--- tags: - mteb model-index: - name: piccolo-base-zh results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 49.16558217326158 - type: cos_sim_spearman value: 51.4049475858823 - type: euclidean_pearson value: 49.85853741070363 - type: euclidean_spearman value: 51.501428092542234 - type: manhattan_pearson value: 49.746099634926296 - type: manhattan_spearman value: 51.41081804320127 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 52.385361699031854 - type: cos_sim_spearman value: 52.59114913702212 - type: euclidean_pearson value: 54.994530439418355 - type: euclidean_spearman value: 52.54102886188004 - type: manhattan_pearson value: 54.9503071669608 - type: manhattan_spearman value: 52.51465652540901 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.236 - type: f1 value: 39.43040092463147 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 60.98952187211432 - type: cos_sim_spearman value: 62.68189713123115 - type: euclidean_pearson value: 61.089426749761344 - type: euclidean_spearman value: 62.41743375544581 - type: manhattan_pearson value: 61.14747216341409 - type: manhattan_spearman value: 62.488918956547046 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 38.36392300667918 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 35.645927581489175 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 85.25085782849087 - type: mrr value: 87.77154761904762 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 86.15357754080844 - type: mrr value: 88.53547619047617 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.683 - type: map_at_10 value: 35.522999999999996 - type: map_at_100 value: 37.456 - type: map_at_1000 value: 37.576 - type: map_at_3 value: 31.584 - type: map_at_5 value: 33.684999999999995 - type: mrr_at_1 value: 36.459 - type: mrr_at_10 value: 44.534 - type: mrr_at_100 value: 45.6 - type: mrr_at_1000 value: 45.647 - type: mrr_at_3 value: 42.186 - type: mrr_at_5 value: 43.482 - type: ndcg_at_1 value: 36.459 - type: ndcg_at_10 value: 42.025 - type: ndcg_at_100 value: 49.754 - type: ndcg_at_1000 value: 51.815999999999995 - type: ndcg_at_3 value: 37.056 - type: ndcg_at_5 value: 38.962 - type: precision_at_1 value: 36.459 - type: precision_at_10 value: 9.485000000000001 - type: precision_at_100 value: 1.567 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 21.13 - type: precision_at_5 value: 15.209 - type: recall_at_1 value: 23.683 - type: recall_at_10 value: 52.190999999999995 - type: recall_at_100 value: 84.491 - type: recall_at_1000 value: 98.19600000000001 - type: recall_at_3 value: 37.09 - type: recall_at_5 value: 43.262 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 74.20324714371618 - type: cos_sim_ap value: 82.32631646194994 - type: cos_sim_f1 value: 76.64052827073876 - type: cos_sim_precision value: 68.58725761772854 - type: cos_sim_recall value: 86.83656768763151 - type: dot_accuracy value: 70.33072760072159 - type: dot_ap value: 77.46972172609794 - type: dot_f1 value: 73.6668924804026 - type: dot_precision value: 62.84676354029062 - type: dot_recall value: 88.98760813654431 - type: euclidean_accuracy value: 74.78051713770296 - type: euclidean_ap value: 82.65778389584023 - type: euclidean_f1 value: 77.1843623157445 - type: euclidean_precision value: 71.05211406096362 - type: euclidean_recall value: 84.47509936871639 - type: manhattan_accuracy value: 74.76849067949489 - type: manhattan_ap value: 82.55694030572194 - type: manhattan_f1 value: 77.1776459569154 - type: manhattan_precision value: 69.5423855963991 - type: manhattan_recall value: 86.69628244096329 - type: max_accuracy value: 74.78051713770296 - type: max_ap value: 82.65778389584023 - type: max_f1 