--- tags: - mteb - sentence-transformers model-index: - name: piccolo-large-zh-v2 results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 56.76055988260572 - type: cos_sim_spearman value: 61.49271876861677 - type: euclidean_pearson value: 59.14524585320711 - type: euclidean_spearman value: 60.63579339225774 - type: manhattan_pearson value: 59.14662752965445 - type: manhattan_spearman value: 60.635190265737904 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 56.21706298831197 - type: cos_sim_spearman value: 59.19831457688953 - type: euclidean_pearson value: 62.37752017633299 - type: euclidean_spearman value: 58.79400967473204 - type: manhattan_pearson value: 62.37015943212308 - type: manhattan_spearman value: 58.79232537600814 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.440000000000005 - type: f1 value: 46.67381446305019 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 70.99026329599994 - type: cos_sim_spearman value: 72.87565357908989 - type: euclidean_pearson value: 71.17690439270028 - type: euclidean_spearman value: 72.50428109969029 - type: manhattan_pearson value: 71.17262321033088 - type: manhattan_spearman value: 72.49845447987437 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 57.92713421071616 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 48.096546680932235 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 89.31003741715936 - type: mrr value: 91.38075396825397 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 90.13769781784876 - type: mrr value: 92.14329365079365 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.931 - type: map_at_10 value: 40.647 - type: map_at_100 value: 42.519 - type: map_at_1000 value: 42.616 - type: map_at_3 value: 36.144999999999996 - type: map_at_5 value: 38.717 - type: mrr_at_1 value: 40.935 - type: mrr_at_10 value: 49.684 - type: mrr_at_100 value: 50.598 - type: mrr_at_1000 value: 50.632999999999996 - type: mrr_at_3 value: 47.07 - type: mrr_at_5 value: 48.49 - type: ndcg_at_1 value: 40.935 - type: ndcg_at_10 value: 47.583999999999996 - type: ndcg_at_100 value: 54.69199999999999 - type: ndcg_at_1000 value: 56.314 - type: ndcg_at_3 value: 41.973 - type: ndcg_at_5 value: 44.334 - type: precision_at_1 value: 40.935 - type: precision_at_10 value: 10.585 - type: precision_at_100 value: 1.637 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.881 - type: precision_at_5 value: 17.399 - type: recall_at_1 value: 26.931 - type: recall_at_10 value: 59.006 - type: recall_at_100 value: 88.247 - type: recall_at_1000 value: 99.045 - type: recall_at_3 value: 42.064 - type: recall_at_5 value: 49.266 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 86.08538785327721 - type: cos_sim_ap value: 92.64373114205229 - type: cos_sim_f1 value: 86.89951395953432 - type: cos_sim_precision value: 84.11378555798687 - type: cos_sim_recall value: 89.87608136544307 - type: dot_accuracy value: 72.66386049308478 - type: dot_ap value: 81.053422935767 - type: dot_f1 value: 75.19933726830277 - type: dot_precision value: 67.4907063197026 - type: dot_recall value: 84.89595510872107 - type: euclidean_accuracy value: 85.52014431749849 - type: euclidean_ap value: 91.90647782899615 - type: euclidean_f1 value: 86.26361413647477 - type: euclidean_precision value: 82.2071595001059 - type: euclidean_recall value: 90.74117371989713 - type: manhattan_accuracy value: 85.48406494287433 - type: manhattan_ap value: 91.89657919524385 - type: manhattan_f1 value: 86.20413761572752 - type: manhattan_precision value: 84.324686940966 - type: manhattan_recall value: 88.16927753097966 - type: max_accuracy value: 86.08538785327721 - type: max_ap value: 92.64373114205229 - type: max_f1 value: 86.89951395953432 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 75.50099999999999 - type: map_at_10 value: 83.43 - type: map_at_100 value: 83.577 - type: map_at_1000 value: 83.57900000000001 - type: map_at_3 value: 82.06400000000001 - type: map_at_5 value: 82.88600000000001 - type: mrr_at_1 value: 75.869 - type: mrr_at_10 value: 83.536 - type: mrr_at_100 value: 83.682 - type: mrr_at_1000 value: 83.68299999999999 - type: mrr_at_3 value: 82.244 - type: mrr_at_5 value: 82.998 - type: ndcg_at_1 value: 75.764 - type: ndcg_at_10 value: 86.777 - type: ndcg_at_100 value: 87.36 - type: ndcg_at_1000 value: 87.424 - type: ndcg_at_3 value: 84.10300000000001 - type: ndcg_at_5 value: 85.532 - type: precision_at_1 value: 75.764 - type: precision_at_10 value: 9.8 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 30.207 - type: precision_at_5 value: 18.82 - type: recall_at_1 value: 75.50099999999999 - type: recall_at_10 value: 96.997 - type: recall_at_100 value: 99.473 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 89.831 - type: recall_at_5 value: 93.256 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 27.094 - type: map_at_10 value: 82.418 - type: map_at_100 value: 85.05 - type: map_at_1000 value: 85.083 - type: map_at_3 value: 57.68600000000001 - type: map_at_5 value: 72.476 - type: mrr_at_1 value: 92.25 - type: mrr_at_10 value: 94.621 - type: mrr_at_100 value: 94.675 - type: mrr_at_1000 value: 94.677 - type: mrr_at_3 value: 94.375 - type: mrr_at_5 value: 94.52199999999999 - type: ndcg_at_1 value: 92.25 - type: ndcg_at_10 value: 89.13600000000001 - type: ndcg_at_100 value: 91.532 - type: ndcg_at_1000 value: 91.836 - type: ndcg_at_3 value: 88.50099999999999 - type: ndcg_at_5 value: 87.251 - type: precision_at_1 value: 92.25 - type: precision_at_10 value: 42.295 - type: precision_at_100 value: 4.812 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 79.167 - type: precision_at_5 value: 66.56 - type: recall_at_1 value: 27.094 - type: recall_at_10 value: 89.816 - type: recall_at_100 value: 97.855 - type: recall_at_1000 value: 99.384 - type: recall_at_3 value: 59.557 - type: recall_at_5 value: 76.395 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.6 - type: map_at_10 value: 62.985 - type: map_at_100 value: 63.532999999999994 - type: map_at_1000 value: 63.546 - type: map_at_3 value: 60.617 - type: map_at_5 value: 62.017 - type: mrr_at_1 value: 53.6 - type: mrr_at_10 value: 62.985 - type: mrr_at_100 value: 63.532999999999994 - type: mrr_at_1000 value: 63.546 - type: mrr_at_3 value: 60.617 - type: mrr_at_5 value: 62.017 - type: ndcg_at_1 value: 53.6 - type: ndcg_at_10 value: 67.755 - type: ndcg_at_100 value: 70.366 - type: ndcg_at_1000 value: 70.696 - type: ndcg_at_3 value: 62.89900000000001 - type: ndcg_at_5 value: 65.437 - type: precision_at_1 value: 53.6 - type: precision_at_10 value: 8.28 - type: precision_at_100 value: 0.9490000000000001 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.166999999999998 - type: precision_at_5 value: 15.14 - type: recall_at_1 value: 53.6 - type: recall_at_10 value: 82.8 - type: recall_at_100 value: 94.89999999999999 - type: recall_at_1000 value: 97.5 - type: recall_at_3 value: 69.5 - type: recall_at_5 value: 75.7 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 52.104655636783384 - type: f1 value: 41.025743582860514 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 88.57410881801127 - type: ap value: 59.49612312498937 - type: f1 value: 83.70595013666741 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 74.00327736048256 - type: cos_sim_spearman value: 79.5459672237356 - type: euclidean_pearson value: 79.18300205389669 - type: euclidean_spearman value: 79.21872988987533 - type: manhattan_pearson value: 79.1715470733081 - type: manhattan_spearman value: 79.20756273498812 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 66.94600000000001 - type: map_at_10 value: 75.947 - type: map_at_100 value: 76.268 - type: map_at_1000 value: 76.28 - type: map_at_3 value: 74.13300000000001 - type: map_at_5 value: 75.28399999999999 - type: mrr_at_1 value: 69.241 - type: mrr_at_10 value: 76.532 - type: mrr_at_100 value: 76.816 - type: mrr_at_1000 value: 76.827 - type: mrr_at_3 value: 74.95 - type: mrr_at_5 value: 75.957 - type: ndcg_at_1 value: 69.241 - type: ndcg_at_10 value: 79.54299999999999 - type: ndcg_at_100 value: 80.95 - type: ndcg_at_1000 value: 