--- tags: - mteb - sentence-similarity - sentence-transformers - Sentence Transformers model-index: - name: gte-small-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: 35.80906032378281 - type: cos_sim_spearman value: 36.688967176174415 - type: euclidean_pearson value: 35.70701955438158 - type: euclidean_spearman value: 36.6889470691436 - type: manhattan_pearson value: 35.832741768286944 - type: manhattan_spearman value: 36.831888591957195 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 44.667266488330384 - type: cos_sim_spearman value: 45.77390794946174 - type: euclidean_pearson value: 48.14272832901943 - type: euclidean_spearman value: 45.77390569666109 - type: manhattan_pearson value: 48.187667158563094 - type: manhattan_spearman value: 45.80979161966117 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.690000000000005 - type: f1 value: 36.868257131984016 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 49.03674224607541 - type: cos_sim_spearman value: 49.63568854885055 - type: euclidean_pearson value: 49.47441886441355 - type: euclidean_spearman value: 49.63567815431205 - type: manhattan_pearson value: 49.76480072909559 - type: manhattan_spearman value: 49.977789367288224 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 39.538126779019755 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 37.333105487031766 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: None metrics: - type: map value: 86.08142426347963 - type: mrr value: 88.04269841269841 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: None metrics: - type: map value: 87.25694119382474 - type: mrr value: 89.36853174603175 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 23.913999999999998 - type: map_at_10 value: 35.913000000000004 - type: map_at_100 value: 37.836 - type: map_at_1000 value: 37.952000000000005 - type: map_at_3 value: 31.845000000000002 - type: map_at_5 value: 34.0 - type: mrr_at_1 value: 36.884 - type: mrr_at_10 value: 44.872 - type: mrr_at_100 value: 45.899 - type: mrr_at_1000 value: 45.945 - type: mrr_at_3 value: 42.331 - type: mrr_at_5 value: 43.674 - type: ndcg_at_1 value: 36.884 - type: ndcg_at_10 value: 42.459 - type: ndcg_at_100 value: 50.046 - type: ndcg_at_1000 value: 52.092000000000006 - type: ndcg_at_3 value: 37.225 - type: ndcg_at_5 value: 39.2 - type: precision_at_1 value: 36.884 - type: precision_at_10 value: 9.562 - type: precision_at_100 value: 1.572 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 21.122 - type: precision_at_5 value: 15.274 - type: recall_at_1 value: 23.913999999999998 - type: recall_at_10 value: 52.891999999999996 - type: recall_at_100 value: 84.328 - type: recall_at_1000 value: 98.168 - type: recall_at_3 value: 37.095 - type: recall_at_5 value: 43.396 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 68.91160553217077 - type: cos_sim_ap value: 76.45769658379533 - type: cos_sim_f1 value: 72.07988702844463 - type: cos_sim_precision value: 63.384779137839274 - type: cos_sim_recall value: 83.53986439092822 - type: dot_accuracy value: 68.91160553217077 - type: dot_ap value: 76.47279917239219 - type: dot_f1 value: 72.07988702844463 - type: dot_precision value: 63.384779137839274 - type: dot_recall value: 83.53986439092822 - type: euclidean_accuracy value: 68.91160553217077 - type: euclidean_ap value: 76.45768544225383 - type: euclidean_f1 value: 72.07988702844463 - type: euclidean_precision value: 63.384779137839274 - type: euclidean_recall value: 83.53986439092822 - type: manhattan_accuracy value: 69.21226698737222 - type: manhattan_ap value: 76.6623683693766 - type: manhattan_f1 value: 72.14058164628506 - type: manhattan_precision value: 64.35643564356435 - type: manhattan_recall value: 82.06686930091185 - type: max_accuracy value: 69.21226698737222 - type: max_ap value: 76.6623683693766 - type: max_f1 value: 72.14058164628506 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 48.419000000000004 - type: map_at_10 value: 57.367999999999995 - type: map_at_100 value: 58.081 - type: map_at_1000 value: 58.108000000000004 - type: map_at_3 value: 55.251 - type: map_at_5 value: 56.53399999999999 - type: mrr_at_1 value: 48.472 - type: mrr_at_10 value: 57.359 - type: mrr_at_100 value: 58.055 - type: mrr_at_1000 value: 58.082 - type: mrr_at_3 value: 55.303999999999995 - type: mrr_at_5 value: 56.542 - type: ndcg_at_1 value: 48.472 - type: ndcg_at_10 value: 61.651999999999994 - type: ndcg_at_100 value: 65.257 - type: ndcg_at_1000 value: 65.977 - type: ndcg_at_3 value: 57.401 - type: ndcg_at_5 value: 59.681 - type: precision_at_1 value: 48.472 - type: precision_at_10 value: 7.576 - type: precision_at_100 value: 