value: 77.1843623157445 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 72.99799999999999 - type: map_at_10 value: 81.271 - type: map_at_100 value: 81.53399999999999 - type: map_at_1000 value: 81.535 - type: map_at_3 value: 80.049 - type: map_at_5 value: 80.793 - type: mrr_at_1 value: 73.13 - type: mrr_at_10 value: 81.193 - type: mrr_at_100 value: 81.463 - type: mrr_at_1000 value: 81.464 - type: mrr_at_3 value: 80.067 - type: mrr_at_5 value: 80.741 - type: ndcg_at_1 value: 73.34 - type: ndcg_at_10 value: 84.503 - type: ndcg_at_100 value: 85.643 - type: ndcg_at_1000 value: 85.693 - type: ndcg_at_3 value: 82.135 - type: ndcg_at_5 value: 83.401 - type: precision_at_1 value: 73.34 - type: precision_at_10 value: 9.536 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 29.54 - type: precision_at_5 value: 18.398 - type: recall_at_1 value: 72.99799999999999 - type: recall_at_10 value: 94.31 - type: recall_at_100 value: 99.368 - type: recall_at_1000 value: 99.789 - type: recall_at_3 value: 87.935 - type: recall_at_5 value: 90.991 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.537 - type: map_at_10 value: 81.292 - type: map_at_100 value: 84.031 - type: map_at_1000 value: 84.066 - type: map_at_3 value: 56.571000000000005 - type: map_at_5 value: 71.082 - type: mrr_at_1 value: 91.2 - type: mrr_at_10 value: 93.893 - type: mrr_at_100 value: 93.955 - type: mrr_at_1000 value: 93.95700000000001 - type: mrr_at_3 value: 93.61699999999999 - type: mrr_at_5 value: 93.767 - type: ndcg_at_1 value: 91.2 - type: ndcg_at_10 value: 88.255 - type: ndcg_at_100 value: 90.813 - type: ndcg_at_1000 value: 91.144 - type: ndcg_at_3 value: 87.435 - type: ndcg_at_5 value: 85.961 - type: precision_at_1 value: 91.2 - type: precision_at_10 value: 42.14 - type: precision_at_100 value: 4.817 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 78.467 - type: precision_at_5 value: 65.75999999999999 - type: recall_at_1 value: 26.537 - type: recall_at_10 value: 89.262 - type: recall_at_100 value: 97.783 - type: recall_at_1000 value: 99.49799999999999 - type: recall_at_3 value: 58.573 - type: recall_at_5 value: 75.154 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 48.5 - type: map_at_10 value: 57.898 - type: map_at_100 value: 58.599000000000004 - type: map_at_1000 value: 58.616 - type: map_at_3 value: 55.1 - type: map_at_5 value: 56.80500000000001 - type: mrr_at_1 value: 48.5 - type: mrr_at_10 value: 57.898 - type: mrr_at_100 value: 58.599000000000004 - type: mrr_at_1000 value: 58.616 - type: mrr_at_3 value: 55.1 - type: mrr_at_5 value: 56.80500000000001 - type: ndcg_at_1 value: 48.5 - type: ndcg_at_10 value: 62.876 - type: ndcg_at_100 value: 66.00200000000001 - type: ndcg_at_1000 value: 66.467 - type: ndcg_at_3 value: 57.162 - type: ndcg_at_5 value: 60.263999999999996 - type: precision_at_1 value: 48.5 - type: precision_at_10 value: 7.870000000000001 - type: precision_at_100 value: 0.927 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 21.032999999999998 - type: precision_at_5 value: 14.14 - type: recall_at_1 value: 48.5 - type: recall_at_10 value: 78.7 - type: recall_at_100 value: 92.7 - type: recall_at_1000 value: 96.39999999999999 - type: recall_at_3 value: 63.1 - type: recall_at_5 value: 70.7 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 44.34782608695652 - type: f1 value: 36.401426200836205 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 84.25891181988743 - type: ap value: 50.54636280166089 - type: f1 value: 78.55080202541332 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 70.02878561337955 - type: cos_sim_spearman value: 75.39509553139982 - type: euclidean_pearson value: 73.92598696939956 - type: