--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: stella-large-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: 54.48093298255762 - type: cos_sim_spearman value: 59.105354109068685 - type: euclidean_pearson value: 57.761189988643444 - type: euclidean_spearman value: 59.10537421115596 - type: manhattan_pearson value: 56.94359297051431 - type: manhattan_spearman value: 58.37611109821567 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 54.39711127600595 - type: cos_sim_spearman value: 58.190191920824454 - type: euclidean_pearson value: 61.80082379352729 - type: euclidean_spearman value: 58.19018966860797 - type: manhattan_pearson value: 60.927601060396206 - type: manhattan_spearman value: 57.78832902694192 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.31600000000001 - type: f1 value: 44.45281663598873 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 69.12211326097868 - type: cos_sim_spearman value: 71.0741302039443 - type: euclidean_pearson value: 69.89070483887852 - type: euclidean_spearman value: 71.07413020351787 - type: manhattan_pearson value: 69.62345441260962 - type: manhattan_spearman value: 70.8517591280618 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 41.937723608805314 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 40.34373057675427 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 88.98896401788376 - type: mrr value: 90.97119047619047 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 89.59718540244556 - type: mrr value: 91.41246031746032 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.954 - type: map_at_10 value: 40.144999999999996 - type: map_at_100 value: 42.083999999999996 - type: map_at_1000 value: 42.181000000000004 - type: map_at_3 value: 35.709 - type: map_at_5 value: 38.141000000000005 - type: mrr_at_1 value: 40.71 - type: mrr_at_10 value: 48.93 - type: mrr_at_100 value: 49.921 - type: mrr_at_1000 value: 49.958999999999996 - type: mrr_at_3 value: 46.32 - type: mrr_at_5 value: 47.769 - type: ndcg_at_1 value: 40.71 - type: ndcg_at_10 value: 46.869 - type: ndcg_at_100 value: 54.234 - type: ndcg_at_1000 value: 55.854000000000006 - type: ndcg_at_3 value: 41.339 - type: ndcg_at_5 value: 43.594 - type: precision_at_1 value: 40.71 - type: precision_at_10 value: 10.408000000000001 - type: precision_at_100 value: 1.635 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.348 - type: precision_at_5 value: 16.929 - type: recall_at_1 value: 26.954 - type: recall_at_10 value: 57.821999999999996 - type: recall_at_100 value: 88.08200000000001 - type: recall_at_1000 value: 98.83800000000001 - type: recall_at_3 value: 41.221999999999994 - type: recall_at_5 value: 48.241 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 83.6680697534576 - type: cos_sim_ap value: 90.77401562455269 - type: cos_sim_f1 value: 84.68266427450101 - type: cos_sim_precision value: 81.36177547942253 - type: cos_sim_recall value: 88.28618190320317 - type: dot_accuracy value: 83.6680697534576 - type: dot_ap value: 90.76429465198817 - type: dot_f1 value: 84.68266427450101 - type: dot_precision value: 81.36177547942253 - type: dot_recall value: 88.28618190320317 - type: euclidean_accuracy value: 83.6680697534576 - type: euclidean_ap value: 90.77401909305344 - type: euclidean_f1 value: 84.68266427450101 - type: euclidean_precision value: 81.36177547942253 - type: euclidean_recall value: 88.28618190320317 - type: manhattan_accuracy value: 83.40348767288035 - type: manhattan_ap value: 90.57002020310819 - type: manhattan_f1 value: 84.51526032315978 - type: manhattan_precision value: 81.25134843581445 - type: manhattan_recall value: 88.05237315875614 - type: max_accuracy value: 83.6680697534576 - type: max_ap value: 90.77401909305344 - type: max_f1 value: 84.68266427450101 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 69.705 - type: map_at_10 value: 78.648 - type: map_at_100 value: 78.888 - type: map_at_1000 value: 78.89399999999999 - type: map_at_3 value: 77.151 - type: map_at_5 value: 77.98 - type: mrr_at_1 value: 69.863 - type: mrr_at_10 value: 78.62599999999999 - type: mrr_at_100 value: 78.861 - type: mrr_at_1000 value: 78.867 - type: mrr_at_3 value: 77.204 - type: mrr_at_5 value: 78.005 - type: ndcg_at_1 value: 69.968 - type: ndcg_at_10 value: 82.44399999999999 - type: ndcg_at_100 value: 83.499 - type: ndcg_at_1000 value: 83.647 - type: ndcg_at_3 value: 79.393 - type: ndcg_at_5 value: 80.855 - type: precision_at_1 value: 69.968 - type: precision_at_10 value: 9.515 - type: precision_at_100 value: 0.9990000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 28.802 - type: precision_at_5 value: 18.019 - type: recall_at_1 value: 69.705 - type: recall_at_10 value: 94.152 - type: recall_at_100 value: 98.84100000000001 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 85.774 - type: recall_at_5 value: 89.252 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.88 - type: map_at_10 value: 79.857 - type: map_at_100 value: 82.636 - type: map_at_1000 value: 82.672 - type: map_at_3 value: 55.184 - type: map_at_5 value: 70.009 - type: mrr_at_1 value: 89.64999999999999 - type: mrr_at_10 value: 92.967 - type: mrr_at_100 value: 93.039 - type: mrr_at_1000 value: 93.041 - type: mrr_at_3 value: 92.65 - type: mrr_at_5 value: 92.86 - type: ndcg_at_1 value: 89.64999999999999 - type: ndcg_at_10 value: 87.126 - type: ndcg_at_100 value: 89.898 - type: ndcg_at_1000 value: 90.253 - type: ndcg_at_3 value: 86.012 - type: ndcg_at_5 value: 85.124 - type: precision_at_1 value: 89.64999999999999 - type: precision_at_10 value: 41.735 - type: precision_at_100 value: 4.797 - type: precision_at_1000 value: 0.488 - type: precision_at_3 value: 77.267 - type: precision_at_5 value: 65.48 - type: recall_at_1 value: 25.88 - type: recall_at_10 value: 88.28399999999999 - type: recall_at_100 value: 97.407 - type: recall_at_1000 value: 99.29299999999999 - type: recall_at_3 value: 57.38799999999999 - type: recall_at_5 value: 74.736 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.2 - type: map_at_10 value: 63.556000000000004 - type: map_at_100 value: 64.033 - type: map_at_1000 value: 64.044 - type: map_at_3 value: 60.983 - type: map_at_5 value: 62.588 - type: mrr_at_1 value: 53.2 - type: mrr_at_10 value: 63.556000000000004 - type: mrr_at_100 value: 64.033 - type: mrr_at_1000 value: 64.044 - type: mrr_at_3 value: 60.983 - type: mrr_at_5 value: 62.588 - type: ndcg_at_1 value: 53.2 - type: ndcg_at_10 value: 68.61699999999999 - type: ndcg_at_100 value: 70.88499999999999 - type: ndcg_at_1000 value: 71.15899999999999 - type: ndcg_at_3 value: 63.434000000000005 - type: ndcg_at_5 value: 66.301 - type: precision_at_1 value: 53.2 - type: precision_at_10 value: 8.450000000000001 - type: precision_at_100 value: 0.95 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 23.5 - type: precision_at_5 value: 15.479999999999999 - type: recall_at_1 value: 53.2 - type: recall_at_10 value: 84.5 - type: recall_at_100 value: 95.0 - type: recall_at_1000 value: 97.1 - type: recall_at_3 value: 70.5 - type: recall_at_5 value: 77.4 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 50.63485956136976 - type: f1 value: 38.286307407751266 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 86.11632270168855 - type: ap value: 54.43932599806482 - type: f1 value: 80.85485110996076 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 72.47315152994804 - type: cos_sim_spearman value: 78.26531600908152 - type: euclidean_pearson value: 77.8560788714531 - type: euclidean_spearman value: 78.26531157334841 - type: manhattan_pearson value: 77.70593783974188 - type: manhattan_spearman value: 78.13880812439999 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 28.088177976572222 - type: mrr value: 27.125 