--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: stella-mrl-large-zh-v3.5-1792d results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 54.33822814973567 - type: cos_sim_spearman value: 58.85457316132848 - type: euclidean_pearson value: 57.57048145477383 - type: euclidean_spearman value: 58.854593263425095 - type: manhattan_pearson value: 57.55884028558309 - type: manhattan_spearman value: 58.84474216217465 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 54.219652875381875 - type: cos_sim_spearman value: 58.079506691583546 - type: euclidean_pearson value: 61.646366330471736 - type: euclidean_spearman value: 58.07951006894859 - type: manhattan_pearson value: 61.64460832085762 - type: manhattan_spearman value: 58.08054699349972 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.593999999999994 - type: f1 value: 44.73150848183217 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 69.16841007040091 - type: cos_sim_spearman value: 71.04760904227217 - type: euclidean_pearson value: 69.95126084376611 - type: euclidean_spearman value: 71.04760904184589 - type: manhattan_pearson value: 69.92512024129407 - type: manhattan_spearman value: 71.02613161257672 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 43.032332399653306 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 40.41603958793544 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 89.33487924447584 - type: mrr value: 91.34623015873017 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 89.17795270698021 - type: mrr value: 91.0956746031746 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.809 - type: map_at_10 value: 39.906000000000006 - type: map_at_100 value: 41.858000000000004 - type: map_at_1000 value: 41.954 - type: map_at_3 value: 35.435 - type: map_at_5 value: 37.978 - type: mrr_at_1 value: 40.660000000000004 - type: mrr_at_10 value: 48.787000000000006 - type: mrr_at_100 value: 49.796 - type: mrr_at_1000 value: 49.832 - type: mrr_at_3 value: 46.166000000000004 - type: mrr_at_5 value: 47.675 - type: ndcg_at_1 value: 40.660000000000004 - type: ndcg_at_10 value: 46.614 - type: ndcg_at_100 value: 54.037 - type: ndcg_at_1000 value: 55.654 - type: ndcg_at_3 value: 41.032000000000004 - type: ndcg_at_5 value: 43.464999999999996 - type: precision_at_1 value: 40.660000000000004 - type: precision_at_10 value: 10.35 - type: precision_at_100 value: 1.6340000000000001 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.122 - type: precision_at_5 value: 16.944 - type: recall_at_1 value: 26.809 - type: recall_at_10 value: 57.474000000000004 - type: recall_at_100 value: 87.976 - type: recall_at_1000 value: 98.74199999999999 - type: recall_at_3 value: 40.819 - type: recall_at_5 value: 48.175000000000004 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 83.4996993385448 - type: cos_sim_ap value: 90.66238348446467 - type: cos_sim_f1 value: 84.39077936333699 - type: cos_sim_precision value: 79.53651975998345 - type: cos_sim_recall value: 89.87608136544307 - type: dot_accuracy value: 83.4996993385448 - type: dot_ap value: 90.64660919236363 - type: dot_f1 value: 84.39077936333699 - type: dot_precision value: 79.53651975998345 - type: dot_recall value: 89.87608136544307 - type: euclidean_accuracy value: 83.4996993385448 - type: euclidean_ap value: 90.66238269557765 - type: euclidean_f1 value: 84.39077936333699 - type: euclidean_precision value: 79.53651975998345 - type: euclidean_recall value: 89.87608136544307 - type: manhattan_accuracy value: 83.35538184004811 - type: manhattan_ap value: 90.6446013420276 - type: manhattan_f1 value: 84.37465196569775 - type: manhattan_precision