euclidean_spearman value: 75.5471147196853 - type: manhattan_pearson value: 73.88049486090739 - type: manhattan_spearman value: 75.51361990583285 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.739 - type: map_at_10 value: 74.039 - type: map_at_100 value: 74.38 - type: map_at_1000 value: 74.39099999999999 - type: map_at_3 value: 72.074 - type: map_at_5 value: 73.29299999999999 - type: mrr_at_1 value: 66.92 - type: mrr_at_10 value: 74.636 - type: mrr_at_100 value: 74.94 - type: mrr_at_1000 value: 74.95 - type: mrr_at_3 value: 72.911 - type: mrr_at_5 value: 73.981 - type: ndcg_at_1 value: 66.92 - type: ndcg_at_10 value: 77.924 - type: ndcg_at_100 value: 79.471 - type: ndcg_at_1000 value: 79.73400000000001 - type: ndcg_at_3 value: 74.17200000000001 - type: ndcg_at_5 value: 76.236 - type: precision_at_1 value: 66.92 - type: precision_at_10 value: 9.5 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 27.989000000000004 - type: precision_at_5 value: 17.874000000000002 - type: recall_at_1 value: 64.739 - type: recall_at_10 value: 89.324 - type: recall_at_100 value: 96.342 - type: recall_at_1000 value: 98.38900000000001 - type: recall_at_3 value: 79.378 - type: recall_at_5 value: 84.28099999999999 - 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: 68.97108271687962 - type: f1 value: 66.8625981386677 - 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: 73.32212508406187 - type: f1 value: 73.33875034670166 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 49.0 - type: map_at_10 value: 55.022999999999996 - type: map_at_100 value: 55.550999999999995 - type: map_at_1000 value: 55.608000000000004 - type: map_at_3 value: 53.417 - type: map_at_5 value: 54.372 - type: mrr_at_1 value: 49.3 - type: mrr_at_10 value: 55.176 - type: mrr_at_100 value: 55.703 - type: mrr_at_1000 value: 55.76 - type: mrr_at_3 value: 53.567 - type: mrr_at_5 value: 54.522000000000006 - type: ndcg_at_1 value: 49.0 - type: ndcg_at_10 value: 58.089999999999996 - type: ndcg_at_100 value: 60.988 - type: ndcg_at_1000 value: 62.580999999999996 - type: ndcg_at_3 value: 54.803000000000004 - type: ndcg_at_5 value: 56.508 - type: precision_at_1 value: 49.0 - type: precision_at_10 value: 6.78 - type: precision_at_100 value: 0.8210000000000001 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 19.6 - type: precision_at_5 value: 12.58 - type: recall_at_1 value: 49.0 - type: recall_at_10 value: 67.80000000000001 - type: recall_at_100 value: 82.1 - type: recall_at_1000 value: 94.8 - type: recall_at_3 value: 58.8 - type: recall_at_5 value: 62.9 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 28.87237408060796 - type: mrr value: 27.83015873015873 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 70.25 - type: f1 value: 70.29055400149645 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 65.56578234975636 - type: cos_sim_ap value: 70.89354058570412 - type: cos_sim_f1 value: 71.21024370095002 - type: cos_sim_precision value: 58.48032564450475 - type: cos_sim_recall value: 91.02428722280888 - type: dot_accuracy value: 64.86193827828912 - type: dot_ap value: 70.17697803463875 - type: dot_f1 value: 70.68676716917922 - type: dot_precision value: 58.57043719639139 - type: dot_recall value: 89.1235480464625 - type: euclidean_accuracy value: 64.86193827828912 - type: euclidean_ap value: 70.26847152773904 - type: euclidean_f1 value: 70.9984152139461 - type: euclidean_precision value: 56.81674064679771 - type: euclidean_recall value: 94.61457233368532 - type: manhattan_accuracy value: 65.40335679480238 - type: manhattan_ap value: 70.22941558736018 - type: manhattan_f1 value: 71.09712937475423 - type: manhattan_precision value: 56.64160401002506 - type: manhattan_recall value: 95.45934530095037 - type: max_accuracy value: 65.56578234975636 - type: max_ap value: 70.89354058570412 - type: max_f1 value: 71.21024370095002 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 89.92999999999999 - type: ap value: 87.16059195012956 - type: f1 value: 89.90917477839415 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 27.74161502387672 - type: cos_sim_spearman value: 31.58353529723325 - type: euclidean_pearson value: 32.43729673844635 - type: euclidean_spearman value: 31.59527486602242 - type: manhattan_pearson value: 32.37467059678786 - type: manhattan_spearman value: 31.44408004951894 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 36.233749845501194 - type: cos_sim_spearman value: 36.47808586229587 - type: euclidean_pearson value: 32.663447466546806 - type: euclidean_spearman value: 34.45830454037139 - type: manhattan_pearson value: 32.80239212096335 - type: manhattan_spearman value: 34.581060433895125 - 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: 63.05131937664673 - type: cos_sim_spearman value: 66.51353746725948 - type: euclidean_pearson value: 61.24016998745561 - type: euclidean_spearman value: 66.07115266049276 - type: manhattan_pearson value: 64.55660243659054 - type: manhattan_spearman value: 66.80282149562386 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 70.45533692882996 - type: cos_sim_spearman value: 70.6045637565602 - type: euclidean_pearson value: 72.75588977483554 - type: euclidean_spearman value: 73.36630581886473 - type: manhattan_pearson value: 72.72517409326954 - type: manhattan_spearman value: 73.35358940437355 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 66.45779474032288 - type: mrr value: 76.0782192023729 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.458 - type: map_at_10 value: 74.355 - type: map_at_100 value: 78.158 - type: map_at_1000 value: 78.233 - type: map_at_3 value: 52.2 - type: map_at_5 value: 64.14 - type: mrr_at_1 value: 88.37 - type: mrr_at_10 value: 91.117 - type: mrr_at_100 value: 91.231 - type: mrr_at_1000 value: 91.23599999999999 - type: mrr_at_3 value: 90.645 - type: mrr_at_5 value: 90.948 - type: ndcg_at_1 value: 88.37 - type: ndcg_at_10 value: 82.384 - type: ndcg_at_100 value: 86.431 - type: ndcg_at_1000 value: 87.163 - type: ndcg_at_3 value: 83.993 - type: ndcg_at_5 value: 82.411 - type: precision_at_1 value: 88.37 - type: precision_at_10 value: 41.131 - type: precision_at_100 value: 4.9799999999999995 - type: precision_at_1000 value: 0.515 - type: precision_at_3 value: 73.651 - type: precision_at_5 value: 61.634 - type: recall_at_1 value: 26.458 - type: recall_at_10 value: 81.3 - type: recall_at_100 value: 94.342 - type: recall_at_1000 value: 98.103 - type: recall_at_3 value: 54.020999999999994 - type: recall_at_5 value: 67.781 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 46.814 - type: f1 value: 45.580027683507666 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 61.43613064816144 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 53.01838461793776 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 59.3 - type: map_at_10 value: 69.158 - type: map_at_100 value: 69.60300000000001 - type: map_at_1000 value: 69.611 - type: map_at_3 value: 67.467 - type: map_at_5 value: 68.432 - type: mrr_at_1 value: 59.199999999999996 - type: mrr_at_10 value: 69.108 - type: mrr_at_100 value: 69.553 - type: mrr_at_1000 value: 69.56099999999999 - type: mrr_at_3 value: 67.417 - type: mrr_at_5 value: 68.382 - type: ndcg_at_1 value: 59.3 - type: ndcg_at_10 value: 73.54 - type: ndcg_at_100 value: 75.652 - type: ndcg_at_1000 value: 75.868 - type: ndcg_at_3 value: 