81.252 - type: ndcg_at_3 value: 76.119 - type: ndcg_at_5 value: 78.069 - type: precision_at_1 value: 69.241 - type: precision_at_10 value: 9.576 - type: precision_at_100 value: 1.026 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 28.571999999999996 - type: precision_at_5 value: 18.181 - type: recall_at_1 value: 66.94600000000001 - type: recall_at_10 value: 90.024 - type: recall_at_100 value: 96.3 - type: recall_at_1000 value: 98.656 - type: recall_at_3 value: 81.026 - type: recall_at_5 value: 85.658 - 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: 77.71015467383997 - type: f1 value: 74.32345894845358 - 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: 85.63214525891055 - type: f1 value: 84.65303466003252 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 55.50000000000001 - type: map_at_10 value: 61.66199999999999 - type: map_at_100 value: 62.13999999999999 - type: map_at_1000 value: 62.187000000000005 - type: map_at_3 value: 59.967000000000006 - type: map_at_5 value: 60.927 - type: mrr_at_1 value: 55.7 - type: mrr_at_10 value: 61.76199999999999 - type: mrr_at_100 value: 62.241 - type: mrr_at_1000 value: 62.287000000000006 - type: mrr_at_3 value: 60.06700000000001 - type: mrr_at_5 value: 61.027 - type: ndcg_at_1 value: 55.50000000000001 - type: ndcg_at_10 value: 64.878 - type: ndcg_at_100 value: 67.464 - type: ndcg_at_1000 value: 68.745 - type: ndcg_at_3 value: 61.367000000000004 - type: ndcg_at_5 value: 63.117999999999995 - type: precision_at_1 value: 55.50000000000001 - type: precision_at_10 value: 7.51 - type: precision_at_100 value: 0.878 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 21.8 - type: precision_at_5 value: 13.94 - type: recall_at_1 value: 55.50000000000001 - type: recall_at_10 value: 75.1 - type: recall_at_100 value: 87.8 - type: recall_at_1000 value: 97.89999999999999 - type: recall_at_3 value: 65.4 - type: recall_at_5 value: 69.69999999999999 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 33.386980266936106 - type: mrr value: 32.11904761904762 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 79.08666666666666 - type: f1 value: 78.93142205976953 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 84.35300487276665 - type: cos_sim_ap value: 87.83572265803564 - type: cos_sim_f1 value: 85.42713567839195 - type: cos_sim_precision value: 81.49568552253116 - type: cos_sim_recall value: 89.7571277719113 - type: dot_accuracy value: 72.87493232268544 - type: dot_ap value: 80.29032993894747 - type: dot_f1 value: 76.5938475256353 - type: dot_precision value: 66.28086419753086 - type: dot_recall value: 90.70749736008447 - type: euclidean_accuracy value: 82.34975636166757 - type: euclidean_ap value: 85.73873757468064 - type: euclidean_f1 value: 83.56713426853707 - type: euclidean_precision value: 79.50428979980934 - type: euclidean_recall value: 88.0675818373812 - type: manhattan_accuracy value: 82.45804006497022 - type: manhattan_ap value: 85.7176464290469 - type: manhattan_f1 value: 83.65095285857572 - type: manhattan_precision value: 79.65616045845272 - type: manhattan_recall value: 88.0675818373812 - type: max_accuracy value: 84.35300487276665 - type: max_ap value: 87.83572265803564 - type: max_f1 value: 85.42713567839195 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 94.61999999999999 - type: ap value: 92.74140430219491 - type: f1 value: 94.60775857122515 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 39.75749234575995 - type: cos_sim_spearman value: 46.48035295363829 - type: euclidean_pearson value: 45.38711981599582 - type: euclidean_spearman value: 46.13915356562481 - type: manhattan_pearson value: 45.420770530489065 - type: manhattan_spearman value: 46.179913441143775 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.02008249965321 - type: cos_sim_spearman value: 45.906917552219156 - type: euclidean_pearson value: 36.600317631983316 - type: euclidean_spearman value: 41.97740958824762 - type: manhattan_pearson value: 36.54329048509785 - type: manhattan_spearman value: 41.91222171040451 