--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb model-index: - name: Dmeta-embedding results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 65.60825224706932 - type: cos_sim_spearman value: 71.12862586297193 - type: euclidean_pearson value: 70.18130275750404 - type: euclidean_spearman value: 71.12862586297193 - type: manhattan_pearson value: 70.14470398075396 - type: manhattan_spearman value: 71.05226975911737 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 65.52386345655479 - type: cos_sim_spearman value: 64.64245253181382 - type: euclidean_pearson value: 73.20157662981914 - type: euclidean_spearman value: 64.64245253178956 - type: manhattan_pearson value: 73.22837571756348 - type: manhattan_spearman value: 64.62632334391418 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.925999999999995 - type: f1 value: 42.82555191308971 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 71.35236446393156 - type: cos_sim_spearman value: 72.29629643702184 - type: euclidean_pearson value: 70.94570179874498 - type: euclidean_spearman value: 72.29629297226953 - type: manhattan_pearson value: 70.84463025501125 - type: manhattan_spearman value: 72.24527021975821 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 40.24232916894152 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 39.167806226929706 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 88.48837920106357 - type: mrr value: 90.36861111111111 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 89.17878171657071 - type: mrr value: 91.35805555555555 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.751 - type: map_at_10 value: 38.946 - type: map_at_100 value: 40.855000000000004 - type: map_at_1000 value: 40.953 - type: map_at_3 value: 34.533 - type: map_at_5 value: 36.905 - type: mrr_at_1 value: 39.235 - type: mrr_at_10 value: 47.713 - type: mrr_at_100 value: 48.71 - type: mrr_at_1000 value: 48.747 - type: mrr_at_3 value: 45.086 - type: mrr_at_5 value: 46.498 - type: ndcg_at_1 value: 39.235 - type: ndcg_at_10 value: 45.831 - type: ndcg_at_100 value: 53.162 - type: ndcg_at_1000 value: 54.800000000000004 - type: ndcg_at_3 value: 40.188 - type: ndcg_at_5 value: 42.387 - type: precision_at_1 value: 39.235 - type: precision_at_10 value: 10.273 - type: precision_at_100 value: 1.627 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 22.772000000000002 - type: precision_at_5 value: 16.524 - type: recall_at_1 value: 25.751 - type: recall_at_10 value: 57.411 - type: recall_at_100 value: 87.44 - type: recall_at_1000 value: 98.386 - type: recall_at_3 value: 40.416000000000004 - type: recall_at_5 value: 47.238 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 83.59591100420926 - type: cos_sim_ap value: 90.65538153970263 - type: cos_sim_f1 value: 84.76466651795673 - type: cos_sim_precision value: 81.04073363190446 - type: cos_sim_recall value: 88.84732288987608 - type: dot_accuracy value: 83.59591100420926 - type: dot_ap value: 90.64355541781003 - type: dot_f1 value: 84.76466651795673 - type: dot_precision value: 81.04073363190446 - type: dot_recall value: 88.84732288987608 - type: euclidean_accuracy value: 83.59591100420926 - type: euclidean_ap value: 90.6547878194287 - type: euclidean_f1 value: 84.76466651795673 - type: euclidean_precision value: 81.04073363190446 - type: euclidean_recall value: 88.84732288987608 - type: manhattan_accuracy value: 83.51172579675286 - type: manhattan_ap value: 90.59941589844144 - type: manhattan_f1 value: 84.51827242524917 - type: manhattan_precision value: 80.28613507258574 - type: manhattan_recall value: 