--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: stella-base-zh-v3-1792d results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 54.5145388936202 - type: cos_sim_spearman value: 59.223125058197134 - type: euclidean_pearson value: 57.819377838734695 - type: euclidean_spearman value: 59.22310494948463 - type: manhattan_pearson value: 57.44029759610327 - type: manhattan_spearman value: 58.88336250854381 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 54.544243591344866 - type: cos_sim_spearman value: 58.43052988038229 - type: euclidean_pearson value: 62.1608405146189 - type: euclidean_spearman value: 58.43052762862396 - type: manhattan_pearson value: 61.88443779892169 - type: manhattan_spearman value: 58.26899143609596 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.343999999999994 - type: f1 value: 44.46931958420461 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 68.52081000538426 - type: cos_sim_spearman value: 70.44089935351529 - type: euclidean_pearson value: 69.24671010626395 - type: euclidean_spearman value: 70.44090281761693 - type: manhattan_pearson value: 69.00737718109357 - type: manhattan_spearman value: 70.24344902456502 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 42.86119436460332 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 39.97521728440642 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 88.34151862240452 - type: mrr value: 90.40380952380953 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 89.06288758814637 - type: mrr value: 90.91285714285713 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.651000000000003 - type: map_at_10 value: 38.576 - type: map_at_100 value: 40.534 - type: map_at_1000 value: 40.64 - type: map_at_3 value: 34.016000000000005 - type: map_at_5 value: 36.675999999999995 - type: mrr_at_1 value: 39.06 - type: mrr_at_10 value: 47.278 - type: mrr_at_100 value: 48.272999999999996 - type: mrr_at_1000 value: 48.314 - type: mrr_at_3 value: 44.461 - type: mrr_at_5 value: 46.107 - type: ndcg_at_1 value: 39.06 - type: ndcg_at_10 value: 45.384 - type: ndcg_at_100 value: 52.796 - type: ndcg_at_1000 value: 54.55 - type: ndcg_at_3 value: 39.497 - type: ndcg_at_5 value: 42.189 - type: precision_at_1 value: 39.06 - type: precision_at_10 value: 10.17 - type: precision_at_100 value: 1.6179999999999999 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 22.247 - type: precision_at_5 value: 16.529 - type: recall_at_1 value: 25.651000000000003 - type: recall_at_10 value: 56.82899999999999 - type: recall_at_100 value: 87.134 - type: recall_at_1000 value: 98.709 - type: recall_at_3 value: 39.461 - type: recall_at_5 value: 47.329 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 83.1870114251353 - type: cos_sim_ap value: 90.42393852164342 - type: cos_sim_f1 value: 84.10685985963323 - type: cos_sim_precision value: 81.5229317533465 - type: cos_sim_recall value: 86.85994856207621 - type: dot_accuracy value: 83.1870114251353 - type: dot_ap value: 90.41339758845682 - type: dot_f1 value: 84.10685985963323 - type: dot_precision value: 81.5229317533465 - type: dot_recall value: 86.85994856207621 - type: euclidean_accuracy value: 83.1870114251353 - type: euclidean_ap value: 90.42393581056393 - type: euclidean_f1 value: 84.10685985963323 - type: euclidean_precision value: 81.5229317533465 - type: euclidean_recall value: 86.85994856207621 - type: manhattan_accuracy value: 82.77811184606134 - type: manhattan_ap value: 90.18115714681704 - type: manhattan_f1 value: 83.75083130126357 - type: manhattan_precision value: 79.62065331928345 - type: manhattan_recall value: 88.33294365209258 - type: max_accuracy value: 83.1870114251353 - type: max_ap value: 90.42393852164342 - type: max_f1 value: 84.10685985963323 