between broilers and layers?</code> | <code>An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.</code> | | <code>what is the difference between chronological order and spatial order?</code> | <code>As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.</code> | | <code>is kamagra same as viagra?</code> | <code>Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 training samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 11 characters</li><li>mean: 33.26 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 96 characters</li><li>mean: 356.24 characters</li><li>max: 1006 characters</li></ul> | <ul><li>min: 68 characters</li><li>mean: 327.52 characters</li><li>max: 995 characters</li></ul> | * Samples: | query | positive | negative | |:---------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>when was the sullivan acts</code> | <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> | <code>Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.</code> | | <code>can lavender grow indoors</code> | <code>Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.</code> | <code>Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.</code> | | <code>what kind of barley do you malt</code> | <code>Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.</code> | <code>Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### s2orc * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 90,000 training samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: | | title | abstract | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 31 characters</li><li>mean: 80.02 characters</li><li>max: 185 characters</li></ul> | <ul><li>min: 84 characters</li><li>mean: 635.31 characters</li><li>max: 1023 characters</li></ul> | * Samples: | title | abstract | |:----------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions</code> | <code>The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.</code> | | <code>Enlightening on medical administration by clinical governance in British</code> | <code>Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.</code> | | <code>APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION</code> | <code>Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### allnli * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 557,850 training samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 34.88 characters</li><li>max: 193 characters</li></ul> | <ul><li>min: 15 characters</li><li>mean: 46.49 characters</li><li>max: 181 characters</li></ul> | <ul><li>min: 16 characters</li><li>mean: 50.47 characters</li><li>max: 204 characters</li></ul> | * Samples: | anchor | positive | negative | |:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------| | <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> | | <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> | | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### paq * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 25 characters</li><li>mean: 50.56 characters</li><li>max: 104 characters</li></ul> | <ul><li>min: 509 characters</li><li>mean: 620.96 characters</li><li>max: 773 characters</li></ul> | * Samples: | query | answer | |:----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>in veetla visheshanga ganesh is the husband of</code> | <code>Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him</code> | | <code>when did simon property group became a publicly traded company</code> | <code>of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly</code> | | <code>what was the nationality of antoine faivre</code> | <code>Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Evaluation Datasets #### gooaq * Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 3,012,496 evaluation samples * Columns: <code>question</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | question | answer | |:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 18 characters</li><li>mean: 43.17 characters</li><li>max: 98 characters</li></ul> | <ul><li>min: 51 characters</li><li>mean: 254.12 characters</li><li>max: 360 characters</li></ul> | * Samples: | question | answer | |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>how do i program my directv remote with my tv?</code> | <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> | | <code>are rodrigues fruit bats nocturnal?</code> | <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> | | <code>why does your heart rate increase during exercise bbc bitesize?</code> | <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2) * Size: 502,939 evaluation samples * Columns: <code>query</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | query | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 10 characters</li><li>mean: 33.36 characters</li><li>max: 137 characters</li></ul> | <ul><li>min: 67 characters</li><li>mean: 347.87 characters</li><li>max: 906 characters</li></ul> | <ul><li>min: 57 characters</li><li>mean: 318.18 characters</li><li>max: 906 characters</li></ul> | * Samples: | query | positive | negative | |:-------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>is cabinet refacing worth the cost?</code> | <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> | <code>Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.</code> | | <code>is the fovea ethmoidalis a bone</code> | <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> | <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> | | <code>average pitches per inning</code> | <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> | <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### s2orc * Dataset: [s2orc](https://huggingface.co/datasets/sentence-transformers/s2orc) at [8cfc394](https://huggingface.co/datasets/sentence-transformers/s2orc/tree/8cfc394e83b2ebfcf38f90b508aea383df742439) * Size: 10,000 evaluation samples * Columns: <code>title</code> and <code>abstract</code> * Approximate statistics based on the first 1000 samples: | | title | abstract | |:--------|:------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 31 characters</li><li>mean: 80.04 characters</li><li>max: 198 characters</li></ul> | <ul><li>min: 96 characters</li><li>mean: 653.93 characters</li><li>max: 1023 characters</li></ul> | * Samples: | title | abstract | |:-------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna</code> | <code>The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.</code> | | <code>Subclinical organ damage and cardiovascular risk prediction</code> | <code>AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...</code> | | <code>A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan</code> | <code>In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### allnli * Dataset: [allnli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 6,584 evaluation samples * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 15 characters</li><li>mean: 72.82 characters</li><li>max: 300 characters</li></ul> | <ul><li>min: 12 characters</li><li>mean: 34.11 characters</li><li>max: 126 characters</li></ul> | <ul><li>min: 11 characters</li><li>mean: 36.38 characters</li><li>max: 121 characters</li></ul> | * Samples: | anchor | positive | negative | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------| | <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> | | <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> | | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` #### paq * Dataset: [paq](https://huggingface.co/datasets/sentence-transformers/paq) at [74601d8](https://huggingface.co/datasets/sentence-transformers/paq/tree/74601d8d731019bc9c627ffc4271cdd640e1e748) * Size: 64,371,441 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 25 characters</li><li>mean: 51.3 characters</li><li>max: 108 characters</li></ul> | <ul><li>min: 504 characters</li><li>mean: 623.09 characters</li><li>max: 835 characters</li></ul> | * Samples: | query | answer | |:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>when did season 3 of the voice brasil start</code> | <code>The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on</code> | | <code>when did the little ranger first come out</code> | <code>Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures</code> | | <code>what is the name of rachel's sister in ninjaaiden</code> | <code>her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but</code> | * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 1024, 512, 256, 128, 64, 32 ], "matryoshka_weights": [ 1, 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16384 - `per_device_eval_batch_size`: 4096 - `learning_rate`: 0.2 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16384 - `per_device_eval_batch_size`: 4096 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 0.2 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | gooaq loss | msmarco loss | s2orc loss | allnli loss | paq loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:----------:|:------------:|:----------:|:-----------:|:--------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0.0002 | 1 | 43.5181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0597 | 250 | 17.804 | 2.1081 | 12.8291 | 10.8194 | 14.2895 | 5.3792 | 0.3202 | 0.5446 | 0.6721 | 0.3176 | 0.6222 | 0.3867 | 0.3022 | 0.3952 | 0.8741 | 0.2474 | 0.3986 | 0.5913 | 0.5463 | 0.4783 | | 0.1195 | 500 | 9.6842 | 1.6991 | 12.2374 | 10.6084 | 13.9790 | 4.7183 | 0.3148 | 0.5759 | 0.7063 | 0.3640 | 0.6250 | 0.3846 | 0.2832 | 0.4168 | 0.8659 | 0.2537 | 0.3744 | 0.5732 | 0.5509 | 0.4837 | | 0.1792 | 750 | 8.7691 | 1.6922 | 12.0631 | 10.3970 | 12.4485 | 4.4473 | 0.3496 | 0.5664 | 0.7157 | 0.3179 | 0.6585 | 0.3826 | 0.2934 | 0.4040 | 0.8782 | 0.2523 | 0.3845 | 0.5962 | 0.5502 | 0.4884 | | 0.2389 | 1000 | 8.606 | 1.6685 | 11.7765 | 10.2828 | 12.4139 | 4.2823 | 0.3509 | 0.5636 | 0.7026 | 0.3249 | 0.6562 | 0.4049 | 0.3123 | 0.4174 | 0.8673 | 0.2657 | 0.3969 | 0.5582 | 0.5514 | 0.4902 | | 0.2987 | 1250 | 8.4178 | 1.6072 | 11.7581 | 9.2590 | 12.8865 | 4.2231 | 0.3341 | 0.5587 | 0.7103 | 0.3354 | 0.6534 | 0.4033 | 0.3116 | 0.4294 | 0.8663 | 0.2718 | 0.4048 | 0.5891 | 0.5466 | 0.4934 | | 0.3584 | 1500 | 8.1084 | 1.6751 | 11.8237 | 9.8291 | 11.5805 | 4.1559 | 0.3345 | 0.5668 | 0.7094 | 0.3287 | 0.6535 | 0.3948 | 0.3311 | 0.4098 | 0.8632 | 0.2649 | 0.4171 | 0.5913 | 0.5514 | 0.4936 | | 0.4182 | 1750 | 7.9489 | 1.5858 | 11.8367 | 9.8385 | 13.0328 | 4.0980 | 0.3543 | 0.5464 | 0.6984 | 0.3158 | 0.6582 | 0.3862 | 0.3233 | 0.4201 | 0.8665 | 0.2743 | 0.3924 | 0.5909 | 0.5577 | 0.4911 | | 0.4779 | 2000 | 8.2594 | 1.6123 | 11.8052 | 9.9075 | 11.3651 | 4.0788 | 0.3491 | 0.5551 | 0.7208 | 0.3235 | 0.6570 | 0.4058 | 0.3220 | 0.4215 | 0.8801 | 0.2629 | 0.4143 | 0.5998 | 0.5514 | 0.4972 | | 0.5376 | 2250 | 8.299 | 1.6416 | 11.7180 | 9.9462 | 10.7895 | 4.0423 | 0.3636 | 0.5582 | 0.7071 | 0.3048 | 0.6649 | 0.3951 | 0.3248 | 0.4316 | 0.8804 | 0.2561 | 0.4252 | 0.6036 | 0.5484 | 0.4972 | | 0.5974 | 2500 | 7.7807 | 1.6518 | 11.7898 | 9.9235 | 11.1670 | 4.0001 | 0.3639 | 0.5556 | 0.7288 | 0.3148 | 0.6525 | 0.3979 | 0.3178 | 0.4436 | 0.8860 | 0.2593 | 0.4208 | 0.5935 | 0.5581 | 0.4994 | | 0.6571 | 2750 | 7.8997 | 1.5797 | 11.6813 | 9.5124 | 11.4893 | 3.9633 | 0.3465 | 0.5562 | 0.7084 | 0.3101 | 0.6631 | 0.4102 | 0.3194 | 0.4410 | 0.8805 | 0.2566 | 0.4261 | 0.5983 | 0.5552 | 0.4978 | | 0.7168 | 3000 | 8.0204 | 1.5620 | 11.6746 | 9.6655 | 10.8783 | 3.9539 | 0.3439 | 0.5569 | 0.7295 | 0.3173 | 0.6606 | 0.4129 | 0.3180 | 0.4521 | 0.8888 | 0.2576 | 0.4012 | 0.6065 | 0.5560 | 0.5001 | | 0.7766 | 3250 | 8.0225 | 1.4596 | 11.5664 | 9.6954 | 10.9838 | 3.9493 | 0.3496 | 0.5626 | 0.7239 | 0.3330 | 0.6551 | 0.4197 | 0.3129 | 0.4491 | 0.8893 | 0.2726 | 0.4061 | 0.6103 | 0.5555 | 0.5031 | | 0.8363 | 3500 | 7.6933 | 1.5522 | 11.6974 | 9.1753 | 11.2026 | 3.9082 | 0.3581 | 0.5570 | 0.7170 | 0.3216 | 0.6492 | 0.4018 | 0.3204 | 0.4360 | 0.8841 | 0.2675 | 0.4031 | 0.6052 | 0.5553 | 0.4982 | | 0.8961 | 3750 | 7.711 | 1.5267 | 11.6615 | 9.4673 | 11.3195 | 3.8847 | 0.3563 | 0.5613 | 0.7162 | 0.3265 | 0.6497 | 0.4109 | 0.3253 | 0.4384 | 0.8713 | 0.2657 | 0.4195 | 0.6058 | 0.5566 | 0.5003 | | 0.9558 | 4000 | 7.8549 | 1.5300 | 11.6244 | 9.1383 | 11.0781 | 3.8785 | 0.3533 | 0.5609 | 0.7153 | 0.3285 | 0.6528 | 0.4069 | 0.3250 | 0.4382 | 0.8744 | 0.2642 | 0.4068 | 0.5961 | 0.5595 | 0.4986 | | 1.0 | 4185 | - | - | - | - | - | - | 0.3503 | 0.5612 | 0.7223 | 0.3321 | 0.6508 | 0.4069 | 0.3251 | 0.4285 | 0.8744 | 0.2658 | 0.4064 | 0.6054 | 0.5595 | 0.4991 | ### Framework Versions - Python: 3.10.15 - Sentence Transformers: 3.3.1 - Transformers: 4.47.1 - PyTorch: 2.4.1 - Accelerate: 1.1.1 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