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: None metrics: - type: cos_sim_pearson value: 60.97044608578288 - type: cos_sim_spearman value: 63.76187490245927 - type: euclidean_pearson value: 60.74245987426317 - type: euclidean_spearman value: 63.32990713078846 - type: manhattan_pearson value: 60.62422616577702 - type: manhattan_spearman value: 63.256612476686826 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 76.28185867362305 - type: cos_sim_spearman value: 78.71478656159289 - type: euclidean_pearson value: 79.80734359535234 - type: euclidean_spearman value: 79.85403491297063 - type: manhattan_pearson value: 79.79454037962215 - type: manhattan_spearman value: 79.82796402623201 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 67.14759526113295 - type: mrr value: 77.36422096484723 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 28.177999999999997 - type: map_at_10 value: 78.77199999999999 - type: map_at_100 value: 82.365 - type: map_at_1000 value: 82.422 - type: map_at_3 value: 55.452999999999996 - type: map_at_5 value: 68.12700000000001 - type: mrr_at_1 value: 91.097 - type: mrr_at_10 value: 93.52000000000001 - type: mrr_at_100 value: 93.587 - type: mrr_at_1000 value: 93.589 - type: mrr_at_3 value: 93.136 - type: mrr_at_5 value: 93.381 - type: ndcg_at_1 value: 91.097 - type: ndcg_at_10 value: 86.136 - type: ndcg_at_100 value: 89.515 - type: ndcg_at_1000 value: 90.049 - type: ndcg_at_3 value: 87.41600000000001 - type: ndcg_at_5 value: 86.115 - type: precision_at_1 value: 91.097 - type: precision_at_10 value: 42.597 - type: precision_at_100 value: 5.043 - type: precision_at_1000 value: 0.517 - type: precision_at_3 value: 76.239 - type: precision_at_5 value: 63.93 - type: recall_at_1 value: 28.177999999999997 - type: recall_at_10 value: 85.182 - type: recall_at_100 value: 96.174 - type: recall_at_1000 value: 98.848 - type: recall_at_3 value: 57.150999999999996 - type: recall_at_5 value: 71.50999999999999 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 54.521 - type: f1 value: 52.53528052282081 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 74.2003249023509 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 68.4277378629746 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 58.599999999999994 - type: map_at_10 value: 68.671 - type: map_at_100 value: 69.148 - type: map_at_1000 value: 69.157 - type: map_at_3 value: 66.9 - type: map_at_5 value: 68.045 - type: mrr_at_1 value: 58.599999999999994 - type: mrr_at_10 value: 68.671 - type: mrr_at_100 value: 69.148 - type: mrr_at_1000 value: 69.157 - type: mrr_at_3 value: 66.9 - type: mrr_at_5 value: 68.045 - type: ndcg_at_1 value: 58.599999999999994 - type: ndcg_at_10 value: 73.099 - type: ndcg_at_100 value: 75.33 - type: ndcg_at_1000 value: 75.58500000000001 - type: ndcg_at_3 value: 69.502 - type: ndcg_at_5 value: 71.542 - type: precision_at_1 value: 58.599999999999994 - type: precision_at_10 value: 8.68 - type: precision_at_100 value: 0.97 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.667 - type: precision_at_5 value: 16.38 - type: recall_at_1 value: 58.599999999999994 - type: recall_at_10 value: 86.8 - type: recall_at_100 value: 97.0 - type: recall_at_1000 value: 99.1 - type: recall_at_3 value: 77.0 - type: recall_at_5 value: 81.89999999999999 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 89.58999999999999 - type: ap value: 75.69899834265364 - type: f1 value: 88.2026184757175 --- [EN](README.md) | [简体中文](README_zh.md) **News** **[2024-05-16]** Due to certain internal company considerations, we have temporarily removed the model weights. It will be uploaded again after passing our internal review process. Please temporarily access this model via API: https://platform.sensenova.cn/doc?path=/chat/Embeddings/Embeddings.md **[2024-05-14]** We have currently release our model weights, training code, and tech report. Discussions are welcome. For training code, please refer to our [github](https://github.com/hjq133/piccolo-embedding) For training details, please