value: 80.5614632071459 - type: manhattan_recall value: 88.56675239653963 - type: max_accuracy value: 83.4996993385448 - type: max_ap value: 90.66238348446467 - type: max_f1 value: 84.39077936333699 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 68.967 - type: map_at_10 value: 77.95299999999999 - type: map_at_100 value: 78.213 - type: map_at_1000 value: 78.21900000000001 - type: map_at_3 value: 76.30799999999999 - type: map_at_5 value: 77.316 - type: mrr_at_1 value: 69.125 - type: mrr_at_10 value: 77.886 - type: mrr_at_100 value: 78.141 - type: mrr_at_1000 value: 78.147 - type: mrr_at_3 value: 76.291 - type: mrr_at_5 value: 77.29700000000001 - type: ndcg_at_1 value: 69.231 - type: ndcg_at_10 value: 81.867 - type: ndcg_at_100 value: 82.982 - type: ndcg_at_1000 value: 83.12 - type: ndcg_at_3 value: 78.592 - type: ndcg_at_5 value: 80.39 - type: precision_at_1 value: 69.231 - type: precision_at_10 value: 9.494 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 28.591 - type: precision_at_5 value: 18.061 - type: recall_at_1 value: 68.967 - type: recall_at_10 value: 93.941 - type: recall_at_100 value: 98.84100000000001 - type: recall_at_1000 value: 99.895 - type: recall_at_3 value: 85.142 - type: recall_at_5 value: 89.46300000000001 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.824 - type: map_at_10 value: 79.396 - type: map_at_100 value: 82.253 - type: map_at_1000 value: 82.295 - type: map_at_3 value: 54.83 - type: map_at_5 value: 69.536 - type: mrr_at_1 value: 89.7 - type: mrr_at_10 value: 92.929 - type: mrr_at_100 value: 93.013 - type: mrr_at_1000 value: 93.015 - type: mrr_at_3 value: 92.658 - type: mrr_at_5 value: 92.841 - type: ndcg_at_1 value: 89.7 - type: ndcg_at_10 value: 86.797 - type: ndcg_at_100 value: 89.652 - type: ndcg_at_1000 value: 90.047 - type: ndcg_at_3 value: 85.651 - type: ndcg_at_5 value: 84.747 - type: precision_at_1 value: 89.7 - type: precision_at_10 value: 41.61 - type: precision_at_100 value: 4.788 - type: precision_at_1000 value: 0.488 - type: precision_at_3 value: 76.833 - type: precision_at_5 value: 65.14 - type: recall_at_1 value: 25.824 - type: recall_at_10 value: 87.896 - type: recall_at_100 value: 97.221 - type: recall_at_1000 value: 99.29599999999999 - type: recall_at_3 value: 57.178 - type: recall_at_5 value: 74.348 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 52.5 - type: map_at_10 value: 63.04 - type: map_at_100 value: 63.548 - type: map_at_1000 value: 63.56 - type: map_at_3 value: 60.483 - type: map_at_5 value: 62.22800000000001 - type: mrr_at_1 value: 52.5 - type: mrr_at_10 value: 63.04 - type: mrr_at_100 value: 63.548 - type: mrr_at_1000 value: 63.56 - type: mrr_at_3 value: 60.483 - type: mrr_at_5 value: 62.22800000000001 - type: ndcg_at_1 value: 52.5 - type: ndcg_at_10 value: 68.099 - type: ndcg_at_100 value: 70.48400000000001 - type: ndcg_at_1000 value: 70.769 - type: ndcg_at_3 value: 63.01 - type: ndcg_at_5 value: 66.148 - type: precision_at_1 value: 52.5 - type: precision_at_10 value: 8.39 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 23.433 - type: precision_at_5 value: 15.58 - type: recall_at_1 value: 52.5 - type: recall_at_10 value: 83.89999999999999 - type: recall_at_100 value: 94.89999999999999 - type: recall_at_1000 value: 97.1 - type: recall_at_3 value: 70.3 - type: recall_at_5 value: 77.9 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 50.742593305117346 - type: f1 value: 38.7451988564002 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 86.09756097560977 - type: ap value: 54.39255221143281 - type: f1 value: 80.8326851537251 