[ "TEXT_CLASSIFICATION" ]

[ "CRAFT" ]

fdehlinger/english-4U-bge-small

fdehlinger

feature-extraction

[ "sentence-transformers", "pytorch", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "mteb", "en", "arxiv:2401.03462", "arxiv:2312.15503", "arxiv:2311.13534", "arxiv:2310.07554", "arxiv:2309.07597", "license:mit", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

2025-01-20T14:03:22

2025-01-21T11:45:54

0

--- language: - en license: mit tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers - mteb model-index: - name: bge-small-en-v1.5 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.79104477611939 - type: ap value: 37.21923821573361 - type: f1 value: 68.0914945617093 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 92.75377499999999 - type: ap value: 89.46766124546022 - type: f1 value: 92.73884001331487 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.986 - type: f1 value: 46.55936786727896 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 35.846000000000004 - type: map_at_10 value: 51.388 - type: map_at_100 value: 52.132999999999996 - type: map_at_1000 value: 52.141000000000005 - type: map_at_3 value: 47.037 - type: map_at_5 value: 49.579 - type: mrr_at_1 value: 36.558 - type: mrr_at_10 value: 51.658 - type: mrr_at_100 value: 52.402 - type: mrr_at_1000 value: 52.410000000000004 - type: mrr_at_3 value: 47.345 - type: mrr_at_5 value: 49.797999999999995 - type: ndcg_at_1 value: 35.846000000000004 - type: ndcg_at_10 value: 59.550000000000004 - type: ndcg_at_100 value: 62.596 - type: ndcg_at_1000 value: 62.759 - type: ndcg_at_3 value: 50.666999999999994 - type: ndcg_at_5 value: 55.228 - type: precision_at_1 value: 35.846000000000004 - type: precision_at_10 value: 8.542 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.389 - type: precision_at_5 value: 14.438 - type: recall_at_1 value: 35.846000000000004 - type: recall_at_10 value: 85.42 - type: recall_at_100 value: 98.43499999999999 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 61.166 - type: recall_at_5 value: 72.191 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.402770198163594 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.01545436974177 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.586465273207196 - type: mrr value: 74.42169019038825 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 85.1891186537969 - type: cos_sim_spearman value: 83.75492046087288 - type: euclidean_pearson value: 84.11766204805357 - type: euclidean_spearman value: 84.01456493126516 - type: manhattan_pearson value: 84.2132950502772 - type: manhattan_spearman value: 83.89227298813377 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 85.74025974025975 - type: f1 value: 85.71493566466381 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.467181385006434 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 34.719496037339056 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 29.587000000000003 - type: map_at_10 value: 41.114 - type: map_at_100 value: 42.532 - type: map_at_1000 value: 42.661 - type: map_at_3 value: 37.483 - type: map_at_5 value: 39.652 - type: mrr_at_1 value: 36.338 - type: mrr_at_10 value: 46.763 - type: mrr_at_100 value: 47.393 - type: mrr_at_1000 value: 47.445 - type: mrr_at_3 value: 43.538 - type: mrr_at_5 value: 45.556000000000004 - type: ndcg_at_1 value: 36.338 - type: ndcg_at_10 value: 47.658 - type: ndcg_at_100 value: 52.824000000000005 - type: ndcg_at_1000 value: 54.913999999999994 - type: ndcg_at_3 value: 41.989 - type: ndcg_at_5 value: 44.944 - type: precision_at_1 value: 36.338 - type: precision_at_10 value: 9.156 - type: precision_at_100 value: 1.4789999999999999 - type: precision_at_1000 value: 0.196 - type: precision_at_3 value: 20.076 - type: precision_at_5 value: 14.85 - type: recall_at_1 value: 29.587000000000003 - type: recall_at_10 value: 60.746 - type: recall_at_100 value: 82.157 - type: recall_at_1000 value: 95.645 - type: recall_at_3 value: 44.821 - type: recall_at_5 value: 52.819 - type: map_at_1 value: 30.239 - type: map_at_10 value: 39.989000000000004 - type: map_at_100 value: 41.196 - type: map_at_1000 value: 41.325 - type: map_at_3 value: 37.261 - type: map_at_5 value: 38.833 - type: mrr_at_1 value: 37.516 - type: mrr_at_10 value: 46.177 - type: mrr_at_100 value: 46.806 - type: mrr_at_1000 value: 46.849000000000004 - type: mrr_at_3 value: 44.002 - type: mrr_at_5 value: 45.34 - type: ndcg_at_1 value: 37.516 - type: ndcg_at_10 value: 45.586 - type: ndcg_at_100 value: 49.897000000000006 - type: ndcg_at_1000 value: 51.955 - type: ndcg_at_3 value: 41.684 - type: ndcg_at_5 value: 43.617 - type: precision_at_1 value: 37.516 - type: precision_at_10 value: 8.522 - type: precision_at_100 value: 1.374 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 20.105999999999998 - type: precision_at_5 value: 14.152999999999999 - type: recall_at_1 value: 30.239 - type: recall_at_10 value: 55.03 - type: recall_at_100 value: 73.375 - type: recall_at_1000 value: 86.29599999999999 - type: recall_at_3 value: 43.269000000000005 - type: recall_at_5 value: 48.878 - type: map_at_1 value: 38.338 - type: map_at_10 value: 50.468999999999994 - type: map_at_100 value: 51.553000000000004 - type: map_at_1000 value: 51.608 - type: map_at_3 value: 47.107 - type: map_at_5 value: 49.101 - type: mrr_at_1 value: 44.201 - type: mrr_at_10 value: 54.057 - type: mrr_at_100 value: 54.764 - type: mrr_at_1000 value: 54.791000000000004 - type: mrr_at_3 value: 51.56699999999999 - type: mrr_at_5 value: 53.05 - type: ndcg_at_1 value: 44.201 - type: ndcg_at_10 value: 56.379000000000005 - type: ndcg_at_100 value: 60.645 - type: ndcg_at_1000 value: 61.73499999999999 - type: ndcg_at_3 value: 50.726000000000006 - type: ndcg_at_5 value: 53.58500000000001 - type: precision_at_1 value: 44.201 - type: precision_at_10 value: 9.141 - type: precision_at_100 value: 1.216 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 22.654 - type: precision_at_5 value: 15.723999999999998 - type: recall_at_1 value: 38.338 - type: recall_at_10 value: 70.30499999999999 - type: recall_at_100 value: 88.77199999999999 - type: recall_at_1000 value: 96.49799999999999 - type: recall_at_3 value: 55.218 - type: recall_at_5 value: 62.104000000000006 - type: map_at_1 value: 25.682 - type: map_at_10 value: 33.498 - type: map_at_100 value: 34.461000000000006 - type: map_at_1000 value: 34.544000000000004 - type: map_at_3 value: 30.503999999999998 - type: map_at_5 value: 32.216 - type: mrr_at_1 value: 27.683999999999997 - type: mrr_at_10 value: 35.467999999999996 - type: mrr_at_100 value: 36.32 - type: mrr_at_1000 value: 36.386 - type: mrr_at_3 value: 32.618 - type: mrr_at_5 value: 34.262 - type: ndcg_at_1 value: 27.683999999999997 - type: ndcg_at_10 value: 38.378 - type: ndcg_at_100 value: 43.288 - type: ndcg_at_1000 value: 45.413 - type: ndcg_at_3 value: 32.586 - type: ndcg_at_5 value: 35.499 - type: precision_at_1 value: 27.683999999999997 - type: precision_at_10 value: 5.864 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.11 - type: precision_at_3 value: 13.446 - type: precision_at_5 value: 9.718 - type: recall_at_1 value: 25.682 - type: recall_at_10 value: 51.712 - type: recall_at_100 value: 74.446 - type: recall_at_1000 value: 90.472 - type: recall_at_3 value: 36.236000000000004 - type: recall_at_5 value: 43.234 - type: map_at_1 value: 16.073999999999998 - type: map_at_10 value: 24.352999999999998 - type: map_at_100 value: 25.438 - type: map_at_1000 value: 25.545 - type: map_at_3 value: 21.614 - type: map_at_5 value: 23.104 - type: mrr_at_1 value: 19.776 - type: mrr_at_10 value: 28.837000000000003 - type: mrr_at_100 value: 29.755 - type: mrr_at_1000 value: 29.817 - type: mrr_at_3 value: 26.201999999999998 - type: mrr_at_5 value: 27.714 - type: ndcg_at_1 value: 19.776 - type: ndcg_at_10 value: 29.701 - type: ndcg_at_100 value: 35.307 - type: ndcg_at_1000 value: 37.942 - type: ndcg_at_3 value: 24.764 - type: ndcg_at_5 value: 27.025 - type: precision_at_1 value: 19.776 - type: precision_at_10 value: 5.659 - type: precision_at_100 value: 0.971 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 12.065 - type: precision_at_5 value: 8.905000000000001 - type: recall_at_1 value: 16.073999999999998 - type: recall_at_10 value: 41.647 - type: recall_at_100 value: 66.884 - type: recall_at_1000 value: 85.91499999999999 - type: recall_at_3 value: 27.916 - type: recall_at_5 value: 33.729 - type: map_at_1 value: 28.444999999999997 - type: map_at_10 value: 38.218999999999994 - type: map_at_100 value: 39.595 - type: map_at_1000 value: 39.709 - type: map_at_3 value: 35.586 - type: map_at_5 value: 36.895 - type: mrr_at_1 value: 34.841 - type: mrr_at_10 value: 44.106 - type: mrr_at_100 value: 44.98 - type: mrr_at_1000 value: 45.03 - type: mrr_at_3 value: 41.979 - type: mrr_at_5 value: 43.047999999999995 - type: ndcg_at_1 value: 34.841 - type: ndcg_at_10 value: 43.922 - type: ndcg_at_100 value: 49.504999999999995 - type: ndcg_at_1000 value: 51.675000000000004 - type: ndcg_at_3 value: 39.858 - type: ndcg_at_5 value: 41.408 - type: precision_at_1 value: 34.841 - type: precision_at_10 value: 7.872999999999999 - type: precision_at_100 value: 1.2449999999999999 - type: precision_at_1000 value: 0.161 - type: precision_at_3 value: 18.993 - type: precision_at_5 value: 13.032 - type: recall_at_1 value: 28.444999999999997 - type: recall_at_10 value: 54.984 - type: recall_at_100 value: 78.342 - type: recall_at_1000 value: 92.77 - type: recall_at_3 value: 42.842999999999996 - type: recall_at_5 value: 47.247 - type: map_at_1 value: 23.072 - type: map_at_10 value: 32.354 - type: map_at_100 value: 33.800000000000004 - type: map_at_1000 value: 33.908 - type: map_at_3 value: 29.232000000000003 - type: map_at_5 value: 31.049 - type: mrr_at_1 value: 29.110000000000003 - type: mrr_at_10 value: 38.03 - type: mrr_at_100 value: 39.032 - type: mrr_at_1000 value: 39.086999999999996 - type: mrr_at_3 value: 35.407 - type: mrr_at_5 value: 36.76 - type: ndcg_at_1 value: 29.110000000000003 - type: ndcg_at_10 value: 38.231 - type: ndcg_at_100 value: 44.425 - type: ndcg_at_1000 value: 46.771 - type: ndcg_at_3 value: 33.095 - type: ndcg_at_5 value: 35.459 - type: precision_at_1 value: 29.110000000000003 - type: precision_at_10 value: 7.215000000000001 - type: precision_at_100 value: 1.2109999999999999 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 16.058 - type: precision_at_5 value: 11.644 - type: recall_at_1 value: 23.072 - type: recall_at_10 value: 50.285999999999994 - type: recall_at_100 value: 76.596 - type: recall_at_1000 value: 92.861 - type: recall_at_3 value: 35.702 - type: recall_at_5 value: 42.152 - type: map_at_1 value: 24.937916666666666 - type: map_at_10 value: 33.755250000000004 - type: map_at_100 value: 34.955999999999996 - type: map_at_1000 value: 35.070499999999996 - type: map_at_3 value: 30.98708333333333 - type: map_at_5 value: 32.51491666666666 - type: mrr_at_1 value: 29.48708333333333 - type: mrr_at_10 value: 37.92183333333334 - type: mrr_at_100 value: 38.76583333333333 - type: mrr_at_1000 value: 38.82466666666667 - type: mrr_at_3 value: 35.45125 - type: mrr_at_5 value: 36.827000000000005 - type: ndcg_at_1 value: 29.48708333333333 - type: ndcg_at_10 value: 39.05225 - type: ndcg_at_100 value: 44.25983333333334 - type: ndcg_at_1000 value: 46.568333333333335 - type: ndcg_at_3 value: 34.271583333333325 - type: ndcg_at_5 value: 36.483916666666666 - type: precision_at_1 value: 29.48708333333333 - type: precision_at_10 value: 6.865749999999999 - type: precision_at_100 value: 1.1195833333333332 - type: precision_at_1000 value: 0.15058333333333335 - type: precision_at_3 value: 15.742083333333333 - type: precision_at_5 value: 11.221916666666667 - type: recall_at_1 value: 24.937916666666666 - type: recall_at_10 value: 50.650416666666665 - type: recall_at_100 value: 73.55383333333334 - type: recall_at_1000 value: 89.61691666666667 - type: recall_at_3 value: 37.27808333333334 - type: recall_at_5 value: 42.99475 - type: map_at_1 value: 23.947 - type: map_at_10 value: 30.575000000000003 - type: map_at_100 value: 31.465 - type: map_at_1000 value: 31.558000000000003 - type: map_at_3 value: 28.814 - type: map_at_5 value: 29.738999999999997 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.415 - type: mrr_at_100 value: 34.18 - type: mrr_at_1000 value: 34.245 - type: mrr_at_3 value: 31.621 - type: mrr_at_5 value: 32.549 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.482 - type: ndcg_at_100 value: 38.915 - type: ndcg_at_1000 value: 41.355 - type: ndcg_at_3 value: 31.139 - type: ndcg_at_5 value: 32.589 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.322 - type: precision_at_100 value: 0.8160000000000001 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 13.344000000000001 - type: precision_at_5 value: 8.988 - type: recall_at_1 value: 23.947 - type: recall_at_10 value: 43.647999999999996 - type: recall_at_100 value: 63.851 - type: recall_at_1000 value: 82.0 - type: recall_at_3 value: 34.288000000000004 - type: recall_at_5 value: 38.117000000000004 - type: map_at_1 value: 16.197 - type: map_at_10 value: 22.968 - type: map_at_100 value: 24.095 - type: map_at_1000 value: 24.217 - type: map_at_3 value: 20.771 - type: map_at_5 value: 21.995 - type: mrr_at_1 value: 19.511 - type: mrr_at_10 value: 26.55 - type: mrr_at_100 value: 27.500999999999998 - type: mrr_at_1000 value: 27.578999999999997 - type: mrr_at_3 value: 24.421 - type: mrr_at_5 value: 25.604 - type: ndcg_at_1 value: 19.511 - type: ndcg_at_10 value: 27.386 - type: ndcg_at_100 value: 32.828 - type: ndcg_at_1000 value: 35.739 - type: ndcg_at_3 value: 23.405 - type: ndcg_at_5 value: 25.255 - type: precision_at_1 value: 19.511 - type: precision_at_10 value: 5.017 - type: precision_at_100 value: 0.91 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 11.023 - type: precision_at_5 value: 8.025 - type: recall_at_1 value: 16.197 - type: recall_at_10 value: 37.09 - type: recall_at_100 value: 61.778 - type: recall_at_1000 value: 82.56599999999999 - type: recall_at_3 value: 26.034000000000002 - type: recall_at_5 value: 30.762 - type: map_at_1 value: 25.41 - type: map_at_10 value: 33.655 - type: map_at_100 value: 34.892 - type: map_at_1000 value: 34.995 - type: map_at_3 value: 30.94 - type: map_at_5 value: 32.303 - type: mrr_at_1 value: 29.477999999999998 - type: mrr_at_10 value: 37.443 - type: mrr_at_100 value: 38.383 - type: mrr_at_1000 value: 38.440000000000005 - type: mrr_at_3 value: 34.949999999999996 - type: mrr_at_5 value: 36.228 - type: ndcg_at_1 value: 29.477999999999998 - type: ndcg_at_10 value: 38.769 - type: ndcg_at_100 value: 44.245000000000005 - type: ndcg_at_1000 value: 46.593 - type: ndcg_at_3 value: 33.623 - type: ndcg_at_5 value: 35.766 - type: precision_at_1 value: 29.477999999999998 - type: precision_at_10 value: 6.455 - type: precision_at_100 value: 1.032 - type: precision_at_1000 value: 0.135 - type: precision_at_3 value: 14.893999999999998 - type: precision_at_5 value: 10.485 - type: recall_at_1 value: 25.41 - type: recall_at_10 value: 50.669 - type: recall_at_100 value: 74.084 - type: recall_at_1000 value: 90.435 - type: recall_at_3 value: 36.679 - type: recall_at_5 value: 41.94 - type: map_at_1 value: 23.339 - type: map_at_10 value: 31.852000000000004 - type: map_at_100 value: 33.411 - type: map_at_1000 value: 33.62 - type: map_at_3 value: 28.929 - type: map_at_5 value: 30.542 - type: mrr_at_1 value: 28.063 - type: mrr_at_10 value: 36.301 - type: mrr_at_100 value: 37.288 - type: mrr_at_1000 value: 37.349 - type: mrr_at_3 value: 33.663 - type: mrr_at_5 value: 35.165 - type: ndcg_at_1 value: 28.063 - type: ndcg_at_10 value: 37.462 - type: ndcg_at_100 value: 43.620999999999995 - type: ndcg_at_1000 value: 46.211 - type: ndcg_at_3 value: 32.68 - type: ndcg_at_5 value: 34.981 - type: precision_at_1 value: 28.063 - type: precision_at_10 value: 7.1739999999999995 - type: precision_at_100 value: 1.486 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 15.217 - type: precision_at_5 value: 11.265 - type: recall_at_1 value: 23.339 - type: recall_at_10 value: 48.376999999999995 - type: recall_at_100 value: 76.053 - type: recall_at_1000 value: 92.455 - type: recall_at_3 value: 34.735 - type: recall_at_5 value: 40.71 - type: map_at_1 value: 18.925 - type: map_at_10 value: 26.017000000000003 - type: map_at_100 value: 27.034000000000002 - type: map_at_1000 value: 27.156000000000002 - type: map_at_3 value: 23.604 - type: map_at_5 value: 24.75 - type: mrr_at_1 value: 20.333000000000002 - type: mrr_at_10 value: 27.915 - type: mrr_at_100 value: 28.788000000000004 - type: mrr_at_1000 value: 28.877999999999997 - type: mrr_at_3 value: 25.446999999999996 - type: mrr_at_5 value: 26.648 - type: ndcg_at_1 value: 20.333000000000002 - type: ndcg_at_10 value: 30.673000000000002 - type: ndcg_at_100 value: 35.618 - type: ndcg_at_1000 value: 38.517 - type: ndcg_at_3 value: 25.71 - type: ndcg_at_5 value: 27.679 - type: precision_at_1 value: 20.333000000000002 - type: precision_at_10 value: 4.9910000000000005 - type: precision_at_100 value: 0.8130000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 11.029 - type: precision_at_5 value: 7.8740000000000006 - type: recall_at_1 value: 18.925 - type: recall_at_10 value: 43.311 - type: recall_at_100 value: 66.308 - type: recall_at_1000 value: 87.49 - type: recall_at_3 value: 29.596 - type: recall_at_5 value: 34.245 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 13.714 - type: map_at_10 value: 23.194 - type: map_at_100 value: 24.976000000000003 - type: map_at_1000 value: 25.166 - type: map_at_3 value: 19.709 - type: map_at_5 value: 21.523999999999997 - type: mrr_at_1 value: 30.619000000000003 - type: mrr_at_10 value: 42.563 - type: mrr_at_100 value: 43.386 - type: mrr_at_1000 value: 43.423 - type: mrr_at_3 value: 39.555 - type: mrr_at_5 value: 41.268 - type: ndcg_at_1 value: 30.619000000000003 - type: ndcg_at_10 value: 31.836 - type: ndcg_at_100 value: 38.652 - type: ndcg_at_1000 value: 42.088 - type: ndcg_at_3 value: 26.733 - type: ndcg_at_5 value: 28.435 - type: precision_at_1 value: 30.619000000000003 - type: precision_at_10 value: 9.751999999999999 - type: precision_at_100 value: 1.71 - type: precision_at_1000 value: 0.23500000000000001 - type: precision_at_3 value: 19.935 - type: precision_at_5 value: 14.984 - type: recall_at_1 value: 13.714 - type: recall_at_10 value: 37.26 - type: recall_at_100 value: 60.546 - type: recall_at_1000 value: 79.899 - type: recall_at_3 value: 24.325 - type: recall_at_5 value: 29.725 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 8.462 - type: map_at_10 value: 18.637 - type: map_at_100 value: 26.131999999999998 - type: map_at_1000 value: 27.607 - type: map_at_3 value: 13.333 - type: map_at_5 value: 15.654000000000002 - type: mrr_at_1 value: 66.25 - type: mrr_at_10 value: 74.32600000000001 - type: mrr_at_100 value: 74.60900000000001 - type: mrr_at_1000 value: 74.62 - type: mrr_at_3 value: 72.667 - type: mrr_at_5 value: 73.817 - type: ndcg_at_1 value: 53.87499999999999 - type: ndcg_at_10 value: 40.028999999999996 - type: ndcg_at_100 value: 44.199 - type: ndcg_at_1000 value: 51.629999999999995 - type: ndcg_at_3 value: 44.113 - type: ndcg_at_5 value: 41.731 - type: precision_at_1 value: 66.25 - type: precision_at_10 value: 31.900000000000002 - type: precision_at_100 value: 10.043000000000001 - type: precision_at_1000 value: 1.926 - type: precision_at_3 value: 47.417 - type: precision_at_5 value: 40.65 - type: recall_at_1 value: 8.462 - type: recall_at_10 value: 24.293 - type: recall_at_100 value: 50.146 - type: recall_at_1000 value: 74.034 - type: recall_at_3 value: 14.967 - type: recall_at_5 value: 18.682000000000002 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 47.84499999999999 - type: f1 value: 42.48106691979349 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 74.034 - type: map_at_10 value: 82.76 - type: map_at_100 value: 82.968 - type: map_at_1000 value: 82.98299999999999 - type: map_at_3 value: 81.768 - type: map_at_5 value: 82.418 - type: mrr_at_1 value: 80.048 - type: mrr_at_10 value: 87.64999999999999 - type: mrr_at_100 value: 87.712 - type: mrr_at_1000 value: 87.713 - type: mrr_at_3 value: 87.01100000000001 - type: mrr_at_5 value: 87.466 - type: ndcg_at_1 value: 80.048 - type: ndcg_at_10 value: 86.643 - type: ndcg_at_100 value: 87.361 - type: ndcg_at_1000 value: 87.606 - type: ndcg_at_3 value: 85.137 - type: ndcg_at_5 value: 86.016 - type: precision_at_1 value: 80.048 - type: precision_at_10 value: 10.372 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.638 - type: precision_at_5 value: 20.177 - type: recall_at_1 value: 74.034 - type: recall_at_10 value: 93.769 - type: recall_at_100 value: 96.569 - type: recall_at_1000 value: 98.039 - type: recall_at_3 value: 89.581 - type: recall_at_5 value: 91.906 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 20.5 - type: map_at_10 value: 32.857 - type: map_at_100 value: 34.589 - type: map_at_1000 value: 34.778 - type: map_at_3 value: 29.160999999999998 - type: map_at_5 value: 31.033 - type: mrr_at_1 value: 40.123 - type: mrr_at_10 value: 48.776 - type: mrr_at_100 value: 49.495 - type: mrr_at_1000 value: 49.539 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 47.654 - type: ndcg_at_1 value: 40.123 - type: ndcg_at_10 value: 40.343 - type: ndcg_at_100 value: 46.56 - type: ndcg_at_1000 value: 49.777 - type: ndcg_at_3 value: 37.322 - type: ndcg_at_5 value: 37.791000000000004 - type: precision_at_1 value: 40.123 - type: precision_at_10 value: 11.08 - type: precision_at_100 value: 1.752 - type: precision_at_1000 value: 0.232 - type: precision_at_3 value: 24.897 - type: precision_at_5 value: 17.809 - type: recall_at_1 value: 20.5 - type: recall_at_10 value: 46.388 - type: recall_at_100 value: 69.552 - type: recall_at_1000 value: 89.011 - type: recall_at_3 value: 33.617999999999995 - type: recall_at_5 value: 38.211 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 39.135999999999996 - type: map_at_10 value: 61.673 - type: map_at_100 value: 62.562 - type: map_at_1000 value: 62.62 - type: map_at_3 value: 58.467999999999996 - type: map_at_5 value: 60.463 - type: mrr_at_1 value: 78.271 - type: mrr_at_10 value: 84.119 - type: mrr_at_100 value: 84.29299999999999 - type: mrr_at_1000 value: 84.299 - type: mrr_at_3 value: 83.18900000000001 - type: mrr_at_5 value: 83.786 - type: ndcg_at_1 value: 78.271 - type: ndcg_at_10 value: 69.935 - type: ndcg_at_100 value: 73.01299999999999 - type: ndcg_at_1000 value: 74.126 - type: ndcg_at_3 value: 65.388 - type: ndcg_at_5 value: 67.906 - type: precision_at_1 value: 78.271 - type: precision_at_10 value: 14.562 - type: precision_at_100 value: 1.6969999999999998 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 41.841 - type: precision_at_5 value: 27.087 - type: recall_at_1 value: 39.135999999999996 - type: recall_at_10 value: 72.809 - type: recall_at_100 value: 84.86200000000001 - type: recall_at_1000 value: 92.208 - type: recall_at_3 value: 62.76199999999999 - type: recall_at_5 value: 67.718 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.60600000000001 - type: ap value: 86.6579587804335 - type: f1 value: 90.5938853929307 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 21.852 - type: map_at_10 value: 33.982 - type: map_at_100 value: 35.116 - type: map_at_1000 value: 35.167 - type: map_at_3 value: 30.134 - type: map_at_5 value: 32.340999999999994 - type: mrr_at_1 value: 22.479 - type: mrr_at_10 value: 34.594 - type: mrr_at_100 value: 35.672 - type: mrr_at_1000 value: 35.716 - type: mrr_at_3 value: 30.84 - type: mrr_at_5 value: 32.998 - type: ndcg_at_1 value: 22.493 - type: ndcg_at_10 value: 40.833000000000006 - type: ndcg_at_100 value: 46.357 - type: ndcg_at_1000 value: 47.637 - type: ndcg_at_3 value: 32.995999999999995 - type: ndcg_at_5 value: 36.919000000000004 - type: precision_at_1 value: 22.493 - type: precision_at_10 value: 6.465999999999999 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.030999999999999 - type: precision_at_5 value: 10.413 - type: recall_at_1 value: 21.852 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 87.611 - type: recall_at_1000 value: 97.441 - type: recall_at_3 value: 40.583999999999996 - type: recall_at_5 value: 49.992999999999995 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.36069311445507 - type: f1 value: 93.16456330371453 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 74.74692202462381 - type: f1 value: 58.17903579421599 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 74.80833893745796 - type: f1 value: 72.70786592684664 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 78.69872225958305 - type: f1 value: 78.61626934504731 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 33.058658628717694 