89.22141688099134 - type: max_accuracy value: 83.59591100420926 - type: max_ap value: 90.65538153970263 - type: max_f1 value: 84.76466651795673 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 63.251000000000005 - type: map_at_10 value: 72.442 - type: map_at_100 value: 72.79299999999999 - type: map_at_1000 value: 72.80499999999999 - type: map_at_3 value: 70.293 - type: map_at_5 value: 71.571 - type: mrr_at_1 value: 63.541000000000004 - type: mrr_at_10 value: 72.502 - type: mrr_at_100 value: 72.846 - type: mrr_at_1000 value: 72.858 - type: mrr_at_3 value: 70.39 - type: mrr_at_5 value: 71.654 - type: ndcg_at_1 value: 63.541000000000004 - type: ndcg_at_10 value: 76.774 - type: ndcg_at_100 value: 78.389 - type: ndcg_at_1000 value: 78.678 - type: ndcg_at_3 value: 72.47 - type: ndcg_at_5 value: 74.748 - type: precision_at_1 value: 63.541000000000004 - type: precision_at_10 value: 9.115 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 26.379 - type: precision_at_5 value: 16.965 - type: recall_at_1 value: 63.251000000000005 - type: recall_at_10 value: 90.253 - type: recall_at_100 value: 97.576 - type: recall_at_1000 value: 99.789 - type: recall_at_3 value: 78.635 - type: recall_at_5 value: 84.141 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.597 - type: map_at_10 value: 72.411 - type: map_at_100 value: 75.58500000000001 - type: map_at_1000 value: 75.64800000000001 - type: map_at_3 value: 49.61 - type: map_at_5 value: 62.527 - type: mrr_at_1 value: 84.65 - type: mrr_at_10 value: 89.43900000000001 - type: mrr_at_100 value: 89.525 - type: mrr_at_1000 value: 89.529 - type: mrr_at_3 value: 89 - type: mrr_at_5 value: 89.297 - type: ndcg_at_1 value: 84.65 - type: ndcg_at_10 value: 81.47 - type: ndcg_at_100 value: 85.198 - type: ndcg_at_1000 value: 85.828 - type: ndcg_at_3 value: 79.809 - type: ndcg_at_5 value: 78.55 - type: precision_at_1 value: 84.65 - type: precision_at_10 value: 39.595 - type: precision_at_100 value: 4.707 - type: precision_at_1000 value: 0.485 - type: precision_at_3 value: 71.61699999999999 - type: precision_at_5 value: 60.45 - type: recall_at_1 value: 23.597 - type: recall_at_10 value: 83.34 - type: recall_at_100 value: 95.19800000000001 - type: recall_at_1000 value: 98.509 - type: recall_at_3 value: 52.744 - type: recall_at_5 value: 68.411 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.1 - type: map_at_10 value: 63.359 - type: map_at_100 value: 63.9 - type: map_at_1000 value: 63.909000000000006 - type: map_at_3 value: 60.95 - type: map_at_5 value: 62.305 - type: mrr_at_1 value: 53.1 - type: mrr_at_10 value: 63.359 - type: mrr_at_100 value: 63.9 - type: mrr_at_1000 value: 63.909000000000006 - type: mrr_at_3 value: 60.95 - type: mrr_at_5 value: 62.305 - type: ndcg_at_1 value: 53.1 - type: ndcg_at_10 value: 68.418 - type: ndcg_at_100 value: 70.88499999999999 - type: ndcg_at_1000 value: 71.135 - type: ndcg_at_3 value: 63.50599999999999 - type: ndcg_at_5 value: 65.92 - type: precision_at_1 value: 53.1 - type: precision_at_10 value: 8.43 - type: precision_at_100 value: 0.955 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.633000000000003 - type: precision_at_5 value: 15.340000000000002 - type: recall_at_1 value: 53.1 - type: recall_at_10 value: 84.3 - type: recall_at_100 value: 95.5 - type: recall_at_1000 value: 97.5 - type: recall_at_3 value: 70.89999999999999 - type: recall_at_5 value: 76.7 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 48.303193535975375 - type: f1 value: 35.96559358693866 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 85.06566604127579 - type: ap