--- license: cc-by-nc-4.0 tags: - feature-extraction - sentence-similarity - mteb language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh inference: false library_name: transformers model-index: - name: jina-embeddings-v3 results: - dataset: config: default name: MTEB AFQMC (default) revision: b44c3b011063adb25877c13823db83bb193913c4 split: validation type: C-MTEB/AFQMC metrics: - type: cosine_pearson value: 41.74237700998808 - type: cosine_spearman value: 43.4726782647566 - type: euclidean_pearson value: 42.244585459479964 - type: euclidean_spearman value: 43.525070045169606 - 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type: nAUC_Recall@10_std(MIRACL) value: 8.791982704261589 - type: nAUC_Recall@1_diff1(MIRACL) value: 28.698973487482206 - type: nAUC_Recall@1_max(MIRACL) value: 2.9217687660885034 - type: nAUC_Recall@1_std(MIRACL) value: -1.1247408800976524 - type: nAUC_Recall@20_diff1(MIRACL) value: -13.312171017942623 - type: nAUC_Recall@20_max(MIRACL) value: 24.19847346821666 - type: nAUC_Recall@20_std(MIRACL) value: 15.8157702609797 - type: nAUC_Recall@3_diff1(MIRACL) value: 16.909128321353343 - type: nAUC_Recall@3_max(MIRACL) value: 6.552122731902991 - type: nAUC_Recall@3_std(MIRACL) value: 1.9963898223457228 - type: nAUC_Recall@5_diff1(MIRACL) value: 9.990292655247721 - type: nAUC_Recall@5_max(MIRACL) value: 9.361722273507574 - type: nAUC_Recall@5_std(MIRACL) value: 3.270918827854495 task: type: Reranking - dataset: config: default name: MTEB SensitiveTopicsClassification (default) revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2 split: test type: ai-forever/sensitive-topics-classification metrics: - 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type: v_measure value: 39.96377851800628 - type: v_measure_std value: 0.9793033243093288 task: type: Clustering - dataset: config: default name: MTEB CLSClusteringS2S (default) revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f split: test type: C-MTEB/CLSClusteringS2S metrics: - type: main_score value: 38.788850224595784 - type: v_measure value: 38.788850224595784 - type: v_measure_std value: 1.0712604145916924 task: type: Clustering - dataset: config: default name: MTEB CMedQAv1 revision: 8d7f1e942507dac42dc58017c1a001c3717da7df split: test type: C-MTEB/CMedQAv1-reranking metrics: - type: map value: 77.95952507806115 - type: mrr value: 80.8643253968254 - type: main_score value: 77.95952507806115 task: type: Reranking - dataset: config: default name: MTEB CMedQAv2 revision: 23d186750531a14a0357ca22cd92d712fd512ea0 split: test type: C-MTEB/CMedQAv2-reranking metrics: - type: map value: 78.21522500165045 - type: mrr value: 81.28194444444443 - type: main_score value: 78.21522500165045 task: type: Reranking - dataset: config: default name: MTEB CQADupstackAndroidRetrieval (default) revision: f46a197baaae43b4f621051089b82a364682dfeb split: test type: mteb/cqadupstack-android metrics: - type: map_at_1 value: 33.377 - type: map_at_10 value: 46.371 - type: map_at_100 value: 47.829 - type: map_at_1000 value: 47.94 - type: map_at_20 value: 47.205000000000005 - type: map_at_3 value: 42.782 - type: map_at_5 value: 44.86 - type: mrr_at_1 value: 41.345 - type: mrr_at_10 value: 52.187 - type: mrr_at_100 value: 52.893 - type: mrr_at_1000 value: 52.929 - type: mrr_at_20 value: 52.637 - type: mrr_at_3 value: 49.714000000000006 - type: mrr_at_5 value: 51.373000000000005 - type: ndcg_at_1 value: 41.345 - type: ndcg_at_10 value: 52.946000000000005 - type: ndcg_at_100 value: 57.92699999999999 - type: ndcg_at_1000 value: 59.609 - type: ndcg_at_20 value: 54.900999999999996 - type: ndcg_at_3 value: 48.357 - type: ndcg_at_5 value: 50.739000000000004 - type: precision_at_1 value: 41.345 - type: precision_at_10 value: 10.186 - type: precision_at_100 value: 1.554 - type: precision_at_1000 value: 0.2 - type: precision_at_20 value: 5.959 - type: precision_at_3 value: 23.796 - type: precision_at_5 value: 17.024 - type: recall_at_1 value: 33.377 - type: recall_at_10 value: 65.067 - type: recall_at_100 value: 86.04899999999999 - type: recall_at_1000 value: 96.54899999999999 - type: recall_at_20 value: 72.071 - type: recall_at_3 value: 51.349999999999994 - type: recall_at_5 value: 58.41 - type: main_score value: 52.946000000000005 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackEnglishRetrieval (default) revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 split: test type: mteb/cqadupstack-english metrics: - type: map_at_1 value: 31.097 - type: map_at_10 value: 42.183 - type: map_at_100 value: 43.580999999999996 - type: map_at_1000 value: 43.718 - type: map_at_20 value: 42.921 - type: map_at_3 value: 38.963 - type: map_at_5 value: 40.815 - type: mrr_at_1 value: 39.745000000000005 - type: mrr_at_10 value: 48.736000000000004 - type: mrr_at_100 value: 49.405 - type: mrr_at_1000 value: 49.452 - type: mrr_at_20 value: 49.118 - type: mrr_at_3 value: 46.497 - type: mrr_at_5 value: 47.827999999999996 - type: ndcg_at_1 value: 39.745000000000005 - type: ndcg_at_10 value: 48.248000000000005 - type: ndcg_at_100 value: 52.956 - type: ndcg_at_1000 value: 54.99699999999999 - type: ndcg_at_20 value: 50.01 - type: ndcg_at_3 value: 43.946000000000005 - type: ndcg_at_5 value: 46.038000000000004 - type: precision_at_1 value: 39.745000000000005 - type: precision_at_10 value: 9.229 - type: precision_at_100 value: 1.5070000000000001 - type: precision_at_1000 value: 0.199 - type: precision_at_20 value: 5.489999999999999 - type: precision_at_3 value: 21.38 - type: precision_at_5 value: 15.274 - type: recall_at_1 value: 31.097 - type: recall_at_10 value: 58.617 - type: recall_at_100 value: 78.55199999999999 - type: recall_at_1000 value: 91.13900000000001 - type: recall_at_20 value: 64.92 - type: recall_at_3 value: 45.672000000000004 - type: recall_at_5 value: 51.669 - type: main_score value: 48.248000000000005 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGamingRetrieval (default) revision: 4885aa143210c98657558c04aaf3dc47cfb54340 split: test type: mteb/cqadupstack-gaming metrics: - type: map_at_1 value: 39.745000000000005 - type: map_at_10 value: 52.063 - type: map_at_100 value: 53.077 - type: map_at_1000 value: 53.13 - type: map_at_20 value: 52.66 - type: map_at_3 value: 48.662 - type: map_at_5 value: 50.507000000000005 - type: mrr_at_1 value: 45.391999999999996 - type: mrr_at_10 value: 55.528 - type: mrr_at_100 value: 56.16100000000001 - type: mrr_at_1000 value: 56.192 - type: mrr_at_20 value: 55.923 - type: mrr_at_3 value: 52.93600000000001 - type: mrr_at_5 value: 54.435 - type: ndcg_at_1 value: 45.391999999999996 - type: ndcg_at_10 value: 58.019 - type: ndcg_at_100 value: 61.936 - type: ndcg_at_1000 value: 63.015 - type: ndcg_at_20 value: 59.691 - type: ndcg_at_3 value: 52.294 - type: ndcg_at_5 value: 55.017 - type: precision_at_1 value: 45.391999999999996 - type: precision_at_10 value: 9.386 - type: precision_at_100 value: 1.232 - type: precision_at_1000 value: 0.136 - type: precision_at_20 value: 5.223 - type: precision_at_3 value: 23.177 - type: precision_at_5 value: 15.9 - type: recall_at_1 value: 39.745000000000005 - type: recall_at_10 value: 72.08099999999999 - type: recall_at_100 value: 88.85300000000001 - type: recall_at_1000 value: 96.569 - type: recall_at_20 value: 78.203 - type: recall_at_3 value: 56.957 - type: recall_at_5 value: 63.63100000000001 - type: main_score value: 58.019 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGisRetrieval (default) revision: 5003b3064772da1887988e05400cf3806fe491f2 split: test type: mteb/cqadupstack-gis metrics: - type: map_at_1 value: 26.651999999999997 - type: map_at_10 value: 35.799 - type: map_at_100 value: 36.846000000000004 - type: map_at_1000 value: 36.931000000000004 - type: map_at_20 value: 36.341 - type: map_at_3 value: 32.999 - type: map_at_5 value: 34.597 - type: mrr_at_1 value: 28.814 - type: mrr_at_10 value: 37.869 - type: mrr_at_100 value: 38.728 - type: mrr_at_1000 value: 38.795 - type: mrr_at_20 value: 38.317 - type: mrr_at_3 value: 35.235 - type: mrr_at_5 value: 36.738 - type: ndcg_at_1 value: 28.814 - type: ndcg_at_10 value: 41.028 - type: ndcg_at_100 value: 46.162 - type: ndcg_at_1000 value: 48.15 - type: ndcg_at_20 value: 42.824 - type: ndcg_at_3 value: 35.621 - type: ndcg_at_5 value: 38.277 - type: precision_at_1 value: 28.814 - type: precision_at_10 value: 6.361999999999999 - type: precision_at_100 value: 0.9450000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_20 value: 3.6159999999999997 - type: precision_at_3 value: 15.140999999999998 - type: precision_at_5 value: 10.712000000000002 - type: recall_at_1 value: 26.651999999999997 - type: recall_at_10 value: 55.038 - type: recall_at_100 value: 78.806 - type: recall_at_1000 value: 93.485 - type: recall_at_20 value: 61.742 - type: recall_at_3 value: 40.682 - type: recall_at_5 value: 46.855000000000004 - type: main_score value: 41.028 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackMathematicaRetrieval (default) revision: 90fceea13679c63fe563ded68f3b6f06e50061de split: test type: mteb/cqadupstack-mathematica metrics: - type: map_at_1 value: 17.627000000000002 - type: map_at_10 value: 26.436999999999998 - type: map_at_100 value: 27.85 - type: map_at_1000 value: 27.955999999999996 - type: map_at_20 value: 27.233 - type: map_at_3 value: 23.777 - type: map_at_5 value: 25.122 - type: mrr_at_1 value: 22.387999999999998 - type: mrr_at_10 value: 31.589 - type: mrr_at_100 value: 32.641999999999996 - type: mrr_at_1000 value: 32.696999999999996 - type: mrr_at_20 value: 32.201 - type: mrr_at_3 value: 28.98 - type: mrr_at_5 value: 30.342000000000002 - type: ndcg_at_1 value: 22.387999999999998 - type: ndcg_at_10 value: 32.129999999999995 - type: ndcg_at_100 value: 38.562999999999995 - type: ndcg_at_1000 value: 40.903 - type: ndcg_at_20 value: 34.652 - type: ndcg_at_3 value: 27.26 - type: ndcg_at_5 value: 29.235 - type: precision_at_1 value: 22.387999999999998 - type: precision_at_10 value: 5.970000000000001 - type: precision_at_100 value: 1.068 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_20 value: 3.6999999999999997 - type: precision_at_3 value: 13.267000000000001 - type: precision_at_5 value: 9.403 - type: recall_at_1 value: 17.627000000000002 - type: recall_at_10 value: 44.71 - type: recall_at_100 value: 72.426 - type: recall_at_1000 value: 88.64699999999999 - type: recall_at_20 value: 53.65 - type: recall_at_3 value: 30.989 - type: recall_at_5 value: 36.237 - type: main_score value: 32.129999999999995 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackPhysicsRetrieval (default) revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 split: test type: mteb/cqadupstack-physics metrics: - type: map_at_1 value: 30.891000000000002 - type: map_at_10 value: 41.519 - type: map_at_100 value: 42.896 - type: map_at_1000 value: 42.992999999999995 - type: map_at_20 value: 42.287 - type: map_at_3 value: 37.822 - type: map_at_5 value: 39.976 - type: mrr_at_1 value: 37.921 - type: mrr_at_10 value: 47.260999999999996 - type: mrr_at_100 value: 48.044 - type: mrr_at_1000 value: 48.08 - type: mrr_at_20 value: 47.699999999999996 - type: mrr_at_3 value: 44.513999999999996 - type: mrr_at_5 value: 46.064 - type: ndcg_at_1 value: 37.921 - type: ndcg_at_10 value: 47.806 - type: ndcg_at_100 value: 53.274 - type: ndcg_at_1000 value: 55.021 - type: ndcg_at_20 value: 49.973 - type: ndcg_at_3 value: 42.046 - type: ndcg_at_5 value: 44.835 - type: precision_at_1 value: 37.921 - type: precision_at_10 value: 8.767999999999999 - type: precision_at_100 value: 1.353 - type: precision_at_1000 value: 0.168 - type: precision_at_20 value: 5.135 - type: precision_at_3 value: 20.051 - type: precision_at_5 value: 14.398 - type: recall_at_1 value: 30.891000000000002 - type: recall_at_10 value: 60.897999999999996 - type: recall_at_100 value: 83.541 - type: recall_at_1000 value: 94.825 - type: recall_at_20 value: 68.356 - type: recall_at_3 value: 44.65 - type: recall_at_5 value: 51.919000000000004 - type: main_score value: 47.806 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackProgrammersRetrieval (default) revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 split: test type: mteb/cqadupstack-programmers metrics: - type: map_at_1 value: 27.654 - type: map_at_10 value: 38.025999999999996 - type: map_at_100 value: 39.425 - type: map_at_1000 value: 39.528 - type: map_at_20 value: 38.838 - type: map_at_3 value: 34.745 - type: map_at_5 value: 36.537 - type: mrr_at_1 value: 34.018 - type: mrr_at_10 value: 43.314 - type: mrr_at_100 value: 44.283 - type: mrr_at_1000 value: 44.327 - type: mrr_at_20 value: 43.929 - type: mrr_at_3 value: 40.868 - type: mrr_at_5 value: 42.317 - type: ndcg_at_1 value: 34.018 - type: ndcg_at_10 value: 43.887 - type: ndcg_at_100 value: 49.791000000000004 - type: ndcg_at_1000 value: 51.834 - type: ndcg_at_20 value: 46.376 - type: ndcg_at_3 value: 38.769999999999996 - type: ndcg_at_5 value: 41.144 - type: precision_at_1 value: 34.018 - type: precision_at_10 value: 8.001999999999999 - type: precision_at_100 value: 1.2630000000000001 - type: precision_at_1000 value: 0.16 - type: precision_at_20 value: 4.737 - type: precision_at_3 value: 18.417 - type: precision_at_5 value: 13.150999999999998 - type: recall_at_1 value: 27.654 - type: recall_at_10 value: 56.111 - type: recall_at_100 value: 81.136 - type: recall_at_1000 value: 94.788 - type: recall_at_20 value: 65.068 - type: recall_at_3 value: 41.713 - type: recall_at_5 value: 48.106 - type: main_score value: 43.887 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackRetrieval (default) revision: CQADupstackRetrieval_is_a_combined_dataset split: test type: CQADupstackRetrieval_is_a_combined_dataset metrics: - type: main_score value: 42.58858333333333 - type: ndcg_at_10 value: 42.58858333333333 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackStatsRetrieval (default) revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a split: test type: mteb/cqadupstack-stats metrics: - type: map_at_1 value: 24.501 - type: map_at_10 value: 32.814 - type: map_at_100 value: 33.754 - type: map_at_1000 value: 33.859 - type: map_at_20 value: 33.324 - type: map_at_3 value: 30.758000000000003 - type: map_at_5 value: 31.936999999999998 - type: mrr_at_1 value: 27.761000000000003 - type: mrr_at_10 value: 35.662 - type: mrr_at_100 value: 36.443999999999996 - type: mrr_at_1000 value: 36.516999999999996 - type: mrr_at_20 value: 36.085 - type: mrr_at_3 value: 33.742 - type: mrr_at_5 value: 34.931 - type: ndcg_at_1 value: 27.761000000000003 - type: ndcg_at_10 value: 37.208000000000006 - type: ndcg_at_100 value: 41.839 - type: ndcg_at_1000 value: 44.421 - type: ndcg_at_20 value: 38.917 - type: ndcg_at_3 value: 33.544000000000004 - type: ndcg_at_5 value: 35.374 - type: precision_at_1 value: 27.761000000000003 - type: precision_at_10 value: 5.92 - type: precision_at_100 value: 0.899 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 3.4130000000000003 - type: precision_at_3 value: 15.031 - type: precision_at_5 value: 10.306999999999999 - type: recall_at_1 value: 24.501 - type: recall_at_10 value: 47.579 - type: recall_at_100 value: 69.045 - type: recall_at_1000 value: 88.032 - type: recall_at_20 value: 54.125 - type: recall_at_3 value: 37.202 - type: recall_at_5 value: 41.927 - type: main_score value: 37.208000000000006 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackTexRetrieval (default) revision: 46989137a86843e03a6195de44b09deda022eec7 split: test type: mteb/cqadupstack-tex metrics: - type: map_at_1 value: 18.29 - type: map_at_10 value: 26.183 - type: map_at_100 value: 27.351999999999997 - type: map_at_1000 value: 27.483999999999998 - type: map_at_20 value: 26.798 - type: map_at_3 value: 23.629 - type: map_at_5 value: 24.937 - type: mrr_at_1 value: 22.299 - type: mrr_at_10 value: 30.189 - type: mrr_at_100 value: 31.098 - type: mrr_at_1000 value: 31.177 - type: mrr_at_20 value: 30.697000000000003 - type: mrr_at_3 value: 27.862 - type: mrr_at_5 value: 29.066 - type: ndcg_at_1 value: 22.299 - type: ndcg_at_10 value: 31.202 - type: ndcg_at_100 value: 36.617 - type: ndcg_at_1000 value: 39.544000000000004 - type: ndcg_at_20 value: 33.177 - type: ndcg_at_3 value: 26.639000000000003 - type: ndcg_at_5 value: 28.526 - type: precision_at_1 value: 22.299 - type: precision_at_10 value: 5.8020000000000005 - type: precision_at_100 value: 1.0070000000000001 - type: precision_at_1000 value: 