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.85561739360599 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.290259910144385 - type: mrr value: 32.44223046102856 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.288 - type: map_at_10 value: 12.267999999999999 - type: map_at_100 value: 15.557000000000002 - type: map_at_1000 value: 16.98 - type: map_at_3 value: 8.866 - type: map_at_5 value: 10.418 - type: mrr_at_1 value: 43.653 - type: mrr_at_10 value: 52.681 - type: mrr_at_100 value: 53.315999999999995 - type: mrr_at_1000 value: 53.357 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 51.903999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.305 - type: ndcg_at_100 value: 30.825999999999997 - type: ndcg_at_1000 value: 39.393 - type: ndcg_at_3 value: 39.931 - type: ndcg_at_5 value: 37.519999999999996 - type: precision_at_1 value: 43.653 - type: precision_at_10 value: 25.728 - type: precision_at_100 value: 7.932 - type: precision_at_1000 value: 2.07 - type: precision_at_3 value: 38.184000000000005 - type: precision_at_5 value: 32.879000000000005 - type: recall_at_1 value: 5.288 - type: recall_at_10 value: 16.195 - type: recall_at_100 value: 31.135 - type: recall_at_1000 value: 61.531000000000006 - type: recall_at_3 value: 10.313 - type: recall_at_5 value: 12.754999999999999 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 28.216 - type: map_at_10 value: 42.588 - type: map_at_100 value: 43.702999999999996 - type: map_at_1000 value: 43.739 - type: map_at_3 value: 38.177 - type: map_at_5 value: 40.754000000000005 - type: mrr_at_1 value: 31.866 - type: mrr_at_10 value: 45.189 - type: mrr_at_100 value: 46.056000000000004 - type: mrr_at_1000 value: 46.081 - type: mrr_at_3 value: 41.526999999999994 - type: mrr_at_5 value: 43.704 - type: ndcg_at_1 value: 31.837 - type: ndcg_at_10 value: 50.178 - type: ndcg_at_100 value: 54.98800000000001 - type: ndcg_at_1000 value: 55.812 - type: ndcg_at_3 value: 41.853 - type: ndcg_at_5 value: 46.153 - type: precision_at_1 value: 31.837 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 1.1119999999999999 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.023 - type: precision_at_5 value: 13.911000000000001 - type: recall_at_1 value: 28.216 - type: recall_at_10 value: 70.8 - type: recall_at_100 value: 91.857 - type: recall_at_1000 value: 97.941 - type: recall_at_3 value: 49.196 - type: recall_at_5 value: 59.072 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.22800000000001 - type: map_at_10 value: 85.115 - type: map_at_100 value: 85.72 - type: map_at_1000 value: 85.737 - type: map_at_3 value: 82.149 - type: map_at_5 value: 84.029 - type: mrr_at_1 value: 81.96 - type: mrr_at_10 value: 88.00200000000001 - type: mrr_at_100 value: 88.088 - type: mrr_at_1000 value: 88.089 - type: mrr_at_3 value: 87.055 - type: mrr_at_5 value: 87.715 - type: ndcg_at_1 value: 82.01 - type: ndcg_at_10 value: 88.78 - type: ndcg_at_100 value: 89.91 - type: ndcg_at_1000 value: 90.013 - type: ndcg_at_3 value: 85.957 - type: ndcg_at_5 value: 87.56 - type: precision_at_1 value: 82.01 - type: precision_at_10 value: 13.462 - type: precision_at_100 value: 1.528 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.553 - type: precision_at_5 value: 24.732000000000003 - type: recall_at_1 value: 71.22800000000001 - type: recall_at_10 value: 95.69 - type: recall_at_100 value: 99.531 - type: recall_at_1000 value: 99.98 - type: recall_at_3 value: 87.632 - type: recall_at_5 value: 92.117 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 52.31768034366916 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 60.640266772723606 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.7780000000000005 - type: map_at_10 value: 12.299 - type: map_at_100 value: 14.363000000000001 - type: map_at_1000 value: 14.71 - type: map_at_3 value: 8.738999999999999 - type: map_at_5 value: 10.397 - type: mrr_at_1 value: 23.599999999999998 - type: mrr_at_10 value: 34.845 - type: mrr_at_100 value: 35.916 - type: mrr_at_1000 value: 35.973 - type: mrr_at_3 value: 31.7 - type: mrr_at_5 value: 33.535 - type: ndcg_at_1 value: 23.599999999999998 - type: ndcg_at_10 value: 20.522000000000002 - type: ndcg_at_100 value: 28.737000000000002 - type: ndcg_at_1000 value: 34.596 - type: ndcg_at_3 value: 19.542 - type: ndcg_at_5 value: 16.958000000000002 - type: precision_at_1 value: 23.599999999999998 - type: precision_at_10 value: 10.67 - type: precision_at_100 value: 2.259 - type: precision_at_1000 value: 0.367 - type: precision_at_3 value: 18.333 - type: precision_at_5 value: 14.879999999999999 - type: recall_at_1 value: 4.7780000000000005 - type: recall_at_10 value: 21.617 - type: recall_at_100 value: 45.905 - type: recall_at_1000 value: 74.42 - type: recall_at_3 value: 11.148 - type: recall_at_5 value: 15.082999999999998 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.22372750297885 - type: cos_sim_spearman value: 79.40972617119405 - type: euclidean_pearson value: 80.6101072020434 - type: euclidean_spearman value: 79.53844217225202 - type: manhattan_pearson value: 80.57265975286111 - type: manhattan_spearman value: 79.46335611792958 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 85.43713315520749 - type: cos_sim_spearman value: 77.44128693329532 - type: euclidean_pearson value: 81.63869928101123 - type: euclidean_spearman value: 77.29512977961515 - type: manhattan_pearson value: 81.63704185566183 - type: manhattan_spearman value: 77.29909412738657 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 81.59451537860527 - type: cos_sim_spearman value: 82.97994638856723 - type: euclidean_pearson value: 82.89478688288412 - type: euclidean_spearman value: 83.58740751053104 - type: manhattan_pearson value: 82.69140840941608 - type: manhattan_spearman value: 83.33665956040555 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 82.00756527711764 - type: cos_sim_spearman value: 81.83560996841379 - type: euclidean_pearson value: 82.07684151976518 - type: euclidean_spearman value: 82.00913052060511 - type: manhattan_pearson value: 82.05690778488794 - type: manhattan_spearman value: 82.02260252019525 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.13710262895447 - type: cos_sim_spearman value: 87.26412811156248 - type: euclidean_pearson value: 86.94151453230228 - type: euclidean_spearman value: 87.5363796699571 - type: manhattan_pearson value: 86.86989424083748 - type: manhattan_spearman value: 87.47315940781353 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 83.0230597603627 - type: cos_sim_spearman value: 84.93344499318864 - type: euclidean_pearson value: 84.23754743431141 - type: euclidean_spearman value: 85.09707376597099 - type: manhattan_pearson value: 84.04325160987763 - type: manhattan_spearman value: 84.89353071339909 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 86.75620824563921 - type: cos_sim_spearman value: 87.15065513706398 - type: euclidean_pearson value: 88.26281533633521 - type: euclidean_spearman value: 87.51963738643983 - type: manhattan_pearson value: 88.25599267618065 - type: manhattan_spearman value: 87.58048736047483 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 64.74645319195137 - type: cos_sim_spearman value: 65.29996325037214 - type: euclidean_pearson value: 67.04297794086443 - type: euclidean_spearman value: 65.43841726694343 - type: manhattan_pearson value: 67.39459955690904 - type: manhattan_spearman value: 65.92864704413651 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 84.31291020270801 - type: cos_sim_spearman value: 85.86473738688068 - type: euclidean_pearson value: 85.65537275064152 - type: euclidean_spearman value: 86.13087454209642 - type: manhattan_pearson value: 85.43946955047609 - type: manhattan_spearman value: 85.91568175344916 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 85.93798118350695 - type: mrr value: 95.93536274908824 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 57.594 - type: map_at_10 value: 66.81899999999999 - type: map_at_100 value: 67.368 - type: map_at_1000 value: 67.4 - type: map_at_3 value: 64.061 - type: map_at_5 value: 65.47 - type: mrr_at_1 value: 60.667 - type: mrr_at_10 value: 68.219 - type: mrr_at_100 value: 68.655 - type: mrr_at_1000 value: 68.684 - type: mrr_at_3 value: 66.22200000000001 - type: mrr_at_5 value: 67.289 - type: ndcg_at_1 value: 60.667 - type: ndcg_at_10 value: 71.275 - type: ndcg_at_100 value: 73.642 - type: ndcg_at_1000 value: 74.373 - type: ndcg_at_3 value: 66.521 - type: ndcg_at_5 value: 68.581 - type: precision_at_1 value: 60.667 - type: precision_at_10 value: 9.433 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.556 - type: precision_at_5 value: 16.8 - type: recall_at_1 value: 57.594 - type: recall_at_10 value: 83.622 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 70.64399999999999 - type: recall_at_5 value: 75.983 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.85841584158416 - type: cos_sim_ap value: 96.66996142314342 - type: cos_sim_f1 value: 92.83208020050125 - type: cos_sim_precision value: 93.06532663316584 - type: cos_sim_recall value: 92.60000000000001 - type: dot_accuracy value: 99.85841584158416 - type: dot_ap value: 96.6775307676576 - type: dot_f1 value: 92.69289729177312 - type: dot_precision value: 94.77533960292581 - type: dot_recall value: 90.7 - type: euclidean_accuracy value: 99.86138613861387 - type: euclidean_ap value: 96.6338454403108 - type: euclidean_f1 value: 92.92214357937311 - type: euclidean_precision value: 93.96728016359918 - type: euclidean_recall value: 91.9 - type: manhattan_accuracy value: 99.86237623762376 - type: manhattan_ap value: 96.60370449645053 - type: manhattan_f1 value: 92.91177970423253 - type: manhattan_precision value: 94.7970863683663 - type: manhattan_recall value: 91.10000000000001 - type: max_accuracy value: 99.86237623762376 - type: max_ap value: 96.6775307676576 - type: max_f1 value: 92.92214357937311 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 60.77977058695198 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 35.2725272535638 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 53.64052466362125 - type: mrr value: 54.533067014684654 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.677624219206578 - type: cos_sim_spearman value: 30.121368518123447 - type: dot_pearson value: 30.69870088041608 - type: dot_spearman value: 29.61284927093751 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.22 - type: map_at_10 value: 1.855 - type: map_at_100 value: 9.885 - type: map_at_1000 value: 23.416999999999998 - type: map_at_3 value: 0.637 - type: map_at_5 value: 1.024 - type: mrr_at_1 value: 88.0 - type: mrr_at_10 value: 93.067 - type: mrr_at_100 value: 93.067 - type: mrr_at_1000 value: 93.067 - type: mrr_at_3 value: 92.667 - type: mrr_at_5 value: 93.067 - type: ndcg_at_1 value: 82.0 - type: ndcg_at_10 value: 75.899 - type: ndcg_at_100 value: 55.115 - type: ndcg_at_1000 value: 48.368 - type: ndcg_at_3 value: 79.704 - type: ndcg_at_5 value: 78.39699999999999 - type: precision_at_1 value: 88.0 - type: precision_at_10 value: 79.60000000000001 - type: precision_at_100 value: 56.06 - type: precision_at_1000 value: 21.206 - type: precision_at_3 value: 84.667 - type: precision_at_5 value: 83.2 - type: recall_at_1 value: 0.22 - type: recall_at_10 value: 2.078 - type: recall_at_100 value: 13.297 - type: recall_at_1000 value: 44.979 - type: recall_at_3 value: 0.6689999999999999 - type: recall_at_5 value: 1.106 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.258 - type: map_at_10 value: 10.439 - type: map_at_100 value: 16.89 - type: map_at_1000 value: 18.407999999999998 - type: map_at_3 value: 5.668 - type: map_at_5 value: 7.718 - type: mrr_at_1 value: 32.653 - type: mrr_at_10 value: 51.159 - type: mrr_at_100 value: 51.714000000000006 - type: mrr_at_1000 value: 51.714000000000006 - type: mrr_at_3 value: 47.959 - type: mrr_at_5 value: 50.407999999999994 - type: ndcg_at_1 value: 29.592000000000002 - type: ndcg_at_10 value: 26.037 - type: ndcg_at_100 value: 37.924 - type: ndcg_at_1000 value: 49.126999999999995 - type: ndcg_at_3 value: 30.631999999999998 - type: ndcg_at_5 value: 28.571 - type: precision_at_1 value: 32.653 - type: precision_at_10 value: 22.857 - type: precision_at_100 value: 7.754999999999999 - type: precision_at_1000 value: 1.529 - type: precision_at_3 value: 34.014 - type: precision_at_5 value: 29.796 - type: recall_at_1 value: 2.258 - type: recall_at_10 value: 16.554 - type: recall_at_100 value: 48.439 - type: recall_at_1000 value: 82.80499999999999 - type: recall_at_3 value: 7.283 - type: recall_at_5 value: 10.732 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 69.8858 - type: ap value: 13.835684144362109 - type: f1 value: 53.803351693244586 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 60.50650820599886 - type: f1 value: 60.84357825979259 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 48.52131044852134 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.59337187816654 - type: cos_sim_ap value: 73.23925826533437 - type: cos_sim_f1 value: 67.34693877551021 - type: cos_sim_precision value: 62.40432237730752 - type: cos_sim_recall value: 73.13984168865434 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.30723963807422 - type: dot_f1 value: 66.47051612112296 - type: dot_precision value: 62.0792305930845 - type: dot_recall value: 71.53034300791556 - type: euclidean_accuracy value: 85.61125350181797 - type: euclidean_ap value: 73.32843720487845 - type: euclidean_f1 value: 67.36549633745895 - type: euclidean_precision value: 64.60755813953489 - type: euclidean_recall value: 70.36939313984169 - type: manhattan_accuracy value: 85.63509566668654 - type: manhattan_ap value: 73.16658488311325 - type: manhattan_f1 value: 67.20597386434349 - type: manhattan_precision value: 63.60424028268551 - type: manhattan_recall value: 71.2401055408971 - type: max_accuracy value: 85.63509566668654 - type: max_ap value: 73.32843720487845 - type: max_f1 value: 67.36549633745895 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 88.33779640625606 - type: cos_sim_ap value: 84.83868375898157 - type: cos_sim_f1 value: 77.16506154017773 - type: cos_sim_precision value: 74.62064005753327 - type: cos_sim_recall value: 79.88912842623961 - type: dot_accuracy value: 88.02732176815307 - type: dot_ap value: 83.95089283763002 - type: dot_f1 value: 76.29635101196631 - type: dot_precision value: 73.31771720613288 - type: dot_recall value: 79.52725592854944 - type: euclidean_accuracy value: 88.44452206310397 - type: euclidean_ap value: 84.98384576824827 - type: euclidean_f1 value: 77.29311047696697 - type: euclidean_precision value: 74.51232583065381 - type: euclidean_recall value: 80.28949799815214 - type: manhattan_accuracy value: 88.47362906042613 - type: manhattan_ap value: 84.91421462218432 - type: manhattan_f1 value: 77.05107637204792 - type: manhattan_precision value: 74.74484256243214 - type: manhattan_recall value: 79.50415768401602 - type: max_accuracy value: 88.47362906042613 - type: max_ap value: 84.98384576824827 - type: max_f1 value: 77.29311047696697 --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) - **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) ## News - 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. [Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: - 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: - 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire: - 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary>  - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . ## Frequently asked questions <details> <summary>1. How to fine-tune bge embedding model?</summary>  Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. </details> <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>  **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary>  For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples for using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` #### Usage of the ONNX files ```python from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5') model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5') model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx") # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') model_output_ort = model_ort(**encoded_input) # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # model_output and model_output_ort are identical ``` #### Usage via infinity Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch" )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

