--- base_model: jinaai/jina-embeddings-v2-small-en datasets: - jinaai/negation-dataset language: en license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb - llama-cpp - gguf-my-repo inference: false model-index: - name: jina-embedding-s-en-v2 results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.35820895522387 - type: ap value: 33.99931933598115 - type: f1 value: 65.3853685535555 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 82.90140000000001 - type: ap value: 78.01434597815617 - type: f1 value: 82.83357802722676 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.88999999999999 - type: f1 value: 39.209432767163456 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 23.257 - type: map_at_10 value: 37.946000000000005 - type: map_at_100 value: 39.17 - type: map_at_1000 value: 39.181 - type: map_at_3 value: 32.99 - type: map_at_5 value: 35.467999999999996 - type: mrr_at_1 value: 23.541999999999998 - type: mrr_at_10 value: 38.057 - type: mrr_at_100 value: 39.289 - type: mrr_at_1000 value: 39.299 - type: mrr_at_3 value: 33.096 - type: mrr_at_5 value: 35.628 - type: ndcg_at_1 value: 23.257 - type: ndcg_at_10 value: 46.729 - type: ndcg_at_100 value: 51.900999999999996 - type: ndcg_at_1000 value: 52.16 - type: ndcg_at_3 value: 36.323 - type: ndcg_at_5 value: 40.766999999999996 - type: precision_at_1 value: 23.257 - type: precision_at_10 value: 7.510999999999999 - type: precision_at_100 value: 0.976 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 15.339 - type: precision_at_5 value: 11.350999999999999 - type: recall_at_1 value: 23.257 - type: recall_at_10 value: 75.107 - type: recall_at_100 value: 97.58200000000001 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 46.017 - type: recall_at_5 value: 56.757000000000005 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.02420878391967 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 35.16136856000258 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 59.61809790513646 - type: mrr value: 73.07215406938397 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 82.0167350090749 - type: cos_sim_spearman value: 80.51569002630401 - type: euclidean_pearson value: 81.46820525099726 - type: euclidean_spearman value: 80.51569002630401 - type: manhattan_pearson value: 81.35596555056757 - type: manhattan_spearman value: 80.12592210903303 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 78.25 - type: f1 value: 77.34950913540605 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 35.57238596005698 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.066444306196683 - task: type: Retrieval dataset: name: MTEB CQADupstackAndroidRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 31.891000000000002 - type: map_at_10 value: 42.772 - type: map_at_100 value: 44.108999999999995 - type: map_at_1000 value: 44.236 - type: map_at_3 value: 39.289 - type: map_at_5 value: 41.113 - type: mrr_at_1 value: 39.342 - type: mrr_at_10 value: 48.852000000000004 - type: mrr_at_100 value: 49.534 - type: mrr_at_1000 value: 49.582 - type: mrr_at_3 value: 46.089999999999996 - type: mrr_at_5 value: 47.685 - type: ndcg_at_1 value: 39.342 - type: ndcg_at_10 value: 48.988 - type: ndcg_at_100 value: 53.854 - type: ndcg_at_1000 value: 55.955 - type: ndcg_at_3 value: 43.877 - type: ndcg_at_5 value: 46.027 - type: precision_at_1 value: 39.342 - type: precision_at_10 value: 9.285 - type: precision_at_100 value: 1.488 - type: precision_at_1000 value: 0.194 - type: precision_at_3 value: 20.696 - type: precision_at_5 value: 14.878 - type: recall_at_1 value: 31.891000000000002 - type: recall_at_10 value: 60.608 - type: recall_at_100 value: 81.025 - type: recall_at_1000 value: 94.883 - type: recall_at_3 value: 45.694 - type: recall_at_5 value: 51.684 - type: map_at_1 value: 28.778 - type: map_at_10 value: 37.632 - type: map_at_100 value: 38.800000000000004 - type: map_at_1000 value: 38.934999999999995 - type: map_at_3 value: 35.293 - type: map_at_5 value: 36.547000000000004 - type: mrr_at_1 value: 35.35 - type: mrr_at_10 value: 42.936 - type: mrr_at_100 value: 43.69 - type: mrr_at_1000 value: 43.739 - type: mrr_at_3 value: 41.062 - type: mrr_at_5 value: 42.097 - type: ndcg_at_1 value: 35.35 - type: ndcg_at_10 value: 42.528 - type: ndcg_at_100 value: 