0.14400000000000002 - type: precision_at_20 value: 3.505 - type: precision_at_3 value: 12.698 - type: precision_at_5 value: 9.174 - type: recall_at_1 value: 18.29 - type: recall_at_10 value: 42.254999999999995 - type: recall_at_100 value: 66.60000000000001 - type: recall_at_1000 value: 87.31400000000001 - type: recall_at_20 value: 49.572 - type: recall_at_3 value: 29.342000000000002 - type: recall_at_5 value: 34.221000000000004 - type: main_score value: 31.202 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackUnixRetrieval (default) revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 split: test type: mteb/cqadupstack-unix metrics: - type: map_at_1 value: 27.722 - type: map_at_10 value: 37.698 - type: map_at_100 value: 38.899 - type: map_at_1000 value: 38.998 - type: map_at_20 value: 38.381 - type: map_at_3 value: 34.244 - type: map_at_5 value: 36.295 - type: mrr_at_1 value: 32.183 - type: mrr_at_10 value: 41.429 - type: mrr_at_100 value: 42.308 - type: mrr_at_1000 value: 42.358000000000004 - type: mrr_at_20 value: 41.957 - type: mrr_at_3 value: 38.401999999999994 - type: mrr_at_5 value: 40.294999999999995 - type: ndcg_at_1 value: 32.183 - type: ndcg_at_10 value: 43.519000000000005 - type: ndcg_at_100 value: 48.786 - type: ndcg_at_1000 value: 50.861999999999995 - type: ndcg_at_20 value: 45.654 - type: ndcg_at_3 value: 37.521 - type: ndcg_at_5 value: 40.615 - type: precision_at_1 value: 32.183 - 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type: recall_at_100 value: 35.052 - type: recall_at_1000 value: 66.67999999999999 - type: recall_at_20 value: 21.343999999999998 - type: recall_at_3 value: 10.259 - type: recall_at_5 value: 13.406 - type: main_score value: 36.623 task: type: Retrieval - dataset: config: default name: MTEB NQ (default) revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 split: test type: mteb/nq metrics: - type: map_at_1 value: 41.411 - type: map_at_10 value: 57.179 - type: map_at_100 value: 57.945 - type: map_at_1000 value: 57.967999999999996 - type: map_at_20 value: 57.687 - type: map_at_3 value: 53.46300000000001 - type: map_at_5 value: 55.696999999999996 - type: mrr_at_1 value: 46.233999999999995 - type: mrr_at_10 value: 59.831999999999994 - type: mrr_at_100 value: 60.33500000000001 - type: mrr_at_1000 value: 60.348 - type: mrr_at_20 value: 60.167 - type: mrr_at_3 value: 56.972 - type: mrr_at_5 value: 58.74 - type: ndcg_at_1 value: 46.205 - type: ndcg_at_10 value: 64.23100000000001 - type: ndcg_at_100 value: 67.242 - type: ndcg_at_1000 value: 67.72500000000001 - type: ndcg_at_20 value: 65.77300000000001 - type: ndcg_at_3 value: 57.516 - type: ndcg_at_5 value: 61.11600000000001 - type: precision_at_1 value: 46.205 - type: precision_at_10 value: 9.873 - type: precision_at_100 value: 1.158 - type: precision_at_1000 value: 0.12 - type: precision_at_20 value: 5.319 - type: precision_at_3 value: 25.424999999999997 - type: precision_at_5 value: 17.375 - type: recall_at_1 value: 41.411 - type: recall_at_10 value: 82.761 - type: recall_at_100 value: 95.52199999999999 - type: recall_at_1000 value: 99.02499999999999 - type: recall_at_20 value: 88.34 - type: recall_at_3 value: 65.73 - type: recall_at_5 value: 73.894 - type: main_score value: 64.23100000000001 task: type: Retrieval - dataset: config: default name: MTEB Ocnli (default) revision: 66e76a618a34d6d565d5538088562851e6daa7ec split: validation type: C-MTEB/OCNLI metrics: - type: cosine_accuracy value: 62.3714131023281 - type: cosine_accuracy_threshold value: 79.70921993255615 - 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type: accuracy value: 91.88000000000001 - type: ap value: 89.52463684448476 - type: ap_weighted value: 89.52463684448476 - type: f1 value: 91.86313022306673 - type: f1_weighted value: 91.87806318146912 - type: main_score value: 91.88000000000001 task: type: Classification - dataset: config: en name: MTEB OpusparcusPC (en) revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a split: test.full type: GEM/opusparcus metrics: - type: cosine_accuracy value: 92.65578635014838 - type: cosine_accuracy_threshold value: 