[ "SEMANTIC_SIMILARITY", "SUMMARIZATION" ]

[ "BEAR", "BIOSSES", "SCIFACT" ]

Adjoumani/BaouleTokenizer_V1

Adjoumani

null

[ "region:us" ]

2025-02-02T19:37:05

2025-02-02T20:09:06

0

--- {} --- ```markdown --- language: - "baq" # Code ISO 639-3 pour le Baoulé - "fr" # Français tags: - "translation" - "low-resource" - "african-nlp" - "tonal-language" license: "apache-2.0" datasets: - "custom" metrics: - "bleu" - "ter" - "chrF" widget: - text: "Mɔ́kɛ́ mɩnɩn wɛ?" example_title: "Salutation basique" pipeline_tag: "translation" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 Premier tokenizer SentencePiece spécialisé pour la langue Baoulé (Côte d'Ivoire) 🇨🇮 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/Adjoumani/BaouleTokenizer_V1) ## Fonctionnalités Clés ✅ Prise en charge complète des caractères tonals Baoulé (ɛ́, ɩ̄, ɔ̀, etc.) ✅ Optimisé pour les modèles de traduction automatique (Transformer) ✅ Vocabulaire de 206 tokens avec couverture linguistique complète ✅ Intégration native avec 🤗 Transformers et Tokenizers ✅ Compatible avec Google Traduction Custom Model et Amazon Translate ## Installation et Utilisation ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Utilisation du tokenizer text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'e', '▁l', 'a', 'f', 'i', '▁s', 'u', '▁k', 'ɛ', '▁b', 'é', '▁t', 'r', 'á', 'n', '▁a', 's', 'i', 'ɛ', '’', 'n', '▁s', 'u', '▁w', 'a', '’', 'n', ',', '▁b', 'e', '▁b', 'u', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'é', '▁k', 'ɔ', '́', '▁ɲ', 'a', 'n', 'm', 'i', 'ɛ', 'n'] ``` ## Détails Techniques | Paramètre | Valeur | |--------------------|----------------------| | Architecture | SentencePiece BPE | | Taille du vocabulaire | 206 | | Caractères couverts | 1.0 (Unicode) | | Tokens spéciaux | [BOS], [EOS], [UNK], [PAD] | | Langues cibles | Français ↔ Baoulé | | Encodage | UTF-8 | ## Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : | Caractère | Code Unicode | Exemple | |-----------|--------------|---------| | ɛ́ | U+025B U+0301| Mɔ́kɛ́ | | ɩ̄ | U+0269 U+0304| Ɩ̄tɩ̄ | | ɔ̀ | U+0254 U+0300| Kɔ̀lɔ̀ | | ɛ̂ | U+025B U+0302| Ɛ̂sɛ̂ | ## Cas d'Usage Recommandés - Traduction automatique Français-Baoulé - Synthèse vocale pour systèmes d'assistance vocale - Reconnaissance de la parole Baoulé - Outils éducatifs numériques - Préservation du patrimoine linguistique ## Meilleures Pratiques ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` ## Jeu de Données d'Entraînement Données collectées grâce à : - Traductions de textes bibliques : Les données ont été extraites en grande partie depuis [Glosbe](https://www.glosbe.com/) et structurées manuellement pour assurer une qualité et une précision optimales. Le contenu a été nettoyé pour supprimer les balises HTML indésirables et formaté de manière cohérente. - Corpus oral transcrit (projet UNESCO) - Phrases quotidiennes annotées - Textes gouvernementaux bilingues **Taille du corpus** : 1500 phrases alignées (en cours d'expansion) ## Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2025, author = {Koffi Wilfried Adjoumani}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` ## Licence Apache 2.0 - [Voir la licence complète](LICENSE) ## Contribuer Nous encourageons les contributions notamment pour : - L'expansion du vocabulaire - L'annotation des tons - L'ajout de dialectes régionaux Contact : [[email protected]](mailto:[email protected]) --- **Mots-clés SEO** : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine --- ```

[ "TRANSLATION" ]

[ "CAS" ]

Adjoumani/baoule-tokenizer

Adjoumani

null

[ "region:us" ]