46.983000000000004 - type: ndcg_at_1000 value: 49.187999999999995 - type: ndcg_at_3 value: 39.271 - type: ndcg_at_5 value: 40.654 - type: precision_at_1 value: 35.35 - type: precision_at_10 value: 7.828 - type: precision_at_100 value: 1.3010000000000002 - type: precision_at_1000 value: 0.17700000000000002 - type: precision_at_3 value: 18.96 - type: precision_at_5 value: 13.120999999999999 - type: recall_at_1 value: 28.778 - type: recall_at_10 value: 50.775000000000006 - type: recall_at_100 value: 69.66799999999999 - type: recall_at_1000 value: 83.638 - type: recall_at_3 value: 40.757 - type: recall_at_5 value: 44.86 - type: map_at_1 value: 37.584 - type: map_at_10 value: 49.69 - type: map_at_100 value: 50.639 - type: map_at_1000 value: 50.702999999999996 - type: map_at_3 value: 46.61 - type: map_at_5 value: 48.486000000000004 - type: mrr_at_1 value: 43.009 - type: mrr_at_10 value: 52.949999999999996 - type: mrr_at_100 value: 53.618 - type: mrr_at_1000 value: 53.65299999999999 - type: mrr_at_3 value: 50.605999999999995 - type: mrr_at_5 value: 52.095 - type: ndcg_at_1 value: 43.009 - type: ndcg_at_10 value: 55.278000000000006 - type: ndcg_at_100 value: 59.134 - type: ndcg_at_1000 value: 60.528999999999996 - type: ndcg_at_3 value: 50.184 - type: ndcg_at_5 value: 52.919000000000004 - type: precision_at_1 value: 43.009 - type: precision_at_10 value: 8.821 - type: precision_at_100 value: 1.161 - type: precision_at_1000 value: 0.133 - type: precision_at_3 value: 22.424 - type: precision_at_5 value: 15.436 - type: recall_at_1 value: 37.584 - type: recall_at_10 value: 68.514 - type: recall_at_100 value: 85.099 - type: recall_at_1000 value: 95.123 - type: recall_at_3 value: 55.007 - type: recall_at_5 value: 61.714999999999996 - type: map_at_1 value: 24.7 - type: map_at_10 value: 32.804 - type: map_at_100 value: 33.738 - type: map_at_1000 value: 33.825 - type: map_at_3 value: 30.639 - type: map_at_5 value: 31.781 - type: mrr_at_1 value: 26.328000000000003 - type: mrr_at_10 value: 34.679 - type: mrr_at_100 value: 35.510000000000005 - type: mrr_at_1000 value: 35.577999999999996 - type: mrr_at_3 value: 32.58 - type: mrr_at_5 value: 33.687 - type: ndcg_at_1 value: 26.328000000000003 - type: ndcg_at_10 value: 37.313 - type: ndcg_at_100 value: 42.004000000000005 - type: ndcg_at_1000 value: 44.232 - type: ndcg_at_3 value: 33.076 - type: ndcg_at_5 value: 34.966 - type: precision_at_1 value: 26.328000000000003 - type: precision_at_10 value: 5.627 - type: precision_at_100 value: 0.8410000000000001 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 14.011000000000001 - type: precision_at_5 value: 9.582 - type: recall_at_1 value: 24.7 - type: recall_at_10 value: 49.324 - type: recall_at_100 value: 71.018 - type: recall_at_1000 value: 87.905 - type: recall_at_3 value: 37.7 - type: recall_at_5 value: 42.281 - type: map_at_1 value: 14.350999999999999 - type: map_at_10 value: 21.745 - type: map_at_100 value: 22.731 - type: map_at_1000 value: 22.852 - type: map_at_3 value: 19.245 - type: map_at_5 value: 20.788 - type: mrr_at_1 value: 18.159 - type: mrr_at_10 value: 25.833000000000002 - type: mrr_at_100 value: 26.728 - type: mrr_at_1000 value: 26.802 - type: mrr_at_3 value: 23.383000000000003 - type: mrr_at_5 value: 24.887999999999998 - type: ndcg_at_1 value: 18.159 - type: ndcg_at_10 value: 26.518000000000004 - type: ndcg_at_100 value: 31.473000000000003 - type: ndcg_at_1000 value: 34.576 - type: ndcg_at_3 value: 21.907 - type: ndcg_at_5 value: 24.39 - type: precision_at_1 value: 18.159 - type: precision_at_10 value: 4.938 - type: precision_at_100 value: 0.853 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.655000000000001 - type: precision_at_5 value: 7.985 - type: recall_at_1 value: 14.350999999999999 - type: recall_at_10 value: 37.284 - type: recall_at_100 value: 59.11300000000001 - type: recall_at_1000 value: 81.634 - type: recall_at_3 value: 24.753 - type: recall_at_5 value: 30.979 - type: map_at_1 value: 26.978 - type: map_at_10 value: 36.276 - type: map_at_100 value: 37.547000000000004 - type: map_at_1000 value: 37.678 - type: map_at_3 value: 33.674 - type: map_at_5 value: 35.119 - type: mrr_at_1 value: 32.916000000000004 - type: mrr_at_10 value: 41.798 - type: mrr_at_100 value: 42.72 - type: mrr_at_1000 value: 