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 72.32408066246728 - type: cos_sim_spearman value: 78.25773378380241 - type: euclidean_pearson value: 77.87824677060661 - type: euclidean_spearman value: 78.25773599854358 - type: manhattan_pearson value: 77.86648277798515 - type: manhattan_spearman value: 78.24642917155661 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 28.846601097874608 - type: mrr value: 27.902777777777775 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.533 - type: map_at_10 value: 75.58399999999999 - type: map_at_100 value: 75.91 - type: map_at_1000 value: 75.921 - type: map_at_3 value: 73.847 - type: map_at_5 value: 74.929 - type: mrr_at_1 value: 68.854 - type: mrr_at_10 value: 76.20700000000001 - type: mrr_at_100 value: 76.498 - type: mrr_at_1000 value: 76.508 - type: mrr_at_3 value: 74.71600000000001 - type: mrr_at_5 value: 75.653 - type: ndcg_at_1 value: 68.854 - type: ndcg_at_10 value: 79.209 - type: ndcg_at_100 value: 80.67 - type: ndcg_at_1000 value: 80.95 - type: ndcg_at_3 value: 75.923 - type: ndcg_at_5 value: 77.74799999999999 - type: precision_at_1 value: 68.854 - type: precision_at_10 value: 9.547 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.582 - type: precision_at_5 value: 18.112000000000002 - type: recall_at_1 value: 66.533 - type: recall_at_10 value: 89.736 - type: recall_at_100 value: 96.34 - type: recall_at_1000 value: 98.52 - type: recall_at_3 value: 81.047 - type: recall_at_5 value: 85.38900000000001 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 73.27841291190316 - type: f1 value: 70.82287701665152 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 76.20040349697376 - type: f1 value: 75.92782428878164 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 56.39999999999999 - type: map_at_10 value: 62.122 - type: map_at_100 value: 62.692 - type: map_at_1000 value: 62.739 - type: map_at_3 value: 60.617 - type: map_at_5 value: 61.582 - type: mrr_at_1 value: 56.39999999999999 - type: mrr_at_10 value: 62.125 - type: mrr_at_100 value: 62.696 - type: mrr_at_1000 value: 62.742 - type: mrr_at_3 value: 60.617 - type: mrr_at_5 value: 61.602000000000004 - type: ndcg_at_1 value: 56.39999999999999 - type: ndcg_at_10 value: 64.986 - type: ndcg_at_100 value: 67.889 - type: ndcg_at_1000 value: 69.16499999999999 - type: ndcg_at_3 value: 61.951 - type: ndcg_at_5 value: 63.685 - type: precision_at_1 value: 56.39999999999999 - type: precision_at_10 value: 7.3999999999999995 - type: precision_at_100 value: 0.8789999999999999 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 21.933 - type: precision_at_5 value: 14.000000000000002 - type: recall_at_1 value: 56.39999999999999 - type: recall_at_10 value: 74.0 - type: recall_at_100 value: 87.9 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 65.8 - type: recall_at_5 value: 70.0 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 76.64 - type: f1 value: 76.5446299028248 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 82.34975636166757 - type: cos_sim_ap value: 85.51352392694149 - type: cos_sim_f1 value: 83.53057199211045 - type: cos_sim_precision value: 78.35337650323775 - type: cos_sim_recall value: 89.44033790918691 - type: dot_accuracy value: 82.34975636166757 - type: dot_ap value: 85.51347115601486 - type: dot_f1 value: 83.53057199211045 - type: dot_precision value: 78.35337650323775 - type: dot_recall value: 89.44033790918691 - type: euclidean_accuracy value: 82.34975636166757 - type: euclidean_ap value: 85.51352392694149 - type: euclidean_f1 value: 83.53057199211045 - type: euclidean_precision value: 78.35337650323775 - type: euclidean_recall value: 