2025-02-04T03:26:31

2025-02-04T03:26:32

0

--- {} --- Votre fichier `README.md` est déjà bien structuré, mais je vais l'améliorer pour qu'il soit encore plus conforme aux principes de référencement (SEO) de Hugging Face et Google. Voici une version optimisée : --- ### **README.md Optimisé** ```markdown --- language: - baq - bci - fr tags: - african-nlp - low-resource-language - sentencepiece - tokenizer - baoule - cote-divoire - translation - tonal-language datasets: - custom license: apache-2.0 library_name: transformers pipeline_tag: text2text-generation widget: - text: "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" example_title: "Exemple de traduction Baoulé" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 **Premier tokenizer spécialisé pour la langue Baoulé (Côte d'Ivoire)** 🇨🇮 Ce tokenizer a été conçu spécifiquement pour la traduction automatique entre le français et le baoulé, une langue tonale africaine parlée en Côte d'Ivoire. [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/Adjoumani/BaouleTokenizer_V1) ## 📋 Fonctionnalités Clés ✅ **Prise en charge complète des caractères tonaux Baoulé** (ɛ́, ɩ̄, ɔ̀, etc.) ✅ **Optimisé pour les modèles de traduction automatique** basés sur Transformer ✅ **Vocabulaire compact** avec une taille de 206 tokens et une couverture linguistique complète ✅ **Intégration native avec 🤗 Transformers et Tokenizers** ✅ Compatible avec **Google Translate Custom Model**, **Amazon Translate**, et autres outils de NLP --- ## 🚀 Installation et Utilisation Installez les bibliothèques nécessaires : ```bash pip install transformers sentencepiece ``` Chargez et utilisez le tokenizer : ```python from transformers import AutoTokenizer # Charger le tokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Exemple d'utilisation text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', ...] ``` --- ## 📊 Détails Techniques | Paramètre | Valeur | |--------------------|----------------------| | Architecture | SentencePiece BPE | | Taille du vocabulaire | 206 | | Caractères couverts | 1.0 (Unicode) | | Tokens spéciaux | `[BOS]`, `[EOS]`, `[UNK]`, `[PAD]` | | Langues cibles | Français ↔ Baoulé | | Encodage | UTF-8 | --- ## 🎵 Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : | Caractère | Code Unicode | Exemple | |-----------|--------------|--------------| | ɛ́ | U+025B U+0301 | Mɔ́kɛ́ | | ɩ̄ | U+0269 U+0304 | Ɩ̄tɩ̄ | | ɔ̀ | U+0254 U+0300 | Kɔ̀lɔ̀ | | ɛ̂ | U+025B U+0302 | Ɛ̂sɛ̂ | --- ## 💡 Cas d'Usage Recommandés - **Traduction automatique** entre le français et le baoulé - **Synthèse vocale** pour systèmes d'assistance vocale - **Reconnaissance de la parole** Baoulé - Outils éducatifs numériques pour apprendre le baoulé - Préservation du patrimoine linguistique africain --- ## 🛠️ Meilleures Pratiques Gérez les phrases longues et ajoutez des tokens personnalisés si nécessaire : ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` --- ## 📚 Jeu de Données d'Entraînement Les données d'entraînement ont été collectées à partir des sources suivantes : - **Traductions de textes bibliques** : Les données ont été extraites depuis [Glosbe](https://fr.glosbe.com/bci/fr) et enrichies manuellement pour assurer une qualité optimale. - **Corpus générés par IA** : Textes générés en français via [Google AI Studio](https://ai.studio.google.com/) et traduits en baoulé via Google Translate. - **Corpus oral transcrit** : Phrases quotidiennes annotées dans le cadre de projets UNESCO. - **Textes gouvernementaux bilingues** : Documents officiels traduits en baoulé. **Taille du corpus** : ~1500 phrases alignées (en cours d'expansion). --- ## 📝 Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2023, author = {Adjoumani Kouakou}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` --- ## 📜 Licence Apache 2.0 - [Voir la licence complète](LICENSE) --- ## 🤝 Contribuer Nous encourageons les contributions pour améliorer ce tokenizer : - Expansion du vocabulaire - Annotation des tons manquants - Ajout de dialectes régionaux Pour toute question ou suggestion, contactez-nous à : [[email protected]](mailto:[email protected]) --- **Mots-clés SEO** : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine ``` --- ### **Améliorations Apportées** 1. **Structure YAML** : Ajout de tags comme `african-nlp`, `cote-divoire`, etc., pour améliorer la visibilité sur Hugging Face. 2. **SEO** : Inclusion de mots-clés pertinents pour le référencement Google (ex. "NLP Africain", "Langues Tonales"). 3. **Clarté** : Simplification des sections pour rendre le README plus accessible. 4. **Sources de données** : Description claire des sources utilisées pour entraîner le tokenizer. 5. **Citation** : Ajout d'une section pour faciliter la citation du modèle dans des publications académiques. 6. **Contribution** : Encouragement explicite des contributions pour enrichir le tokenizer. Ce README est maintenant prêt à être utilisé pour publier votre tokenizer sur Hugging Face ! 😊

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Adjoumani/baouleTokenizer

Adjoumani

null

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2025-02-08T15:06:48

2025-02-08T15:15:13

0

--- {} --- ```markdown --- language: - baq - bci - fr tags: - African NLP - low-resource language - sentencepiece - tokenizer - Baoulé - Côte d'Ivoire - translation - tonal language datasets: - custom license: apache-2.0 library_name: transformers pipeline_tag: text2text-generation widget: - text: "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" example_title: "Traduction de base" --- # Tokenizer Baoulé : Modèle de Traduction Français-Baoulé 🌍 Premier tokenizer SentencePiece spécialisé pour la langue Baoulé (Côte d'Ivoire) 🇨🇮 [![Hugging Face Hub](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-blue)](https://huggingface.co/votre_username/baoule-tokenizer) ## Fonctionnalités Clés ✅ Prise en charge complète des caractères tonals Baoulé (ɛ́, ɩ̄, ɔ̀, etc.) ✅ Optimisé pour les modèles de traduction automatique (Transformer) ✅ Vocabulaire de 206 tokens avec couverture linguistique complète ✅ Intégration native avec 🤗 Transformers et Tokenizers ✅ Compatible avec Google Traduction Custom Model et Amazon Translate ## Installation et Utilisation ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Adjoumani/BaouleTokenizer_V1") # Utilisation du tokenizer text = "Wafa sɛ yɛ ɔ fata kɛ be nga be lafi su kɛ bé trán asiɛ’n su wa’n, be bu be nga bé kɔ́ ɲanmiɛn" encoded = tokenizer.encode(text) decoded = tokenizer.decode(encoded) print(f"Tokens: {tokenizer.tokenize(text)}") # Output: ['W', 'a', 'f', 'a', '▁s', 'ɛ', '▁y', 'ɛ', '▁ɔ', '▁f', 'a', 't', 'a', '▁k', 'ɛ', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'e', '▁l', 'a', 'f', 'i', '▁s', 'u', '▁k', 'ɛ', '▁b', 'é', '▁t', 'r', 'á', 'n', '▁a', 's', 'i', 'ɛ', '’', 'n', '▁s', 'u', '▁w', 'a', '’', 'n', ',', '▁b', 'e', '▁b', 'u', '▁b', 'e', '▁n', 'g', 'a', '▁b', 'é', '▁k', 'ɔ', '́', '▁ɲ', 'a', 'n', 'm', 'i', 'ɛ', 'n'] ``` ## Détails Techniques | Paramètre | Valeur | |--------------------|----------------------| | Architecture | SentencePiece BPE | | Taille du vocabulaire | 206 | | Caractères couverts | 1.0 (Unicode) | | Tokens spéciaux | [BOS], [EOS], [UNK], [PAD] | | Langues cibles | Français ↔ Baoulé | | Encodage | UTF-8 | ## Tons Supportés Le tokenizer gère tous les tons Baoulé selon la norme Unicode : | Caractère | Code Unicode | Exemple | |-----------|--------------|---------| | ɛ́ | U+025B U+0301| Mɔ́kɛ́ | | ɩ̄ | U+0269 U+0304| Ɩ̄tɩ̄ | | ɔ̀ | U+0254 U+0300| Kɔ̀lɔ̀ | | ɛ̂ | U+025B U+0302| Ɛ̂sɛ̂ | ## Cas d'Usage Recommandés - Traduction automatique Français-Baoulé - Synthèse vocale pour systèmes d'assistance vocale - Reconnaissance de la parole Baoulé - Outils éducatifs numériques - Préservation du patrimoine linguistique ## Meilleures Pratiques ```python # Pour gérer les phrases longues tokenizer.model_max_length = 512 # Ajout de tokens personnalisés new_tokens = ["<dialect:NDÊ>", "<dialect:SAFOUÈ>"] tokenizer.add_tokens(new_tokens) ``` ## Jeu de Données d'Entraînement Données collectées grâce à : - Traductions de textes bibliques : Les données ont été extraites en grande partie depuis [Glosbe](https://www.glosbe.com/) et structurées manuellement pour assurer une qualité et une précision optimales. Le contenu a été nettoyé pour supprimer les balises HTML indésirables et formaté de manière cohérente. - Corpus oral transcrit (projet UNESCO) - Phrases quotidiennes annotées - Textes gouvernementaux bilingues **Taille du corpus** : 1500 phrases alignées (en cours d'expansion) ## Citation Si vous utilisez ce tokenizer dans vos recherches, merci de citer : ```bibtex @misc{BaouleTokenizer2023, author = {Votre Nom}, title = {Baoulé Tokenizer for Low-Resource Machine Translation}, year = {2023}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/Adjoumani/BaouleTokenizer_V1}} } ``` ## Licence Apache 2.0 - [Voir la licence complète](LICENSE) ## Contribuer Nous encourageons les contributions notamment pour : - L'expansion du vocabulaire - L'annotation des tons - L'ajout de dialectes régionaux Contact : [[email protected]](mailto:[email protected]) --- **Mots-clés SEO** : Tokenizer Baoulé, Traduction Français-Baoulé, NLP Africain, Langues Tonales, Côte d'Ivoire AI, Modèle Linguistique Basse Ressource, SentencePiece Baoulé, Préservation Langue Africaine ```

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