value: 52.0596483757231 - type: f1 value: 79.5196835127668 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 74.48499423626059 - type: cos_sim_spearman value: 78.75806756061169 - type: euclidean_pearson value: 78.47917601852879 - type: euclidean_spearman value: 78.75807199272622 - type: manhattan_pearson value: 78.40207586289772 - type: manhattan_spearman value: 78.6911776964119 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 24.75987466552363 - type: mrr value: 23.40515873015873 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 58.026999999999994 - type: map_at_10 value: 67.50699999999999 - type: map_at_100 value: 67.946 - type: map_at_1000 value: 67.96600000000001 - type: map_at_3 value: 65.503 - type: map_at_5 value: 66.649 - type: mrr_at_1 value: 60.20100000000001 - type: mrr_at_10 value: 68.271 - type: mrr_at_100 value: 68.664 - type: mrr_at_1000 value: 68.682 - type: mrr_at_3 value: 66.47800000000001 - type: mrr_at_5 value: 67.499 - type: ndcg_at_1 value: 60.20100000000001 - type: ndcg_at_10 value: 71.697 - type: ndcg_at_100 value: 73.736 - type: ndcg_at_1000 value: 74.259 - type: ndcg_at_3 value: 67.768 - type: ndcg_at_5 value: 69.72 - type: precision_at_1 value: 60.20100000000001 - type: precision_at_10 value: 8.927999999999999 - type: precision_at_100 value: 0.9950000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 25.883 - type: precision_at_5 value: 16.55 - type: recall_at_1 value: 58.026999999999994 - type: recall_at_10 value: 83.966 - type: recall_at_100 value: 93.313 - type: recall_at_1000 value: 97.426 - type: recall_at_3 value: 73.342 - type: recall_at_5 value: 77.997 - 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: 71.1600537995965 - type: f1 value: 68.8126216609964 - 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: 73.54068594485541 - type: f1 value: 73.46845879869848 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.900000000000006 - type: map_at_10 value: 61.363 - type: map_at_100 value: 61.924 - type: map_at_1000 value: 61.967000000000006 - type: map_at_3 value: 59.767 - type: map_at_5 value: 60.802 - type: mrr_at_1 value: 55.1 - type: mrr_at_10 value: 61.454 - type: mrr_at_100 value: 62.016000000000005 - type: mrr_at_1000 value: 62.059 - type: mrr_at_3 value: 59.882999999999996 - type: mrr_at_5 value: 60.893 - type: ndcg_at_1 value: 54.900000000000006 - type: ndcg_at_10 value: 64.423 - type: ndcg_at_100 value: 67.35900000000001 - type: ndcg_at_1000 value: 68.512 - type: ndcg_at_3 value: 61.224000000000004 - type: ndcg_at_5 value: 63.083 - type: precision_at_1 value: 54.900000000000006 - type: precision_at_10 value: 7.3999999999999995 - type: precision_at_100 value: 0.882 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.8 - type: precision_at_5 value: 13.98 - type: recall_at_1 value: 54.900000000000006 - type: recall_at_10 value: 74 - type: recall_at_100 value: 88.2 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 65.4 - type: recall_at_5 value: 69.89999999999999 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 75.15666666666667 - type: f1 value: 74.8306375354435 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 83.10774228478614 - type: cos_sim_ap value: 87.17679348388666 - type: cos_sim_f1 value: 84.59302325581395 - type: cos_sim_precision value: 78.15577439570276 - type: cos_sim_recall value: 92.18585005279832 - type: dot_accuracy value: 83.10774228478614 - type: dot_ap value: 87.17679348388666 - type: dot_f1 value: 84.59302325581395 - type: dot_precision value: 78.15577439570276 - type: dot_recall value: 92.18585005279832 - type: euclidean_accuracy value: 83.10774228478614 - type: euclidean_ap value: 87.17679348388666 - type: euclidean_f1 value: 84.59302325581395 - type: euclidean_precision value: 78.15577439570276 - type: euclidean_recall value: 92.18585005279832 - type: manhattan_accuracy value: 82.67460747157553 - type: manhattan_ap value: 86.94296334435238 - type: manhattan_f1 value: 84.32327166504382 - type: manhattan_precision value: 78.22944896115628 - type: manhattan_recall value: 91.4466737064414 - type: max_accuracy value: 83.10774228478614 - type: max_ap value: 87.17679348388666 - type: max_f1 value: 84.59302325581395 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 93.24999999999999 - type: ap value: 90.98617641063584 - type: f1 value: 93.23447883650289 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 41.071417937737856 - type: cos_sim_spearman value: 45.049199344455424 - type: euclidean_pearson value: 44.913450096830786 - type: euclidean_spearman value: 45.05733424275291 - type: manhattan_pearson value: 44.881623825912065 - type: manhattan_spearman value: 44.989923561416596 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 41.38238052689359 - type: cos_sim_spearman value: 42.61949690594399 - type: euclidean_pearson value: 40.61261500356766 - type: euclidean_spearman value: 42.619626605620724 - type: manhattan_pearson value: 40.8886109204474 - type: manhattan_spearman value: 42.75791523010463 - 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: 62.10977863727196 - type: cos_sim_spearman value: 63.843727112473225 - type: euclidean_pearson value: 63.25133487817196 - type: euclidean_spearman value: 63.843727112473225 - type: manhattan_pearson value: 63.58749018644103 - type: manhattan_spearman value: 63.83820575456674 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 79.30616496720054 - type: cos_sim_spearman value: 80.767935782436 - type: euclidean_pearson value: 80.4160642670106 - type: euclidean_spearman value: 80.76820284024356 - type: manhattan_pearson value: 80.27318714580251 - type: manhattan_spearman value: 80.61030164164964 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.26242871142425 - type: mrr value: 76.20689863623174 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.240999999999996 - type: map_at_10 value: 73.009 - type: map_at_100 value: 76.893 - type: map_at_1000 value: 76.973 - type: map_at_3 value: 51.339 - type: map_at_5 value: 63.003 - type: mrr_at_1 value: 87.458 - type: mrr_at_10 value: 90.44 - type: mrr_at_100 value: 90.558 - type: mrr_at_1000 value: 90.562 - type: mrr_at_3 value: 89.89 - type: mrr_at_5 value: 90.231 - type: ndcg_at_1 value: 87.458 - type: ndcg_at_10 value: 81.325 - type: ndcg_at_100 value: 85.61999999999999 - type: ndcg_at_1000 value: 86.394 - type: ndcg_at_3 value: 82.796 - type: ndcg_at_5 value: 81.219 - type: precision_at_1 value: 87.458 - type: precision_at_10 value: 40.534 - type: precision_at_100 value: 4.96 - type: precision_at_1000 value: 0.514 - type: precision_at_3 value: 72.444 - type: precision_at_5 value: 60.601000000000006 - type: recall_at_1 value: 26.240999999999996 - type: recall_at_10 value: 80.42 - type: recall_at_100 value: 94.118 - type: recall_at_1000 value: 98.02199999999999 - type: recall_at_3 value: 53.174 - type: recall_at_5 value: 66.739 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 52.40899999999999 - type: f1 value: 50.68532128056062 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 65.57616085176686 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 58.844999922904925 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 58.4 - type: map_at_10 value: 68.64 - type: map_at_100 value: 69.062 - type: map_at_1000 value: 69.073 - type: map_at_3 value: 66.567 - type: map_at_5 value: 67.89699999999999 - type: mrr_at_1 value: 58.4 - type: mrr_at_10 value: 68.64 - type: mrr_at_100 value: 69.062 - type: mrr_at_1000 value: 69.073 - type: mrr_at_3 value: 66.567 - type: mrr_at_5 value: 67.89699999999999 - type: ndcg_at_1 value: 58.4 - type: ndcg_at_10 value: 73.30600000000001 - type: ndcg_at_100 value: 75.276 - type: ndcg_at_1000 value: 75.553 - type: ndcg_at_3 value: 69.126 - type: ndcg_at_5 value: 71.519 - type: precision_at_1 value: 58.4 - type: precision_at_10 value: 8.780000000000001 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.5 - type: precision_at_5 value: 16.46 - type: recall_at_1 value: 58.4 - type: recall_at_10 value: 87.8 - type: recall_at_100 value: 96.8 - type: recall_at_1000 value: 99 - type: recall_at_3 value: 76.5 - type: recall_at_5 value: 82.3 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 86.21000000000001 - type: ap value: 69.17460264576461 - type: f1 value: 84.68032984659226 license: apache-2.0 language: - zh - en ---
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Dmeta-embedding

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Dmeta-embedding 是一款跨领域、跨任务、开箱即用的中文 Embedding 模型，适用于搜索、问答、智能客服、LLM+RAG 等各种业务场景。 优势特点如下： - 多任务、场景泛化性能优异，目前已取得 [MTEB](https://huggingface.co/spaces/mteb/leaderboard) 中文榜单第二成绩（2024.01.25） - 模型参数大小仅 400MB，对比参数量超过 GB 级模型，可以极大降低推理成本 - 支持上下文窗口长度达到 1024，对于长文本检索、RAG 等场景更适配 ## Usage 目前模型支持通过 [Sentence-Transformers](#sentence-transformers), [Langchain](#langchain), [Huggingface Transformers](#huggingface-transformers) 等主流框架进行推理，具体用法参考各个框架的示例。 ### Sentence-Transformers Dmeta-embedding 模型支持通过 [sentence-transformers](https://www.SBERT.net) 来加载推理： ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] model = SentenceTransformer('DMetaSoul/Dmeta-embedding') embs1 = model.encode(texts1, normalize_embeddings=True) embs2 = model.encode(texts2, normalize_embeddings=True) # 计算两两相似度 similarity = embs1 @ embs2.T print(similarity) # 获取 texts1[i] 对应的最相似 texts2[j] for i in range(len(texts1)): scores = [] for j in range(len(texts2)): scores.append([texts2[j], similarity[i][j]]) scores = sorted(scores, key=lambda x:x[1], reverse=True) print(f"查询文本：{texts1[i]}") for text2, score in scores: print(f"相似文本：{text2}，打分：{score}") print() ``` 示例输出如下： ``` 查询文本：胡子长得太快怎么办？ 相似文本：胡子长得快怎么办？，打分：0.9535336494445801 相似文本：怎样使胡子不浓密！，打分：0.6776421070098877 相似文本：香港买手表哪里好，打分：0.2297907918691635 相似文本：在杭州手机到哪里买，打分：0.11386542022228241 查询文本：在香港哪里买手表好 相似文本：香港买手表哪里好，打分：0.9843372106552124 相似文本：在杭州手机到哪里买，打分：0.45211508870124817 相似文本：胡子长得快怎么办？，打分：0.19985519349575043 相似文本：怎样使胡子不浓密！，打分：0.18558596074581146 ``` ### Langchain Dmeta-embedding 模型支持通过 LLM 工具框架 [langchain](https://www.langchain.com/) 来加载推理： ``` pip install -U langchain ``` ```python import torch import numpy as np from langchain.embeddings import HuggingFaceEmbeddings model_name = "DMetaSoul/Dmeta-embedding" model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, ) texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] embs1 = model.embed_documents(texts1) embs2 = model.embed_documents(texts2) embs1, embs2 = np.array(embs1), np.array(embs2) # 计算两两相似度 similarity = embs1 @ embs2.T print(similarity) # 获取 texts1[i] 对应的最相似 texts2[j] for i in range(len(texts1)): scores = [] for j in range(len(texts2)): scores.append([texts2[j], similarity[i][j]]) scores = sorted(scores, key=lambda x:x[1], reverse=True) print(f"查询文本：{texts1[i]}") for text2, score in scores: print(f"相似文本：{text2}，打分：{score}") print() ``` ### HuggingFace Transformers Dmeta-embedding 模型支持通过 [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) 框架来加载推理： ``` pip install -U transformers ``` ```python import torch from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings 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) def cls_pooling(model_output): return model_output[0][:, 0] texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/Dmeta-embedding') model = AutoModel.from_pretrained('DMetaSoul/Dmeta-embedding') model.eval() with torch.no_grad(): inputs1 = tokenizer(texts1, padding=True, truncation=True, return_tensors='pt') inputs2 = tokenizer(texts2, padding=True, truncation=True, return_tensors='pt') model_output1 = model(**inputs1) model_output2 = model(**inputs2) embs1, embs2 = cls_pooling(model_output1), cls_pooling(model_output2) embs1 = torch.nn.functional.normalize(embs1, p=2, dim=1).numpy() embs2 = torch.nn.functional.normalize(embs2, p=2, dim=1).numpy() # 计算两两相似度 similarity = embs1 @ embs2.T print(similarity) # 获取 texts1[i] 对应的最相似 texts2[j] for i in range(len(texts1)): scores = [] for j in range(len(texts2)): scores.append([texts2[j], similarity[i][j]]) scores = sorted(scores, key=lambda x:x[1], reverse=True) print(f"查询文本：{texts1[i]}") for text2, score in scores: print(f"相似文本：{text2}，打分：{score}") print() ``` ## Evaluation Dmeta-embedding 模型在 [MTEB 中文榜单](https://huggingface.co/spaces/mteb/leaderboard)取得开源第一的成绩（2024.01.25，Baichuan 榜单第一、未开源），具体关于评测数据和代码可参考 MTEB 官方[仓库](https://github.com/embeddings-benchmark/mteb)。 **MTEB Chinese**: 该[榜单数据集](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB)由智源研究院团队（BAAI）收集整理，包含 6 个经典任务共计 35 个中文数据集，涵盖了分类、检索、排序、句对、STS 等任务，是目前 Embedding 模型全方位能力评测的全球权威榜单。 | Model | Vendor | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------------------------------------------------------------------------------- | ------ |:-------------------:|:-----:|:---------:|:-----:|:------------------:|:--------------:|:---------:|:----------:| | [Dmeta-embedding](https://huggingface.co/DMetaSoul/Dmeta-embedding) | 数元灵 | 1024 | 67.51 | 70.41 | 64.09 | 88.92 | 70 | 67.17 | 50.96 | | [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | 阿里达摩院 | 1024 | 66.72 | 72.49 | 57.82 | 84.41 | 71.34 | 67.4 | 53.07 | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 智源 | 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) | 智源 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | OpenAI | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 个人 | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 个人 | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | ## FAQ
1. 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？ 简单来说，模型优异的泛化能力来自于预训练数据的广泛和多样，以及模型优化时面向多任务场景设计了不同优化目标。 具体来说，技术要点有： 1）首先是大规模弱标签对比学习。业界经验表明开箱即用的语言模型在 Embedding 相关任务上表现不佳，但由于监督数据标注、获取成本较高，因此大规模、高质量的弱标签学习成为一条可选技术路线。通过在互联网上论坛、新闻、问答社区、百科等半结构化数据中提取弱标签，并利用大模型进行低质过滤，得到 10 亿级别弱监督文本对数据。 2）其次是高质量监督学习。我们收集整理了大规模开源标注的语句对数据集，包含百科、教育、金融、医疗、法律、新闻、学术等多个领域共计 3000 万句对样本。同时挖掘难负样本对，借助对比学习更好的进行模型优化。 3）最后是检索任务针对性优化。考虑到搜索、问答以及 RAG 等场景是 Embedding 模型落地的重要应用阵地，为了增强模型跨领域、跨场景的效果性能，我们专门针对检索任务进行了模型优化，核心在于从问答、检索等数据中挖掘难负样本，借助稀疏和稠密检索等多种手段，构造百万级难负样本对数据集，显著提升了模型跨领域的检索性能。
2. 模型可以商用吗？ 我们的开源模型基于 Apache-2.0 协议，完全支持免费商用。
3. 如何复现 MTEB 评测结果？ 我们在模型仓库中提供了脚本 mteb_eval.py，您可以直接运行此脚本来复现我们的评测结果。
4. 后续规划有哪些？ 我们将不断致力于为社区提供效果优异、推理轻量、多场景开箱即用的 Embedding 模型，同时我们也会将 Embedding 逐步整合到目前已经的技术生态中，跟随社区一起成长！
## Contact 您如果在使用过程中，遇到任何问题，欢迎前往[讨论区](https://huggingface.co/DMetaSoul/Dmeta-embedding/discussions)建言献策。 您也可以联系我们：赵中昊 , 肖文斌 , 孙凯 ## License Dmeta-embedding 模型采用 Apache-2.0 License，开源模型可以进行免费商用私有部署。