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 68.388 - type: map_at_10 value: 76.819 - type: map_at_100 value: 77.153 - type: map_at_1000 value: 77.16 - type: map_at_3 value: 74.98700000000001 - type: map_at_5 value: 76.101 - type: mrr_at_1 value: 68.599 - type: mrr_at_10 value: 76.844 - type: mrr_at_100 value: 77.168 - type: mrr_at_1000 value: 77.17500000000001 - type: mrr_at_3 value: 75.044 - type: mrr_at_5 value: 76.208 - type: ndcg_at_1 value: 68.599 - type: ndcg_at_10 value: 80.613 - type: ndcg_at_100 value: 82.017 - type: ndcg_at_1000 value: 82.19300000000001 - type: ndcg_at_3 value: 76.956 - type: ndcg_at_5 value: 78.962 - type: precision_at_1 value: 68.599 - type: precision_at_10 value: 9.336 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.678000000000004 - type: precision_at_5 value: 17.619 - type: recall_at_1 value: 68.388 - type: recall_at_10 value: 92.36 - type: recall_at_100 value: 98.52499999999999 - type: recall_at_1000 value: 99.895 - type: recall_at_3 value: 82.53399999999999 - type: recall_at_5 value: 87.355 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.1 - type: map_at_10 value: 77.71000000000001 - type: map_at_100 value: 80.638 - type: map_at_1000 value: 80.679 - type: map_at_3 value: 53.187 - type: map_at_5 value: 67.735 - type: mrr_at_1 value: 87.8 - type: mrr_at_10 value: 91.8 - type: mrr_at_100 value: 91.893 - type: mrr_at_1000 value: 91.89500000000001 - type: mrr_at_3 value: 91.51700000000001 - type: mrr_at_5 value: 91.704 - type: ndcg_at_1 value: 87.8 - type: ndcg_at_10 value: 85.55 - type: ndcg_at_100 value: 88.626 - type: ndcg_at_1000 value: 89.021 - type: ndcg_at_3 value: 83.94 - type: ndcg_at_5 value: 83.259 - type: precision_at_1 value: 87.8 - type: precision_at_10 value: 41.295 - type: precision_at_100 value: 4.781 - type: precision_at_1000 value: 0.488 - type: precision_at_3 value: 75.3 - type: precision_at_5 value: 64.13 - type: recall_at_1 value: 25.1 - type: recall_at_10 value: 87.076 - type: recall_at_100 value: 97.095 - type: recall_at_1000 value: 99.129 - type: recall_at_3 value: 56.013999999999996 - type: recall_at_5 value: 73.2 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.300000000000004 - type: map_at_10 value: 63.01 - type: map_at_100 value: 63.574 - type: map_at_1000 value: 63.587 - type: map_at_3 value: 60.783 - type: map_at_5 value: 62.098 - type: mrr_at_1 value: 53.300000000000004 - type: mrr_at_10 value: 63.01 - type: mrr_at_100 value: 63.574 - type: mrr_at_1000 value: 63.587 - type: mrr_at_3 value: 60.783 - type: mrr_at_5 value: 62.098 - type: ndcg_at_1 value: 53.300000000000004 - type: ndcg_at_10 value: 67.876 - type: ndcg_at_100 value: 70.434 - type: ndcg_at_1000 value: 70.753 - type: ndcg_at_3 value: 63.275000000000006 - type: ndcg_at_5 value: 65.654 - type: precision_at_1 value: 53.300000000000004 - type: precision_at_10 value: 8.32 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 23.5 - type: precision_at_5 value: 15.260000000000002 - type: recall_at_1 value: 53.300000000000004 - type: recall_at_10 value: 83.2 - type: recall_at_100 value: 94.8 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 70.5 - type: recall_at_5 value: 76.3 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 49.92689495959984 - type: f1 value: 37.784780470986625 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 86.26641651031895 - type: ap value: 54.50750244841821 - type: f1 value: 80.94927946681523 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 72.3980811478615 - type: cos_sim_spearman value: 78.26906056425528 - type: euclidean_pearson value: 77.87705501225068 - type: euclidean_spearman value: 78.26905834518651 - type: manhattan_pearson value: 77.77154630197 - type: manhattan_spearman value: 78.1940918602169 