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gte-Qwen2-1.5B-instruct - GGUF
- Model creator: https://huggingface.co/Alibaba-NLP/
- Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/
Original model description:
tags: - mteb - sentence-transformers - transformers - Qwen2 - sentence-similarity license: apache-2.0 model-index: - name: gte-qwen2-7B-instruct results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 83.98507462686567 - type: ap value: 50.93015252587014 - type: f1 value: 78.50416599051215 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - type: accuracy value: 96.61065 - type: ap value: 94.89174052954196 - type: f1 value: 96.60942596940565 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 55.614000000000004 - type: f1 value: 54.90553480294904 task: type: Classification - dataset: config: default name: MTEB ArguAna revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics: - type: map_at_1 value: 45.164 - type: map_at_10 value: 61.519 - type: map_at_100 value: 61.769 - type: map_at_1000 value: 61.769 - type: map_at_3 value: 57.443999999999996 - type: map_at_5 value: 60.058 - type: mrr_at_1 value: 46.088 - type: mrr_at_10 value: 61.861 - type: mrr_at_100 value: 62.117999999999995 - type: mrr_at_1000 value: 62.117999999999995 - type: mrr_at_3 value: 57.729 - type: mrr_at_5 value: 60.392 - type: ndcg_at_1 value: 45.164 - type: ndcg_at_10 value: 69.72 - type: ndcg_at_100 value: 70.719 - type: ndcg_at_1000 value: 70.719 - type: ndcg_at_3 value: 61.517999999999994 - type: ndcg_at_5 value: 66.247 - type: precision_at_1 value: 45.164 - type: precision_at_10 value: 9.545 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 24.443 - type: precision_at_5 value: 16.97 - type: recall_at_1 value: 45.164 - type: recall_at_10 value: 95.448 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 73.329 - type: recall_at_5 value: 84.851 task: type: Retrieval - dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics: - type: v_measure value: 50.511868162026175 task: type: Clustering - dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics: - type: v_measure value: 45.007803189284004 task: type: Clustering - dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics: - type: map value: 64.55292107723382 - type: mrr value: 77.66158818097877 task: type: Reranking - dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cos_sim_pearson value: 85.65459047085452 - type: cos_sim_spearman value: 82.10729255710761 - type: euclidean_pearson value: 82.78079159312476 - type: euclidean_spearman value: 80.50002701880933 - type: manhattan_pearson value: 82.41372641383016 - type: manhattan_spearman value: 80.57412509272639 task: type: STS - dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics: - type: accuracy value: 87.30844155844156 - type: f1 value: 87.25307322443255 task: type: Classification - dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics: - type: v_measure value: 43.20754608934859 task: type: Clustering - dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics: - type: v_measure value: 38.818037697335505 task: type: Clustering - dataset: config: default name: MTEB CQADupstackAndroidRetrieval revision: f46a197baaae43b4f621051089b82a364682dfeb split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 35.423 - type: map_at_10 value: 47.198 - type: map_at_100 value: 48.899 - type: map_at_1000 value: 49.004 - type: map_at_3 value: 43.114999999999995 - type: map_at_5 value: 45.491 - type: mrr_at_1 value: 42.918 - type: mrr_at_10 value: 53.299 - type: mrr_at_100 value: 54.032000000000004 - type: mrr_at_1000 value: 54.055 - type: mrr_at_3 value: 50.453 - type: mrr_at_5 value: 52.205999999999996 - type: ndcg_at_1 value: 42.918 - type: ndcg_at_10 value: 53.98 - type: ndcg_at_100 value: 59.57 - type: ndcg_at_1000 value: 60.879000000000005 - type: ndcg_at_3 value: 48.224000000000004 - type: ndcg_at_5 value: 50.998 - type: precision_at_1 value: 42.918 - type: precision_at_10 value: 10.299999999999999 - type: precision_at_100 value: 1.687 - type: precision_at_1000 value: 0.211 - type: precision_at_3 value: 22.842000000000002 - type: precision_at_5 value: 16.681 - type: recall_at_1 value: 35.423 - type: recall_at_10 value: 66.824 - type: recall_at_100 value: 89.564 - type: recall_at_1000 value: 97.501 - type: recall_at_3 value: 50.365 - type: recall_at_5 value: 57.921 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackEnglishRetrieval revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 33.205 - type: map_at_10 value: 44.859 - type: map_at_100 value: 46.135 - type: map_at_1000 value: 46.259 - type: map_at_3 value: 41.839 - type: map_at_5 value: 43.662 - type: mrr_at_1 value: 41.146 - type: mrr_at_10 value: 50.621 - type: mrr_at_100 value: 51.207 - type: mrr_at_1000 value: 51.246 - type: mrr_at_3 value: 48.535000000000004 - type: mrr_at_5 value: 49.818 - type: ndcg_at_1 value: 41.146 - type: ndcg_at_10 value: 50.683 - type: ndcg_at_100 value: 54.82 - type: ndcg_at_1000 value: 56.69 - type: ndcg_at_3 value: 46.611000000000004 - type: ndcg_at_5 value: 48.66 - type: precision_at_1 value: 41.146 - type: precision_at_10 value: 9.439 - type: precision_at_100 value: 1.465 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 22.59 - type: precision_at_5 value: 15.86 - type: recall_at_1 value: 33.205 - type: recall_at_10 value: 61.028999999999996 - type: recall_at_100 value: 78.152 - type: recall_at_1000 value: 89.59700000000001 - type: recall_at_3 value: 49.05 - type: recall_at_5 value: 54.836 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGamingRetrieval revision: 4885aa143210c98657558c04aaf3dc47cfb54340 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 41.637 - type: map_at_10 value: 55.162 - type: map_at_100 value: 56.142 - type: map_at_1000 value: 56.188 - type: map_at_3 value: 51.564 - type: map_at_5 value: 53.696 - type: mrr_at_1 value: 47.524 - type: mrr_at_10 value: 58.243 - type: mrr_at_100 value: 58.879999999999995 - type: mrr_at_1000 value: 58.9 - type: mrr_at_3 value: 55.69499999999999 - type: mrr_at_5 value: 57.284 - type: ndcg_at_1 value: 47.524 - type: ndcg_at_10 value: 61.305 - type: ndcg_at_100 value: 65.077 - type: ndcg_at_1000 value: 65.941 - type: ndcg_at_3 value: 55.422000000000004 - type: ndcg_at_5 value: 58.516 - type: precision_at_1 value: 47.524 - type: precision_at_10 value: 9.918000000000001 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.765 - type: precision_at_5 value: 17.204 - type: recall_at_1 value: 41.637 - type: recall_at_10 value: 76.185 - type: recall_at_100 value: 92.149 - type: recall_at_1000 value: 98.199 - type: recall_at_3 value: 60.856 - type: recall_at_5 value: 68.25099999999999 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGisRetrieval revision: 5003b3064772da1887988e05400cf3806fe491f2 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 26.27 - type: map_at_10 value: 37.463 - type: map_at_100 value: 38.434000000000005 - type: map_at_1000 value: 38.509 - type: map_at_3 value: 34.226 - type: map_at_5 value: 36.161 - type: mrr_at_1 value: 28.588 - type: mrr_at_10 value: 39.383 - type: mrr_at_100 value: 40.23 - type: mrr_at_1000 value: 40.281 - type: mrr_at_3 value: 36.422 - type: mrr_at_5 value: 38.252 - type: ndcg_at_1 value: 28.588 - type: ndcg_at_10 value: 43.511 - type: ndcg_at_100 value: 48.274 - type: ndcg_at_1000 value: 49.975 - type: ndcg_at_3 value: 37.319 - type: ndcg_at_5 value: 40.568 - type: precision_at_1 value: 28.588 - type: precision_at_10 value: 6.893000000000001 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 16.347 - type: precision_at_5 value: 11.661000000000001 - type: recall_at_1 value: 26.27 - type: recall_at_10 value: 60.284000000000006 - type: recall_at_100 value: 81.902 - type: recall_at_1000 value: 94.43 - type: recall_at_3 value: 43.537 - type: recall_at_5 value: 51.475 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackMathematicaRetrieval revision: 90fceea13679c63fe563ded68f3b6f06e50061de split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 18.168 - type: map_at_10 value: 28.410000000000004 - type: map_at_100 value: 29.78 - type: map_at_1000 value: 29.892999999999997 - type: map_at_3 value: 25.238 - type: map_at_5 value: 26.96 - type: mrr_at_1 value: 23.507 - type: mrr_at_10 value: 33.382 - type: mrr_at_100 value: 34.404 - type: mrr_at_1000 value: 34.467999999999996 - type: mrr_at_3 value: 30.637999999999998 - type: mrr_at_5 value: 32.199 - type: ndcg_at_1 value: 23.507 - type: ndcg_at_10 value: 34.571000000000005 - type: ndcg_at_100 value: 40.663 - type: ndcg_at_1000 value: 43.236000000000004 - type: ndcg_at_3 value: 29.053 - type: ndcg_at_5 value: 31.563999999999997 - type: precision_at_1 value: 23.507 - type: precision_at_10 value: 6.654 - type: precision_at_100 value: 1.113 - type: precision_at_1000 value: 0.146 - type: precision_at_3 value: 14.427999999999999 - type: precision_at_5 value: 10.498000000000001 - type: recall_at_1 value: 18.168 - type: recall_at_10 value: 48.443000000000005 - type: recall_at_100 value: 74.47 - type: recall_at_1000 value: 92.494 - type: recall_at_3 value: 33.379999999999995 - type: recall_at_5 value: 39.76 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackPhysicsRetrieval revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 32.39 - type: map_at_10 value: 44.479 - type: map_at_100 value: 45.977000000000004 - type: map_at_1000 value: 46.087 - type: map_at_3 value: 40.976 - type: map_at_5 value: 43.038 - type: mrr_at_1 value: 40.135 - type: mrr_at_10 value: 50.160000000000004 - type: mrr_at_100 value: 51.052 - type: mrr_at_1000 value: 51.087 - type: mrr_at_3 value: 47.818 - type: mrr_at_5 value: 49.171 - type: ndcg_at_1 value: 40.135 - type: ndcg_at_10 value: 50.731 - type: ndcg_at_100 value: 56.452000000000005 - type: ndcg_at_1000 value: 58.123000000000005 - type: ndcg_at_3 value: 45.507 - type: ndcg_at_5 value: 48.11 - type: precision_at_1 value: 40.135 - type: precision_at_10 value: 9.192 - type: precision_at_100 value: 1.397 - type: precision_at_1000 value: 0.169 - type: precision_at_3 value: 21.816 - type: precision_at_5 value: 15.476 - type: recall_at_1 value: 32.39 - type: recall_at_10 value: 63.597 - type: recall_at_100 value: 86.737 - type: recall_at_1000 value: 97.039 - type: recall_at_3 value: 48.906 - type: recall_at_5 value: 55.659000000000006 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackProgrammersRetrieval revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 28.397 - type: map_at_10 value: 39.871 - type: map_at_100 value: 41.309000000000005 - type: map_at_1000 value: 41.409 - type: map_at_3 value: 36.047000000000004 - type: map_at_5 value: 38.104 - type: mrr_at_1 value: 34.703 - type: mrr_at_10 value: 44.773 - type: mrr_at_100 value: 45.64 - type: mrr_at_1000 value: 45.678999999999995 - type: mrr_at_3 value: 41.705 - type: mrr_at_5 value: 43.406 - type: ndcg_at_1 value: 34.703 - type: ndcg_at_10 value: 46.271 - type: ndcg_at_100 value: 52.037 - type: ndcg_at_1000 value: 53.81700000000001 - type: ndcg_at_3 value: 39.966 - type: ndcg_at_5 value: 42.801 - type: precision_at_1 value: 34.703 - type: precision_at_10 value: 8.744 - type: precision_at_100 value: 1.348 - type: precision_at_1000 value: 0.167 - type: precision_at_3 value: 19.102 - type: precision_at_5 value: 13.836 - type: recall_at_1 value: 28.397 - type: recall_at_10 value: 60.299 - type: recall_at_100 value: 84.595 - type: recall_at_1000 value: 96.155 - type: recall_at_3 value: 43.065 - type: recall_at_5 value: 50.371 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackRetrieval revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 28.044333333333338 - type: map_at_10 value: 38.78691666666666 - type: map_at_100 value: 40.113 - type: map_at_1000 value: 40.22125 - type: map_at_3 value: 35.52966666666667 - type: map_at_5 value: 37.372749999999996 - type: mrr_at_1 value: 33.159083333333335 - type: mrr_at_10 value: 42.913583333333335 - type: mrr_at_100 value: 43.7845 - type: mrr_at_1000 value: 43.830333333333336 - type: mrr_at_3 value: 40.29816666666667 - type: mrr_at_5 value: 41.81366666666667 - type: ndcg_at_1 value: 33.159083333333335 - type: ndcg_at_10 value: 44.75750000000001 - type: ndcg_at_100 value: 50.13658333333334 - type: ndcg_at_1000 value: 52.037 - type: ndcg_at_3 value: 39.34258333333334 - type: ndcg_at_5 value: 41.93708333333333 - type: precision_at_1 value: 33.159083333333335 - type: precision_at_10 value: 7.952416666666667 - type: precision_at_100 value: 1.2571666666666668 - type: precision_at_1000 value: 0.16099999999999998 - type: precision_at_3 value: 18.303833333333337 - type: precision_at_5 value: 13.057083333333333 - type: recall_at_1 value: 28.044333333333338 - type: recall_at_10 value: 58.237249999999996 - type: recall_at_100 value: 81.35391666666666 - type: recall_at_1000 value: 94.21283333333334 - type: recall_at_3 value: 43.32341666666667 - type: recall_at_5 value: 49.94908333333333 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackStatsRetrieval revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 