0.932 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 21.25 - type: precision_at_5 value: 13.888 - type: recall_at_1 value: 48.419000000000004 - type: recall_at_10 value: 74.97399999999999 - type: recall_at_100 value: 92.202 - type: recall_at_1000 value: 97.893 - type: recall_at_3 value: 63.541000000000004 - type: recall_at_5 value: 68.994 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 22.328 - type: map_at_10 value: 69.11 - type: map_at_100 value: 72.47 - type: map_at_1000 value: 72.54599999999999 - type: map_at_3 value: 46.938 - type: map_at_5 value: 59.56 - type: mrr_at_1 value: 81.35 - type: mrr_at_10 value: 87.066 - type: mrr_at_100 value: 87.212 - type: mrr_at_1000 value: 87.21799999999999 - type: mrr_at_3 value: 86.558 - type: mrr_at_5 value: 86.931 - type: ndcg_at_1 value: 81.35 - type: ndcg_at_10 value: 78.568 - type: ndcg_at_100 value: 82.86099999999999 - type: ndcg_at_1000 value: 83.628 - type: ndcg_at_3 value: 76.716 - type: ndcg_at_5 value: 75.664 - type: precision_at_1 value: 81.35 - type: precision_at_10 value: 38.545 - type: precision_at_100 value: 4.657 - type: precision_at_1000 value: 0.484 - type: precision_at_3 value: 69.18299999999999 - type: precision_at_5 value: 58.67 - type: recall_at_1 value: 22.328 - type: recall_at_10 value: 80.658 - type: recall_at_100 value: 94.093 - type: recall_at_1000 value: 98.137 - type: recall_at_3 value: 50.260000000000005 - type: recall_at_5 value: 66.045 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 43.1 - type: map_at_10 value: 52.872 - type: map_at_100 value: 53.556000000000004 - type: map_at_1000 value: 53.583000000000006 - type: map_at_3 value: 50.14999999999999 - type: map_at_5 value: 51.925 - type: mrr_at_1 value: 43.1 - type: mrr_at_10 value: 52.872 - type: mrr_at_100 value: 53.556000000000004 - type: mrr_at_1000 value: 53.583000000000006 - type: mrr_at_3 value: 50.14999999999999 - type: mrr_at_5 value: 51.925 - type: ndcg_at_1 value: 43.1 - type: ndcg_at_10 value: 57.907 - type: ndcg_at_100 value: 61.517999999999994 - type: ndcg_at_1000 value: 62.175000000000004 - type: ndcg_at_3 value: 52.425 - type: ndcg_at_5 value: 55.631 - type: precision_at_1 value: 43.1 - type: precision_at_10 value: 7.380000000000001 - type: precision_at_100 value: 0.9129999999999999 - type: precision_at_1000 value: 0.096 - type: precision_at_3 value: 19.667 - type: precision_at_5 value: 13.36 - type: recall_at_1 value: 43.1 - type: recall_at_10 value: 73.8 - type: recall_at_100 value: 91.3 - type: recall_at_1000 value: 96.39999999999999 - type: recall_at_3 value: 59.0 - type: recall_at_5 value: 66.8 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: None metrics: - type: accuracy value: 41.146594844170835 - type: f1 value: 28.544218732704845 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: None metrics: - type: accuracy value: 82.83302063789868 - type: ap value: 48.881798834997056 - type: f1 value: 77.28655923994657 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 66.05467125345538 - type: cos_sim_spearman value: 72.71921060562211 - type: euclidean_pearson value: 71.28539457113986 - type: euclidean_spearman value: 72.71920173126693 - type: manhattan_pearson value: 71.23750818174456 - type: manhattan_spearman value: 72.61025268693467 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics: - type: map value: 26.127712982639483 - type: mrr value: 24.87420634920635 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 62.517 - type: map_at_10 value: 71.251 - type: map_at_100 value: 71.647 - type: map_at_1000 value: 71.665 - type: map_at_3 value: 69.28 - type: map_at_5 value: 70.489 - type: mrr_at_1 value: 64.613 - type: mrr_at_10 value: 71.89 - type: mrr_at_100 value: 72.243 - type: mrr_at_1000 value: 72.259 - type: mrr_at_3 value: 70.138 - type: mrr_at_5 value: 71.232 - type: ndcg_at_1 value: 64.613 - type: ndcg_at_10 value: 75.005 - type: ndcg_at_100 value: 76.805 - type: ndcg_at_1000 value: 77.281 - type: ndcg_at_3 value: 71.234 - type: ndcg_at_5 value: 73.294 - type: precision_at_1 value: 64.613 - type: precision_at_10 value: 9.142 - type: precision_at_100 value: 1.004 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 26.781 - type: precision_at_5 value: 17.149 - type: recall_at_1 value: 62.517 - type: recall_at_10 value: 85.997 - type: recall_at_100 value: 94.18299999999999 - type: recall_at_1000 value: 97.911 - type: recall_at_3 value: 75.993 - type: recall_at_5 value: 80.88300000000001 - 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: 59.27706792199058 - type: f1 value: 56.77545011902468 - 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: 66.47948890383321 - type: f1 value: 66.4502180376861 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 54.2 - type: map_at_10 value: 59.858 - type: map_at_100 value: 60.46 - type: map_at_1000 value: 60.507 - type: map_at_3 value: 58.416999999999994 - type: map_at_5 value: 59.331999999999994 - type: mrr_at_1 value: 54.2 - type: mrr_at_10 value: 59.862 - type: mrr_at_100 value: 60.463 - type: mrr_at_1000 value: 60.51 - type: mrr_at_3 value: 58.416999999999994 - type: mrr_at_5 value: 59.352000000000004 - type: ndcg_at_1 value: 54.2 - type: ndcg_at_10 value: 62.643 - type: ndcg_at_100 value: 65.731 - type: ndcg_at_1000 value: 67.096 - type: ndcg_at_3 value: 59.727 - type: ndcg_at_5 value: 61.375 - type: precision_at_1 value: 54.2 - type: precision_at_10 value: 7.140000000000001 - type: precision_at_100 value: 0.8619999999999999 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.166999999999998 - type: precision_at_5 value: 13.5 - type: recall_at_1 value: 54.2 - type: recall_at_10 value: 71.39999999999999 - type: recall_at_100 value: 86.2 - type: recall_at_1000 value: 97.2 - type: recall_at_3 value: 63.5 - type: recall_at_5 value: 67.5 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: None metrics: - type: accuracy value: 68.19666666666666 - type: f1 value: 67.58581661416034 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 60.530590146182995 - type: cos_sim_ap value: 63.53656091243922 - type: cos_sim_f1 value: 68.09929603556874 - type: cos_sim_precision value: 52.45433789954338 - type: cos_sim_recall value: 97.04329461457233 - type: dot_accuracy value: 60.530590146182995 - type: dot_ap value: 63.53660452157237 - type: dot_f1 value: 68.09929603556874 - type: dot_precision value: 52.45433789954338 - type: dot_recall value: 97.04329461457233 - type: euclidean_accuracy value: 60.530590146182995 - type: euclidean_ap value: 63.53678735855631 - type: euclidean_f1 value: 68.09929603556874 - type: euclidean_precision value: 52.45433789954338 - type: euclidean_recall value: 97.04329461457233 - type: manhattan_accuracy value: 60.47644829453167 - type: manhattan_ap value: 63.5622508250315 - type: manhattan_f1 value: 68.1650700073692 - type: manhattan_precision value: 52.34861346915677 - type: manhattan_recall value: 97.67687434002113 - type: max_accuracy value: 60.530590146182995 - type: max_ap value: 63.5622508250315 - type: max_f1 value: 68.1650700073692 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: None metrics: - type: accuracy value: 89.13 - type: ap value: 87.21879260137172 - type: f1 value: 89.12359325300508 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 12.035577637900758 - type: cos_sim_spearman value: 12.76524190663864 - type: euclidean_pearson value: 14.4012689427106 - type: euclidean_spearman value: 12.765328992583608 - type: manhattan_pearson value: 14.458505202938946 - type: manhattan_spearman value: 12.763238700117896 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 34.809415339934006 - type: cos_sim_spearman value: 36.96728615916954 - type: euclidean_pearson value: 35.56113673772396 - type: euclidean_spearman value: 36.96842963389308 - type: manhattan_pearson value: 35.5447066178264 - type: manhattan_spearman value: 36.97514513480951 - 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: 66.39448692338551 - type: cos_sim_spearman value: 66.72211526923901 - type: euclidean_pearson value: 65.72981824553035 - type: euclidean_spearman value: 66.72211526923901 - type: manhattan_pearson value: 65.52315559414296 - type: manhattan_spearman value: 66.61931702511545 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: None metrics: - type: cos_sim_pearson value: 76.73608064460915 - type: cos_sim_spearman value: 76.51424826130031 - type: euclidean_pearson value: 76.17930213372487 - type: euclidean_spearman value: 76.51342756283478 - type: manhattan_pearson value: 75.87085607319342 - type: manhattan_spearman value: 76.22676341477134 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: None metrics: - type: map value: 65.38779931543048 - type: mrr value: 74.79313763420059 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 25.131999999999998 - type: map_at_10 value: 69.131 - type: map_at_100 value: 72.943 - type: map_at_1000 value: 73.045 - type: map_at_3 value: 48.847 - type: map_at_5 value: 59.842 - type: mrr_at_1 value: 85.516 - type: mrr_at_10 value: 88.863 - type: mrr_at_100 value: 88.996 - type: mrr_at_1000 value: 89.00099999999999 - type: mrr_at_3 value: 88.277 - type: mrr_at_5 value: 88.64800000000001 - type: ndcg_at_1 value: 85.516 - type: ndcg_at_10 value: 78.122 - type: ndcg_at_100 value: 82.673 - type: ndcg_at_1000 value: 83.707 - type: ndcg_at_3 value: 80.274 - type: ndcg_at_5 value: 78.405 - type: precision_at_1 value: 85.516 - type: precision_at_10 value: 38.975 - type: precision_at_100 value: 4.833 - type: precision_at_1000 value: 0.509 - type: precision_at_3 value: 70.35 - type: precision_at_5 value: 58.638 - type: recall_at_1 value: 25.131999999999998 - type: recall_at_10 value: 76.848 - type: recall_at_100 value: 91.489 - type: recall_at_1000 value: 96.709 - type: recall_at_3 value: 50.824000000000005 - type: recall_at_5 value: 63.89 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: None metrics: - type: accuracy value: 49.65 - type: f1 value: 47.66791473245483 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 63.78843565968542 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 55.14095244943176 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 53.800000000000004 - type: map_at_10 value: 63.312000000000005 - type: map_at_100 value: 63.93600000000001 - type: map_at_1000 value: 63.955 - type: map_at_3 value: 61.283 - type: map_at_5 value: 62.553000000000004 - type: mrr_at_1 value: 53.800000000000004 - type: mrr_at_10 value: 63.312000000000005 - type: mrr_at_100 value: 63.93600000000001 - type: mrr_at_1000 value: 63.955 - type: mrr_at_3 value: 61.283 - type: mrr_at_5 value: 62.553000000000004 - type: ndcg_at_1 value: 53.800000000000004 - type: ndcg_at_10 value: 67.693 - type: ndcg_at_100 value: 70.552 - type: ndcg_at_1000 value: 71.06099999999999 - type: ndcg_at_3 value: 63.632 - type: ndcg_at_5 value: 65.90899999999999 - type: precision_at_1 value: 53.800000000000004 - type: precision_at_10 value: 8.129999999999999 - type: precision_at_100 value: 0.943 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 23.467 - type: precision_at_5 value: 15.18 - type: recall_at_1 value: 53.800000000000004 - type: recall_at_10 value: 81.3 - type: recall_at_100 value: 94.3 - type: recall_at_1000 value: 98.3 - type: recall_at_3 value: 70.39999999999999 - type: recall_at_5 value: 75.9 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: None metrics: - type: accuracy value: 84.96000000000001 - type: ap value: 66.89917287702019 - type: f1 value: 83.0239988458119 language: - en license: mit --- # gte-small-zh General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. ## Model List | Models | Language | Max Sequence Length | Dimension | Model Size | |:-----: | :-----: |:-----: |:-----: |:-----: | |[GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 0.67GB | |[GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.21GB | |[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.10GB | |[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB | |[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | |[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | ## Metrics We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). - Evaluation results on CMTEB | Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) | | ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ | | **gte-large-zh** | 0.65 | 1024 | 512 | **66.72** | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 | | gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 | | stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | | stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | | bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 | | stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 | | stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | | piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | | piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | | gte-small-zh | 0.1 | 512 | 512 | 60.04 | 64.35 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 | | bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 | | m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | |text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 | ## Usage Code example ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel input_texts = [ "中国的首都是哪里", "你喜欢去哪里旅游", "北京", "今天中午吃什么" ] tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small-zh") model = AutoModel.from_pretrained("thenlper/gte-small-zh") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('thenlper/gte-small-zh') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ### Limitation This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ### Citation If you find our paper or models helpful, please consider citing them as follows: ``` @article{li2023towards, title={Towards general text embeddings with multi-stage contrastive learning}, author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, journal={arXiv preprint arXiv:2308.03281}, year={2023} } ```