42.778 - type: mrr_at_3 value: 39.493 - type: mrr_at_5 value: 40.927 - type: ndcg_at_1 value: 32.916000000000004 - type: ndcg_at_10 value: 41.81 - type: ndcg_at_100 value: 47.284 - type: ndcg_at_1000 value: 49.702 - type: ndcg_at_3 value: 37.486999999999995 - type: ndcg_at_5 value: 39.597 - type: precision_at_1 value: 32.916000000000004 - type: precision_at_10 value: 7.411 - type: precision_at_100 value: 1.189 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.581 - type: precision_at_5 value: 12.397 - type: recall_at_1 value: 26.978 - type: recall_at_10 value: 52.869 - type: recall_at_100 value: 75.78399999999999 - type: recall_at_1000 value: 91.545 - type: recall_at_3 value: 40.717 - type: recall_at_5 value: 46.168 - type: map_at_1 value: 24.641 - type: map_at_10 value: 32.916000000000004 - type: map_at_100 value: 34.165 - type: map_at_1000 value: 34.286 - type: map_at_3 value: 30.335 - type: map_at_5 value: 31.569000000000003 - type: mrr_at_1 value: 30.593999999999998 - type: mrr_at_10 value: 38.448 - type: mrr_at_100 value: 39.299 - type: mrr_at_1000 value: 39.362 - type: mrr_at_3 value: 36.244 - type: mrr_at_5 value: 37.232 - type: ndcg_at_1 value: 30.593999999999998 - type: ndcg_at_10 value: 38.2 - type: ndcg_at_100 value: 43.742 - type: ndcg_at_1000 value: 46.217000000000006 - type: ndcg_at_3 value: 33.925 - type: ndcg_at_5 value: 35.394 - type: precision_at_1 value: 30.593999999999998 - type: precision_at_10 value: 6.895 - type: precision_at_100 value: 1.1320000000000001 - type: precision_at_1000 value: 0.153 - type: precision_at_3 value: 16.096 - type: precision_at_5 value: 11.05 - type: recall_at_1 value: 24.641 - type: recall_at_10 value: 48.588 - type: recall_at_100 value: 72.841 - type: recall_at_1000 value: 89.535 - type: recall_at_3 value: 36.087 - type: recall_at_5 value: 40.346 - type: map_at_1 value: 24.79425 - type: map_at_10 value: 33.12033333333333 - type: map_at_100 value: 34.221333333333334 - type: map_at_1000 value: 34.3435 - type: map_at_3 value: 30.636583333333338 - type: map_at_5 value: 31.974083333333326 - type: mrr_at_1 value: 29.242416666666664 - type: mrr_at_10 value: 37.11675 - type: mrr_at_100 value: 37.93783333333334 - type: mrr_at_1000 value: 38.003083333333336 - type: mrr_at_3 value: 34.904666666666664 - type: mrr_at_5 value: 36.12916666666667 - type: ndcg_at_1 value: 29.242416666666664 - type: ndcg_at_10 value: 38.03416666666667 - type: ndcg_at_100 value: 42.86674999999999 - type: ndcg_at_1000 value: 45.34550000000001 - type: ndcg_at_3 value: 33.76466666666666 - type: ndcg_at_5 value: 35.668666666666674 - type: precision_at_1 value: 29.242416666666664 - type: precision_at_10 value: 6.589833333333334 - type: precision_at_100 value: 1.0693333333333332 - type: precision_at_1000 value: 0.14641666666666667 - type: precision_at_3 value: 15.430749999999998 - type: precision_at_5 value: 10.833833333333333 - type: recall_at_1 value: 24.79425 - type: recall_at_10 value: 48.582916666666655 - type: recall_at_100 value: 69.88499999999999 - type: recall_at_1000 value: 87.211 - type: recall_at_3 value: 36.625499999999995 - type: recall_at_5 value: 41.553999999999995 - type: map_at_1 value: 22.767 - type: map_at_10 value: 28.450999999999997 - type: map_at_100 value: 29.332 - type: map_at_1000 value: 29.426000000000002 - type: map_at_3 value: 26.379 - type: map_at_5 value: 27.584999999999997 - type: mrr_at_1 value: 25.46 - type: mrr_at_10 value: 30.974 - type: mrr_at_100 value: 31.784000000000002 - type: mrr_at_1000 value: 31.857999999999997 - type: mrr_at_3 value: 28.962 - type: mrr_at_5 value: 30.066 - type: ndcg_at_1 value: 25.46 - type: ndcg_at_10 value: 32.041 - type: ndcg_at_100 value: 36.522 - type: ndcg_at_1000 value: 39.101 - type: ndcg_at_3 value: 28.152 - type: ndcg_at_5 value: 30.03 - type: precision_at_1 value: 25.46 - type: precision_at_10 value: 4.893 - type: precision_at_100 value: 0.77 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 11.605 - type: precision_at_5 value: 8.19 - type: recall_at_1 value: 22.767 - type: recall_at_10 value: 40.71 - type: recall_at_100 value: 61.334999999999994 - type: recall_at_1000 value: 80.567 - type: recall_at_3 value: 30.198000000000004 - type: recall_at_5 value: 34.803 - type: map_at_1 value: 16.722 - type: map_at_10 