27.838 - type: map_at_10 value: 36.04 - type: map_at_100 value: 37.113 - type: map_at_1000 value: 37.204 - type: map_at_3 value: 33.585 - type: map_at_5 value: 34.845 - type: mrr_at_1 value: 30.982 - type: mrr_at_10 value: 39.105000000000004 - type: mrr_at_100 value: 39.98 - type: mrr_at_1000 value: 40.042 - type: mrr_at_3 value: 36.912 - type: mrr_at_5 value: 38.062000000000005 - type: ndcg_at_1 value: 30.982 - type: ndcg_at_10 value: 40.982 - type: ndcg_at_100 value: 46.092 - type: ndcg_at_1000 value: 48.25 - type: ndcg_at_3 value: 36.41 - type: ndcg_at_5 value: 38.379999999999995 - type: precision_at_1 value: 30.982 - type: precision_at_10 value: 6.534 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 15.745999999999999 - type: precision_at_5 value: 10.828 - type: recall_at_1 value: 27.838 - type: recall_at_10 value: 52.971000000000004 - type: recall_at_100 value: 76.357 - type: recall_at_1000 value: 91.973 - type: recall_at_3 value: 40.157 - type: recall_at_5 value: 45.147999999999996 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackTexRetrieval revision: 46989137a86843e03a6195de44b09deda022eec7 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 19.059 - type: map_at_10 value: 27.454 - type: map_at_100 value: 28.736 - type: map_at_1000 value: 28.865000000000002 - type: map_at_3 value: 24.773999999999997 - type: map_at_5 value: 26.266000000000002 - type: mrr_at_1 value: 23.125 - type: mrr_at_10 value: 31.267 - type: mrr_at_100 value: 32.32 - type: mrr_at_1000 value: 32.394 - type: mrr_at_3 value: 28.894 - type: mrr_at_5 value: 30.281000000000002 - type: ndcg_at_1 value: 23.125 - type: ndcg_at_10 value: 32.588 - type: ndcg_at_100 value: 38.432 - type: ndcg_at_1000 value: 41.214 - type: ndcg_at_3 value: 27.938000000000002 - type: ndcg_at_5 value: 30.127 - type: precision_at_1 value: 23.125 - type: precision_at_10 value: 5.9639999999999995 - type: precision_at_100 value: 1.047 - type: precision_at_1000 value: 0.148 - type: precision_at_3 value: 13.294 - type: precision_at_5 value: 9.628 - type: recall_at_1 value: 19.059 - type: recall_at_10 value: 44.25 - type: recall_at_100 value: 69.948 - type: recall_at_1000 value: 89.35300000000001 - type: recall_at_3 value: 31.114000000000004 - type: recall_at_5 value: 36.846000000000004 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackUnixRetrieval revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 28.355999999999998 - type: map_at_10 value: 39.055 - type: map_at_100 value: 40.486 - type: map_at_1000 value: 40.571 - type: map_at_3 value: 35.69 - type: map_at_5 value: 37.605 - type: mrr_at_1 value: 33.302 - type: mrr_at_10 value: 42.986000000000004 - type: mrr_at_100 value: 43.957 - type: mrr_at_1000 value: 43.996 - type: mrr_at_3 value: 40.111999999999995 - type: mrr_at_5 value: 41.735 - type: ndcg_at_1 value: 33.302 - type: ndcg_at_10 value: 44.962999999999994 - type: ndcg_at_100 value: 50.917 - type: ndcg_at_1000 value: 52.622 - type: ndcg_at_3 value: 39.182 - type: ndcg_at_5 value: 41.939 - type: precision_at_1 value: 33.302 - type: precision_at_10 value: 7.779999999999999 - type: precision_at_100 value: 1.203 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 18.035 - type: precision_at_5 value: 12.873000000000001 - type: recall_at_1 value: 28.355999999999998 - type: recall_at_10 value: 58.782000000000004 - type: recall_at_100 value: 84.02199999999999 - type: recall_at_1000 value: 95.511 - type: recall_at_3 value: 43.126999999999995 - type: recall_at_5 value: 50.14999999999999 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWebmastersRetrieval revision: 160c094312a0e1facb97e55eeddb698c0abe3571 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 27.391 - type: map_at_10 value: 37.523 - type: map_at_100 value: 39.312000000000005 - type: map_at_1000 value: 39.54 - type: map_at_3 value: 34.231 - type: map_at_5 value: 36.062 - type: mrr_at_1 value: 32.016 - type: mrr_at_10 value: 41.747 - type: mrr_at_100 value: 42.812 - type: mrr_at_1000 value: 42.844 - type: mrr_at_3 value: 39.129999999999995 - type: mrr_at_5 value: 40.524 - type: ndcg_at_1 value: 32.016 - type: ndcg_at_10 value: 43.826 - type: ndcg_at_100 value: 50.373999999999995 - type: ndcg_at_1000 value: 52.318 - type: ndcg_at_3 value: 38.479 - type: ndcg_at_5 value: 40.944 - type: precision_at_1 value: 32.016 - type: precision_at_10 value: 8.280999999999999 - type: precision_at_100 value: 1.6760000000000002 - type: precision_at_1000 value: 0.25 - type: precision_at_3 value: 18.05 - type: precision_at_5 value: 13.083 - type: recall_at_1 value: 27.391 - type: recall_at_10 value: 56.928999999999995 - type: recall_at_100 value: 85.169 - type: recall_at_1000 value: 96.665 - type: recall_at_3 value: 42.264 - type: recall_at_5 value: 48.556 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWordpressRetrieval revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: BeIR/cqadupstack metrics: - type: map_at_1 value: 18.398 - type: map_at_10 value: 27.929 - type: map_at_100 value: 29.032999999999998 - type: map_at_1000 value: 29.126 - type: map_at_3 value: 25.070999999999998 - type: map_at_5 value: 26.583000000000002 - type: mrr_at_1 value: 19.963 - type: mrr_at_10 value: 29.997 - type: mrr_at_100 value: 30.9 - type: mrr_at_1000 value: 30.972 - type: mrr_at_3 value: 27.264 - type: mrr_at_5 value: 28.826 - type: ndcg_at_1 value: 19.963 - type: ndcg_at_10 value: 33.678999999999995 - type: ndcg_at_100 value: 38.931 - type: ndcg_at_1000 value: 41.379 - type: ndcg_at_3 value: 28.000000000000004 - type: ndcg_at_5 value: 30.637999999999998 - type: precision_at_1 value: 19.963 - type: precision_at_10 value: 5.7299999999999995 - type: precision_at_100 value: 0.902 - type: precision_at_1000 value: 0.122 - type: precision_at_3 value: 12.631 - type: precision_at_5 value: 9.057 - type: recall_at_1 value: 18.398 - type: recall_at_10 value: 49.254 - type: recall_at_100 value: 73.182 - type: recall_at_1000 value: 91.637 - type: recall_at_3 value: 34.06 - type: recall_at_5 value: 40.416000000000004 task: type: Retrieval - dataset: config: default name: MTEB ClimateFEVER revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 split: test type: mteb/climate-fever metrics: - type: map_at_1 value: 19.681 - type: map_at_10 value: 32.741 - type: map_at_100 value: 34.811 - type: map_at_1000 value: 35.003 - type: map_at_3 value: 27.697 - type: map_at_5 value: 30.372 - type: mrr_at_1 value: 44.951 - type: mrr_at_10 value: 56.34400000000001 - type: mrr_at_100 value: 56.961 - type: mrr_at_1000 value: 56.987 - type: mrr_at_3 value: 53.681 - type: mrr_at_5 value: 55.407 - type: ndcg_at_1 value: 44.951 - type: ndcg_at_10 value: 42.905 - type: ndcg_at_100 value: 49.95 - type: ndcg_at_1000 value: 52.917 - type: ndcg_at_3 value: 36.815 - type: ndcg_at_5 value: 38.817 - type: precision_at_1 value: 44.951 - type: precision_at_10 value: 12.989999999999998 - type: precision_at_100 value: 2.068 - type: precision_at_1000 value: 0.263 - type: precision_at_3 value: 27.275 - type: precision_at_5 value: 20.365 - type: recall_at_1 value: 19.681 - type: recall_at_10 value: 48.272999999999996 - type: recall_at_100 value: 71.87400000000001 - type: recall_at_1000 value: 87.929 - type: recall_at_3 value: 32.653999999999996 - type: recall_at_5 value: 39.364 task: type: Retrieval - dataset: config: default name: MTEB DBPedia revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 split: test type: mteb/dbpedia metrics: - type: map_at_1 value: 10.231 - type: map_at_10 value: 22.338 - type: map_at_100 value: 31.927 - type: map_at_1000 value: 33.87 - type: map_at_3 value: 15.559999999999999 - type: map_at_5 value: 18.239 - type: mrr_at_1 value: 75.0 - type: mrr_at_10 value: 81.303 - type: mrr_at_100 value: 81.523 - type: mrr_at_1000 value: 81.53 - type: mrr_at_3 value: 80.083 - type: mrr_at_5 value: 80.758 - type: ndcg_at_1 value: 64.625 - type: ndcg_at_10 value: 48.687000000000005 - type: ndcg_at_100 value: 52.791 - type: ndcg_at_1000 value: 60.041999999999994 - type: ndcg_at_3 value: 53.757999999999996 - type: ndcg_at_5 value: 50.76500000000001 - type: precision_at_1 value: 75.0 - type: precision_at_10 value: 38.3 - type: precision_at_100 value: 12.025 - type: precision_at_1000 value: 2.3970000000000002 - type: precision_at_3 value: 55.417 - type: precision_at_5 value: 47.5 - type: recall_at_1 value: 10.231 - type: recall_at_10 value: 27.697 - type: recall_at_100 value: 57.409 - type: recall_at_1000 value: 80.547 - type: recall_at_3 value: 16.668 - type: recall_at_5 value: 20.552 task: type: Retrieval - dataset: config: default name: MTEB EmotionClassification revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 split: test type: mteb/emotion metrics: - type: accuracy value: 61.365 - type: f1 value: 56.7540827912991 task: type: Classification - dataset: config: default name: MTEB FEVER revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 split: test type: mteb/fever metrics: - type: map_at_1 value: 83.479 - type: map_at_10 value: 88.898 - type: map_at_100 value: 89.11 - type: map_at_1000 value: 89.12400000000001 - type: map_at_3 value: 88.103 - type: map_at_5 value: 88.629 - type: mrr_at_1 value: 89.934 - type: mrr_at_10 value: 93.91000000000001 - type: mrr_at_100 value: 93.937 - type: mrr_at_1000 value: 93.938 - type: mrr_at_3 value: 93.62700000000001 - type: mrr_at_5 value: 93.84599999999999 - type: ndcg_at_1 value: 89.934 - type: ndcg_at_10 value: 91.574 - type: ndcg_at_100 value: 92.238 - type: ndcg_at_1000 value: 92.45 - type: ndcg_at_3 value: 90.586 - type: ndcg_at_5 value: 91.16300000000001 - type: precision_at_1 value: 89.934 - type: precision_at_10 value: 10.555 - type: precision_at_100 value: 1.1159999999999999 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 33.588 - type: precision_at_5 value: 20.642 - type: recall_at_1 value: 83.479 - type: recall_at_10 value: 94.971 - type: recall_at_100 value: 97.397 - type: recall_at_1000 value: 98.666 - type: recall_at_3 value: 92.24799999999999 - type: recall_at_5 value: 93.797 task: type: Retrieval - dataset: config: default name: MTEB FiQA2018 revision: 27a168819829fe9bcd655c2df245fb19452e8e06 split: test type: mteb/fiqa metrics: - type: map_at_1 value: 27.16 - type: map_at_10 value: 45.593 - type: map_at_100 value: 47.762 - type: map_at_1000 value: 47.899 - type: map_at_3 value: 39.237 - type: map_at_5 value: 42.970000000000006 - type: mrr_at_1 value: 52.623 - type: mrr_at_10 value: 62.637 - type: mrr_at_100 value: 63.169 - type: mrr_at_1000 value: 63.185 - type: mrr_at_3 value: 59.928000000000004 - type: mrr_at_5 value: 61.702999999999996 - type: ndcg_at_1 value: 52.623 - type: ndcg_at_10 value: 54.701 - type: ndcg_at_100 value: 61.263 - type: ndcg_at_1000 value: 63.134 - type: ndcg_at_3 value: 49.265 - type: ndcg_at_5 value: 51.665000000000006 - type: precision_at_1 value: 52.623 - type: precision_at_10 value: 15.185 - type: precision_at_100 value: 2.202 - type: precision_at_1000 value: 0.254 - type: precision_at_3 value: 32.767 - type: precision_at_5 value: 24.722 - type: recall_at_1 value: 27.16 - type: recall_at_10 value: 63.309000000000005 - type: recall_at_100 value: 86.722 - type: recall_at_1000 value: 97.505 - type: recall_at_3 value: 45.045 - type: recall_at_5 value: 54.02400000000001 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA revision: ab518f4d6fcca38d87c25209f94beba119d02014 split: test type: mteb/hotpotqa metrics: - type: map_at_1 value: 42.573 - type: map_at_10 value: 59.373 - type: map_at_100 value: 60.292 - type: map_at_1000 value: 60.358999999999995 - type: map_at_3 value: 56.159000000000006 - type: map_at_5 value: 58.123999999999995 - type: mrr_at_1 value: 85.14500000000001 - type: mrr_at_10 value: 89.25999999999999 - type: mrr_at_100 value: 89.373 - type: mrr_at_1000 value: 89.377 - type: mrr_at_3 value: 88.618 - type: mrr_at_5 value: 89.036 - type: ndcg_at_1 value: 85.14500000000001 - type: ndcg_at_10 value: 68.95 - type: ndcg_at_100 value: 71.95 - type: ndcg_at_1000 value: 73.232 - type: ndcg_at_3 value: 64.546 - type: ndcg_at_5 value: 66.945 - type: precision_at_1 value: 85.14500000000001 - type: precision_at_10 value: 13.865 - type: precision_at_100 value: 1.619 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 39.703 - type: precision_at_5 value: 25.718000000000004 - type: recall_at_1 value: 42.573 - type: recall_at_10 value: 69.325 - type: recall_at_100 value: 80.932 - type: recall_at_1000 value: 89.446 - type: recall_at_3 value: 59.553999999999995 - type: recall_at_5 value: 64.294 task: type: Retrieval - dataset: config: default name: MTEB ImdbClassification revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics: - type: accuracy value: 95.8336 - type: ap value: 93.78862962194073 - type: f1 value: 95.83192650728371 task: type: Classification - dataset: config: default name: MTEB MSMARCO revision: c5a29a104738b98a9e76336939199e264163d4a0 split: dev type: mteb/msmarco metrics: - type: map_at_1 value: 23.075000000000003 - type: map_at_10 value: 36.102000000000004 - type: map_at_100 value: 37.257 - type: map_at_1000 value: 37.3 - type: map_at_3 value: 32.144 - type: map_at_5 value: 34.359 - type: mrr_at_1 value: 23.711 - type: mrr_at_10 value: 36.671 - type: mrr_at_100 value: 37.763999999999996 - type: mrr_at_1000 value: 37.801 - type: mrr_at_3 value: 32.775 - type: mrr_at_5 value: 34.977000000000004 - type: ndcg_at_1 value: 23.711 - type: ndcg_at_10 value: 43.361 - type: ndcg_at_100 value: 48.839 - type: ndcg_at_1000 value: 49.88 - type: ndcg_at_3 value: 35.269 - type: ndcg_at_5 value: 39.224 - type: precision_at_1 value: 23.711 - type: precision_at_10 value: 6.866999999999999 - type: precision_at_100 value: 0.96 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 15.096000000000002 - type: precision_at_5 value: 11.083 - type: recall_at_1 value: 23.075000000000003 - type: recall_at_10 value: 65.756 - type: recall_at_100 value: 90.88199999999999 - type: recall_at_1000 value: 98.739 - type: recall_at_3 value: 43.691 - type: recall_at_5 value: 53.15800000000001 task: type: Retrieval - dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 97.69493844049248 - type: f1 value: 97.55048089616261 task: type: Classification - dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 88.75968992248062 - type: f1 value: 72.26321223399123 task: type: Classification - dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 82.40080699394754 - type: f1 value: 79.62590029057968 task: type: Classification - dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 84.49562878278414 - type: f1 value: 84.0040193313333 task: type: Classification - dataset: config: default name: MTEB MedrxivClusteringP2P revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics: - type: v_measure value: 39.386760057101945 task: type: Clustering - dataset: config: default name: MTEB MedrxivClusteringS2S revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics: - type: v_measure value: 37.89687154075537 task: type: Clustering - dataset: config: default name: MTEB MindSmallReranking revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 split: test type: mteb/mind_small metrics: - type: map value: 33.94151656057482 - type: mrr value: 35.32684700746953 task: type: Reranking - dataset: config: default name: MTEB NFCorpus revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 split: test type: mteb/nfcorpus metrics: - type: map_at_1 value: 6.239999999999999 - type: map_at_10 value: 14.862 - type: map_at_100 value: 18.955 - type: map_at_1000 value: 20.694000000000003 - type: map_at_3 value: 10.683 - type: map_at_5 value: 12.674 - type: mrr_at_1 value: 50.15500000000001 - type: mrr_at_10 value: 59.697 - type: mrr_at_100 value: 60.095 - type: mrr_at_1000 value: 60.129999999999995 - type: mrr_at_3 value: 58.35900000000001 - type: mrr_at_5 value: 58.839 - type: ndcg_at_1 value: 48.452 - type: ndcg_at_10 value: 39.341 - type: ndcg_at_100 value: 35.866 - type: ndcg_at_1000 value: 45.111000000000004 - type: ndcg_at_3 value: 44.527 - type: ndcg_at_5 value: 42.946 - type: precision_at_1 value: 50.15500000000001 - type: precision_at_10 value: 29.536 - type: precision_at_100 value: 9.142 - type: precision_at_1000 value: 2.2849999999999997 - type: precision_at_3 value: 41.899 - type: precision_at_5 value: 37.647000000000006 - type: recall_at_1 value: 6.239999999999999 - type: recall_at_10 value: 19.278000000000002 - type: recall_at_100 value: 36.074 - type: recall_at_1000 value: 70.017 - type: recall_at_3 value: 12.066 - type: recall_at_5 value: 15.254000000000001 task: type: Retrieval - dataset: config: default name: MTEB NQ revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 split: test type: mteb/nq metrics: - type: map_at_1 value: 39.75 - type: map_at_10 value: 56.443 - type: map_at_100 value: 57.233999999999995 - type: map_at_1000 value: 57.249 - type: map_at_3 value: 52.032999999999994 - type: map_at_5 value: 54.937999999999995 - type: mrr_at_1 value: 44.728 - type: mrr_at_10 value: 58.939 - type: mrr_at_100 value: 59.489000000000004 - type: mrr_at_1000 value: 59.499 - type: mrr_at_3 value: 55.711999999999996 - type: mrr_at_5 value: 57.89 - type: ndcg_at_1 value: 44.728 - type: ndcg_at_10 value: 63.998999999999995 - type: ndcg_at_100 value: 67.077 - type: ndcg_at_1000 value: 67.40899999999999 - type: ndcg_at_3 value: 56.266000000000005 - type: ndcg_at_5 value: 60.88 - type: precision_at_1 value: 44.728 - type: precision_at_10 value: 10.09 - type: precision_at_100 value: 1.1809999999999998 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 25.145 - type: precision_at_5 value: 17.822 - type: recall_at_1 value: 39.75 - type: recall_at_10 value: 84.234 - type: recall_at_100 value: 97.055 - type: recall_at_1000 value: 99.517 - type: recall_at_3 value: 64.851 - type: recall_at_5 value: 75.343 task: type: Retrieval - dataset: config: default name: MTEB QuoraRetrieval revision: None split: test type: mteb/quora metrics: - type: map_at_1 value: 72.085 - type: map_at_10 value: 86.107 - type: map_at_100 value: 86.727 - type: map_at_1000 value: 86.74 - type: map_at_3 value: 83.21 - type: map_at_5 value: 85.06 - type: mrr_at_1 value: 82.94 - type: mrr_at_10 value: 88.845 - type: mrr_at_100 value: 88.926 - type: mrr_at_1000 value: 88.927 - type: mrr_at_3 value: 87.993 - type: mrr_at_5 value: 88.62299999999999 - type: ndcg_at_1 value: 82.97 - type: ndcg_at_10 value: 89.645 - type: ndcg_at_100 value: 90.717 - type: ndcg_at_1000 value: 90.78 - type: ndcg_at_3 value: 86.99900000000001 - type: ndcg_at_5 value: 88.52600000000001 - type: precision_at_1 value: 82.97 - type: precision_at_10 value: 13.569 - type: precision_at_100 value: 1.539 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.043 - type: precision_at_5 value: 24.992 - type: recall_at_1 value: 72.085 - type: recall_at_10 value: 96.262 - type: recall_at_100 value: 99.77000000000001 - type: recall_at_1000 value: 99.997 - type: recall_at_3 value: 88.652 - type: recall_at_5 value: 93.01899999999999 task: type: Retrieval - dataset: config: default name: MTEB RedditClustering revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics: - type: v_measure value: 55.82153952668092 task: type: Clustering - dataset: config: default name: MTEB RedditClusteringP2P revision: 282350215ef01743dc01b456c7f5241fa8937f16 split: test type: mteb/reddit-clustering-p2p metrics: - type: v_measure value: 62.094465801879295 task: type: Clustering - dataset: config: default name: MTEB SCIDOCS revision: None split: test type: mteb/scidocs metrics: - type: map_at_1 value: 5.688 - type: map_at_10 value: 15.201999999999998 - type: map_at_100 value: 18.096 - type: map_at_1000 value: 18.481 - type: map_at_3 value: 10.734 - type: map_at_5 value: 12.94 - type: mrr_at_1 value: 28.000000000000004 - type: mrr_at_10 value: 41.101 - type: mrr_at_100 value: 42.202 - type: mrr_at_1000 value: 42.228 - type: mrr_at_3 value: 37.683 - type: mrr_at_5 value: 39.708 - type: ndcg_at_1 value: 28.000000000000004 - type: ndcg_at_10 value: 24.976000000000003 - type: ndcg_at_100 value: 35.129 - type: ndcg_at_1000 value: 40.77 - type: ndcg_at_3 value: 23.787 - type: ndcg_at_5 value: 20.816000000000003 - type: precision_at_1 value: 28.000000000000004 - type: precision_at_10 value: 13.04 - type: precision_at_100 value: 2.761 - type: precision_at_1000 value: 0.41000000000000003 - type: precision_at_3 value: 22.6 - type: precision_at_5 value: 18.52 - type: recall_at_1 value: 5.688 - type: recall_at_10 value: 26.43 - type: recall_at_100 value: 56.02 - type: recall_at_1000 value: 83.21 - type: recall_at_3 value: 13.752 - type: recall_at_5 value: 18.777 task: type: Retrieval - dataset: config: default name: MTEB SICK-R revision: a6ea5a8cab320b040a23452cc28066d9beae2cee split: test type: mteb/sickr-sts metrics: - type: cos_sim_pearson value: 85.15084859283178 - type: cos_sim_spearman value: 80.49030614009419 - type: euclidean_pearson value: 81.84574978672468 - type: euclidean_spearman value: 79.89787150656818 - type: manhattan_pearson value: 81.63076538567131 - type: manhattan_spearman value: 79.69867352121841 task: type: STS - dataset: config: default name: MTEB STS12 revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cos_sim_pearson value: 84.64097921490992 - type: cos_sim_spearman value: 77.25370084896514 - type: euclidean_pearson value: 82.71210826468788 - type: euclidean_spearman value: 78.50445584994826 - type: manhattan_pearson value: 82.92580164330298 - type: manhattan_spearman value: 78.69686891301019 task: type: STS - dataset: config: default name: MTEB STS13 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cos_sim_pearson value: 87.24596417308994 - type: cos_sim_spearman value: 87.79454220555091 - type: euclidean_pearson value: 87.40242561671164 - type: euclidean_spearman value: 88.25955597373556 - type: manhattan_pearson value: 87.25160240485849 - type: manhattan_spearman value: 88.155794979818 task: type: STS - dataset: config: default name: MTEB STS14 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cos_sim_pearson value: 84.44914233422564 - type: cos_sim_spearman value: 82.91015471820322 - type: euclidean_pearson value: 84.7206656630327 - type: euclidean_spearman value: 83.86408872059216 - type: manhattan_pearson value: 84.72816725158454 - type: manhattan_spearman value: 84.01603388572788 task: type: STS - dataset: config: default name: MTEB STS15 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cos_sim_pearson value: 87.6168026237477 - type: cos_sim_spearman value: 88.45414278092397 - type: euclidean_pearson value: 88.57023240882022 - type: euclidean_spearman value: 89.04102190922094 - type: manhattan_pearson value: 88.66695535796354 - type: manhattan_spearman value: 89.19898476680969 task: type: STS - dataset: config: default name: MTEB STS16 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cos_sim_pearson value: 84.27925826089424 - type: cos_sim_spearman value: 85.45291099550461 - type: euclidean_pearson value: 83.63853036580834 - type: euclidean_spearman value: 84.33468035821484 - type: manhattan_pearson value: 83.72778773251596 - type: manhattan_spearman value: 84.51583132445376 task: type: STS - dataset: config: en-en name: MTEB STS17 (en-en) revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d split: test type: mteb/sts17-crosslingual-sts metrics: - type: cos_sim_pearson value: 89.67375185692552 - type: cos_sim_spearman value: 90.32542469203855 - type: euclidean_pearson value: 89.63513717951847 - type: euclidean_spearman value: 89.87760271003745 - type: manhattan_pearson value: 89.28381452982924 - type: manhattan_spearman value: 89.53568197785721 task: type: STS - dataset: config: en name: MTEB STS22 (en) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 66.24644693819846 - type: cos_sim_spearman value: 66.09889420525377 - type: euclidean_pearson value: 63.72551583520747 - type: euclidean_spearman value: 63.01385470780679 - type: manhattan_pearson value: 64.09258157214097 - type: manhattan_spearman value: 63.080517752822594 task: type: STS - dataset: config: default name: MTEB STSBenchmark revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cos_sim_pearson value: 86.27321463839989 - type: cos_sim_spearman value: 86.37572865993327 - type: euclidean_pearson value: 86.36268020198149 - type: euclidean_spearman value: 86.31089339478922 - type: manhattan_pearson value: 86.4260445761947 - type: manhattan_spearman value: 86.45885895320457 task: type: STS - dataset: config: default name: MTEB SciDocsRR revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: map value: 86.52456702387798 - type: mrr value: 96.34556529164372 task: type: Reranking - dataset: config: default name: MTEB SciFact revision: 0228b52cf27578f30900b9e5271d331663a030d7 split: test type: mteb/scifact metrics: - type: map_at_1 value: 61.99400000000001 - type: map_at_10 value: 73.38799999999999 - type: map_at_100 value: 73.747 - type: map_at_1000 value: 73.75 - type: map_at_3 value: 70.04599999999999 - type: map_at_5 value: 72.095 - type: mrr_at_1 value: 65.0 - type: mrr_at_10 value: 74.42800000000001 - type: mrr_at_100 value: 74.722 - type: mrr_at_1000 value: 74.725 - type: mrr_at_3 value: 72.056 - type: mrr_at_5 value: 73.60600000000001 - type: ndcg_at_1 value: 65.0 - type: ndcg_at_10 value: 78.435 - type: ndcg_at_100 value: 79.922 - type: ndcg_at_1000 value: 80.00500000000001 - type: ndcg_at_3 value: 73.05199999999999 - type: ndcg_at_5 value: 75.98 - type: precision_at_1 value: 65.0 - type: precision_at_10 value: 10.5 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 28.555999999999997 - type: precision_at_5 value: 19.0 - type: recall_at_1 value: 61.99400000000001 - type: recall_at_10 value: 92.72200000000001 - type: recall_at_100 value: 99.333 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 78.739 - type: recall_at_5 value: 85.828 task: type: Retrieval - dataset: config: default name: MTEB SprintDuplicateQuestions revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics: - type: cos_sim_accuracy value: 99.79009900990098 - type: cos_sim_ap value: 95.3203137438653 - type: cos_sim_f1 value: 89.12386706948641 - type: cos_sim_precision value: 89.75659229208925 - type: cos_sim_recall value: 88.5 - type: dot_accuracy value: 99.67821782178218 - type: dot_ap value: 89.94069840000675 - type: dot_f1 value: 83.45902463549521 - type: dot_precision value: 83.9231547017189 - type: dot_recall value: 83.0 - type: euclidean_accuracy value: 99.78613861386138 - type: euclidean_ap value: 95.10648259135526 - type: euclidean_f1 value: 88.77338877338877 - type: euclidean_precision value: 92.42424242424242 - type: euclidean_recall value: 85.39999999999999 - type: manhattan_accuracy value: 99.7950495049505 - type: manhattan_ap value: 95.29987661320946 - type: manhattan_f1 value: 89.21313183949972 - type: manhattan_precision value: 93.14472252448314 - type: manhattan_recall value: 85.6 - type: max_accuracy value: 99.7950495049505 - type: max_ap value: 95.3203137438653 - type: max_f1 value: 89.21313183949972 task: type: PairClassification - dataset: config: default name: MTEB StackExchangeClustering revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: v_measure value: 67.65446577183913 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: v_measure value: 46.30749237193961 task: type: Clustering - dataset: config: default name: MTEB StackOverflowDupQuestions revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics: - type: map value: 54.91481849959949 - type: mrr value: 55.853506175197346 task: type: Reranking - dataset: config: default name: MTEB SummEval revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cos_sim_pearson value: 30.08196549170419 - type: cos_sim_spearman value: 31.16661390597077 - type: dot_pearson value: 29.892258410943466 - type: dot_spearman value: 30.51328811965085 task: type: Summarization - dataset: config: default name: MTEB TRECCOVID revision: None split: test type: mteb/trec-covid metrics: - type: map_at_1 value: 0.23900000000000002 - type: map_at_10 value: 2.173 - type: map_at_100 value: 14.24 - type: map_at_1000 value: 35.309000000000005 - type: map_at_3 value: 0.7100000000000001 - type: map_at_5 value: 1.163 - type: mrr_at_1 value: 92.0 - type: mrr_at_10 value: 96.0 - type: mrr_at_100 value: 96.0 - type: mrr_at_1000 value: 96.0 - type: mrr_at_3 value: 96.0 - type: mrr_at_5 value: 96.0 - type: ndcg_at_1 value: 90.0 - type: ndcg_at_10 value: 85.382 - type: ndcg_at_100 value: 68.03 - type: ndcg_at_1000 value: 61.021 - type: ndcg_at_3 value: 89.765 - type: ndcg_at_5 value: 88.444 - type: precision_at_1 value: 92.0 - type: precision_at_10 value: 88.0 - type: precision_at_100 value: 70.02000000000001 - type: precision_at_1000 value: 26.984 - type: precision_at_3 value: 94.0 - type: precision_at_5 value: 92.80000000000001 - type: recall_at_1 value: 0.23900000000000002 - type: recall_at_10 value: 2.313 - type: recall_at_100 value: 17.049 - type: recall_at_1000 value: 57.489999999999995 - type: recall_at_3 value: 0.737 - type: recall_at_5 value: 1.221 task: type: Retrieval - dataset: config: default name: MTEB Touche2020 revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f split: test type: mteb/touche2020 metrics: - type: map_at_1 value: 2.75 - type: map_at_10 value: 11.29 - type: map_at_100 value: 18.032999999999998 - type: map_at_1000 value: 19.746 - type: map_at_3 value: 6.555 - type: map_at_5 value: 8.706999999999999 - type: mrr_at_1 value: 34.694 - type: mrr_at_10 value: 50.55 - type: mrr_at_100 value: 51.659 - type: mrr_at_1000 value: 51.659 - type: mrr_at_3 value: 47.278999999999996 - type: mrr_at_5 value: 49.728 - type: ndcg_at_1 value: 32.653 - type: ndcg_at_10 value: 27.894000000000002 - type: ndcg_at_100 value: 39.769 - type: ndcg_at_1000 value: 51.495999999999995 - type: ndcg_at_3 value: 32.954 - type: ndcg_at_5 value: 31.502999999999997 - type: precision_at_1 value: 34.694 - type: precision_at_10 value: 23.265 - type: precision_at_100 value: 7.898 - type: precision_at_1000 value: 1.58 - type: precision_at_3 value: 34.694 - type: precision_at_5 value: 31.429000000000002 - type: recall_at_1 value: 2.75 - type: recall_at_10 value: 16.953 - type: recall_at_100 value: 48.68 - type: recall_at_1000 value: 85.18599999999999 - type: recall_at_3 value: 7.710999999999999 - type: recall_at_5 value: 11.484 task: type: Retrieval - dataset: config: default name: MTEB ToxicConversationsClassification revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c split: test type: mteb/toxic_conversations_50k metrics: - type: accuracy value: 82.66099999999999 - type: ap value: 25.555698090238337 - type: f1 value: 66.48402012461622 task: type: Classification - dataset: config: default name: MTEB TweetSentimentExtractionClassification revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics: - type: accuracy value: 72.94567062818335 - type: f1 value: 73.28139189595674 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: v_measure value: 49.581627240203474 task: type: Clustering - dataset: config: default name: MTEB TwitterSemEval2015 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics: - type: cos_sim_accuracy value: 87.78089050485785 - type: cos_sim_ap value: 79.64487116574168 - type: cos_sim_f1 value: 72.46563021970964 - type: cos_sim_precision value: 70.62359128474831 - type: cos_sim_recall value: 74.40633245382587 - type: dot_accuracy value: 86.2609524944865 - type: dot_ap value: 75.513046857613 - type: dot_f1 value: 68.58213616489695 - type: dot_precision value: 65.12455516014235 - type: dot_recall value: 72.42744063324538 - type: euclidean_accuracy value: 87.6080348095607 - type: euclidean_ap value: 79.00204933649795 - type: euclidean_f1 value: 72.14495342605589 - type: euclidean_precision value: 69.85421299728193 - type: euclidean_recall value: 74.5910290237467 - type: manhattan_accuracy value: 87.59611372712642 - type: manhattan_ap value: 78.78523756706264 - type: manhattan_f1 value: 71.86499137718648 - type: manhattan_precision value: 67.39833641404806 - type: manhattan_recall value: 76.96569920844327 - type: max_accuracy value: 87.78089050485785 - type: max_ap value: 79.64487116574168 - type: max_f1 value: 72.46563021970964 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cos_sim_accuracy value: 89.98719292117825 - type: cos_sim_ap value: 87.58146137353202 - type: cos_sim_f1 value: 80.28543232369239 - type: cos_sim_precision value: 79.1735289714029 - type: cos_sim_recall value: 81.42901139513397 - type: dot_accuracy value: 88.9199363526992 - type: dot_ap value: 84.98499998630417 - type: dot_f1 value: 78.21951400757969 - type: dot_precision value: 75.58523624874336 - type: dot_recall value: 81.04404065291038 - type: euclidean_accuracy value: 89.77374160748244 - type: euclidean_ap value: 87.35151562835209 - type: euclidean_f1 value: 79.92160922940393 - type: euclidean_precision value: 76.88531587933979 - type: euclidean_recall value: 83.20757622420696 - type: manhattan_accuracy value: 89.72717041176699 - type: manhattan_ap value: 87.34065592142515 - type: manhattan_f1 value: 79.85603419187943 - type: manhattan_precision value: 77.82243332115455 - type: manhattan_recall value: 81.99876809362489 - type: max_accuracy value: 89.98719292117825 - type: max_ap value: 87.58146137353202 - type: max_f1 value: 80.28543232369239 task: type: PairClassification - dataset: config: default name: MTEB AFQMC revision: b44c3b011063adb25877c13823db83bb193913c4 split: validation type: C-MTEB/AFQMC metrics: - type: cos_sim_pearson value: 53.45954203592337 - type: cos_sim_spearman value: 58.42154680418638 - type: euclidean_pearson value: 56.41543791722753 - type: euclidean_spearman value: 58.39328016640146 - type: manhattan_pearson value: 56.318510356833876 - type: manhattan_spearman value: 58.28423447818184 task: type: STS - dataset: config: default name: MTEB ATEC revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 split: test type: C-MTEB/ATEC metrics: - type: cos_sim_pearson value: 50.78356460675945 - type: cos_sim_spearman value: 55.6530411663269 - type: euclidean_pearson value: 56.50763660417816 - type: euclidean_spearman value: 55.733823335669065 - type: manhattan_pearson value: 56.45323093512866 - type: manhattan_spearman value: 55.63248619032702 task: type: STS - dataset: config: zh name: MTEB AmazonReviewsClassification (zh) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 47.209999999999994 - type: f1 value: 46.08892432018655 task: type: Classification - dataset: config: default name: MTEB BQ revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 split: test type: C-MTEB/BQ metrics: - type: cos_sim_pearson value: 70.25573992001478 - type: cos_sim_spearman value: 73.85247134951433 - type: euclidean_pearson value: 72.60033082168442 - type: euclidean_spearman value: 73.72445893756499 - type: manhattan_pearson value: 72.59932284620231 - type: manhattan_spearman value: 73.68002490614583 task: type: STS - dataset: config: default name: MTEB CLSClusteringP2P revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 split: test type: C-MTEB/CLSClusteringP2P metrics: - type: v_measure value: 45.21317724305628 task: type: Clustering - dataset: config: default name: MTEB CLSClusteringS2S revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f split: test type: C-MTEB/CLSClusteringS2S metrics: - type: v_measure value: 42.49825170976724 task: type: Clustering - dataset: config: default name: MTEB CMedQAv1 revision: 8d7f1e942507dac42dc58017c1a001c3717da7df split: test type: C-MTEB/CMedQAv1-reranking metrics: - type: map value: 88.15661686810597 - type: mrr value: 90.11222222222223 task: type: Reranking - dataset: config: default name: MTEB CMedQAv2 revision: 23d186750531a14a0357ca22cd92d712fd512ea0 split: test type: C-MTEB/CMedQAv2-reranking metrics: - type: map value: 88.1204726064383 - type: mrr value: 90.20142857142858 task: type: Reranking - dataset: config: default name: MTEB CmedqaRetrieval revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 split: dev type: C-MTEB/CmedqaRetrieval metrics: - type: map_at_1 value: 27.224999999999998 - type: map_at_10 value: 40.169 - type: map_at_100 value: 42.0 - type: map_at_1000 value: 42.109 - type: map_at_3 value: 35.76 - type: map_at_5 value: 38.221 - type: mrr_at_1 value: 40.56 - type: mrr_at_10 value: 49.118 - type: mrr_at_100 value: 50.092999999999996 - type: mrr_at_1000 value: 50.133 - type: mrr_at_3 value: 46.507 - type: mrr_at_5 value: 47.973 - type: ndcg_at_1 value: 40.56 - type: ndcg_at_10 value: 46.972 - type: ndcg_at_100 value: 54.04 - type: ndcg_at_1000 value: 55.862 - type: ndcg_at_3 value: 41.36 - type: ndcg_at_5 value: 43.704 - type: precision_at_1 value: 40.56 - type: precision_at_10 value: 10.302999999999999 - type: precision_at_100 value: 1.606 - type: precision_at_1000 value: 0.184 - type: precision_at_3 value: 23.064 - type: precision_at_5 value: 16.764000000000003 - type: recall_at_1 value: 27.224999999999998 - type: recall_at_10 value: 58.05200000000001 - type: recall_at_100 value: 87.092 - type: recall_at_1000 value: 99.099 - type: recall_at_3 value: 41.373 - type: recall_at_5 value: 48.453 task: type: Retrieval - dataset: config: default name: MTEB Cmnli revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 split: validation type: C-MTEB/CMNLI metrics: - type: cos_sim_accuracy value: 77.40228502705953 - type: cos_sim_ap value: 86.22359172956327 - type: cos_sim_f1 value: 78.96328293736501 - type: cos_sim_precision value: 73.36945615091311 - type: cos_sim_recall value: 85.48047696983868 - type: dot_accuracy value: 75.53818400481059 - type: dot_ap value: 83.70164011305312 - type: dot_f1 value: 77.67298719348754 - type: dot_precision value: 67.49482401656314 - type: dot_recall value: 91.46598082768296 - type: euclidean_accuracy value: 77.94347564642213 - type: euclidean_ap value: 86.4652108728609 - type: euclidean_f1 value: 79.15555555555555 - type: euclidean_precision value: 75.41816641964853 - type: euclidean_recall value: 83.28267477203647 - type: manhattan_accuracy value: 77.45039085989175 - type: manhattan_ap value: 86.09986583900665 - type: manhattan_f1 value: 78.93669264438988 - type: manhattan_precision value: 72.63261296660117 - type: manhattan_recall value: 86.43909282207154 - type: max_accuracy value: 77.94347564642213 - type: max_ap value: 86.4652108728609 - type: max_f1 value: 79.15555555555555 task: type: PairClassification - dataset: config: default name: MTEB CovidRetrieval revision: 1271c7809071a13532e05f25fb53511ffce77117 split: dev type: C-MTEB/CovidRetrieval metrics: - type: map_at_1 value: 69.336 - type: map_at_10 value: 77.16 - type: map_at_100 value: 77.47500000000001 - type: map_at_1000 value: 77.482 - type: map_at_3 value: 75.42999999999999 - type: map_at_5 value: 76.468 - type: mrr_at_1 value: 69.44200000000001 - type: mrr_at_10 value: 77.132 - type: mrr_at_100 value: 77.43299999999999 - type: mrr_at_1000 value: 77.44 - type: mrr_at_3 value: 75.395 - type: mrr_at_5 value: 76.459 - type: ndcg_at_1 value: 69.547 - type: ndcg_at_10 value: 80.794 - type: ndcg_at_100 value: 82.245 - type: ndcg_at_1000 value: 82.40899999999999 - type: ndcg_at_3 value: 77.303 - type: ndcg_at_5 value: 79.168 - type: precision_at_1 value: 69.547 - type: precision_at_10 value: 9.305 - type: precision_at_100 value: 0.9979999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 27.749000000000002 - type: precision_at_5 value: 17.576 - type: recall_at_1 value: 69.336 - type: recall_at_10 value: 92.097 - type: recall_at_100 value: 98.736 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 82.64 - type: recall_at_5 value: 87.144 task: type: Retrieval - dataset: config: default name: MTEB DuRetrieval revision: a1a333e290fe30b10f3f56498e3a0d911a693ced split: dev type: C-MTEB/DuRetrieval metrics: - type: map_at_1 value: 26.817999999999998 - type: map_at_10 value: 82.67 - type: map_at_100 value: 85.304 - type: map_at_1000 value: 85.334 - type: map_at_3 value: 57.336 - type: map_at_5 value: 72.474 - type: mrr_at_1 value: 91.45 - type: mrr_at_10 value: 94.272 - type: mrr_at_100 value: 94.318 - type: mrr_at_1000 value: 94.32000000000001 - type: mrr_at_3 value: 94.0 - type: mrr_at_5 value: 94.17699999999999 - type: ndcg_at_1 value: 91.45 - type: ndcg_at_10 value: 89.404 - type: ndcg_at_100 value: 91.724 - type: ndcg_at_1000 value: 91.973 - type: ndcg_at_3 value: 88.104 - type: ndcg_at_5 value: 87.25699999999999 - type: precision_at_1 value: 91.45 - type: precision_at_10 value: 42.585 - type: precision_at_100 value: 4.838 - type: precision_at_1000 value: 0.49 - type: precision_at_3 value: 78.8 - type: precision_at_5 value: 66.66 - type: recall_at_1 value: 26.817999999999998 - type: recall_at_10 value: 90.67 - type: recall_at_100 value: 98.36200000000001 - type: recall_at_1000 value: 99.583 - type: recall_at_3 value: 59.614999999999995 - type: recall_at_5 value: 77.05199999999999 task: type: Retrieval - dataset: config: default name: MTEB EcomRetrieval revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 split: dev type: C-MTEB/EcomRetrieval metrics: - type: map_at_1 value: 47.699999999999996 - type: map_at_10 value: 57.589999999999996 - type: map_at_100 value: 58.226 - type: map_at_1000 value: 58.251 - type: map_at_3 value: 55.233 - type: map_at_5 value: 56.633 - type: mrr_at_1 value: 47.699999999999996 - type: mrr_at_10 value: 57.589999999999996 - type: mrr_at_100 value: 58.226 - type: mrr_at_1000 value: 58.251 - type: mrr_at_3 value: 55.233 - type: mrr_at_5 value: 56.633 - type: ndcg_at_1 value: 47.699999999999996 - type: ndcg_at_10 value: 62.505 - type: ndcg_at_100 value: 65.517 - type: ndcg_at_1000 value: 66.19800000000001 - type: ndcg_at_3 value: 57.643 - type: ndcg_at_5 value: 60.181 - type: precision_at_1 value: 47.699999999999996 - type: precision_at_10 value: 7.8 - type: precision_at_100 value: 0.919 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 21.532999999999998 - type: precision_at_5 value: 14.16 - type: recall_at_1 value: 47.699999999999996 - type: recall_at_10 value: 78.0 - type: recall_at_100 value: 91.9 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 64.60000000000001 - type: recall_at_5 value: 70.8 task: type: Retrieval - dataset: config: default name: MTEB IFlyTek revision: 421605374b29664c5fc098418fe20ada9bd55f8a split: validation type: C-MTEB/IFlyTek-classification metrics: - type: accuracy value: 44.84801846864178 - type: f1 value: 37.47347897956339 task: type: Classification - dataset: config: default name: MTEB JDReview revision: b7c64bd89eb87f8ded463478346f76731f07bf8b split: test type: C-MTEB/JDReview-classification metrics: - type: accuracy value: 85.81613508442777 - type: ap value: 52.68244615477374 - type: f1 value: 80.0445640948843 task: type: Classification - dataset: config: default name: MTEB LCQMC revision: 17f9b096f80380fce5ed12a9be8be7784b337daf split: test type: C-MTEB/LCQMC metrics: - type: cos_sim_pearson value: 69.57786502217138 - type: cos_sim_spearman value: 75.39106054489906 - type: euclidean_pearson value: 73.72082954602402 - type: euclidean_spearman value: 75.14421475913619 - type: manhattan_pearson value: 73.62463076633642 - type: manhattan_spearman value: 75.01301565104112 task: type: STS - dataset: config: default name: MTEB MMarcoReranking revision: None split: dev type: C-MTEB/Mmarco-reranking metrics: - type: map value: 29.143797057999134 - type: mrr value: 28.08174603174603 task: type: Reranking - dataset: config: default name: MTEB MMarcoRetrieval revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 split: dev type: C-MTEB/MMarcoRetrieval metrics: - type: map_at_1 value: 70.492 - type: map_at_10 value: 79.501 - type: map_at_100 value: 79.728 - type: map_at_1000 value: 79.735 - type: map_at_3 value: 77.77 - type: map_at_5 value: 78.851 - type: mrr_at_1 value: 72.822 - type: mrr_at_10 value: 80.001 - type: mrr_at_100 value: 80.19 - type: mrr_at_1000 value: 80.197 - type: mrr_at_3 value: 78.484 - type: mrr_at_5 value: 79.42099999999999 - type: ndcg_at_1 value: 72.822 - type: ndcg_at_10 value: 83.013 - type: ndcg_at_100 value: 84.013 - type: ndcg_at_1000 value: 84.20400000000001 - type: ndcg_at_3 value: 79.728 - type: ndcg_at_5 value: 81.542 - type: precision_at_1 value: 72.822 - type: precision_at_10 value: 9.917 - type: precision_at_100 value: 1.042 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 29.847 - type: precision_at_5 value: 18.871 - type: recall_at_1 value: 70.492 - type: recall_at_10 value: 93.325 - type: recall_at_100 value: 97.822 - type: recall_at_1000 value: 99.319 - type: recall_at_3 value: 84.636 - type: recall_at_5 value: 88.93100000000001 task: type: Retrieval - dataset: config: zh-CN name: MTEB MassiveIntentClassification (zh-CN) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 76.88298587760592 - type: f1 value: 73.89001762017176 task: type: Classification - dataset: config: zh-CN name: MTEB MassiveScenarioClassification (zh-CN) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 80.76328177538669 - type: f1 value: 80.24718532423358 task: type: Classification - dataset: config: default name: MTEB MedicalRetrieval revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 split: dev type: C-MTEB/MedicalRetrieval metrics: - type: map_at_1 value: 49.6 - type: map_at_10 value: 55.620999999999995 - type: map_at_100 value: 56.204 - type: map_at_1000 value: 56.251 - type: map_at_3 value: 54.132999999999996 - type: map_at_5 value: 54.933 - type: mrr_at_1 value: 49.7 - type: mrr_at_10 value: 55.67100000000001 - type: mrr_at_100 value: 56.254000000000005 - type: mrr_at_1000 value: 56.301 - type: mrr_at_3 value: 54.18300000000001 - type: mrr_at_5 value: 54.983000000000004 - type: ndcg_at_1 value: 49.6 - type: ndcg_at_10 value: 58.645 - type: ndcg_at_100 value: 61.789 - type: ndcg_at_1000 value: 63.219 - type: ndcg_at_3 value: 55.567 - type: ndcg_at_5 value: 57.008 - type: precision_at_1 value: 49.6 - type: precision_at_10 value: 6.819999999999999 - type: precision_at_100 value: 0.836 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 19.900000000000002 - type: precision_at_5 value: 12.64 - type: recall_at_1 value: 49.6 - type: recall_at_10 value: 68.2 - type: recall_at_100 value: 83.6 - type: recall_at_1000 value: 95.3 - type: recall_at_3 value: 59.699999999999996 - type: recall_at_5 value: 63.2 task: type: Retrieval - dataset: config: default name: MTEB MultilingualSentiment revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a split: validation type: C-MTEB/MultilingualSentiment-classification metrics: - type: accuracy value: 74.45666666666666 - type: f1 value: 74.32582402190089 task: type: Classification - dataset: config: default name: MTEB Ocnli revision: 66e76a618a34d6d565d5538088562851e6daa7ec split: validation type: C-MTEB/OCNLI metrics: - type: cos_sim_accuracy value: 80.67135896047645 - type: cos_sim_ap value: 87.60421240712051 - type: cos_sim_f1 value: 82.1304131408661 - type: cos_sim_precision value: 77.68361581920904 - type: cos_sim_recall value: 87.11721224920802 - type: dot_accuracy value: 79.04710341093666 - type: dot_ap value: 85.6370059719336 - type: dot_f1 value: 80.763723150358 - type: dot_precision value: 73.69337979094077 - type: dot_recall value: 89.33474128827878 - type: euclidean_accuracy value: 81.05035192203573 - type: euclidean_ap value: 87.7880240053663 - type: euclidean_f1 value: 82.50244379276637 - type: euclidean_precision value: 76.7970882620564 - type: euclidean_recall value: 89.1235480464625 - type: manhattan_accuracy value: 80.61721710882512 - type: manhattan_ap value: 87.43568120591175 - type: manhattan_f1 value: 81.89526184538653 - type: manhattan_precision value: 77.5992438563327 - type: manhattan_recall value: 86.6948257655755 - type: max_accuracy value: 81.05035192203573 - type: max_ap value: 87.7880240053663 - type: max_f1 value: 82.50244379276637 task: type: PairClassification - dataset: config: default name: MTEB OnlineShopping revision: e610f2ebd179a8fda30ae534c3878750a96db120 split: test type: C-MTEB/OnlineShopping-classification metrics: - type: accuracy value: 93.5 - type: ap value: 91.31357903446782 - type: f1 value: 93.48088994006616 task: type: Classification - dataset: config: default name: MTEB PAWSX revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 split: test type: C-MTEB/PAWSX metrics: - type: cos_sim_pearson value: 36.93293453538077 - type: cos_sim_spearman value: 42.45972506308574 - type: euclidean_pearson value: 42.34945133152159 - type: euclidean_spearman value: 42.331610303674644 - type: manhattan_pearson value: 42.31455070249498 - type: manhattan_spearman value: 42.19887982891834 task: type: STS - dataset: config: default name: MTEB QBQTC revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 split: test type: C-MTEB/QBQTC metrics: - type: cos_sim_pearson value: 33.683290790043785 - type: cos_sim_spearman value: 35.149171171202994 - type: euclidean_pearson value: 32.33806561267862 - type: euclidean_spearman value: 34.483576387347966 - type: manhattan_pearson value: 32.47629754599608 - type: manhattan_spearman value: 34.66434471867615 task: type: STS - dataset: config: zh name: MTEB STS22 (zh) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 66.46322760516104 - type: cos_sim_spearman value: 67.398478319726 - type: euclidean_pearson value: 64.7223480293625 - type: euclidean_spearman value: 66.83118568812951 - type: manhattan_pearson value: 64.88440039828305 - type: manhattan_spearman value: 66.80429458952257 task: type: STS - dataset: config: default name: MTEB STSB revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 split: test type: C-MTEB/STSB metrics: - type: cos_sim_pearson value: 79.08991383232105 - type: cos_sim_spearman value: 79.39715677296854 - type: euclidean_pearson value: 78.63201279320496 - type: euclidean_spearman value: 79.40262660785731 - type: manhattan_pearson value: 78.98138363146906 - type: manhattan_spearman value: 79.79968413014194 task: type: STS - dataset: config: default name: MTEB T2Reranking revision: 76631901a18387f85eaa53e5450019b87ad58ef9 split: dev type: C-MTEB/T2Reranking metrics: - type: map value: 67.43289278789972 - type: mrr value: 77.53012460908535 task: type: Reranking - dataset: config: default name: MTEB T2Retrieval revision: 8731a845f1bf500a4f111cf1070785c793d10e64 split: dev type: C-MTEB/T2Retrieval metrics: - type: map_at_1 value: 27.733999999999998 - type: map_at_10 value: 78.24799999999999 - type: map_at_100 value: 81.765 - type: map_at_1000 value: 81.824 - type: map_at_3 value: 54.92 - type: map_at_5 value: 67.61399999999999 - type: mrr_at_1 value: 90.527 - type: mrr_at_10 value: 92.843 - type: mrr_at_100 value: 92.927 - type: mrr_at_1000 value: 92.93 - type: mrr_at_3 value: 92.45100000000001 - type: mrr_at_5 value: 92.693 - type: ndcg_at_1 value: 90.527 - type: ndcg_at_10 value: 85.466 - type: ndcg_at_100 value: 88.846 - type: ndcg_at_1000 value: 89.415 - type: ndcg_at_3 value: 86.768 - type: ndcg_at_5 value: 85.46000000000001 - type: precision_at_1 value: 90.527 - type: precision_at_10 value: 42.488 - type: precision_at_100 value: 5.024 - type: precision_at_1000 value: 0.516 - type: precision_at_3 value: 75.907 - type: precision_at_5 value: 63.727000000000004 - type: recall_at_1 value: 27.733999999999998 - type: recall_at_10 value: 84.346 - type: recall_at_100 value: 95.536 - type: recall_at_1000 value: 98.42999999999999 - type: recall_at_3 value: 56.455 - type: recall_at_5 value: 70.755 task: type: Retrieval - dataset: config: default name: MTEB TNews revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 split: validation type: C-MTEB/TNews-classification metrics: - type: accuracy value: 49.952000000000005 - type: f1 value: 48.264617195258054 task: type: Classification - dataset: config: default name: MTEB ThuNewsClusteringP2P revision: 5798586b105c0434e4f0fe5e767abe619442cf93 split: test type: C-MTEB/ThuNewsClusteringP2P metrics: - type: v_measure value: 68.23769904483508 task: type: Clustering - dataset: config: default name: MTEB ThuNewsClusteringS2S revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d split: test type: C-MTEB/ThuNewsClusteringS2S metrics: - type: v_measure value: 62.50294403136556 task: type: Clustering - dataset: config: default name: MTEB VideoRetrieval revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 split: dev type: C-MTEB/VideoRetrieval metrics: - type: map_at_1 value: 54.0 - type: map_at_10 value: 63.668 - type: map_at_100 value: 64.217 - type: map_at_1000 value: 64.23100000000001 - type: map_at_3 value: 61.7 - type: map_at_5 value: 62.870000000000005 - type: mrr_at_1 value: 54.0 - type: mrr_at_10 value: 63.668 - type: mrr_at_100 value: 64.217 - type: mrr_at_1000 value: 64.23100000000001 - type: mrr_at_3 value: 61.7 - type: mrr_at_5 value: 62.870000000000005 - type: ndcg_at_1 value: 54.0 - type: ndcg_at_10 value: 68.11399999999999 - type: ndcg_at_100 value: 70.723 - type: ndcg_at_1000 value: 71.123 - type: ndcg_at_3 value: 64.074 - type: ndcg_at_5 value: 66.178 - type: precision_at_1 value: 54.0 - type: precision_at_10 value: 8.200000000000001 - type: precision_at_100 value: 0.941 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 23.633000000000003 - type: precision_at_5 value: 15.2 - type: recall_at_1 value: 54.0 - type: recall_at_10 value: 82.0 - type: recall_at_100 value: 94.1 - type: recall_at_1000 value: 97.3 - type: recall_at_3 value: 70.89999999999999 - type: recall_at_5 value: 76.0 task: type: Retrieval - dataset: config: default name: MTEB Waimai revision: 339287def212450dcaa9df8c22bf93e9980c7023 split: test type: C-MTEB/waimai-classification metrics: - type: accuracy value: 86.63000000000001 - type: ap value: 69.99457882599567 - type: f1 value: 85.07735617998541 task: type: Classification - dataset: config: default name: MTEB 8TagsClustering revision: None split: test type: PL-MTEB/8tags-clustering metrics: - type: v_measure value: 44.594104491193555 task: type: Clustering - dataset: config: default name: MTEB AllegroReviews revision: None split: test type: PL-MTEB/allegro-reviews metrics: - type: accuracy value: 63.97614314115309 - type: f1 value: 52.15634261679283 task: type: Classification - dataset: config: default name: MTEB ArguAna-PL revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 split: test type: clarin-knext/arguana-pl metrics: - type: map_at_1 value: 32.646 - type: map_at_10 value: 47.963 - type: map_at_100 value: 48.789 - type: map_at_1000 value: 48.797000000000004 - type: map_at_3 value: 43.196 - type: map_at_5 value: 46.016 - type: mrr_at_1 value: 33.073 - type: mrr_at_10 value: 48.126000000000005 - type: mrr_at_100 value: 48.946 - type: mrr_at_1000 value: 48.953 - type: mrr_at_3 value: 43.374 - type: mrr_at_5 value: 46.147 - type: ndcg_at_1 value: 32.646 - type: ndcg_at_10 value: 56.481 - type: ndcg_at_100 value: 59.922 - type: ndcg_at_1000 value: 60.07 - type: ndcg_at_3 value: 46.675 - type: ndcg_at_5 value: 51.76500000000001 - type: precision_at_1 value: 32.646 - type: precision_at_10 value: 8.371 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.919 - type: precision_at_5 value: 13.825999999999999 - type: recall_at_1 value: 32.646 - type: recall_at_10 value: 83.71300000000001 - type: recall_at_100 value: 98.578 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 56.757000000000005 - type: recall_at_5 value: 69.132 task: type: Retrieval - dataset: config: default name: MTEB CBD revision: None split: test type: PL-MTEB/cbd metrics: - type: accuracy value: 68.56 - type: ap value: 23.310493680488513 - type: f1 value: 58.85369533105693 task: type: Classification - dataset: config: default name: MTEB CDSC-E revision: None split: test type: PL-MTEB/cdsce-pairclassification metrics: - type: cos_sim_accuracy value: 88.5 - type: cos_sim_ap value: 72.42140924378361 - type: cos_sim_f1 value: 66.0919540229885 - type: cos_sim_precision value: 72.78481012658227 - type: cos_sim_recall value: 60.526315789473685 - type: dot_accuracy value: 88.5 - type: dot_ap value: 72.42140924378361 - type: dot_f1 value: 66.0919540229885 - type: dot_precision value: 72.78481012658227 - type: dot_recall value: 60.526315789473685 - type: euclidean_accuracy value: 88.5 - type: euclidean_ap value: 72.42140924378361 - type: euclidean_f1 value: 66.0919540229885 - type: euclidean_precision value: 72.78481012658227 - type: euclidean_recall value: 60.526315789473685 - type: manhattan_accuracy value: 88.5 - type: manhattan_ap value: 72.49745515311696 - type: manhattan_f1 value: 66.0968660968661 - type: manhattan_precision value: 72.04968944099379 - type: manhattan_recall value: 61.05263157894737 - type: max_accuracy value: 88.5 - type: max_ap value: 72.49745515311696 - type: max_f1 value: 66.0968660968661 task: type: PairClassification - dataset: config: default name: MTEB CDSC-R revision: None split: test type: PL-MTEB/cdscr-sts metrics: - type: cos_sim_pearson value: 90.32269765590145 - type: cos_sim_spearman value: 89.73666311491672 - type: euclidean_pearson value: 88.2933868516544 - type: euclidean_spearman value: 89.73666311491672 - type: manhattan_pearson value: 88.33474590219448 - type: manhattan_spearman value: 89.8548364866583 task: type: STS - dataset: config: default name: MTEB DBPedia-PL revision: 76afe41d9af165cc40999fcaa92312b8b012064a split: test type: clarin-knext/dbpedia-pl metrics: - type: map_at_1 value: 7.632999999999999 - type: map_at_10 value: 16.426 - type: map_at_100 value: 22.651 - type: map_at_1000 value: 24.372 - type: map_at_3 value: 11.706 - type: map_at_5 value: 13.529 - type: mrr_at_1 value: 60.75000000000001 - type: mrr_at_10 value: 68.613 - type: mrr_at_100 value: 69.001 - type: mrr_at_1000 value: 69.021 - type: mrr_at_3 value: 67.0 - type: mrr_at_5 value: 67.925 - type: ndcg_at_1 value: 49.875 - type: ndcg_at_10 value: 36.978 - type: ndcg_at_100 value: 40.031 - type: ndcg_at_1000 value: 47.566 - type: ndcg_at_3 value: 41.148 - type: ndcg_at_5 value: 38.702 - type: precision_at_1 value: 60.75000000000001 - type: precision_at_10 value: 29.7 - type: precision_at_100 value: 9.278 - type: precision_at_1000 value: 2.099 - type: precision_at_3 value: 44.0 - type: precision_at_5 value: 37.6 - type: recall_at_1 value: 7.632999999999999 - type: recall_at_10 value: 22.040000000000003 - type: recall_at_100 value: 44.024 - type: recall_at_1000 value: 67.848 - type: recall_at_3 value: 13.093 - type: recall_at_5 value: 15.973 task: type: Retrieval - dataset: config: default name: MTEB FiQA-PL revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e split: test type: clarin-knext/fiqa-pl metrics: - type: map_at_1 value: 15.473 - type: map_at_10 value: 24.579 - type: map_at_100 value: 26.387 - type: map_at_1000 value: 26.57 - type: map_at_3 value: 21.278 - type: map_at_5 value: 23.179 - type: mrr_at_1 value: 30.709999999999997 - type: mrr_at_10 value: 38.994 - type: mrr_at_100 value: 39.993 - type: mrr_at_1000 value: 40.044999999999995 - type: mrr_at_3 value: 36.342999999999996 - type: mrr_at_5 value: 37.846999999999994 - type: ndcg_at_1 value: 30.709999999999997 - type: ndcg_at_10 value: 31.608999999999998 - type: ndcg_at_100 value: 38.807 - type: ndcg_at_1000 value: 42.208 - type: ndcg_at_3 value: 28.086 - type: ndcg_at_5 value: 29.323 - type: precision_at_1 value: 30.709999999999997 - type: precision_at_10 value: 8.688 - type: precision_at_100 value: 1.608 - type: precision_at_1000 value: 0.22100000000000003 - type: precision_at_3 value: 18.724 - type: precision_at_5 value: 13.950999999999999 - type: recall_at_1 value: 15.473 - type: recall_at_10 value: 38.361000000000004 - type: recall_at_100 value: 65.2 - type: recall_at_1000 value: 85.789 - type: recall_at_3 value: 25.401 - type: recall_at_5 value: 30.875999999999998 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA-PL revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 split: test type: clarin-knext/hotpotqa-pl metrics: - type: map_at_1 value: 38.096000000000004 - type: map_at_10 value: 51.44499999999999 - type: map_at_100 value: 52.325 - type: map_at_1000 value: 52.397000000000006 - type: map_at_3 value: 48.626999999999995 - type: map_at_5 value: 50.342 - type: mrr_at_1 value: 76.19200000000001 - type: mrr_at_10 value: 81.191 - type: mrr_at_100 value: 81.431 - type: mrr_at_1000 value: 81.443 - type: mrr_at_3 value: 80.30199999999999 - type: mrr_at_5 value: 80.85900000000001 - type: ndcg_at_1 value: 76.19200000000001 - type: ndcg_at_10 value: 60.9 - type: ndcg_at_100 value: 64.14699999999999 - type: ndcg_at_1000 value: 65.647 - type: ndcg_at_3 value: 56.818000000000005 - type: ndcg_at_5 value: 59.019999999999996 - type: precision_at_1 value: 76.19200000000001 - type: precision_at_10 value: 12.203 - type: precision_at_100 value: 1.478 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 34.616 - type: precision_at_5 value: 22.515 - type: recall_at_1 value: 38.096000000000004 - type: recall_at_10 value: 61.013 - type: recall_at_100 value: 73.90299999999999 - type: recall_at_1000 value: 83.91 - type: recall_at_3 value: 51.92400000000001 - type: recall_at_5 value: 56.286 task: type: Retrieval - dataset: config: default name: MTEB MSMARCO-PL revision: 8634c07806d5cce3a6138e260e59b81760a0a640 split: test type: clarin-knext/msmarco-pl metrics: - type: map_at_1 value: 1.548 - type: map_at_10 value: 11.049000000000001 - type: map_at_100 value: 28.874 - type: map_at_1000 value: 34.931 - type: map_at_3 value: 4.162 - type: map_at_5 value: 6.396 - type: mrr_at_1 value: 90.69800000000001 - type: mrr_at_10 value: 92.093 - type: mrr_at_100 value: 92.345 - type: mrr_at_1000 value: 92.345 - type: mrr_at_3 value: 91.86 - type: mrr_at_5 value: 91.86 - type: ndcg_at_1 value: 74.031 - type: ndcg_at_10 value: 63.978 - type: ndcg_at_100 value: 53.101 - type: ndcg_at_1000 value: 60.675999999999995 - type: ndcg_at_3 value: 71.421 - type: ndcg_at_5 value: 68.098 - type: precision_at_1 value: 90.69800000000001 - type: precision_at_10 value: 71.86 - type: precision_at_100 value: 31.395 - type: precision_at_1000 value: 5.981 - type: precision_at_3 value: 84.49600000000001 - type: precision_at_5 value: 79.07 - type: recall_at_1 value: 1.548 - type: recall_at_10 value: 12.149000000000001 - type: recall_at_100 value: 40.794999999999995 - type: recall_at_1000 value: 67.974 - type: recall_at_3 value: 4.244 - type: recall_at_5 value: 6.608 task: type: Retrieval - dataset: config: pl name: MTEB MassiveIntentClassification (pl) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 73.55413584398119 - type: f1 value: 69.65610882318181 task: type: Classification - dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 76.37188971082716 - type: f1 value: 75.64847309941361 task: type: Classification - dataset: config: default name: MTEB NFCorpus-PL revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 split: test type: clarin-knext/nfcorpus-pl metrics: - type: map_at_1 value: 4.919 - type: map_at_10 value: 10.834000000000001 - type: map_at_100 value: 13.38 - type: map_at_1000 value: 14.581 - type: map_at_3 value: 8.198 - type: map_at_5 value: 9.428 - type: mrr_at_1 value: 41.176 - type: mrr_at_10 value: 50.083 - type: mrr_at_100 value: 50.559 - type: mrr_at_1000 value: 50.604000000000006 - type: mrr_at_3 value: 47.936 - type: mrr_at_5 value: 49.407000000000004 - type: ndcg_at_1 value: 39.628 - type: ndcg_at_10 value: 30.098000000000003 - type: ndcg_at_100 value: 27.061 - type: ndcg_at_1000 value: 35.94 - type: ndcg_at_3 value: 35.135 - type: ndcg_at_5 value: 33.335 - type: precision_at_1 value: 41.176 - type: precision_at_10 value: 22.259999999999998 - type: precision_at_100 value: 6.712 - type: precision_at_1000 value: 1.9060000000000001 - type: precision_at_3 value: 33.23 - type: precision_at_5 value: 29.04 - type: recall_at_1 value: 4.919 - type: recall_at_10 value: 14.196 - type: recall_at_100 value: 26.948 - type: recall_at_1000 value: 59.211000000000006 - type: recall_at_3 value: 9.44 - type: recall_at_5 value: 11.569 task: type: Retrieval - dataset: config: default name: MTEB NQ-PL revision: f171245712cf85dd4700b06bef18001578d0ca8d split: test type: clarin-knext/nq-pl metrics: - type: map_at_1 value: 25.35 - type: map_at_10 value: 37.884 - type: map_at_100 value: 38.955 - type: map_at_1000 value: 39.007999999999996 - type: map_at_3 value: 34.239999999999995 - type: map_at_5 value: 36.398 - type: mrr_at_1 value: 28.737000000000002 - type: mrr_at_10 value: 39.973 - type: mrr_at_100 value: 40.844 - type: mrr_at_1000 value: 40.885 - type: mrr_at_3 value: 36.901 - type: mrr_at_5 value: 38.721 - type: ndcg_at_1 value: 28.708 - type: ndcg_at_10 value: 44.204 - type: ndcg_at_100 value: 48.978 - type: ndcg_at_1000 value: 50.33 - type: ndcg_at_3 value: 37.36 - type: ndcg_at_5 value: 40.912 - type: precision_at_1 value: 28.708 - type: precision_at_10 value: 7.367 - type: precision_at_100 value: 1.0030000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 17.034 - type: precision_at_5 value: 12.293999999999999 - type: recall_at_1 value: 25.35 - type: recall_at_10 value: 61.411 - type: recall_at_100 value: 82.599 - type: recall_at_1000 value: 92.903 - type: recall_at_3 value: 43.728 - type: recall_at_5 value: 51.854 task: type: Retrieval - dataset: config: default name: MTEB PAC revision: None split: test type: laugustyniak/abusive-clauses-pl metrics: - type: accuracy value: 69.04141326382856 - type: ap value: 77.49422763833996 - type: f1 value: 66.73472657783407 task: type: Classification - dataset: config: default name: MTEB PPC revision: None split: test type: PL-MTEB/ppc-pairclassification metrics: - type: cos_sim_accuracy value: 81.0 - type: cos_sim_ap value: 91.47194213011349 - type: cos_sim_f1 value: 84.73767885532592 - type: cos_sim_precision value: 81.49847094801224 - type: cos_sim_recall value: 88.24503311258279 - type: dot_accuracy value: 81.0 - type: dot_ap value: 91.47194213011349 - type: dot_f1 value: 84.73767885532592 - type: dot_precision value: 81.49847094801224 - type: dot_recall value: 88.24503311258279 - type: euclidean_accuracy value: 81.0 - type: euclidean_ap value: 91.47194213011349 - type: euclidean_f1 value: 84.73767885532592 - type: euclidean_precision value: 81.49847094801224 - type: euclidean_recall value: 88.24503311258279 - type: manhattan_accuracy value: 81.0 - type: manhattan_ap value: 91.46464475050571 - type: manhattan_f1 value: 84.48687350835321 - type: manhattan_precision value: 81.31699846860643 - type: manhattan_recall value: 87.91390728476821 - type: max_accuracy value: 81.0 - type: max_ap value: 91.47194213011349 - type: max_f1 value: 84.73767885532592 task: type: PairClassification - dataset: config: default name: MTEB PSC revision: None split: test type: PL-MTEB/psc-pairclassification metrics: - type: cos_sim_accuracy value: 97.6808905380334 - type: cos_sim_ap value: 99.27948611836348 - type: cos_sim_f1 value: 96.15975422427034 - type: cos_sim_precision value: 96.90402476780186 - type: cos_sim_recall value: 95.42682926829268 - type: dot_accuracy value: 97.6808905380334 - type: dot_ap value: 99.2794861183635 - type: dot_f1 value: 96.15975422427034 - type: dot_precision value: 96.90402476780186 - type: dot_recall value: 95.42682926829268 - type: euclidean_accuracy value: 97.6808905380334 - type: euclidean_ap value: 99.2794861183635 - type: euclidean_f1 value: 96.15975422427034 - type: euclidean_precision value: 96.90402476780186 - type: euclidean_recall value: 95.42682926829268 - type: manhattan_accuracy value: 97.6808905380334 - type: manhattan_ap value: 99.28715055268721 - type: manhattan_f1 value: 96.14791987673343 - type: manhattan_precision value: 97.19626168224299 - type: manhattan_recall value: 95.1219512195122 - type: max_accuracy value: 97.6808905380334 - type: max_ap value: 99.28715055268721 - type: max_f1 value: 96.15975422427034 task: type: PairClassification - dataset: config: default name: MTEB PolEmo2.0-IN revision: None split: test type: PL-MTEB/polemo2_in metrics: - type: accuracy value: 86.16343490304708 - type: f1 value: 83.3442579486744 task: type: Classification - dataset: config: default name: MTEB PolEmo2.0-OUT revision: None split: test type: PL-MTEB/polemo2_out metrics: - type: accuracy value: 68.40080971659918 - type: f1 value: 53.13720751142237 task: type: Classification - dataset: config: default name: MTEB Quora-PL revision: 0be27e93455051e531182b85e85e425aba12e9d4 split: test type: clarin-knext/quora-pl metrics: - type: map_at_1 value: 63.322 - type: map_at_10 value: 76.847 - type: map_at_100 value: 77.616 - type: map_at_1000 value: 77.644 - type: map_at_3 value: 73.624 - type: map_at_5 value: 75.603 - type: mrr_at_1 value: 72.88 - type: mrr_at_10 value: 80.376 - type: mrr_at_100 value: 80.604 - type: mrr_at_1000 value: 80.61 - type: mrr_at_3 value: 78.92 - type: mrr_at_5 value: 79.869 - type: ndcg_at_1 value: 72.89999999999999 - type: ndcg_at_10 value: 81.43 - type: ndcg_at_100 value: 83.394 - type: ndcg_at_1000 value: 83.685 - type: ndcg_at_3 value: 77.62599999999999 - type: ndcg_at_5 value: 79.656 - type: precision_at_1 value: 72.89999999999999 - type: precision_at_10 value: 12.548 - type: precision_at_100 value: 1.4869999999999999 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 34.027 - type: precision_at_5 value: 22.654 - type: recall_at_1 value: 63.322 - type: recall_at_10 value: 90.664 - type: recall_at_100 value: 97.974 - type: recall_at_1000 value: 99.636 - type: recall_at_3 value: 80.067 - type: recall_at_5 value: 85.526 task: type: Retrieval - dataset: config: default name: MTEB SCIDOCS-PL revision: 45452b03f05560207ef19149545f168e596c9337 split: test type: clarin-knext/scidocs-pl metrics: - type: map_at_1 value: 3.95 - type: map_at_10 value: 9.658999999999999 - type: map_at_100 value: 11.384 - type: map_at_1000 value: 11.677 - type: map_at_3 value: 7.055 - type: map_at_5 value: 8.244 - type: mrr_at_1 value: 19.5 - type: mrr_at_10 value: 28.777 - type: mrr_at_100 value: 29.936 - type: mrr_at_1000 value: 30.009999999999998 - type: mrr_at_3 value: 25.55 - type: mrr_at_5 value: 27.284999999999997 - type: ndcg_at_1 value: 19.5 - type: ndcg_at_10 value: 16.589000000000002 - type: ndcg_at_100 value: 23.879 - type: ndcg_at_1000 value: 29.279 - type: ndcg_at_3 value: 15.719 - type: ndcg_at_5 value: 13.572000000000001 - type: precision_at_1 value: 19.5 - type: precision_at_10 value: 8.62 - type: precision_at_100 value: 1.924 - type: precision_at_1000 value: 0.322 - type: precision_at_3 value: 14.6 - type: precision_at_5 value: 11.78 - type: recall_at_1 value: 3.95 - type: recall_at_10 value: 17.477999999999998 - type: recall_at_100 value: 38.99 - type: recall_at_1000 value: 65.417 - type: recall_at_3 value: 8.883000000000001 - type: recall_at_5 value: 11.933 task: type: Retrieval - dataset: config: default name: MTEB SICK-E-PL revision: None split: test type: PL-MTEB/sicke-pl-pairclassification metrics: - type: cos_sim_accuracy value: 83.48960456583775 - type: cos_sim_ap value: 76.31522115825375 - type: cos_sim_f1 value: 70.35573122529645 - type: cos_sim_precision value: 70.9934735315446 - type: cos_sim_recall value: 69.72934472934473 - type: dot_accuracy value: 83.48960456583775 - type: dot_ap value: 76.31522115825373 - type: dot_f1 value: 70.35573122529645 - type: dot_precision value: 70.9934735315446 - type: dot_recall value: 69.72934472934473 - type: euclidean_accuracy value: 83.48960456583775 - type: euclidean_ap value: 76.31522115825373 - type: euclidean_f1 value: 70.35573122529645 - type: euclidean_precision value: 70.9934735315446 - type: euclidean_recall value: 69.72934472934473 - type: manhattan_accuracy value: 83.46922136159804 - type: manhattan_ap value: 76.18474601388084 - type: manhattan_f1 value: 70.34779490856937 - type: manhattan_precision value: 70.83032490974729 - type: manhattan_recall value: 69.87179487179486 - type: max_accuracy value: 83.48960456583775 - type: max_ap value: 76.31522115825375 - type: max_f1 value: 70.35573122529645 task: type: PairClassification - dataset: config: default name: MTEB SICK-R-PL revision: None split: test type: PL-MTEB/sickr-pl-sts metrics: - type: cos_sim_pearson value: 77.95374883876302 - type: cos_sim_spearman value: 73.77630219171942 - type: euclidean_pearson value: 75.81927069594934 - type: euclidean_spearman value: 73.7763211303831 - type: manhattan_pearson value: 76.03126859057528 - type: manhattan_spearman value: 73.96528138013369 task: type: STS - dataset: config: pl name: MTEB STS22 (pl) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 37.388282764841826 - type: cos_sim_spearman value: 40.83477184710897 - type: euclidean_pearson value: 26.754737044177805 - type: euclidean_spearman value: 40.83477184710897 - type: manhattan_pearson value: 26.760453110872458 - type: manhattan_spearman value: 41.034477441383856 task: type: STS - dataset: config: default name: MTEB SciFact-PL revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e split: test type: clarin-knext/scifact-pl metrics: - type: map_at_1 value: 49.15 - type: map_at_10 value: 61.690999999999995 - type: map_at_100 value: 62.348000000000006 - type: map_at_1000 value: 62.38 - type: map_at_3 value: 58.824 - type: map_at_5 value: 60.662000000000006 - type: mrr_at_1 value: 51.333 - type: mrr_at_10 value: 62.731 - type: mrr_at_100 value: 63.245 - type: mrr_at_1000 value: 63.275000000000006 - type: mrr_at_3 value: 60.667 - type: mrr_at_5 value: 61.93300000000001 - type: ndcg_at_1 value: 51.333 - type: ndcg_at_10 value: 67.168 - type: ndcg_at_100 value: 69.833 - type: ndcg_at_1000 value: 70.56700000000001 - type: ndcg_at_3 value: 62.40599999999999 - type: ndcg_at_5 value: 65.029 - type: precision_at_1 value: 51.333 - type: precision_at_10 value: 9.333 - type: precision_at_100 value: 1.0699999999999998 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 25.333 - type: precision_at_5 value: 17.067 - type: recall_at_1 value: 49.15 - type: recall_at_10 value: 82.533 - type: recall_at_100 value: 94.167 - type: recall_at_1000 value: 99.667 - type: recall_at_3 value: 69.917 - type: recall_at_5 value: 76.356 task: type: Retrieval - dataset: config: default name: MTEB TRECCOVID-PL revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd split: test type: clarin-knext/trec-covid-pl metrics: - type: map_at_1 value: 0.261 - type: map_at_10 value: 2.1260000000000003 - type: map_at_100 value: 12.171999999999999 - type: map_at_1000 value: 26.884999999999998 - type: map_at_3 value: 0.695 - type: map_at_5 value: 1.134 - type: mrr_at_1 value: 96.0 - type: mrr_at_10 value: 96.952 - type: mrr_at_100 value: 96.952 - type: mrr_at_1000 value: 96.952 - type: mrr_at_3 value: 96.667 - type: mrr_at_5 value: 96.667 - type: ndcg_at_1 value: 92.0 - type: ndcg_at_10 value: 81.193 - type: ndcg_at_100 value: 61.129 - type: ndcg_at_1000 value: 51.157 - type: ndcg_at_3 value: 85.693 - type: ndcg_at_5 value: 84.129 - type: precision_at_1 value: 96.0 - type: precision_at_10 value: 85.39999999999999 - type: precision_at_100 value: 62.03999999999999 - type: precision_at_1000 value: 22.224 - type: precision_at_3 value: 88.0 - type: precision_at_5 value: 88.0 - type: recall_at_1 value: 0.261 - type: recall_at_10 value: 2.262 - type: recall_at_100 value: 14.981 - type: recall_at_1000 value: 46.837 - type: recall_at_3 value: 0.703 - type: recall_at_5 value: 1.172 task: type: Retrieval - dataset: config: default name: MTEB AlloProfClusteringP2P revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: v_measure value: 70.55290063940157 task: type: Clustering - dataset: config: default name: MTEB AlloProfClusteringS2S revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: v_measure value: 55.41500719337263 task: type: Clustering - dataset: config: default name: MTEB AlloprofReranking revision: 666fdacebe0291776e86f29345663dfaf80a0db9 split: test type: lyon-nlp/mteb-fr-reranking-alloprof-s2p metrics: - type: map value: 73.48697375332002 - type: mrr value: 75.01836585523822 task: type: Reranking - dataset: config: default name: MTEB AlloprofRetrieval revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics: - type: map_at_1 value: 38.454 - type: map_at_10 value: 51.605000000000004 - type: map_at_100 value: 52.653000000000006 - type: map_at_1000 value: 52.697 - type: map_at_3 value: 48.304 - type: map_at_5 value: 50.073 - type: mrr_at_1 value: 43.307 - type: mrr_at_10 value: 54.400000000000006 - type: mrr_at_100 value: 55.147999999999996 - type: mrr_at_1000 value: 55.174 - type: mrr_at_3 value: 51.77 - type: mrr_at_5 value: 53.166999999999994 - type: ndcg_at_1 value: 43.307 - type: ndcg_at_10 value: 57.891000000000005 - type: ndcg_at_100 value: 62.161 - type: ndcg_at_1000 value: 63.083 - type: ndcg_at_3 value: 51.851 - type: ndcg_at_5 value: 54.605000000000004 - type: precision_at_1 value: 43.307 - type: precision_at_10 value: 9.033 - type: precision_at_100 value: 1.172 - type: precision_at_1000 value: 0.127 - type: precision_at_3 value: 22.798 - type: precision_at_5 value: 15.492 - type: recall_at_1 value: 38.454 - type: recall_at_10 value: 74.166 - type: recall_at_100 value: 92.43599999999999 - type: recall_at_1000 value: 99.071 - type: recall_at_3 value: 58.087 - type: recall_at_5 value: 64.568 task: type: Retrieval - dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 53.474 - type: f1 value: 50.38275392350236 task: type: Classification - dataset: config: default name: MTEB BSARDRetrieval revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 split: test type: maastrichtlawtech/bsard metrics: - type: map_at_1 value: 2.252 - type: map_at_10 value: 4.661 - type: map_at_100 value: 5.271 - type: map_at_1000 value: 5.3629999999999995 - type: map_at_3 value: 3.604 - type: map_at_5 value: 4.3020000000000005 - type: mrr_at_1 value: 2.252 - type: mrr_at_10 value: 4.661 - type: mrr_at_100 value: 5.271 - type: mrr_at_1000 value: 5.3629999999999995 - type: mrr_at_3 value: 3.604 - type: mrr_at_5 value: 4.3020000000000005 - type: ndcg_at_1 value: 2.252 - type: ndcg_at_10 value: 6.3020000000000005 - type: ndcg_at_100 value: 10.342 - type: ndcg_at_1000 value: 13.475999999999999 - type: ndcg_at_3 value: 4.0649999999999995 - type: ndcg_at_5 value: 5.344 - type: precision_at_1 value: 2.252 - type: precision_at_10 value: 1.171 - type: precision_at_100 value: 0.333 - type: precision_at_1000 value: 0.059000000000000004 - type: precision_at_3 value: 1.802 - type: precision_at_5 value: 1.712 - type: recall_at_1 value: 2.252 - type: recall_at_10 value: 11.712 - type: recall_at_100 value: 33.333 - type: recall_at_1000 value: 59.458999999999996 - type: recall_at_3 value: 5.405 - type: recall_at_5 value: 8.559 task: type: Retrieval - dataset: config: default name: MTEB HALClusteringS2S revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 split: test type: lyon-nlp/clustering-hal-s2s metrics: - type: v_measure value: 28.301882091023288 task: type: Clustering - dataset: config: default name: MTEB MLSUMClusteringP2P revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics: - type: v_measure value: 45.26992995191701 task: type: Clustering - dataset: config: default name: MTEB MLSUMClusteringS2S revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics: - type: v_measure value: 42.773174876871145 task: type: Clustering - dataset: config: fr name: MTEB MTOPDomainClassification (fr) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 93.47635452552458 - type: f1 value: 93.19922617577213 task: type: Classification - dataset: config: fr name: MTEB MTOPIntentClassification (fr) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 80.2317569683683 - type: f1 value: 56.18060418621901 task: type: Classification - dataset: config: fra name: MTEB MasakhaNEWSClassification (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: accuracy value: 85.18957345971565 - type: f1 value: 80.829981537394 task: type: Classification - dataset: config: fra name: MTEB MasakhaNEWSClusteringP2P (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: v_measure value: 71.04138999801822 task: type: Clustering - dataset: config: fra name: MTEB MasakhaNEWSClusteringS2S (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics: - type: v_measure value: 71.7056263158008 task: type: Clustering - dataset: config: fr name: MTEB MassiveIntentClassification (fr) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 76.65097511768661 - type: f1 value: 73.82441070598712 task: type: Classification - dataset: config: fr name: MTEB MassiveScenarioClassification (fr) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 79.09885675857431 - type: f1 value: 78.28407777434224 task: type: Classification - dataset: config: fr name: MTEB MintakaRetrieval (fr) revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e split: test type: jinaai/mintakaqa metrics: - type: map_at_1 value: 25.307000000000002 - type: map_at_10 value: 36.723 - type: map_at_100 value: 37.713 - type: map_at_1000 value: 37.769000000000005 - type: map_at_3 value: 33.77 - type: map_at_5 value: 35.463 - type: mrr_at_1 value: 25.307000000000002 - type: mrr_at_10 value: 36.723 - type: mrr_at_100 value: 37.713 - type: mrr_at_1000 value: 37.769000000000005 - type: mrr_at_3 value: 33.77 - type: mrr_at_5 value: 35.463 - type: ndcg_at_1 value: 25.307000000000002 - type: ndcg_at_10 value: 42.559999999999995 - type: ndcg_at_100 value: 47.457 - type: ndcg_at_1000 value: 49.162 - type: ndcg_at_3 value: 36.461 - type: ndcg_at_5 value: 39.504 - type: precision_at_1 value: 25.307000000000002 - type: precision_at_10 value: 6.106 - type: precision_at_100 value: 0.8420000000000001 - type: precision_at_1000 value: 0.098 - type: precision_at_3 value: 14.741999999999999 - type: precision_at_5 value: 10.319 - type: recall_at_1 value: 25.307000000000002 - type: recall_at_10 value: 61.056999999999995 - type: recall_at_100 value: 84.152 - type: recall_at_1000 value: 98.03399999999999 - type: recall_at_3 value: 44.226 - type: recall_at_5 value: 51.597 task: type: Retrieval - dataset: config: fr name: MTEB OpusparcusPC (fr) revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a split: test type: GEM/opusparcus metrics: - type: cos_sim_accuracy value: 99.90069513406156 - type: cos_sim_ap value: 100.0 - type: cos_sim_f1 value: 99.95032290114257 - type: cos_sim_precision value: 100.0 - type: cos_sim_recall value: 99.90069513406156 - type: dot_accuracy value: 99.90069513406156 - type: dot_ap value: 100.0 - type: dot_f1 value: 99.95032290114257 - type: dot_precision value: 100.0 - type: dot_recall value: 99.90069513406156 - type: euclidean_accuracy value: 99.90069513406156 - type: euclidean_ap value: 100.0 - type: euclidean_f1 value: 99.95032290114257 - type: euclidean_precision value: 100.0 - type: euclidean_recall value: 99.90069513406156 - type: manhattan_accuracy value: 99.90069513406156 - type: manhattan_ap value: 100.0 - type: manhattan_f1 value: 99.95032290114257 - type: manhattan_precision value: 100.0 - type: manhattan_recall value: 99.90069513406156 - type: max_accuracy value: 99.90069513406156 - type: max_ap value: 100.0 - type: max_f1 value: 99.95032290114257 task: type: PairClassification - dataset: config: fr name: MTEB PawsX (fr) revision: 8a04d940a42cd40658986fdd8e3da561533a3646 split: test type: paws-x metrics: - type: cos_sim_accuracy value: 70.8 - type: cos_sim_ap value: 73.7671529695957 - type: cos_sim_f1 value: 68.80964339527875 - type: cos_sim_precision value: 62.95955882352941 - type: cos_sim_recall value: 75.85825027685493 - type: dot_accuracy value: 70.8 - type: dot_ap value: 73.78345265366947 - type: dot_f1 value: 68.80964339527875 - type: dot_precision value: 62.95955882352941 - type: dot_recall value: 75.85825027685493 - type: euclidean_accuracy value: 70.8 - type: euclidean_ap value: 73.7671529695957 - type: euclidean_f1 value: 68.80964339527875 - type: euclidean_precision value: 62.95955882352941 - type: euclidean_recall value: 75.85825027685493 - type: manhattan_accuracy value: 70.75 - type: manhattan_ap value: 73.78996383615953 - type: manhattan_f1 value: 68.79432624113475 - type: manhattan_precision value: 63.39869281045751 - type: manhattan_recall value: 75.1937984496124 - type: max_accuracy value: 70.8 - type: max_ap value: 73.78996383615953 - type: max_f1 value: 68.80964339527875 task: type: PairClassification - dataset: config: default name: MTEB SICKFr revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a split: test type: Lajavaness/SICK-fr metrics: - type: cos_sim_pearson value: 84.03253762760392 - type: cos_sim_spearman value: 79.68280105762004 - type: euclidean_pearson value: 80.98265050044444 - type: euclidean_spearman value: 79.68233242682867 - type: manhattan_pearson value: 80.9678911810704 - type: manhattan_spearman value: 79.70264097683109 task: type: STS - dataset: config: fr name: MTEB STS22 (fr) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics: - type: cos_sim_pearson value: 80.56896987572884 - type: cos_sim_spearman value: 81.84352499523287 - type: euclidean_pearson value: 80.40831759421305 - type: euclidean_spearman value: 81.84352499523287 - type: manhattan_pearson value: 80.74333857561238 - type: manhattan_spearman value: 82.41503246733892 task: type: STS - dataset: config: fr name: MTEB STSBenchmarkMultilingualSTS (fr) revision: 93d57ef91790589e3ce9c365164337a8a78b7632 split: test type: stsb_multi_mt metrics: - type: cos_sim_pearson value: 82.71826762276979 - type: cos_sim_spearman value: 82.25433354916042 - type: euclidean_pearson value: 81.87115571724316 - type: euclidean_spearman value: 82.25322342890107 - type: manhattan_pearson value: 82.11174867527224 - type: manhattan_spearman value: 82.55905365203084 task: type: STS - dataset: config: default name: MTEB SummEvalFr revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 split: test type: lyon-nlp/summarization-summeval-fr-p2p metrics: - type: cos_sim_pearson value: 30.659441623392887 - type: cos_sim_spearman value: 30.501134097353315 - type: dot_pearson value: 30.659444768851056 - type: dot_spearman value: 30.501134097353315 task: type: Summarization - dataset: config: default name: MTEB SyntecReranking revision: b205c5084a0934ce8af14338bf03feb19499c84d split: test type: lyon-nlp/mteb-fr-reranking-syntec-s2p metrics: - type: map value: 94.03333333333333 - type: mrr value: 94.03333333333333 task: type: Reranking - dataset: config: default name: MTEB SyntecRetrieval revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff split: test type: lyon-nlp/mteb-fr-retrieval-syntec-s2p metrics: - type: map_at_1 value: 79.0 - type: map_at_10 value: 87.61 - type: map_at_100 value: 87.655 - type: map_at_1000 value: 87.655 - type: map_at_3 value: 87.167 - type: map_at_5 value: 87.36699999999999 - type: mrr_at_1 value: 79.0 - type: mrr_at_10 value: 87.61 - type: mrr_at_100 value: 87.655 - type: mrr_at_1000 value: 87.655 - type: mrr_at_3 value: 87.167 - type: mrr_at_5 value: 87.36699999999999 - type: ndcg_at_1 value: 79.0 - type: ndcg_at_10 value: 90.473 - type: ndcg_at_100 value: 90.694 - type: ndcg_at_1000 value: 90.694 - type: ndcg_at_3 value: 89.464 - type: ndcg_at_5 value: 89.851 - type: precision_at_1 value: 79.0 - type: precision_at_10 value: 9.9 - type: precision_at_100 value: 1.0 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 32.0 - type: precision_at_5 value: 19.400000000000002 - type: recall_at_1 value: 79.0 - type: recall_at_10 value: 99.0 - type: recall_at_100 value: 100.0 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 96.0 - type: recall_at_5 value: 97.0 task: type: Retrieval - dataset: config: fr name: MTEB XPQARetrieval (fr) revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f split: test type: jinaai/xpqa metrics: - type: map_at_1 value: 39.395 - type: map_at_10 value: 59.123999999999995 - type: map_at_100 value: 60.704 - type: map_at_1000 value: 60.760000000000005 - type: map_at_3 value: 53.187 - type: map_at_5 value: 56.863 - type: mrr_at_1 value: 62.083 - type: mrr_at_10 value: 68.87299999999999 - type: mrr_at_100 value: 69.46900000000001 - type: mrr_at_1000 value: 69.48299999999999 - type: mrr_at_3 value: 66.8 - type: mrr_at_5 value: 67.928 - type: ndcg_at_1 value: 62.083 - type: ndcg_at_10 value: 65.583 - type: ndcg_at_100 value: 70.918 - type: ndcg_at_1000 value: 71.72800000000001 - type: ndcg_at_3 value: 60.428000000000004 - type: ndcg_at_5 value: 61.853 - type: precision_at_1 value: 62.083 - type: precision_at_10 value: 15.033 - type: precision_at_100 value: 1.9529999999999998 - type: precision_at_1000 value: 0.207 - type: precision_at_3 value: 36.315 - type: precision_at_5 value: 25.955000000000002 - type: recall_at_1 value: 39.395 - type: recall_at_10 value: 74.332 - type: recall_at_100 value: 94.729 - type: recall_at_1000 value: 99.75500000000001 - type: recall_at_3 value: 57.679 - type: recall_at_5 value: 65.036 task: type: Retrieval
gte-Qwen2-1.5B-instruct
gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model.
The model incorporates several key advancements:
- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
- Instruction tuning, applied solely on the query side for streamlined efficiency
- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.
Model Information
- Model Size: 1.5B
- Embedding Dimension: 1536
- Max Input Tokens: 32k
Requirements
transformers>=4.39.2
flash_attn>=2.5.6
Usage
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
]
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt="Instruct: ...\nQuery: " to use a custom prompt of your choice.
Transformers
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
max_length = 8192
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
Evaluation
MTEB & C-MTEB
You can use the scripts/eval_mteb.py to reproduce the following result of gte-Qwen2-1.5B-instruct on MTEB(English)/C-MTEB(Chinese):
| Model Name | MTEB(56) | C-MTEB(35) | MTEB-fr(26) | MTEB-pl(26) |
|---|---|---|---|---|
| bge-base-en-1.5 | 64.23 | - | - | - |
| bge-large-en-1.5 | 63.55 | - | - | - |
| gte-large-en-v1.5 | 65.39 | - | - | - |
| gte-base-en-v1.5 | 64.11 | - | - | - |
| mxbai-embed-large-v1 | 64.68 | - | - | - |
| acge_text_embedding | - | 69.07 | - | - |
| stella-mrl-large-zh-v3.5-1792d | - | 68.55 | - | - |
| gte-large-zh | - | 66.72 | - | - |
| multilingual-e5-base | 59.45 | 56.21 | - | - |
| multilingual-e5-large | 61.50 | 58.81 | - | - |
| e5-mistral-7b-instruct | 66.63 | 60.81 | - | - |
| gte-Qwen1.5-7B-instruct | 67.34 | 69.52 | - | - |
| NV-Embed-v1 | 69.32 | - | - | - |
| gte-Qwen2-7B-instruct | 70.24 | 72.05 | 68.25 | 67.86 |
| gte-Qwen2-1.5B-instruct | 67.16 | 67.65 | 66.60 | 64.04 |
GTE Models
The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).
| Models | Language | Max Sequence Length | Dimension | Model Size (Memory Usage, fp32) |
|---|---|---|---|---|
| GTE-large-zh | Chinese | 512 | 1024 | 1.25GB |
| GTE-base-zh | Chinese | 512 | 512 | 0.41GB |
| GTE-small-zh | Chinese | 512 | 512 | 0.12GB |
| GTE-large | English | 512 | 1024 | 1.25GB |
| GTE-base | English | 512 | 512 | 0.21GB |
| GTE-small | English | 512 | 384 | 0.10GB |
| GTE-large-en-v1.5 | English | 8192 | 1024 | 1.74GB |
| GTE-base-en-v1.5 | English | 8192 | 768 | 0.51GB |
| GTE-Qwen1.5-7B-instruct | Multilingual | 32000 | 4096 | 26.45GB |
| GTE-Qwen2-7B-instruct | Multilingual | 32000 | 3584 | 26.45GB |
| GTE-Qwen2-1.5B-instruct | Multilingual | 32000 | 1536 | 6.62GB |
Citation
If you find our paper or models helpful, please consider cite:
@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}
}
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