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 27.48003475319453 - type: mrr value: 26.400793650793652 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.373 - type: map_at_10 value: 73.604 - type: map_at_100 value: 73.953 - type: map_at_1000 value: 73.965 - type: map_at_3 value: 71.70100000000001 - type: map_at_5 value: 72.859 - type: mrr_at_1 value: 66.676 - type: mrr_at_10 value: 74.248 - type: mrr_at_100 value: 74.56099999999999 - type: mrr_at_1000 value: 74.572 - type: mrr_at_3 value: 72.59100000000001 - type: mrr_at_5 value: 73.592 - type: ndcg_at_1 value: 66.676 - type: ndcg_at_10 value: 77.417 - type: ndcg_at_100 value: 79.006 - type: ndcg_at_1000 value: 79.334 - type: ndcg_at_3 value: 73.787 - type: ndcg_at_5 value: 75.74 - type: precision_at_1 value: 66.676 - type: precision_at_10 value: 9.418 - type: precision_at_100 value: 1.0210000000000001 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 27.832 - type: precision_at_5 value: 17.736 - type: recall_at_1 value: 64.373 - type: recall_at_10 value: 88.565 - type: recall_at_100 value: 95.789 - type: recall_at_1000 value: 98.355 - type: recall_at_3 value: 78.914 - type: recall_at_5 value: 83.56 - 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: 72.0544720914593 - type: f1 value: 69.61749470345791 - 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: 75.30262273032953 - type: f1 value: 75.05097671215634 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 55.1 - type: map_at_10 value: 61.284000000000006 - type: map_at_100 value: 61.794000000000004 - type: map_at_1000 value: 61.838 - type: map_at_3 value: 59.75 - type: map_at_5 value: 60.64000000000001 - type: mrr_at_1 value: 55.300000000000004 - type: mrr_at_10 value: 61.38400000000001 - type: mrr_at_100 value: 61.894000000000005 - type: mrr_at_1000 value: 61.938 - type: mrr_at_3 value: 59.85 - type: mrr_at_5 value: 60.74 - type: ndcg_at_1 value: 55.1 - type: ndcg_at_10 value: 64.345 - type: ndcg_at_100 value: 67.148 - type: ndcg_at_1000 value: 68.36 - type: ndcg_at_3 value: 61.182 - type: ndcg_at_5 value: 62.808 - type: precision_at_1 value: 55.1 - 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: 21.767 - type: precision_at_5 value: 13.86 - type: recall_at_1 value: 55.1 - type: recall_at_10 value: 74 - type: recall_at_100 value: 87.9 - type: recall_at_1000 value: 97.5 - type: recall_at_3 value: 65.3 - type: recall_at_5 value: 69.3 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 76.21666666666667 - type: f1 value: 76.03732395559548 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 81.8083378451543 - type: cos_sim_ap value: 85.43050139514027 - type: cos_sim_f1 value: 83.25969563082965 - type: cos_sim_precision value: 77.79816513761469 - type: cos_sim_recall value: 89.54593453009504 - type: dot_accuracy value: 81.8083378451543 - type: dot_ap value: 85.43050139514027 - type: dot_f1 value: 83.25969563082965 - type: dot_precision value: 77.79816513761469 - type: dot_recall value: 89.54593453009504 - type: euclidean_accuracy value: 81.8083378451543 - type: euclidean_ap value: 85.43050139514027 - type: euclidean_f1 value: 83.25969563082965 - type: euclidean_precision value: 77.79816513761469 - type: euclidean_recall value: 89.54593453009504 - type: manhattan_accuracy value: 81.53762858689767 - type: manhattan_ap value: 84.90556637024838 - type: manhattan_f1 value: 82.90258449304174 - type: manhattan_precision value: 78.30985915492957 - type: manhattan_recall value: 88.0675818373812 - type: max_accuracy value: 81.8083378451543 - type: max_ap value: 85.43050139514027 - type: max_f1 value: 83.25969563082965 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 93.53 - type: ap value: 91.62070655043128 - type: f1 value: 93.51908163199477 