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.428 - type: map_at_10 value: 75.5 - type: map_at_100 value: 75.82600000000001 - type: map_at_1000 value: 75.837 - type: map_at_3 value: 73.74300000000001 - type: map_at_5 value: 74.87 - type: mrr_at_1 value: 68.754 - type: mrr_at_10 value: 76.145 - type: mrr_at_100 value: 76.432 - type: mrr_at_1000 value: 76.442 - type: mrr_at_3 value: 74.628 - type: mrr_at_5 value: 75.612 - type: ndcg_at_1 value: 68.754 - type: ndcg_at_10 value: 79.144 - type: ndcg_at_100 value: 80.60199999999999 - type: ndcg_at_1000 value: 80.886 - type: ndcg_at_3 value: 75.81599999999999 - type: ndcg_at_5 value: 77.729 - type: precision_at_1 value: 68.754 - type: precision_at_10 value: 9.544 - type: precision_at_100 value: 1.026 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.534 - type: precision_at_5 value: 18.138 - type: recall_at_1 value: 66.428 - type: recall_at_10 value: 89.716 - type: recall_at_100 value: 96.313 - type: recall_at_1000 value: 98.541 - type: recall_at_3 value: 80.923 - type: recall_at_5 value: 85.48 - 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: 70.65529957574735 - 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: 76.30127774041695 - type: f1 value: 76.10358226518304 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 56.3 - type: map_at_10 value: 62.193 - type: map_at_100 value: 62.722 - type: map_at_1000 value: 62.765 - type: map_at_3 value: 60.633 - type: map_at_5 value: 61.617999999999995 - type: mrr_at_1 value: 56.3 - type: mrr_at_10 value: 62.193 - type: mrr_at_100 value: 62.722 - type: mrr_at_1000 value: 62.765 - type: mrr_at_3 value: 60.633 - type: mrr_at_5 value: 61.617999999999995 - type: ndcg_at_1 value: 56.3 - type: ndcg_at_10 value: 65.176 - type: ndcg_at_100 value: 67.989 - type: ndcg_at_1000 value: 69.219 - type: ndcg_at_3 value: 62.014 - type: ndcg_at_5 value: 63.766 - type: precision_at_1 value: 56.3 - type: precision_at_10 value: 7.46 - type: precision_at_100 value: 0.8829999999999999 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 22.0 - type: precision_at_5 value: 14.04 - type: recall_at_1 value: 56.3 - type: recall_at_10 value: 74.6 - type: recall_at_100 value: 88.3 - type: recall_at_1000 value: 98.1 - type: recall_at_3 value: 66.0 - type: recall_at_5 value: 70.19999999999999 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 76.44666666666666 - type: f1 value: 76.34548655475949 - 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: 85.44149338593267 - type: cos_sim_f1 value: 83.68654509610647 - type: cos_sim_precision value: 78.46580406654344 - type: cos_sim_recall value: 89.65153115100317 - type: dot_accuracy value: 82.34975636166757 - type: dot_ap value: 85.4415701376729 - type: dot_f1 value: 83.68654509610647 - type: dot_precision value: 78.46580406654344 - type: dot_recall value: 89.65153115100317 - type: euclidean_accuracy value: 82.34975636166757 - type: euclidean_ap value: 85.4415701376729 - type: euclidean_f1 value: 83.68654509610647 - type: euclidean_precision value: 78.46580406654344 - type: euclidean_recall value: 89.65153115100317 - type: manhattan_accuracy value: 81.97076340010828 - type: manhattan_ap value: 84.83614660756733 - type: manhattan_f1 value: 83.34167083541772 - type: manhattan_precision value: 79.18250950570342 - type: manhattan_recall value: 87.96198521647307 - type: max_accuracy value: 82.34975636166757 - type: max_ap value: 85.4415701376729 - type: max_f1 value: 83.68654509610647 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 93.24 - type: ap value: 91.3586656455605 - type: f1 value: 93.22999314249503 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 39.05676042449009 - type: cos_sim_spearman value: 44.996534098358545 - type: euclidean_pearson value: 44.42418609172825 - type: euclidean_spearman value: 44.995941361058996 - type: manhattan_pearson value: 