value: 22.794 - type: map_at_100 value: 23.7 - type: map_at_1000 value: 23.822 - type: map_at_3 value: 20.781 - type: map_at_5 value: 22.024 - type: mrr_at_1 value: 20.061999999999998 - type: mrr_at_10 value: 26.346999999999998 - type: mrr_at_100 value: 27.153 - type: mrr_at_1000 value: 27.233 - type: mrr_at_3 value: 24.375 - type: mrr_at_5 value: 25.593 - type: ndcg_at_1 value: 20.061999999999998 - type: ndcg_at_10 value: 26.785999999999998 - type: ndcg_at_100 value: 31.319999999999997 - type: ndcg_at_1000 value: 34.346 - type: ndcg_at_3 value: 23.219 - type: ndcg_at_5 value: 25.107000000000003 - type: precision_at_1 value: 20.061999999999998 - type: precision_at_10 value: 4.78 - type: precision_at_100 value: 0.83 - type: precision_at_1000 value: 0.125 - type: precision_at_3 value: 10.874 - type: precision_at_5 value: 7.956 - type: recall_at_1 value: 16.722 - type: recall_at_10 value: 35.204 - type: recall_at_100 value: 55.797 - type: recall_at_1000 value: 77.689 - type: recall_at_3 value: 25.245 - type: recall_at_5 value: 30.115 - type: map_at_1 value: 24.842 - type: map_at_10 value: 32.917 - type: map_at_100 value: 33.961000000000006 - type: map_at_1000 value: 34.069 - type: map_at_3 value: 30.595 - type: map_at_5 value: 31.837 - type: mrr_at_1 value: 29.011 - type: mrr_at_10 value: 36.977 - type: mrr_at_100 value: 37.814 - type: mrr_at_1000 value: 37.885999999999996 - type: mrr_at_3 value: 34.966 - type: mrr_at_5 value: 36.043 - type: ndcg_at_1 value: 29.011 - type: ndcg_at_10 value: 37.735 - type: ndcg_at_100 value: 42.683 - type: ndcg_at_1000 value: 45.198 - type: ndcg_at_3 value: 33.650000000000006 - type: ndcg_at_5 value: 35.386 - type: precision_at_1 value: 29.011 - type: precision_at_10 value: 6.259 - type: precision_at_100 value: 0.984 - type: precision_at_1000 value: 0.13 - type: precision_at_3 value: 15.329999999999998 - type: precision_at_5 value: 10.541 - type: recall_at_1 value: 24.842 - type: recall_at_10 value: 48.304 - type: recall_at_100 value: 70.04899999999999 - type: recall_at_1000 value: 87.82600000000001 - type: recall_at_3 value: 36.922 - type: recall_at_5 value: 41.449999999999996 - type: map_at_1 value: 24.252000000000002 - type: map_at_10 value: 32.293 - type: map_at_100 value: 33.816 - type: map_at_1000 value: 34.053 - type: map_at_3 value: 29.781999999999996 - type: map_at_5 value: 31.008000000000003 - type: mrr_at_1 value: 29.051 - type: mrr_at_10 value: 36.722 - type: mrr_at_100 value: 37.663000000000004 - type: mrr_at_1000 value: 37.734 - type: mrr_at_3 value: 34.354 - type: mrr_at_5 value: 35.609 - type: ndcg_at_1 value: 29.051 - type: ndcg_at_10 value: 37.775999999999996 - type: ndcg_at_100 value: 43.221 - type: ndcg_at_1000 value: 46.116 - type: ndcg_at_3 value: 33.403 - type: ndcg_at_5 value: 35.118 - type: precision_at_1 value: 29.051 - type: precision_at_10 value: 7.332 - type: precision_at_100 value: 1.49 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 15.415000000000001 - type: precision_at_5 value: 11.107 - type: recall_at_1 value: 24.252000000000002 - type: recall_at_10 value: 47.861 - type: recall_at_100 value: 72.21600000000001 - type: recall_at_1000 value: 90.886 - type: recall_at_3 value: 35.533 - type: recall_at_5 value: 39.959 - type: map_at_1 value: 20.025000000000002 - type: map_at_10 value: 27.154 - type: map_at_100 value: 28.118 - type: map_at_1000 value: 28.237000000000002 - type: map_at_3 value: 25.017 - type: map_at_5 value: 25.832 - type: mrr_at_1 value: 21.627 - type: mrr_at_10 value: 28.884999999999998 - type: mrr_at_100 value: 29.741 - type: mrr_at_1000 value: 29.831999999999997 - type: mrr_at_3 value: 26.741 - type: mrr_at_5 value: 27.628000000000004 - type: ndcg_at_1 value: 21.627 - type: ndcg_at_10 value: 31.436999999999998 - type: ndcg_at_100 value: 36.181000000000004 - type: ndcg_at_1000 value: 38.986 - type: ndcg_at_3 value: 27.025 - type: ndcg_at_5 value: 28.436 - type: precision_at_1 value: 21.627 - type: precision_at_10 value: 5.009 - type: precision_at_100 value: 0.7929999999999999 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 11.522 - type: precision_at_5 value: 7.763000000000001 - type: recall_at_1 value: 20.025000000000002 - type: recall_at_10 value: 42.954 - type: recall_at_100 value: 64.67500000000001 - type: recall_at_1000 value: 85.301 - type: recall_at_3 value: 30.892999999999997 - type: recall_at_5 value: 34.288000000000004 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 10.079 - type: map_at_10 value: 16.930999999999997 - type: map_at_100 value: 18.398999999999997 - type: map_at_1000 value: 18.561 - type: map_at_3 value: 14.294 - type: map_at_5 value: 15.579 - type: mrr_at_1 value: 22.606 - type: mrr_at_10 value: 32.513 - type: mrr_at_100 value: 33.463 - type: mrr_at_1000 value: 33.513999999999996 - type: mrr_at_3 value: 29.479 - type: mrr_at_5 value: 31.3 - type: ndcg_at_1 value: 22.606 - type: ndcg_at_10 value: 24.053 - type: ndcg_at_100 value: 30.258000000000003 - type: ndcg_at_1000 value: 33.516 - type: ndcg_at_3 value: 19.721 - type: ndcg_at_5 value: 21.144 - type: precision_at_1 value: 22.606 - type: precision_at_10 value: 7.55 - type: precision_at_100 value: 1.399 - type: precision_at_1000 value: 0.2 - type: precision_at_3 value: 14.701 - type: precision_at_5 value: 11.192 - type: recall_at_1 value: 10.079 - type: recall_at_10 value: 28.970000000000002 - type: recall_at_100 value: 50.805 - type: recall_at_1000 value: 69.378 - type: recall_at_3 value: 18.199 - type: recall_at_5 value: 22.442 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 7.794 - type: map_at_10 value: 15.165999999999999 - type: map_at_100 value: 20.508000000000003 - type: map_at_1000 value: 21.809 - type: map_at_3 value: 11.568000000000001 - type: map_at_5 value: 13.059000000000001 - type: mrr_at_1 value: 56.49999999999999 - type: mrr_at_10 value: 65.90899999999999 - type: mrr_at_100 value: 66.352 - type: mrr_at_1000 value: 66.369 - type: mrr_at_3 value: 64.0 - type: mrr_at_5 value: 65.10000000000001 - type: ndcg_at_1 value: 44.25 - type: ndcg_at_10 value: 32.649 - type: ndcg_at_100 value: 36.668 - type: ndcg_at_1000 value: 43.918 - type: ndcg_at_3 value: 37.096000000000004 - type: ndcg_at_5 value: 34.048 - type: precision_at_1 value: 56.49999999999999 - type: precision_at_10 value: 25.45 - type: precision_at_100 value: 8.055 - type: precision_at_1000 value: 1.7489999999999999 - type: precision_at_3 value: 41.0 - type: precision_at_5 value: 32.85 - type: recall_at_1 value: 7.794 - type: recall_at_10 value: 20.101 - type: recall_at_100 value: 42.448 - type: recall_at_1000 value: 65.88000000000001 - type: recall_at_3 value: 12.753 - type: recall_at_5 value: 15.307 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 44.01 - type: f1 value: 38.659680951114964 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 49.713 - type: map_at_10 value: 61.79 - type: map_at_100 value: 62.28 - type: map_at_1000 value: 62.297000000000004 - type: map_at_3 value: 59.361 - type: map_at_5 value: 60.92100000000001 - type: mrr_at_1 value: 53.405 - type: mrr_at_10 value: 65.79899999999999 - type: mrr_at_100 value: 66.219 - type: mrr_at_1000 value: 66.227 - type: mrr_at_3 value: 63.431000000000004 - type: mrr_at_5 value: 64.98 - type: ndcg_at_1 value: 53.405 - type: ndcg_at_10 value: 68.01899999999999 - type: ndcg_at_100 value: 70.197 - type: ndcg_at_1000 value: 70.571 - type: ndcg_at_3 value: 63.352 - type: ndcg_at_5 value: 66.018 - type: precision_at_1 value: 53.405 - type: precision_at_10 value: 9.119 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.107 - type: precision_at_3 value: 25.602999999999998 - type: precision_at_5 value: 16.835 - type: recall_at_1 value: 49.713 - type: recall_at_10 value: 83.306 - type: recall_at_100 value: 92.92 - type: recall_at_1000 value: 95.577 - type: recall_at_3 value: 70.798 - type: recall_at_5 value: 77.254 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 15.310000000000002 - type: map_at_10 value: 26.204 - type: map_at_100 value: 27.932000000000002 - type: map_at_1000 value: 28.121000000000002 - type: map_at_3 value: 22.481 - type: map_at_5 value: 24.678 - type: mrr_at_1 value: 29.784 - type: mrr_at_10 value: 39.582 - type: mrr_at_100 value: 40.52 - type: mrr_at_1000 value: 40.568 - type: mrr_at_3 value: 37.114000000000004 - type: mrr_at_5 value: 38.596000000000004 - type: ndcg_at_1 value: 29.784 - type: ndcg_at_10 value: 33.432 - type: ndcg_at_100 value: 40.281 - type: ndcg_at_1000 value: 43.653999999999996 - type: ndcg_at_3 value: 29.612 - type: ndcg_at_5 value: 31.223 - type: precision_at_1 value: 29.784 - type: precision_at_10 value: 9.645 - type: precision_at_100 value: 1.645 - type: precision_at_1000 value: 0.22499999999999998 - type: precision_at_3 value: 20.165 - type: precision_at_5 value: 15.401000000000002 - type: recall_at_1 value: 15.310000000000002 - type: recall_at_10 value: 40.499 - type: recall_at_100 value: 66.643 - type: recall_at_1000 value: 87.059 - type: recall_at_3 value: 27.492 - type: recall_at_5 value: 33.748 - task: type: Retrieval dataset: name: MTEB HotpotQA type: hotpotqa config: default split: test revision: None metrics: - type: map_at_1 value: 33.599000000000004 - type: map_at_10 value: 47.347 - type: map_at_100 value: 48.191 - type: map_at_1000 value: 48.263 - type: map_at_3 value: 44.698 - type: map_at_5 value: 46.278999999999996 - type: mrr_at_1 value: 67.19800000000001 - type: mrr_at_10 value: 74.054 - type: mrr_at_100 value: 74.376 - type: mrr_at_1000 value: 74.392 - type: mrr_at_3 value: 72.849 - type: mrr_at_5 value: 73.643 - type: ndcg_at_1 value: 67.19800000000001 - type: ndcg_at_10 value: 56.482 - type: ndcg_at_100 value: 59.694 - type: ndcg_at_1000 value: 61.204 - type: ndcg_at_3 value: 52.43299999999999 - type: ndcg_at_5 value: 54.608000000000004 - type: precision_at_1 value: 67.19800000000001 - type: precision_at_10 value: 11.613999999999999 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 32.726 - type: precision_at_5 value: 21.349999999999998 - type: recall_at_1 value: 33.599000000000004 - type: recall_at_10 value: 58.069 - type: recall_at_100 value: 70.736 - type: recall_at_1000 value: 80.804 - type: recall_at_3 value: 49.088 - type: recall_at_5 value: 53.376000000000005 - task: type: Classification dataset: name: MTEB ImdbClassification type: mteb/imdb config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 73.64359999999999 - type: ap value: 67.54685976014599 - type: f1 value: 73.55148707559482 - task: type: Retrieval dataset: name: MTEB MSMARCO type: msmarco config: default split: dev revision: None metrics: - type: map_at_1 value: 19.502 - type: map_at_10 value: 30.816 - type: map_at_100 value: 32.007999999999996 - type: map_at_1000 value: 32.067 - type: map_at_3 value: 27.215 - type: map_at_5 value: 29.304000000000002 - type: mrr_at_1 value: 20.072000000000003 - type: mrr_at_10 value: 31.406 - type: mrr_at_100 value: 32.549 - type: mrr_at_1000 value: 32.602 - type: mrr_at_3 value: 27.839000000000002 - type: mrr_at_5 value: 29.926000000000002 - type: ndcg_at_1 value: 20.086000000000002 - type: ndcg_at_10 value: 37.282 - type: ndcg_at_100 value: 43.206 - type: ndcg_at_1000 value: 44.690000000000005 - type: ndcg_at_3 value: 29.932 - type: ndcg_at_5 value: 33.668 - type: precision_at_1 value: 20.086000000000002 - type: precision_at_10 value: 5.961 - type: precision_at_100 value: 0.898 - type: precision_at_1000 value: 0.10200000000000001 - type: precision_at_3 value: 12.856000000000002 - type: precision_at_5 value: 9.596 - type: recall_at_1 value: 19.502 - type: recall_at_10 value: 57.182 - type: recall_at_100 value: 84.952 - type: recall_at_1000 value: 96.34700000000001 - type: recall_at_3 value: 37.193 - type: recall_at_5 value: 46.157 - task: type: Classification dataset: name: MTEB MTOPDomainClassification (en) type: mteb/mtop_domain config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 93.96488828089375 - type: f1 value: 93.32119260543482 - task: type: Classification dataset: name: MTEB MTOPIntentClassification (en) type: mteb/mtop_intent config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 72.4965800273598 - type: f1 value: 49.34896217536082 - task: type: Classification dataset: name: MTEB MassiveIntentClassification (en) type: mteb/amazon_massive_intent config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 67.60928043039678 - type: f1 value: 64.34244712074538 - task: type: Classification dataset: name: MTEB MassiveScenarioClassification (en) type: mteb/amazon_massive_scenario config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 69.75453934095493 - type: f1 value: 68.39224867489249 - task: type: Clustering dataset: name: MTEB MedrxivClusteringP2P type: mteb/medrxiv-clustering-p2p config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.862573504920082 - task: type: Clustering dataset: name: MTEB MedrxivClusteringS2S type: mteb/medrxiv-clustering-s2s config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 27.511123551196803 - task: type: Reranking dataset: name: MTEB MindSmallReranking type: mteb/mind_small config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 30.99145104942086 - type: mrr value: 32.03606480418627 - task: type: Retrieval dataset: name: MTEB NFCorpus type: nfcorpus config: default split: test revision: None metrics: - type: map_at_1 value: 5.015 - type: map_at_10 value: 11.054 - type: map_at_100 value: 13.773 - type: map_at_1000 value: 15.082999999999998 - type: map_at_3 value: 8.253 - type: map_at_5 value: 9.508999999999999 - type: mrr_at_1 value: 42.105 - type: mrr_at_10 value: 50.44499999999999 - type: mrr_at_100 value: 51.080000000000005 - type: mrr_at_1000 value: 51.129999999999995 - type: mrr_at_3 value: 48.555 - type: mrr_at_5 value: 49.84 - type: ndcg_at_1 value: 40.402 - type: ndcg_at_10 value: 30.403000000000002 - type: ndcg_at_100 value: 28.216 - type: ndcg_at_1000 value: 37.021 - type: ndcg_at_3 value: 35.53 - type: ndcg_at_5 value: 33.202999999999996 - type: precision_at_1 value: 42.105 - type: precision_at_10 value: 22.353 - type: precision_at_100 value: 7.266 - type: precision_at_1000 value: 2.011 - type: precision_at_3 value: 32.921 - type: precision_at_5 value: 28.297 - type: recall_at_1 value: 5.015 - type: recall_at_10 value: 14.393 - type: recall_at_100 value: 28.893 - type: recall_at_1000 value: 60.18 - type: recall_at_3 value: 9.184000000000001 - type: recall_at_5 value: 11.39 - task: type: Retrieval dataset: name: MTEB NQ type: nq config: default split: test revision: None metrics: - type: map_at_1 value: 29.524 - type: map_at_10 value: 44.182 - type: map_at_100 value: 45.228 - type: map_at_1000 value: 45.265 - type: map_at_3 value: 39.978 - type: map_at_5 value: 42.482 - type: mrr_at_1 value: 33.256 - type: mrr_at_10 value: 46.661 - type: mrr_at_100 value: 47.47 - type: mrr_at_1000 value: 47.496 - type: mrr_at_3 value: 43.187999999999995 - type: mrr_at_5 value: 45.330999999999996 - type: ndcg_at_1 value: 33.227000000000004 - type: ndcg_at_10 value: 51.589 - type: ndcg_at_100 value: 56.043 - type: ndcg_at_1000 value: 56.937000000000005 - type: ndcg_at_3 value: 43.751 - type: ndcg_at_5 value: 47.937000000000005 - type: precision_at_1 value: 33.227000000000004 - type: precision_at_10 value: 8.556999999999999 - type: precision_at_100 value: 1.103 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 19.921 - type: precision_at_5 value: 14.396999999999998 - type: recall_at_1 value: 29.524 - type: recall_at_10 value: 71.615 - type: recall_at_100 value: 91.056 - type: recall_at_1000 value: 97.72800000000001 - type: recall_at_3 value: 51.451 - type: recall_at_5 value: 61.119 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 69.596 - type: map_at_10 value: 83.281 - type: map_at_100 value: 83.952 - type: map_at_1000 value: 83.97200000000001 - type: map_at_3 value: 80.315 - type: map_at_5 value: 82.223 - type: mrr_at_1 value: 80.17 - type: mrr_at_10 value: 86.522 - type: mrr_at_100 value: 86.644 - type: mrr_at_1000 value: 86.64500000000001 - type: mrr_at_3 value: 85.438 - type: mrr_at_5 value: 86.21799999999999 - type: ndcg_at_1 value: 80.19 - type: ndcg_at_10 value: 87.19 - type: ndcg_at_100 value: 88.567 - type: ndcg_at_1000 value: 88.70400000000001 - type: ndcg_at_3 value: 84.17999999999999 - type: ndcg_at_5 value: 85.931 - type: precision_at_1 value: 80.19 - type: precision_at_10 value: 13.209000000000001 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 36.717 - type: precision_at_5 value: 24.248 - type: recall_at_1 value: 69.596 - type: recall_at_10 value: 94.533 - type: recall_at_100 value: 99.322 - type: recall_at_1000 value: 99.965 - type: recall_at_3 value: 85.911 - type: recall_at_5 value: 90.809 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 49.27650627571912 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 57.08550946534183 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 4.568 - type: map_at_10 value: 10.862 - type: map_at_100 value: 12.757 - type: map_at_1000 value: 13.031 - type: map_at_3 value: 7.960000000000001 - type: map_at_5 value: 9.337 - type: mrr_at_1 value: 22.5 - type: mrr_at_10 value: 