74.02530312538147 - type: cosine_ap value: 98.3834226153613 - type: cosine_f1 value: 94.92567913890312 - type: cosine_f1_threshold value: 74.02530312538147 - type: cosine_precision value: 95.562435500516 - type: cosine_recall value: 94.29735234215886 - type: dot_accuracy value: 91.54302670623146 - type: dot_accuracy_threshold value: 34452.29187011719 - type: dot_ap value: 98.1237257754439 - type: dot_f1 value: 94.22400803616273 - type: dot_f1_threshold value: 33670.41931152344 - type: dot_precision value: 92.9633300297324 - 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type: max_f1 value: 92.1089063221043 - type: max_precision value: 89.64577656675749 - type: max_recall value: 97.42120343839542 - type: similarity_accuracy value: 87.72178850248403 - type: similarity_accuracy_threshold value: 73.33863377571106 - type: similarity_ap value: 96.98901408834976 - type: similarity_f1 value: 91.89944134078212 - type: similarity_f1_threshold value: 71.45810127258301 - type: similarity_precision value: 89.64577656675749 - type: similarity_recall value: 94.26934097421203 task: type: PairClassification - dataset: config: fr name: MTEB OpusparcusPC (fr) revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a split: test.full type: GEM/opusparcus metrics: - type: cosine_accuracy value: 80.92643051771117 - type: cosine_accuracy_threshold value: 76.68856382369995 - type: cosine_ap value: 93.74622381534307 - type: cosine_f1 value: 87.12328767123287 - type: cosine_f1_threshold value: 71.64022922515869 - type: cosine_precision value: 80.64243448858834 - type: cosine_recall value: 94.73684210526315 - 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dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cosine_pearson value: 88.86627241652141 - type: cosine_spearman value: 89.49011599120688 - type: euclidean_pearson value: 89.3314120073772 - type: euclidean_spearman value: 89.8226502776963 - type: main_score value: 89.49011599120688 - type: manhattan_pearson value: 89.2252179076963 - type: manhattan_spearman value: 89.74573844021225 - type: pearson value: 88.86627241652141 - type: spearman value: 89.49011599120688 task: type: STS - dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cosine_pearson value: 87.22891405215968 - type: cosine_spearman value: 84.9467188157614 - type: euclidean_pearson value: 87.20330004726237 - type: euclidean_spearman value: 85.34806059461808 - type: main_score value: 84.9467188157614 - type: manhattan_pearson value: 87.15224666107623 - 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dataset: config: es-es name: MTEB STS17 (es-es) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics: - type: cosine_pearson value: 88.4084678497171 - type: cosine_spearman value: 88.77640638748285 - type: euclidean_pearson value: 89.60124312475843 - type: euclidean_spearman value: 88.4321442688528 - type: main_score value: 88.77640638748285 - type: manhattan_pearson value: 89.62375118021299 - type: manhattan_spearman value: 88.46998118661577 - type: pearson value: 88.4084678497171 - type: spearman value: 88.77640638748285 task: type: STS - dataset: config: fr-en name: MTEB STS17 (fr-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics: - type: cosine_pearson value: 87.30688801326498 - type: cosine_spearman value: 87.55684697258378 - type: euclidean_pearson value: 87.89672951056794 - type: euclidean_spearman value: 87.28050429201674 - type: main_score value: 87.55684697258378 - type: manhattan_pearson value: 87.74292745320572 - 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dataset: config: pl-en name: MTEB STS22 (pl-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 77.77233721490776 - type: cosine_spearman value: 76.17596588017625 - type: euclidean_pearson value: 74.47600468156611 - type: euclidean_spearman value: 72.61278728057012 - type: main_score value: 76.17596588017625 - type: manhattan_pearson value: 74.48118910099699 - type: manhattan_spearman value: 73.33167419101696 - type: pearson value: 77.77233721490776 - type: spearman value: 76.17596588017625 task: type: STS - dataset: config: pl name: MTEB STS22 (pl) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 42.87453608131507 - type: cosine_spearman value: 45.137849894401185 - type: euclidean_pearson value: 31.66964197694796 - type: euclidean_spearman value: 44.1014900837869 - type: main_score value: 45.137849894401185 - type: manhattan_pearson value: 31.007199259384745 - 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dataset: config: es-en name: MTEB STS22 (es-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 76.8364678896155 - type: cosine_spearman value: 78.45516413087114 - type: euclidean_pearson value: 78.62779318576634 - type: euclidean_spearman value: 78.88760695649488 - type: main_score value: 78.45516413087114 - type: manhattan_pearson value: 78.62131335760031 - type: manhattan_spearman value: 78.81861844200388 - type: pearson value: 76.8364678896155 - type: spearman value: 78.45516413087114 task: type: STS - dataset: config: de-en name: MTEB STS22 (de-en) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 65.16640313911604 - type: cosine_spearman value: 60.887608967403914 - type: euclidean_pearson value: 67.49902244990913 - type: euclidean_spearman value: 59.2458787136538 - type: main_score value: 60.887608967403914 - 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Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications.

The embedding model trained by Jina AI.

jina-embeddings-v3: Multilingual Embeddings With Task LoRA

## Quick Start The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/). ## Intended Usage & Model Info `jina-embeddings-v3` is a **multilingual multi-task text embedding model** designed for a variety of NLP applications. Based on the [Jina-XLM-RoBERTa architecture](https://huggingface.co./jinaai/xlm-roberta-flash-implementation), this model supports Rotary Position Embeddings to handle long input sequences up to **8192 tokens**. Additionally, it features 5 LoRA adapters to generate task-specific embeddings efficiently. ### Key Features: - **Extended Sequence Length:** Supports up to 8192 tokens with RoPE. - **Task-Specific Embedding:** Customize embeddings through the `task` argument with the following options: - `retrieval.query`: Used for query embeddings in asymmetric retrieval tasks - `retrieval.passage`: Used for passage embeddings in asymmetric retrieval tasks - `separation`: Used for embeddings in clustering and re-ranking applications - `classification`: Used for embeddings in classification tasks - `text-matching`: Used for embeddings in tasks that quantify similarity between two texts, such as STS or symmetric retrieval tasks - **Matryoshka Embeddings**: Supports flexible embedding sizes (`32, 64, 128, 256, 512, 768, 1024`), allowing for truncating embeddings to fit your application. ### Supported Languages: While the foundation model supports 100 languages, we've focused our tuning efforts on the following 30 languages: **Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, Georgian, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Latvian, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu,** and **Vietnamese.** ## Usage **
Apply mean pooling when integrating the model.**

### Why Use Mean Pooling? Mean pooling takes all token embeddings from the model's output and averages them at the sentence or paragraph level. This approach has been shown to produce high-quality sentence embeddings. We provide an `encode` function that handles this for you automatically. However, if you're working with the model directly, outside of the `encode` function, you'll need to apply mean pooling manually. Here's how you can do it: ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] input_mask_expanded = ( attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() ) return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( input_mask_expanded.sum(1), min=1e-9 ) sentences = ["How is the weather today?", "What is the current weather like today?"] tokenizer = AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v3") model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") task = 'retrieval.query' task_id = model._adaptation_map[task] adapter_mask = torch.full((len(sentences),), task_id, dtype=torch.int32) with torch.no_grad(): model_output = model(**encoded_input, adapter_mask=adapter_mask) embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) embeddings = F.normalize(embeddings, p=2, dim=1) ```

The easiest way to start using `jina-embeddings-v3` is with the [Jina Embedding API](https://jina.ai/embeddings/). Alternatively, you can use `jina-embeddings-v3` directly via Transformers package: ```bash !pip install transformers torch einops !pip install 'numpy<2' ``` If you run it on a GPU that support [FlashAttention-2](https://github.com/Dao-AILab/flash-attention). By 2024.9.12, it supports Ampere, Ada, or Hopper GPUs (e.g., A100, RTX 3090, RTX 4090, H100), ```bash !pip install flash-attn --no-build-isolation ``` ```python from transformers import AutoModel # Initialize the model model = AutoModel.from_pretrained("jinaai/jina-embeddings-v3", trust_remote_code=True) texts = [ "Follow the white rabbit.", # English "Sigue al conejo blanco.", # Spanish "Suis le lapin blanc.", # French "跟着白兔走。", # Chinese "اتبع الأرنب الأبيض.", # Arabic "Folge dem weißen Kaninchen.", # German ] # When calling the `encode` function, you can choose a `task` based on the use case: # 'retrieval.query', 'retrieval.passage', 'separation', 'classification', 'text-matching' # Alternatively, you can choose not to pass a `task`, and no specific LoRA adapter will be used. embeddings = model.encode(texts, task="text-matching") # Compute similarities print(embeddings[0] @ embeddings[1].T) ``` By default, the model supports a maximum sequence length of 8192 tokens. However, if you want to truncate your input texts to a shorter length, you can pass the `max_length` parameter to the `encode` function: ```python embeddings = model.encode(["Very long ... document"], max_length=2048) ``` In case you want to use **Matryoshka embeddings** and switch to a different dimension, you can adjust it by passing the `truncate_dim` parameter to the `encode` function: ```python embeddings = model.encode(['Sample text'], truncate_dim=256) ``` The latest version (3.1.0) of [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) also supports `jina-embeddings-v3`: ```bash !pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True) task = "retrieval.query" embeddings = model.encode( ["What is the weather like in Berlin today?"], task=task, prompt_name=task, ) ``` **
ONNX Inference.**

You can use ONNX for efficient inference with `jina-embeddings-v3`: ```python import onnxruntime import numpy as np from transformers import AutoTokenizer, PretrainedConfig # Load tokenizer and model config tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3') config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3') # Tokenize input input_text = tokenizer('sample text', return_tensors='np') # ONNX session model_path = 'jina-embeddings-v3/onnx/model.onnx' session = onnxruntime.InferenceSession(model_path) # Prepare inputs for ONNX model task_type = 'text-matching' task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64) inputs = { 'input_ids': input_text['input_ids'], 'attention_mask': input_text['attention_mask'], 'task_id': task_id } # Run model outputs = session.run(None, inputs) ```

## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## License `jina-embeddings-v3` is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the models is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to [contact us](https://jina.ai/contact-sales/). ## Citation If you find `jina-embeddings-v3` useful in your research, please cite the following paper: ```bibtex @misc{sturua2024jinaembeddingsv3multilingualembeddingstask, title={jina-embeddings-v3: Multilingual Embeddings With Task LoRA}, author={Saba Sturua and Isabelle Mohr and Mohammad Kalim Akram and Michael Günther and Bo Wang and Markus Krimmel and Feng Wang and Georgios Mastrapas and Andreas Koukounas and Andreas Koukounas and Nan Wang and Han Xiao}, year={2024}, eprint={2409.10173}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2409.10173}, } ```