43.98118203238076 - type: manhattan_spearman value: 44.51414152788784 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 36.694269474438045 - type: cos_sim_spearman value: 38.686738967031616 - type: euclidean_pearson value: 36.822540068407235 - type: euclidean_spearman value: 38.68690745429757 - type: manhattan_pearson value: 36.77180703308932 - type: manhattan_spearman value: 38.45414914148094 - 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: 65.81209017614124 - type: cos_sim_spearman value: 66.5255285833172 - type: euclidean_pearson value: 66.01848701752732 - type: euclidean_spearman value: 66.5255285833172 - type: manhattan_pearson value: 66.66433676370542 - type: manhattan_spearman value: 67.07086311480214 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 80.60785761283502 - type: cos_sim_spearman value: 82.80278693241074 - type: euclidean_pearson value: 82.47573315938638 - type: euclidean_spearman value: 82.80290808593806 - type: manhattan_pearson value: 82.49682028989669 - type: manhattan_spearman value: 82.84565039346022 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.37886004738723 - type: mrr value: 76.08501655006394 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 28.102 - type: map_at_10 value: 78.071 - type: map_at_100 value: 81.71000000000001 - type: map_at_1000 value: 81.773 - type: map_at_3 value: 55.142 - type: map_at_5 value: 67.669 - type: mrr_at_1 value: 90.9 - type: mrr_at_10 value: 93.29499999999999 - type: mrr_at_100 value: 93.377 - type: mrr_at_1000 value: 93.379 - type: mrr_at_3 value: 92.901 - type: mrr_at_5 value: 93.152 - type: ndcg_at_1 value: 90.9 - type: ndcg_at_10 value: 85.564 - type: ndcg_at_100 value: 89.11200000000001 - type: ndcg_at_1000 value: 89.693 - type: ndcg_at_3 value: 87.024 - type: ndcg_at_5 value: 85.66 - type: precision_at_1 value: 90.9 - type: precision_at_10 value: 42.208 - type: precision_at_100 value: 5.027 - type: precision_at_1000 value: 0.517 - type: precision_at_3 value: 75.872 - type: precision_at_5 value: 63.566 - type: recall_at_1 value: 28.102 - type: recall_at_10 value: 84.44500000000001 - type: recall_at_100 value: 95.91300000000001 - type: recall_at_1000 value: 98.80799999999999 - type: recall_at_3 value: 56.772999999999996 - type: recall_at_5 value: 70.99499999999999 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 53.10599999999999 - type: f1 value: 51.40415523558322 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 69.6145576098232 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 63.7129548775017 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 60.199999999999996 - type: map_at_10 value: 69.724 - type: map_at_100 value: 70.185 - type: map_at_1000 value: 70.196 - type: map_at_3 value: 67.95 - type: map_at_5 value: 69.155 - type: mrr_at_1 value: 60.199999999999996 - type: mrr_at_10 value: 69.724 - type: mrr_at_100 value: 70.185 - type: mrr_at_1000 value: 70.196 - type: mrr_at_3 value: 67.95 - type: mrr_at_5 value: 69.155 - type: ndcg_at_1 value: 60.199999999999996 - type: ndcg_at_10 value: 73.888 - type: ndcg_at_100 value: 76.02799999999999 - type: ndcg_at_1000 value: 76.344 - type: ndcg_at_3 value: 70.384 - type: ndcg_at_5 value: 72.541 - type: precision_at_1 value: 60.199999999999996 - type: precision_at_10 value: 8.67 - type: precision_at_100 value: 0.9650000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.8 - type: precision_at_5 value: 16.520000000000003 - type: recall_at_1 value: 60.199999999999996 - type: recall_at_10 value: 86.7 - type: recall_at_100 value: 96.5 - type: recall_at_1000 value: 99.0 - type: recall_at_3 value: 77.4 - type: recall_at_5 value: 82.6 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 88.08 - type: ap value: 72.66435456846166 - type: f1 value: 86.55995793551286 --- **新闻 | News** **[2024-04-30]** 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