refer to our [tech-report](https://arxiv.org/abs/2405.06932) **[2024-04-22]** piccolo-large-zh-v2 currently ranks first on the C-MTEB list, leading the previous BERT model by about 1.9 points. ## Piccolo-large-zh-v2 piccolo-large-zh-v2 is a Chinese embedding model developed by the general model group from SenseTime Research. This upgraded version of Piccolo aims to prioritize general downstream fine-tuning methods. Piccolo2 primarily leverages an efficient multi-task hybrid loss training approach, effectively harnessing textual data and labels from diverse downstream tasks. In addition, Piccolo2 scales up the embedding dimension and uses MRL training to support more flexible vector dimensions. ## 💡 Model Hightlights The main feature of piccolo2 is that it uses a multi-task hybrid loss during training. For retrieval/sorting tasks, we use the standard InfoNCE with in-batch-negative:

For sts/pair classification tasks, we use cosent loss, which is proved to be better for data with more fine-grained labels(e.g. score values ):

For classification/clustering tasks, by treating text and its semantic labels as positive and negative pairs, we convert the dataset into the format of triples. And then we use InfoNCE to optimize it. However, it’s important to stress that in-batch negatives are no longer used due to the fact that it can easily lead to conflict training targets:

## 📃 Experiments and Results Piccolo2 primarily focuses on the downstream general finetune paradigm. Our open source model uses [stella-v3.5](https://huggingface.co/infgrad/stella-mrl-large-zh-v3.5-1792d) as initialization and trained about 2500 steps on 32 GPUS. For more implementation details, please refer to our [technical report](https://arxiv.org/abs/2405.06932). | Model Name | Model Size (GB) | Dimension | Sequence Length | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | Average (35) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**piccolo-large-zh-v2**](https://huggingface.co/sensenova/piccolo-large-zh-v2) | 1.21 | 1792 | 512 | 74.59 | 62.17 | 90.24 | 70 | 74.36 | 63.5 | 70.95 | | [gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| 26.45 | 32768 |4096 | 73.35 | 67.08 | 88.52 | 66.38 | 70.62 | 62.32 | 69.56| | [acge-text-embedding](https://huggingface.co/aspire/acge_text_embedding) |1.21 | 1792 | 512 | 72.75 | 58.7 | 87.84 | 67.98 | 72.93 | 62.09 | 69.07 | ## 🔨 Usage The piccolo model can be easily accessed in the sentence-transformer package: ```python # for s2s/s2p dataset, you can use piccolo as below from sklearn.preprocessing import normalize from sentence_transformers import SentenceTransformer sentences = ["数据1", "数据2"] matryoshka_dim=1792 # support 256, 512, 768, 1024, 1280, 1536, 1792 model = SentenceTransformer('sensenova/piccolo-large-zh-v2') embeddings_1 = model.encode(sentences, normalize_embeddings=False) embeddings_2 = model.encode(sentences, normalize_embeddings=False) embeddings_1 = normalize(embeddings_1[..., :matryoshka_dim], norm="l2", axis=1) embeddings_2 = normalize(embeddings_2[..., :matryoshka_dim], norm="l2", axis=1) similarity = embeddings_1 @ embeddings_2.T ``` ## 🤗 **Model List** | Model|Language|Description|prompt| |:-|:-:|:-:|:--:| | [sensenova/piccolo-large-zh-v2](https://huggingface.co/sensenova/piccolo-large-zh-v2) | Chinese | version2: finetuning with multi-task hybrid loss training | None | | [sensenova/piccolo-large-zh](https://huggingface.co/sensenova/piccolo-large-zh) | Chinese | version1: pretrain under 400 million chinese text pair | '查询'/'结果' | | [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh) | Chinese | version1: pretrain under 400 million chinese text pair | '查询'/'结果' | ## Citation If you find our tech report, models or code helpful, please cite our report or give a star on github or huggingface! ```bibtex @misc{2405.06932, Author = {Junqin Huang and Zhongjie Hu and Zihao Jing and Mengya Gao and Yichao Wu}, Title = {Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training}, Year = {2024}, Eprint = {arXiv:2405.06932}, } ```