70.074 - type: ndcg_at_5 value: 71.808 - type: precision_at_1 value: 59.3 - type: precision_at_10 value: 8.709999999999999 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 25.867 - type: precision_at_5 value: 16.36 - type: recall_at_1 value: 59.3 - type: recall_at_10 value: 87.1 - type: recall_at_100 value: 96.89999999999999 - type: recall_at_1000 value: 98.6 - type: recall_at_3 value: 77.60000000000001 - type: recall_at_5 value: 81.8 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 84.69999999999999 - type: ap value: 66.65020528563207 - type: f1 value: 83.00542769081453 --- ## piccolo-base-zh piccolo是一个通用embedding模型(中文), 由来自商汤科技的通用模型组完成训练。piccolo借鉴了E5以及GTE的训练流程，采用了两阶段的训练方式。 在第一阶段中，我们搜集和爬取了4亿的中文文本对(可视为弱监督文本对数据)，并采用二元组的softmax对比学习损失来优化模型。 在第二阶段中，我们从搜集整理了2000万人工标注的中文文本对(精标数据)，并采用带有难负样本的三元组的softmax对比学习损失来帮助模型更好地优化。 目前，我们提供了piccolo-base-zh和piccolo-large-zh两个模型。 piccolo is a general text embedding model(chinese), powered by General Model Group from SenseTime Research. Inspired from E5 and GTE, piccolo is trained using a two stage pipeline. On the first stage, we collect and crawl 400 million weakly supervised Chinese text pairs from the Internet, and train the model with the pair(text and text pos) softmax contrastive loss. On the second stage, we collect 20 million human labeled chinese text pairs dataset, and finetune the model with tiplet (text, text_pos, text_neg) contrastive loss. Currently here we offer two different sizes of models, including piccolo-base-zh, piccolo-large-zh. ## Metric 我们将piccolo与其他的开源embedding模型在CMTEB榜单上进行了比较，请参考CMTEB榜单。我们在eval文件夹中提供了复现结果的脚本。 We compared the performance of the piccolo with other embedding models on the C-MTEB benchmark. please refer to the C-MTEB leaderboard. we provide scripts in "eval" folder for results reproducing. | Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**piccolo-large-zh**] | 0.65 | 1024 | 512 | **64.11** | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | [bge-large-zh]| 1.3 | 1024| 512 | 63.96 | 68.32 | 48.39 | 78.94 | 65.11 | 71.52 | 54.98 | | [**piccolo-base-zh**]| 0.2 | 768 | 512 | **63.66** | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | [bge-large-zh-no-instruct]| 1.3 | 1024 | 512 | 63.4 | 68.58 | 50.01 | 76.77 | 64.9 | 70.54 | 53 | | [bge-base-zh]| 0.41 | 768 | 512 | 62.8 | 67.07 | 47.64 | 77.5 | 64.91 | 69.53 | 54.12 | ## Usage 在sentence-transformer package中可以很容易地调用piccolo模型 ```python # for s2s dataset, you can use piccolo as below # 对于短对短数据集，下面是通用的使用方式 from sentence_transformers import SentenceTransformer sentences = ["数据1", "数据2"] model = SentenceTransformer('sensenova/piccolo-base-zh') embeddings_1 = model.encode(sentences, normalize_embeddings=True) embeddings_2 = model.encode(sentences, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p dataset, we recommend to add instruction for passage retrieval # 对于短对长数据集，我们推荐添加instruction，来帮助模型更好地进行检索。 from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["doc_1", "doc_2"] model = SentenceTransformer('sensenova/piccolo-base-zh') q_embeddings = model.encode(["查询：" + q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(["结果：" + p for p in passages], normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` ## Training Detail TODO ## acknowledgement piccolo is powered by Genral Model group from SenseTime Research. [Jinkin](https://huggingface.co/Jinkin) complete code implementation and model training. [Jinkin](https://huggingface.co/Jinkin), [CCCCxxx](https://huggingface.co/CCCCxxx) completed the data collection、processing and model evaluation together. Project is led by [Gaomengya](https://huggingface.co/gaomengya) and [chaorenwu111](https://huggingface.co/chaorenwu111)