--- tags: - mteb model-index: - name: piccolo-embedding_mixed2 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 56.918538280469875 - type: cos_sim_spearman value: 60.95597435855258 - type: euclidean_pearson value: 59.73821610051437 - type: euclidean_spearman value: 60.956778530262454 - type: manhattan_pearson value: 59.739675774225475 - type: manhattan_spearman value: 60.95243600302903 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 56.79417977023184 - type: cos_sim_spearman value: 58.80984726256814 - type: euclidean_pearson value: 63.42225182281334 - type: euclidean_spearman value: 58.80957930593542 - type: manhattan_pearson value: 63.41128425333986 - type: manhattan_spearman value: 58.80784321716389 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 50.074000000000005 - type: f1 value: 47.11468271375511 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 73.3412976021806 - type: cos_sim_spearman value: 75.0799965464816 - type: euclidean_pearson value: 73.7874729086686 - type: euclidean_spearman value: 75.07910973646369 - type: manhattan_pearson value: 73.7716616949607 - type: manhattan_spearman value: 75.06089549008017 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 60.4206935177474 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 49.53654617222264 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 90.96386786978509 - type: mrr value: 92.8897619047619 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 90.41014127763198 - type: mrr value: 92.45039682539682 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.901999999999997 - type: map_at_10 value: 40.321 - type: map_at_100 value: 42.176 - type: map_at_1000 value: 42.282 - type: map_at_3 value: 35.882 - type: map_at_5 value: 38.433 - type: mrr_at_1 value: 40.910000000000004 - type: mrr_at_10 value: 49.309999999999995 - type: mrr_at_100 value: 50.239 - type: mrr_at_1000 value: 50.278 - type: mrr_at_3 value: 46.803 - type: mrr_at_5 value: 48.137 - type: ndcg_at_1 value: 40.785 - type: ndcg_at_10 value: 47.14 - type: ndcg_at_100 value: 54.156000000000006 - type: ndcg_at_1000 value: 55.913999999999994 - type: ndcg_at_3 value: 41.669 - type: ndcg_at_5 value: 43.99 - type: precision_at_1 value: 40.785 - type: precision_at_10 value: 10.493 - type: precision_at_100 value: 1.616 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.723 - type: precision_at_5 value: 17.249 - type: recall_at_1 value: 26.901999999999997 - type: recall_at_10 value: 58.25 - type: recall_at_100 value: 87.10900000000001 - type: recall_at_1000 value: 98.804 - type: recall_at_3 value: 41.804 - type: recall_at_5 value: 48.884 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 86.42212868310283 - type: cos_sim_ap value: 92.83788702972741 - type: cos_sim_f1 value: 87.08912233141307 - type: cos_sim_precision value: 84.24388111888112 - type: cos_sim_recall value: 90.13327098433481 - type: dot_accuracy value: 86.44618159951895 - type: dot_ap value: 92.81146275060858 - type: dot_f1 value: 87.06857911250562 - type: dot_precision value: 83.60232408005164 - type: dot_recall value: 90.83469721767594 - type: euclidean_accuracy value: 86.42212868310283 - type: euclidean_ap value: 92.83805700492603 - type: euclidean_f1 value: 87.08803611738148 - type: euclidean_precision value: 84.18066768492254 - type: euclidean_recall value: 90.20341360766892 - type: manhattan_accuracy value: 86.28983764281419 - type: manhattan_ap value: 92.82818970981005 - type: manhattan_f1 value: 87.12625521832335 - type: manhattan_precision value: 84.19101613606628 - type: manhattan_recall value: 90.27355623100304 - type: max_accuracy value: 86.44618159951895 - type: max_ap value: 92.83805700492603 - type: max_f1 value: 87.12625521832335 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 79.215 - type: map_at_10 value: 86.516 - type: map_at_100 value: 86.6 - type: map_at_1000 value: 86.602 - type: map_at_3 value: 85.52 - type: map_at_5 value: 86.136 - type: mrr_at_1 value: 79.663 - type: mrr_at_10 value: 86.541 - type: mrr_at_100 value: 86.625 - type: mrr_at_1000 value: 86.627 - type: mrr_at_3 value: 85.564 - type: mrr_at_5 value: 86.15899999999999 - type: ndcg_at_1 value: 79.663 - type: ndcg_at_10 value: 89.399 - type: ndcg_at_100 value: 89.727 - type: ndcg_at_1000 value: 89.781 - type: ndcg_at_3 value: 87.402 - type: ndcg_at_5 value: 88.479 - type: precision_at_1 value: 79.663 - type: precision_at_10 value: 9.926 - type: precision_at_100 value: 1.006 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 31.226 - type: precision_at_5 value: 19.283 - type: recall_at_1 value: 79.215 - type: recall_at_10 value: 98.209 - type: recall_at_100 value: 99.579 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 92.703 - type: recall_at_5 value: 95.364 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.391 - type: map_at_10 value: 82.82000000000001 - type: map_at_100 value: 85.5 - type: map_at_1000 value: 85.533 - type: map_at_3 value: 57.802 - type: map_at_5 value: 72.82600000000001 - type: mrr_at_1 value: 92.80000000000001 - type: mrr_at_10 value: 94.83500000000001 - type: mrr_at_100 value: 94.883 - type: mrr_at_1000 value: 94.884 - type: mrr_at_3 value: 94.542 - type: mrr_at_5 value: 94.729 - type: ndcg_at_1 value: 92.7 - type: ndcg_at_10 value: 89.435 - type: ndcg_at_100 value: 91.78699999999999 - type: ndcg_at_1000 value: 92.083 - type: ndcg_at_3 value: 88.595 - type: ndcg_at_5 value: 87.53 - type: precision_at_1 value: 92.7 - type: precision_at_10 value: 42.4 - type: precision_at_100 value: 4.823 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 79.133 - type: precision_at_5 value: 66.8 - type: recall_at_1 value: 27.391 - type: recall_at_10 value: 90.069 - type: recall_at_100 value: 97.875 - type: recall_at_1000 value: 99.436 - type: recall_at_3 value: 59.367999999999995 - type: recall_at_5 value: 76.537 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.800000000000004 - type: map_at_10 value: 65.289 - type: map_at_100 value: 65.845 - type: map_at_1000 value: 65.853 - type: map_at_3 value: 62.766999999999996 - type: map_at_5 value: 64.252 - type: mrr_at_1 value: 54.800000000000004 - type: mrr_at_10 value: 65.255 - type: mrr_at_100 value: 65.81700000000001 - type: mrr_at_1000 value: 65.824 - type: mrr_at_3 value: 62.683 - type: mrr_at_5 value: 64.248 - type: ndcg_at_1 value: 54.800000000000004 - type: ndcg_at_10 value: 70.498 - type: ndcg_at_100 value: 72.82300000000001 - type: ndcg_at_1000 value: 73.053 - type: ndcg_at_3 value: 65.321 - type: ndcg_at_5 value: 67.998 - type: precision_at_1 value: 54.800000000000004 - type: precision_at_10 value: 8.690000000000001 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 24.233 - type: precision_at_5 value: 15.840000000000002 - type: recall_at_1 value: 54.800000000000004 - type: recall_at_10 value: 86.9 - type: recall_at_100 value: 97 - type: recall_at_1000 value: 98.9 - type: recall_at_3 value: 72.7 - type: recall_at_5 value: 79.2 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 51.758368603308966 - type: f1 value: 40.249503783871596 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 89.08067542213884 - type: ap value: 60.31281895139249 - type: f1 value: 84.20883153932607 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 74.04193577551248 - type: cos_sim_spearman value: 79.81875884845549 - type: euclidean_pearson value: 80.02581187503708 - type: euclidean_spearman value: 79.81877215060574 - type: manhattan_pearson value: 80.01767830530258 - type: manhattan_spearman value: 79.81178852172727 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 39.90939429947956 - type: mrr value: 39.71071428571429 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 68.485 - type: map_at_10 value: 78.27199999999999 - type: map_at_100 value: 78.54100000000001 - type: map_at_1000 value: 78.546 - type: map_at_3 value: 76.339 - type: map_at_5 value: 77.61099999999999 - type: mrr_at_1 value: 70.80199999999999 - type: mrr_at_10 value: 78.901 - type: mrr_at_100 value: 79.12400000000001 - type: mrr_at_1000 value: 79.128 - type: mrr_at_3 value: 77.237 - type: mrr_at_5 value: 78.323 - type: ndcg_at_1 value: 70.759 - type: ndcg_at_10 value: 82.191 - type: ndcg_at_100 value: 83.295 - type: ndcg_at_1000 value: 83.434 - type: ndcg_at_3 value: 78.57600000000001 - type: ndcg_at_5 value: 80.715 - type: precision_at_1 value: 70.759 - type: precision_at_10 value: 9.951 - type: precision_at_100 value: 1.049 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.660999999999998 - type: precision_at_5 value: 18.94 - type: recall_at_1 value: 68.485 - type: recall_at_10 value: 93.65 - type: recall_at_100 value: 98.434 - type: recall_at_1000 value: 99.522 - type: recall_at_3 value: 84.20100000000001 - type: recall_at_5 value: 89.261 - 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: 77.45460659045055 - type: f1 value: 73.84987702455533 - 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: 85.29926025554808 - type: f1 value: 84.40636286569843 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 57.599999999999994 - type: map_at_10 value: 64.691 - type: map_at_100 value: 65.237 - type: map_at_1000 value: 65.27 - type: map_at_3 value: 62.733000000000004 - type: map_at_5 value: 63.968 - type: mrr_at_1 value: 58.099999999999994 - type: mrr_at_10 value: 64.952 - type: mrr_at_100 value: 65.513 - type: mrr_at_1000 value: 65.548 - type: mrr_at_3 value: 63 - type: mrr_at_5 value: 64.235 - type: ndcg_at_1 value: 57.599999999999994 - type: ndcg_at_10 value: 68.19 - type: ndcg_at_100 value: 70.98400000000001 - type: ndcg_at_1000 value: 71.811 - type: ndcg_at_3 value: 64.276 - type: ndcg_at_5 value: 66.47999999999999 - type: precision_at_1 value: 57.599999999999994 - type: precision_at_10 value: 7.920000000000001 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 22.900000000000002 - type: precision_at_5 value: 14.799999999999999 - type: recall_at_1 value: 57.599999999999994 - type: recall_at_10 value: 79.2 - type: recall_at_100 value: 92.60000000000001 - type: recall_at_1000 value: 99 - type: recall_at_3 value: 68.7 - type: recall_at_5 value: 74 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 79.45 - type: f1 value: 79.25610578280538 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 85.43584190579317 - type: cos_sim_ap value: 90.89979725191012 - type: cos_sim_f1 value: 86.48383937316358 - type: cos_sim_precision value: 80.6392694063927 - type: cos_sim_recall value: 93.24181626187962 - type: dot_accuracy value: 85.38170005414185 - type: dot_ap value: 90.87532457866699 - type: dot_f1 value: 86.48383937316358 - type: dot_precision value: 80.6392694063927 - type: dot_recall value: 93.24181626187962 - type: euclidean_accuracy value: 85.43584190579317 - type: euclidean_ap value: 90.90126652086121 - type: euclidean_f1 value: 86.48383937316358 - type: euclidean_precision value: 80.6392694063927 - type: euclidean_recall value: 93.24181626187962 - type: manhattan_accuracy value: 85.43584190579317 - type: manhattan_ap value: 90.87896997853466 - type: manhattan_f1 value: 86.47581441263573 - type: manhattan_precision value: 81.18628359592215 - type: manhattan_recall value: 92.5026399155227 - type: max_accuracy value: 85.43584190579317 - type: max_ap value: 90.90126652086121 - type: max_f1 value: 86.48383937316358 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 94.9 - type: ap value: 93.1468223150745 - type: f1 value: 94.88918689508299 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 40.4831743182905 - type: cos_sim_spearman value: 47.4163675550491 - type: euclidean_pearson value: 46.456319899274924 - type: euclidean_spearman value: 47.41567079730661 - type: manhattan_pearson value: 46.48561639930895 - type: manhattan_spearman value: 47.447721653461215 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 42.96423587663398 - type: cos_sim_spearman value: 45.13742225167858 - type: euclidean_pearson value: 39.275452114075435 - type: euclidean_spearman value: 45.137763540967406 - type: manhattan_pearson value: 39.24797626417764 - type: manhattan_spearman value: 45.13817773119268 - 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: 66.26687809086202 - type: cos_sim_spearman value: 66.9569145816897 - type: euclidean_pearson value: 65.72390780809788 - type: euclidean_spearman value: 66.95406938095539 - type: manhattan_pearson value: 65.6220809000381 - type: manhattan_spearman value: 66.88531036320953 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 80.30831700726195 - type: cos_sim_spearman value: 82.05184068558792 - type: euclidean_pearson value: 81.73198597791563 - type: euclidean_spearman value: 82.05326103582206 - type: manhattan_pearson value: 81.70886400949136 - type: manhattan_spearman value: 82.03473274756037 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 69.03398835347575 - type: mrr value: 79.9212528613341 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.515 - type: map_at_10 value: 77.40599999999999 - type: map_at_100 value: 81.087 - type: map_at_1000 value: 81.148 - type: map_at_3 value: 54.327000000000005 - type: map_at_5 value: 66.813 - type: mrr_at_1 value: 89.764 - type: mrr_at_10 value: 92.58 - type: mrr_at_100 value: 92.663 - type: mrr_at_1000 value: 92.666 - type: mrr_at_3 value: 92.15299999999999 - type: mrr_at_5 value: 92.431 - type: ndcg_at_1 value: 89.777 - type: ndcg_at_10 value: 85.013 - type: ndcg_at_100 value: 88.62100000000001 - type: ndcg_at_1000 value: 89.184 - type: ndcg_at_3 value: 86.19200000000001 - type: ndcg_at_5 value: 84.909 - type: precision_at_1 value: 89.777 - type: precision_at_10 value: 42.218 - type: precision_at_100 value: 5.032 - type: precision_at_1000 value: 0.517 - type: precision_at_3 value: 75.335 - type: precision_at_5 value: 63.199000000000005 - type: recall_at_1 value: 27.515 - type: recall_at_10 value: 84.258 - type: recall_at_100 value: 95.908 - type: recall_at_1000 value: 98.709 - type: recall_at_3 value: 56.189 - type: recall_at_5 value: 70.50800000000001 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 54.635999999999996 - type: f1 value: 52.63073912739558 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 78.75676284855221 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 71.95583733802839 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.9 - type: map_at_10 value: 75.622 - type: map_at_100 value: 75.93900000000001 - type: map_at_1000 value: 75.93900000000001 - type: map_at_3 value: 73.933 - type: map_at_5 value: 74.973 - type: mrr_at_1 value: 65 - type: mrr_at_10 value: 75.676 - type: mrr_at_100 value: 75.994 - type: mrr_at_1000 value: 75.994 - type: mrr_at_3 value: 74.05000000000001 - type: mrr_at_5 value: 75.03999999999999 - type: ndcg_at_1 value: 64.9 - type: ndcg_at_10 value: 80.08999999999999 - type: ndcg_at_100 value: 81.44500000000001 - type: ndcg_at_1000 value: 81.45599999999999 - type: ndcg_at_3 value: 76.688 - type: ndcg_at_5 value: 78.53 - type: precision_at_1 value: 64.9 - type: precision_at_10 value: 9.379999999999999 - type: precision_at_100 value: 0.997 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 28.199999999999996 - type: precision_at_5 value: 17.8 - type: recall_at_1 value: 64.9 - type: recall_at_10 value: 93.8 - type: recall_at_100 value: 99.7 - type: recall_at_1000 value: 99.8 - type: recall_at_3 value: 84.6 - type: recall_at_5 value: 89 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 89.34 - type: ap value: 75.20638024616892 - type: f1 value: 87.88648489072128 library_name: sentence-transformers --- # xiaobu-embedding-v2 基于piccolo-embedding[1]，主要改动如下： - 合成数据替换为xiaobu-embedding-v1[2]所积累数据 - 在circle_loss[3]视角下统一处理CMTEB的6类问题，最大优势是可充分利用原始数据集中的多个正例，其次是可一定程度上避免考虑多个不同loss之间的权重问题 ## Usage (Sentence-Transformers) ``` pip install -U sentence-transformers ``` 相似度计算： ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('lier007/xiaobu-embedding-v2') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` ## Reference 1. https://github.com/hjq133/piccolo-embedding 2. https://huggingface.co./lier007/xiaobu-embedding 3. https://arxiv.org/abs/2002.10857