--- tags: - mteb - llama-cpp - gguf-my-repo license: cc-by-nc-4.0 library_name: sentence-transformers base_model: TencentBAC/Conan-embedding-v1 model-index: - name: conan-embedding results: - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 56.613572467148856 - type: cos_sim_spearman value: 60.66446211824284 - type: euclidean_pearson value: 58.42080485872613 - type: euclidean_spearman value: 59.82750030458164 - type: manhattan_pearson value: 58.39885271199772 - type: manhattan_spearman value: 59.817749720366734 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 56.60530380552331 - type: cos_sim_spearman value: 58.63822441736707 - type: euclidean_pearson value: 62.18551665180664 - type: euclidean_spearman value: 58.23168804495912 - type: manhattan_pearson value: 62.17191480770053 - type: manhattan_spearman value: 58.22556219601401 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 50.308 - type: f1 value: 46.927458607895126 - task: type: STS dataset: name: MTEB BQ type: C-MTEB/BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 72.6472074172711 - type: cos_sim_spearman value: 74.50748447236577 - type: euclidean_pearson value: 72.51833296451854 - type: euclidean_spearman value: 73.9898922606105 - type: manhattan_pearson value: 72.50184948939338 - type: manhattan_spearman value: 73.97797921509638 - task: type: Clustering dataset: name: MTEB CLSClusteringP2P type: C-MTEB/CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 60.63545326048343 - task: type: Clustering dataset: name: MTEB CLSClusteringS2S type: C-MTEB/CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 52.64834762325994 - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: None metrics: - type: map value: 91.38528814655234 - type: mrr value: 93.35857142857144 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: None metrics: - type: map value: 89.72084678877096 - type: mrr value: 91.74380952380953 - task: type: Retrieval dataset: name: MTEB CmedqaRetrieval type: C-MTEB/CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.987 - type: map_at_10 value: 40.675 - type: map_at_100 value: 42.495 - type: map_at_1000 value: 42.596000000000004 - type: map_at_3 value: 36.195 - type: map_at_5 value: 38.704 - type: mrr_at_1 value: 41.21 - type: mrr_at_10 value: 49.816 - type: mrr_at_100 value: 50.743 - type: mrr_at_1000 value: 50.77700000000001 - type: mrr_at_3 value: 47.312 - type: mrr_at_5 value: 48.699999999999996 - type: ndcg_at_1 value: 41.21 - type: ndcg_at_10 value: 47.606 - type: ndcg_at_100 value: 54.457 - type: ndcg_at_1000 value: 56.16100000000001 - type: ndcg_at_3 value: 42.108000000000004 - type: ndcg_at_5 value: 44.393 - type: precision_at_1 value: 41.21 - type: precision_at_10 value: 10.593 - type: precision_at_100 value: 1.609 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 23.881 - type: precision_at_5 value: 17.339 - type: recall_at_1 value: 26.987 - type: recall_at_10 value: 58.875 - type: recall_at_100 value: 87.023 - type: recall_at_1000 value: 98.328 - type: recall_at_3 value: 42.265 - type: recall_at_5 value: 49.334 - task: type: PairClassification dataset: name: MTEB Cmnli type: C-MTEB/CMNLI config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 85.91701743836441 - type: cos_sim_ap value: 92.53650618807644 - type: cos_sim_f1 value: 86.80265975431082 - type: cos_sim_precision value: 83.79025239338556 - type: cos_sim_recall value: 90.039747486556 - type: dot_accuracy value: 77.17378232110643 - type: dot_ap value: 85.40244368166546 - type: dot_f1 value: 79.03038001481951 - type: dot_precision value: 72.20502901353966 - type: dot_recall value: 87.2808043020809 - type: euclidean_accuracy value: 84.65423932651834 - type: euclidean_ap value: 