89.44033790918691 - type: manhattan_accuracy value: 82.34975636166757 - type: manhattan_ap value: 85.48313896880585 - type: manhattan_f1 value: 83.52414136386261 - type: manhattan_precision value: 79.00188323917138 - type: manhattan_recall value: 88.59556494192185 - type: max_accuracy value: 82.34975636166757 - type: max_ap value: 85.51352392694149 - type: max_f1 value: 83.53057199211045 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 93.39 - type: ap value: 91.62127505252761 - type: f1 value: 93.38126146765326 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 39.69424895486595 - type: cos_sim_spearman value: 45.357868735202885 - type: euclidean_pearson value: 44.85027304963503 - type: euclidean_spearman value: 45.356945176162064 - type: manhattan_pearson value: 44.866080721344744 - type: manhattan_spearman value: 45.37053172312661 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 37.03908089465844 - type: cos_sim_spearman value: 38.98314179826781 - type: euclidean_pearson value: 37.189386019789545 - type: euclidean_spearman value: 38.98311189555396 - type: manhattan_pearson value: 37.14695118899785 - type: manhattan_spearman value: 38.94957261261034 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 65.08396305098712 - type: cos_sim_spearman value: 66.26346934994216 - type: euclidean_pearson value: 65.56501615370941 - type: euclidean_spearman value: 66.26346934994216 - type: manhattan_pearson value: 65.47984748172154 - type: manhattan_spearman value: 66.25326746119808 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 80.95965207330296 - type: cos_sim_spearman value: 82.96149593569953 - type: euclidean_pearson value: 82.67125448003975 - type: euclidean_spearman value: 82.96141174550262 - type: manhattan_pearson value: 82.64660468206361 - type: manhattan_spearman value: 82.91756025324656 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.43391960680063 - type: mrr value: 76.078440855015 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 28.29 - type: map_at_10 value: 78.441 - type: map_at_100 value: 82.043 - type: map_at_1000 value: 82.10499999999999 - type: map_at_3 value: 55.448 - type: map_at_5 value: 67.982 - type: mrr_at_1 value: 91.18 - type: mrr_at_10 value: 93.498 - type: mrr_at_100 value: 93.57 - type: mrr_at_1000 value: 93.572 - type: mrr_at_3 value: 93.112 - type: mrr_at_5 value: 93.351 - type: ndcg_at_1 value: 91.18 - type: ndcg_at_10 value: 85.849 - type: ndcg_at_100 value: 89.32600000000001 - type: ndcg_at_1000 value: 89.9 - type: ndcg_at_3 value: 87.333 - type: ndcg_at_5 value: 85.91499999999999 - type: precision_at_1 value: 91.18 - type: precision_at_10 value: 42.315000000000005 - type: precision_at_100 value: 5.029 - type: precision_at_1000 value: 0.517 - type: precision_at_3 value: 76.12400000000001 - type: precision_at_5 value: 63.690000000000005 - type: recall_at_1 value: 28.29 - type: recall_at_10 value: 84.679 - type: recall_at_100 value: 95.952 - type: recall_at_1000 value: 98.821 - type: recall_at_3 value: 56.987 - type: recall_at_5 value: 71.15599999999999 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 53.09799999999999 - type: f1 value: 51.397192036892314 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 70.59693805158501 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 63.21127290121542 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 61.3 - type: map_at_10 value: 70.658 - type: map_at_100 value: 71.096 - type: map_at_1000 value: 71.108 - type: map_at_3 value: 69.15 - type: map_at_5 value: 70.125 - type: mrr_at_1 value: 61.3 - type: mrr_at_10 value: 70.658 - type: mrr_at_100 value: 