model_data_json/Alibaba-NLP_gte-Qwen2-1.5B-instruct.json

{
    "model_id": "Alibaba-NLP/gte-Qwen2-1.5B-instruct",
    "downloads": 214128,
    "tags": [
        "sentence-transformers",
        "safetensors",
        "qwen2",
        "text-generation",
        "mteb",
        "transformers",
        "Qwen2",
        "sentence-similarity",
        "custom_code",
        "arxiv:2308.03281",
        "license:apache-2.0",
        "model-index",
        "autotrain_compatible",
        "text-generation-inference",
        "text-embeddings-inference",
        "endpoints_compatible",
        "region:us"
    ],
    "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 ## Usage ### Sentence Transformers Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use to use a custom prompt of your choice. ### Transformers ### infinity_emb Usage via infinity, MIT Licensed. ## 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 | ## Cloud API Services In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud. - Embedding Models: Three versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service. - ReRank Models: The gte-rerank model service is available. Note that the models behind the commercial APIs are not entirely identical to the open-source models. ## Community support ### Fine-tuning GTE models can be fine-tuned with a third party framework SWIFT. ## Citation If you find our paper or models helpful, please consider cite:",
    "model_explanation_gemini": "Generates sentence embeddings for tasks like classification, clustering, retrieval, and semantic textual similarity, optimized for multilingual performance.\n\nModel Features:  \n- Sentence embedding generation  \n- Multilingual capability  \n- Tasks: classification, clustering, retrieval, reranking, STS  \n- Strong performance on MTEB benchmarks  \n\nComparison:  \nOutperforms or matches similar models in accuracy, F1 scores, and retrieval metrics across multiple datasets, particularly in multilingual and semantic similarity tasks.",
    "release_year": "2023",
    "parameter_count": "1.5B",
    "is_fine_tuned": true,
    "category": "Embedding",
    "model_family": "Qwen",
    "api_enhanced": true
}