91.47775530034588 - type: euclidean_f1 value: 85.64471499723298 - type: euclidean_precision value: 81.31567885666246 - type: euclidean_recall value: 90.46060322656068 - type: manhattan_accuracy value: 84.58208057726999 - type: manhattan_ap value: 91.46228709402014 - type: manhattan_f1 value: 85.6631626034444 - type: manhattan_precision value: 82.10075026795283 - type: manhattan_recall value: 89.5487491232172 - type: max_accuracy value: 85.91701743836441 - type: max_ap value: 92.53650618807644 - type: max_f1 value: 86.80265975431082 - task: type: Retrieval dataset: name: MTEB CovidRetrieval type: C-MTEB/CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 83.693 - type: map_at_10 value: 90.098 - type: map_at_100 value: 90.145 - type: map_at_1000 value: 90.146 - type: map_at_3 value: 89.445 - type: map_at_5 value: 89.935 - type: mrr_at_1 value: 83.878 - type: mrr_at_10 value: 90.007 - type: mrr_at_100 value: 90.045 - type: mrr_at_1000 value: 90.046 - type: mrr_at_3 value: 89.34 - type: mrr_at_5 value: 89.835 - type: ndcg_at_1 value: 84.089 - type: ndcg_at_10 value: 92.351 - type: ndcg_at_100 value: 92.54599999999999 - type: ndcg_at_1000 value: 92.561 - type: ndcg_at_3 value: 91.15299999999999 - type: ndcg_at_5 value: 91.968 - type: precision_at_1 value: 84.089 - type: precision_at_10 value: 10.011000000000001 - type: precision_at_100 value: 1.009 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 32.28 - type: precision_at_5 value: 19.789 - type: recall_at_1 value: 83.693 - type: recall_at_10 value: 99.05199999999999 - type: recall_at_100 value: 99.895 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 95.917 - type: recall_at_5 value: 97.893 - task: type: Retrieval dataset: name: MTEB DuRetrieval type: C-MTEB/DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.924 - type: map_at_10 value: 81.392 - type: map_at_100 value: 84.209 - type: map_at_1000 value: 84.237 - type: map_at_3 value: 56.998000000000005 - type: map_at_5 value: 71.40100000000001 - type: mrr_at_1 value: 91.75 - type: mrr_at_10 value: 94.45 - type: mrr_at_100 value: 94.503 - type: mrr_at_1000 value: 94.505 - type: mrr_at_3 value: 94.258 - type: mrr_at_5 value: 94.381 - type: ndcg_at_1 value: 91.75 - type: ndcg_at_10 value: 88.53 - type: ndcg_at_100 value: 91.13900000000001 - type: ndcg_at_1000 value: 91.387 - type: ndcg_at_3 value: 87.925 - type: ndcg_at_5 value: 86.461 - type: precision_at_1 value: 91.75 - type: precision_at_10 value: 42.05 - type: precision_at_100 value: 4.827 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 78.55 - type: precision_at_5 value: 65.82000000000001 - type: recall_at_1 value: 26.924 - type: recall_at_10 value: 89.338 - type: recall_at_100 value: 97.856 - type: recall_at_1000 value: 99.11 - type: recall_at_3 value: 59.202999999999996 - type: recall_at_5 value: 75.642 - task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.800000000000004 - type: map_at_10 value: 65.613 - type: map_at_100 value: 66.185 - type: map_at_1000 value: 66.191 - type: map_at_3 value: 62.8 - type: map_at_5 value: 64.535 - type: mrr_at_1 value: 54.800000000000004 - type: mrr_at_10 value: 65.613 - type: mrr_at_100 value: 66.185 - type: mrr_at_1000 value: 66.191 - type: mrr_at_3 value: 62.8 - type: mrr_at_5 value: 64.535 - type: ndcg_at_1 value: 54.800000000000004 - type: ndcg_at_10 value: 70.991 - type: ndcg_at_100 value: 73.434 - type: ndcg_at_1000 value: 73.587 - type: ndcg_at_3 value: 65.324 - type: ndcg_at_5 value: 68.431 - type: precision_at_1 value: 54.800000000000004 - type: precision_at_10 value: 8.790000000000001 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 24.2 - type: precision_at_5 value: 16.02 - type: recall_at_1 value: 54.800000000000004 - type: recall_at_10 value: 87.9 - type: recall_at_100 value: 98.6 - type: recall_at_1000 value: 99.8 - type: recall_at_3 value: 72.6 - type: recall_at_5 value: 80.10000000000001 - task: type: Classification dataset: name: MTEB IFlyTek type: C-MTEB/IFlyTek-classification config: default split: validation revision: None metrics: - type: accuracy value: 51.94305502116199 - type: f1 value: 39.82197338426721 - task: type: Classification dataset: name: MTEB JDReview type: C-MTEB/JDReview-classification config: default split: test revision: None metrics: - type: accuracy value: 90.31894934333957 - type: ap value: 63.89821836499594 - type: f1 value: 85.93687177603624 - task: type: STS dataset: name: MTEB LCQMC type: C-MTEB/LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 73.18906216730208 - type: cos_sim_spearman value: 79.44570226735877 - type: euclidean_pearson value: 78.8105072242798 - type: euclidean_spearman value: 79.15605680863212 - type: manhattan_pearson value: 78.80576507484064 - type: manhattan_spearman value: 79.14625534068364 - task: type: Reranking dataset: name: MTEB MMarcoReranking type: C-MTEB/Mmarco-reranking config: default split: dev revision: None metrics: - type: map value: 41.58107192600853 - type: mrr value: 41.37063492063492 - task: type: Retrieval dataset: name: MTEB MMarcoRetrieval type: C-MTEB/MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 68.33 - type: map_at_10 value: 78.261 - type: map_at_100 value: 78.522 - type: map_at_1000 value: 78.527 - type: map_at_3 value: 76.236 - type: map_at_5 value: 77.557 - type: mrr_at_1 value: 70.602 - type: mrr_at_10 value: 78.779 - type: mrr_at_100 value: 79.00500000000001 - type: mrr_at_1000 value: 79.01 - type: mrr_at_3 value: 77.037 - type: mrr_at_5 value: 78.157 - type: ndcg_at_1 value: 70.602 - type: ndcg_at_10 value: 82.254 - type: ndcg_at_100 value: 83.319 - type: ndcg_at_1000 value: 83.449 - type: ndcg_at_3 value: 78.46 - type: ndcg_at_5 value: 80.679 - type: precision_at_1 value: 70.602 - type: precision_at_10 value: 9.989 - type: precision_at_100 value: 1.05 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.598999999999997 - type: precision_at_5 value: 18.948 - type: recall_at_1 value: 68.33 - type: recall_at_10 value: 94.00800000000001 - type: recall_at_100 value: 98.589 - type: recall_at_1000 value: 99.60799999999999 - type: recall_at_3 value: 84.057 - type: recall_at_5 value: 89.32900000000001 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (zh-CN) type: mteb/amazon_massive_intent config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 78.13718897108272 - type: f1 value: 74.07613180855328 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (zh-CN) type: mteb/amazon_massive_scenario config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 86.20040349697376 - type: f1 value: 85.05282136519973 - task: type: Retrieval dataset: name: MTEB MedicalRetrieval type: C-MTEB/MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 56.8 - type: map_at_10 value: 64.199 - type: map_at_100 value: 64.89 - type: map_at_1000 value: 64.917 - type: map_at_3 value: 62.383 - type: map_at_5 value: 63.378 - type: mrr_at_1 value: 56.8 - type: mrr_at_10 value: 64.199 - type: mrr_at_100 value: 64.89 - type: mrr_at_1000 value: 64.917 - type: mrr_at_3 value: 62.383 - type: mrr_at_5 value: 63.378 - type: ndcg_at_1 value: 56.8 - type: ndcg_at_10 value: 67.944 - type: ndcg_at_100 value: 71.286 - type: ndcg_at_1000 value: 71.879 - type: ndcg_at_3 value: 64.163 - type: ndcg_at_5 value: 65.96600000000001 - type: precision_at_1 value: 56.8 - type: precision_at_10 value: 7.9799999999999995 - type: precision_at_100 value: 0.954 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.1 - type: precision_at_5 value: 14.74 - type: recall_at_1 value: 56.8 - type: recall_at_10 value: 79.80000000000001 - type: recall_at_100 value: 95.39999999999999 - type: recall_at_1000 value: 99.8 - type: recall_at_3 value: 69.3 - type: recall_at_5 value: 73.7 - task: type: Classification dataset: name: MTEB MultilingualSentiment type: C-MTEB/MultilingualSentiment-classification config: default split: validation revision: None metrics: - type: accuracy value: 78.57666666666667 - type: f1 value: 78.23373528202681 - task: type: PairClassification dataset: name: MTEB Ocnli type: C-MTEB/OCNLI config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 85.43584190579317 - type: cos_sim_ap value: 90.76665640338129 - type: cos_sim_f1 value: 86.5021770682148 - type: cos_sim_precision value: 79.82142857142858 - type: cos_sim_recall value: 94.40337909186906 - type: dot_accuracy value: 78.66811044937737 - type: dot_ap value: 85.84084363880804 - type: dot_f1 value: 80.10075566750629 - type: dot_precision value: 76.58959537572254 - type: dot_recall value: 83.9493136219641 - type: euclidean_accuracy value: 84.46128857606931 - type: euclidean_ap value: 88.62351100230491 - type: euclidean_f1 value: 85.7709469509172 - type: euclidean_precision value: 80.8411214953271 - type: euclidean_recall value: 91.34107708553326 - type: manhattan_accuracy value: 84.51543042772063 - type: manhattan_ap value: 88.53975607870393 - type: manhattan_f1 value: 85.75697211155378 - type: manhattan_precision value: 81.14985862393968 - type: manhattan_recall value: 90.91869060190075 - type: max_accuracy value: 85.43584190579317 - type: max_ap value: 90.76665640338129 - type: max_f1 value: 86.5021770682148 - task: type: Classification dataset: name: MTEB OnlineShopping type: C-MTEB/OnlineShopping-classification config: default split: test revision: None metrics: - type: accuracy value: 95.06999999999998 - type: ap value: 93.45104559324996 - type: f1 value: 95.06036329426092 - task: type: STS dataset: name: MTEB PAWSX type: C-MTEB/PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 40.01998290519605 - type: cos_sim_spearman value: 46.5989769986853 - type: euclidean_pearson value: 45.37905883182924 - type: euclidean_spearman value: 46.22213849806378 - type: manhattan_pearson value: 45.40925124776211 - type: manhattan_spearman value: 46.250705124226386 - task: type: STS dataset: name: MTEB QBQTC type: C-MTEB/QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 42.719516197112526 - type: cos_sim_spearman value: 44.57507789581106 - type: euclidean_pearson value: 35.73062264160721 - type: euclidean_spearman value: 40.473523909913695 - type: manhattan_pearson value: 35.69868964086357 - type: manhattan_spearman value: 40.46349925372903 - task: type: STS dataset: name: MTEB STS22 (zh) type: mteb/sts22-crosslingual-sts config: zh split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 62.340118285801104 - type: cos_sim_spearman value: 67.72781908620632 - type: euclidean_pearson value: 63.161965746091596 - type: euclidean_spearman value: 67.36825684340769 - type: manhattan_pearson value: 63.089863788261425 - type: manhattan_spearman value: 67.40868898995384 - task: type: STS dataset: name: MTEB STSB type: C-MTEB/STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 79.1646360962365 - type: cos_sim_spearman value: 81.24426700767087 - type: euclidean_pearson value: 79.43826409936123 - type: euclidean_spearman value: 79.71787965300125 - type: manhattan_pearson value: 79.43377784961737 - type: manhattan_spearman value: 79.69348376886967 - task: type: Reranking dataset: name: MTEB T2Reranking type: C-MTEB/T2Reranking config: default split: dev revision: None metrics: - type: map value: 68.35595092507496 - type: mrr value: 79.00244892585788 - task: type: Retrieval dataset: name: MTEB T2Retrieval type: C-MTEB/T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.588 - type: map_at_10 value: 75.327 - type: map_at_100 value: 79.095 - type: map_at_1000 value: 79.163 - type: map_at_3 value: 52.637 - type: map_at_5 value: 64.802 - type: mrr_at_1 value: 88.103 - type: mrr_at_10 value: 91.29899999999999 - type: mrr_at_100 value: 91.408 - type: mrr_at_1000 value: 91.411 - type: mrr_at_3 value: 90.801 - type: mrr_at_5 value: 91.12700000000001 - type: ndcg_at_1 value: 88.103 - type: ndcg_at_10 value: 83.314 - type: ndcg_at_100 value: 87.201 - type: ndcg_at_1000 value: 87.83999999999999 - type: ndcg_at_3 value: 84.408 - type: ndcg_at_5 value: 83.078 - type: precision_at_1 value: 88.103 - type: precision_at_10 value: 41.638999999999996 - type: precision_at_100 value: 5.006 - type: precision_at_1000 value: 0.516 - type: precision_at_3 value: 73.942 - type: precision_at_5 value: 62.056 - type: recall_at_1 value: 26.588 - type: recall_at_10 value: 82.819 - type: recall_at_100 value: 95.334 - type: recall_at_1000 value: 98.51299999999999 - type: recall_at_3 value: 54.74 - type: recall_at_5 value: 68.864 - task: type: Classification dataset: name: MTEB TNews type: C-MTEB/TNews-classification config: default split: validation revision: None metrics: - type: accuracy value: 55.029 - type: f1 value: 53.043617905026764 - task: type: Clustering dataset: name: MTEB ThuNewsClusteringP2P type: C-MTEB/ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 77.83675116835911 - task: type: Clustering dataset: name: MTEB ThuNewsClusteringS2S type: C-MTEB/ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 74.19701455865277 - task: type: Retrieval dataset: name: MTEB VideoRetrieval type: C-MTEB/VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 64.7 - type: map_at_10 value: 75.593 - type: map_at_100 value: 75.863 - type: map_at_1000 value: 75.863 - type: map_at_3 value: 73.63300000000001 - type: map_at_5 value: 74.923 - type: mrr_at_1 value: 64.7 - type: mrr_at_10 value: 75.593 - type: mrr_at_100 value: 75.863 - type: mrr_at_1000 value: 75.863 - type: mrr_at_3 value: 73.63300000000001 - type: mrr_at_5 value: 74.923 - type: ndcg_at_1 value: 64.7 - type: ndcg_at_10 value: 80.399 - type: ndcg_at_100 value: 81.517 - type: ndcg_at_1000 value: 81.517 - type: ndcg_at_3 value: 76.504 - type: ndcg_at_5 value: 78.79899999999999 - type: precision_at_1 value: 64.7 - type: precision_at_10 value: 9.520000000000001 - type: precision_at_100 value: 1 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 28.266999999999996 - type: precision_at_5 value: 18.060000000000002 - type: recall_at_1 value: 64.7 - type: recall_at_10 value: 95.19999999999999 - type: recall_at_100 value: 100 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 84.8 - type: recall_at_5 value: 90.3 - task: type: Classification dataset: name: MTEB Waimai type: C-MTEB/waimai-classification config: default split: test revision: None metrics: - type: accuracy value: 89.69999999999999 - type: ap value: 75.91371640164184 - type: f1 value: 88.34067777698694 --- # lagoon999/Conan-embedding-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`TencentBAC/Conan-embedding-v1`](https://huggingface.co/TencentBAC/Conan-embedding-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/TencentBAC/Conan-embedding-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo lagoon999/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo lagoon999/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo lagoon999/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo lagoon999/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -c 2048 ```