71.096 - type: mrr_at_1000 value: 71.108 - type: mrr_at_3 value: 69.15 - type: mrr_at_5 value: 70.125 - type: ndcg_at_1 value: 61.3 - type: ndcg_at_10 value: 74.71 - type: ndcg_at_100 value: 76.783 - type: ndcg_at_1000 value: 77.09899999999999 - type: ndcg_at_3 value: 71.634 - type: ndcg_at_5 value: 73.399 - type: precision_at_1 value: 61.3 - type: precision_at_10 value: 8.72 - type: precision_at_100 value: 0.967 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 26.267000000000003 - type: precision_at_5 value: 16.619999999999997 - type: recall_at_1 value: 61.3 - type: recall_at_10 value: 87.2 - type: recall_at_100 value: 96.7 - type: recall_at_1000 value: 99.2 - type: recall_at_3 value: 78.8 - type: recall_at_5 value: 83.1 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 88.01 - type: ap value: 72.51537272974005 - type: f1 value: 86.49546025793478 --- **新闻 | News** **[2024-04-??]** stella-v4系列预计四月份发布，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语 **。 **[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。 **[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。 **[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。 **[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。 **[2023-09-11]** 开源stella-base-zh和stella-large-zh 欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ # 1 开源模型 本次开源stella-mrl-large-zh-v3.5-1792d模型， 本模型是在stella-large-zh-v3-1792d的基础上使用[MRL](https://arxiv.org/abs/2205.13147)方法训练而成。 其主要特点是**可变的向量维度**。 # 2 使用方法 ```python from sentence_transformers import SentenceTransformer from sklearn.preprocessing import normalize model = SentenceTransformer("infgrad/stella-mrl-large-zh-v3.5-1792d") # 注意先不要normalize! 选取前n维后再normalize vectors = model.encode(["text1", "text2"], normalize_embeddings=False) print(vectors.shape) # shape is [2,1792] # n_dims越大效果越好，但是时空消耗就越大。建议维度选取128的倍数，因为是这么训练的 n_dims = 768 cut_vecs = normalize(vectors[:, :n_dims]) ``` # 3 不同向量维度的CMTEB得分 stella-mrl-large-zh-v3.5-1792d_1024 代表取前1024维。整体趋势是维度越大效果越好。 | Model | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | CMTEB-Score | |:------------------------------------|:---------:|:-----:|:------------------:|:--------------:|:---------:|:----------:|:-----------:| | stella-mrl-large-zh-v3.5-1792d_128 | 70.01 | 62.17 | 87.99 | 70.67 | 66.77 | 53.55 | 67.16 | | stella-mrl-large-zh-v3.5-1792d_256 | 72.19 | 62.41 | 88.09 | 71.22 | 68.32 | 53.38 | 68.02 | | stella-mrl-large-zh-v3.5-1792d_384 | 72.77 | 62.43 | 88.26 | 71.34 | 68.31 | 53.87 | 68.25 | | stella-mrl-large-zh-v3.5-1792d_512 | 73.11 | 62.45 | 88.16 | 71.46 | 68.32 | 53.28 | 68.29 | | stella-mrl-large-zh-v3.5-1792d_640 | 73.27 | 62.49 | 88.21 | 71.46 | 68.69 | 53.63 | 68.42 | | stella-mrl-large-zh-v3.5-1792d_768 | 73.38 | 62.5 | 88.19 | 71.49 | 68.64 | 53.77 | 68.47 | | stella-mrl-large-zh-v3.5-1792d_896 | 73.37 | 62.5 | 88.14 | 71.51 | 68.44 | 54.13 | 68.49 | | stella-mrl-large-zh-v3.5-1792d_1024 | 73.43 | 62.51 | 88.16 | 71.52 | 68.59 | 53.43 | 68.44 | | stella-mrl-large-zh-v3.5-1792d_1152 | 73.46 | 62.49 | 88.16 | 71.57 | 68.55 | 53.67 | 68.49 | | stella-mrl-large-zh-v3.5-1792d_1280 | 73.48 | 62.51 | 88.12 | 71.55 | 68.44 | 53.74 | 68.48 | | stella-mrl-large-zh-v3.5-1792d_1408 | 73.48 | 62.51 | 88.14 | 71.58 | 68.46 | 53.69 | 68.48 | | stella-mrl-large-zh-v3.5-1792d_1536 | 73.49 | 62.5 | 88.11 | 71.55 | 68.5 | 54.06 | 68.52 | | stella-mrl-large-zh-v3.5-1792d_1664 | 73.56 | 62.49 | 88.06 | 71.56 | 68.47 | 54.28 | 68.56 | | stella-mrl-large-zh-v3.5-1792d_1792 | 73.51 | 62.48 | 88.09 | 71.56 | 68.45 | 54.39 | 68.56 | 上述表格中stella-mrl-large-zh-v3.5-1792d_1792的得分为68.56和榜单68.55得分不一致，原因和权重类型有关，小差异请忽略不计。