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 38.451787103814375 - type: cos_sim_spearman value: 43.97299462643919 - type: euclidean_pearson value: 43.63298716626501 - type: euclidean_spearman value: 43.973080252178576 - type: manhattan_pearson value: 43.37465277323481 - type: manhattan_spearman value: 43.71981281220414 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 37.75882451277358 - type: cos_sim_spearman value: 40.0244327844802 - type: euclidean_pearson value: 38.11050875514246 - type: euclidean_spearman value: 40.02440987254504 - type: manhattan_pearson value: 38.03186803221696 - type: manhattan_spearman value: 39.757452890246775 - 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: 65.9133992390713 - type: cos_sim_spearman value: 66.4894937647578 - type: euclidean_pearson value: 66.19047142189935 - type: euclidean_spearman value: 66.4894937647578 - type: manhattan_pearson value: 66.6960935896136 - type: manhattan_spearman value: 66.88179996508133 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 80.55099417946924 - type: cos_sim_spearman value: 83.05000687568048 - type: euclidean_pearson value: 82.62744668792926 - type: euclidean_spearman value: 83.05000687568048 - type: manhattan_pearson value: 82.6543207325763 - type: manhattan_spearman value: 83.06852715971705 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.48634798223672 - type: mrr value: 76.30158461488861 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.483999999999998 - type: map_at_10 value: 76.848 - type: map_at_100 value: 80.541 - type: map_at_1000 value: 80.607 - type: map_at_3 value: 54.111 - type: map_at_5 value: 66.46300000000001 - type: mrr_at_1 value: 90.045 - type: mrr_at_10 value: 92.552 - type: mrr_at_100 value: 92.642 - type: mrr_at_1000 value: 92.645 - type: mrr_at_3 value: 92.134 - type: mrr_at_5 value: 92.391 - type: ndcg_at_1 value: 90.045 - type: ndcg_at_10 value: 84.504 - type: ndcg_at_100 value: 88.23100000000001 - type: ndcg_at_1000 value: 88.85300000000001 - type: ndcg_at_3 value: 85.992 - type: ndcg_at_5 value: 84.548 - type: precision_at_1 value: 90.045 - type: precision_at_10 value: 41.91 - type: precision_at_100 value: 5.017 - type: precision_at_1000 value: 0.516 - type: precision_at_3 value: 75.15899999999999 - type: precision_at_5 value: 62.958000000000006 - type: recall_at_1 value: 27.483999999999998 - type: recall_at_10 value: 83.408 - type: recall_at_100 value: 95.514 - type: recall_at_1000 value: 98.65 - type: recall_at_3 value: 55.822 - type: recall_at_5 value: 69.868 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 53.196 - type: f1 value: 51.51679244513836 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 67.87592101539063 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 62.4675464095125 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 57.9 - type: map_at_10 value: 68.099 - type: map_at_100 value: 68.55499999999999 - type: map_at_1000 value: 68.566 - type: map_at_3 value: 66.4 - type: map_at_5 value: 67.46 - type: mrr_at_1 value: 57.9 - type: mrr_at_10 value: 68.099 - type: mrr_at_100 value: 68.55499999999999 - type: mrr_at_1000 value: 68.566 - type: mrr_at_3 value: 66.4 - type: mrr_at_5 value: 67.46 - type: ndcg_at_1 value: 57.9 - type: ndcg_at_10 value: 72.555 - type: ndcg_at_100 value: 74.715 - type: ndcg_at_1000 value: 75.034 - type: ndcg_at_3 value: 69.102 - type: ndcg_at_5 value: 71.004 - type: precision_at_1 value: 57.9 - type: precision_at_10 value: 8.63 - type: precision_at_100 value: 0.963 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.633 - type: precision_at_5 value: 16.3 - type: recall_at_1 value: 57.9 - type: recall_at_10 value: 86.3 - type: recall_at_100 value: 96.3 - type: recall_at_1000 value: 98.9 - type: recall_at_3 value: 76.9 - type: recall_at_5 value: 81.5 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 87.27000000000001 - type: ap value: 71.10883470119464 - type: f1 value: 85.76618863591946 license: mit --- **新闻 | News** **[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**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 开源清单 本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型，同时开源全量160万对话重写数据集和20万的难负例的检索数据集。 **开源模型：** | ModelName | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score | |---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------| | [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d) | 0.4GB | 512 | 1792 | zh-CN | 通用文本 | 67.96 | | [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | 通用文本 | 68.48 | | [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | **对话文本** | 不适用 | **开源数据：** 1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万 2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万 上述数据集均使用LLM构造，欢迎各位贡献数据集。 # 2 使用方法 ## 2.1 通用编码模型使用方法 直接SentenceTransformer加载即可： ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d") # model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d") vectors = model.encode(["text1", "text2"]) ``` ## 2.2 dialogue编码模型使用方法 **使用场景：** **在一段对话中，需要根据用户语句去检索相关文本，但是对话中的用户语句存在大量的指代和省略，导致直接使用通用编码模型效果不好， 可以使用本项目的专门的dialogue编码模型进行编码** **使用要点：** 1. 对dialogue进行编码时，dialogue中的每个utterance需要是如下格式：`"{ROLE}: {TEXT}"`，然后使用`[SEP]` join一下 2. 整个对话都要送入模型进行编码，如果长度不够就删掉早期的对话，**编码后的向量本质是对话中最后一句话的重写版本的向量！！** 3. 对话用stella-dialogue-large-zh-v3-1792d编码，被检索文本使用stella-large-zh-v3-1792d进行编码，所以本场景是需要2个编码模型的 如果对使用方法还有疑惑，请到下面章节阅读该模型是如何训练的。 使用示例： ```python from sentence_transformers import SentenceTransformer dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d") general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d") # dialogue = ["张三: 吃饭吗", "李四: 等会去"] dialogue = ["A: 最近去打篮球了吗", "B: 没有"] corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"] last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True) corpus_vectors = general_model.encode(corpus, normalize_embeddings=True) # 计算相似度 sims = (last_utterance_vector * corpus_vectors).sum(axis=1) print(sims) ``` # 3 通用编码模型训练技巧分享 ## hard negative 难负例挖掘也是个经典的trick了，几乎总能提升效果 ## dropout-1d dropout已经是深度学习的标配，我们可以稍微改造下使其更适合句向量的训练。 我们在训练时会尝试让每一个token-embedding都可以表征整个句子，而在推理时使用mean_pooling从而达到类似模型融合的效果。 具体操作是在mean_pooling时加入dropout_1d，torch代码如下： ```python vector_dropout = nn.Dropout1d(0.3) # 算力有限，试了0.3和0.5 两个参数，其中0.3更优 last_hidden_state = bert_model(...)[0] last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) last_hidden = vector_dropout(last_hidden) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] ``` # 4 dialogue编码模型细节 ## 4.1 为什么需要一个dialogue编码模型？ 参见本人历史文章：https://www.zhihu.com/pin/1674913544847077376 ## 4.2 训练数据 单条数据示例： ```json { "dialogue": [ "A: 最近去打篮球了吗", "B: 没有" ], "last_utterance_rewrite": "B: 我最近没有去打篮球" } ``` ## 4.3 训练Loss ``` loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) ) ``` dial_model就是要被训练的模型，本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的 existing_model就是现有训练好的**通用编码模型**，本人使用的是stella-large-zh-v3-1792d 已开源dialogue-embedding的全量训练数据，理论上可以复现本模型效果。 Loss下降情况：
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## 4.4 效果 目前还没有专门测试集，本人简单测试了下是有效果的，部分测试结果见文件`dial_retrieval_test.xlsx`。 # 5 后续TODO 1. 更多的dial-rewrite数据 2. 不同EmbeddingDimensions的编码模型 # 6 FAQ Q: 为什么向量维度是1792？\ A: 最初考虑发布768、1024，768+768，1024+1024，1024+768维度，但是时间有限，先做了1792就只发布1792维度的模型。理论上维度越高效果越好。 Q: 如何复现CMTEB效果？\ A: SentenceTransformer加载后直接用官方评测脚本就行，注意对于Classification任务向量需要先normalize一下 Q: 复现的CMTEB效果和本文不一致？\ A: 聚类不一致正常，官方评测代码没有设定seed，其他不一致建议检查代码或联系本人。 Q: 如何选择向量模型？\ A: 没有免费的午餐，在自己测试集上试试，本人推荐bge、e5和stella. Q: 长度为什么只有512，能否更长？\ A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。 Q: 训练资源和算力？\ A: 亿级别的数据，单卡A100要一个月起步