32.6 - type: mrr_at_100 value: 33.603 - type: mrr_at_1000 value: 33.672000000000004 - type: mrr_at_3 value: 29.299999999999997 - type: mrr_at_5 value: 31.25 - type: ndcg_at_1 value: 22.5 - type: ndcg_at_10 value: 18.605 - type: ndcg_at_100 value: 26.029999999999998 - type: ndcg_at_1000 value: 31.256 - type: ndcg_at_3 value: 17.873 - type: ndcg_at_5 value: 15.511 - type: precision_at_1 value: 22.5 - type: precision_at_10 value: 9.58 - type: precision_at_100 value: 2.033 - type: precision_at_1000 value: 0.33 - type: precision_at_3 value: 16.633 - type: precision_at_5 value: 13.54 - type: recall_at_1 value: 4.568 - type: recall_at_10 value: 19.402 - type: recall_at_100 value: 41.277 - type: recall_at_1000 value: 66.963 - type: recall_at_3 value: 10.112 - type: recall_at_5 value: 13.712 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 83.31992291680787 - type: cos_sim_spearman value: 76.7212346922664 - type: euclidean_pearson value: 80.42189271706478 - type: euclidean_spearman value: 76.7212342532493 - type: manhattan_pearson value: 80.33171093031578 - type: manhattan_spearman value: 76.63192883074694 - task: type: STS dataset: name: MTEB STS12 type: mteb/sts12-sts config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 83.16654278886763 - type: cos_sim_spearman value: 73.66390263429565 - type: euclidean_pearson value: 79.7485360086639 - type: euclidean_spearman value: 73.66389870373436 - type: manhattan_pearson value: 79.73652237443706 - type: manhattan_spearman value: 73.65296117151647 - task: type: STS dataset: name: MTEB STS13 type: mteb/sts13-sts config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 82.40389689929246 - type: cos_sim_spearman value: 83.29727595993955 - type: euclidean_pearson value: 82.23970587854079 - type: euclidean_spearman value: 83.29727595993955 - type: manhattan_pearson value: 82.18823600831897 - type: manhattan_spearman value: 83.20746192209594 - task: type: STS dataset: name: MTEB STS14 type: mteb/sts14-sts config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 81.73505246913413 - type: cos_sim_spearman value: 79.1686548248754 - type: euclidean_pearson value: 80.48889135993412 - type: euclidean_spearman value: 79.16864112930354 - type: manhattan_pearson value: 80.40720651057302 - type: manhattan_spearman value: 79.0640155089286 - task: type: STS dataset: name: MTEB STS15 type: mteb/sts15-sts config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 86.3953512879065 - type: cos_sim_spearman value: 87.29947322714338 - type: euclidean_pearson value: 86.59759438529645 - type: euclidean_spearman value: 87.29947511092824 - type: manhattan_pearson value: 86.52097806169155 - type: manhattan_spearman value: 87.22987242146534 - task: type: STS dataset: name: MTEB STS16 type: mteb/sts16-sts config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 82.48565753792056 - type: cos_sim_spearman value: 83.6049720319893 - type: euclidean_pearson value: 82.56452023172913 - type: euclidean_spearman value: 83.60490168191697 - type: manhattan_pearson value: 82.58079941137872 - type: manhattan_spearman value: 83.60975807374051 - task: type: STS dataset: name: MTEB STS17 (en-en) type: mteb/sts17-crosslingual-sts config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics: - type: cos_sim_pearson value: 88.18239976618212 - type: cos_sim_spearman value: 88.23061724730616 - type: euclidean_pearson value: 87.78482472776658 - type: euclidean_spearman value: 88.23061724730616 - type: manhattan_pearson value: 87.75059641730239 - type: manhattan_spearman value: 88.22527413524622 - task: type: STS dataset: name: MTEB STS22 (en) type: mteb/sts22-crosslingual-sts config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 63.42816418706765 - type: cos_sim_spearman value: 63.4569864520124 - type: euclidean_pearson value: 64.35405409953853 - type: euclidean_spearman value: 63.4569864520124 - type: manhattan_pearson value: 63.96649236073056 - type: manhattan_spearman value: 63.01448583722708 - task: type: STS dataset: name: MTEB STSBenchmark type: mteb/stsbenchmark-sts config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 83.41659638047614 - type: cos_sim_spearman value: 84.03893866106175 - type: euclidean_pearson value: 84.2251203953798 - type: euclidean_spearman value: 84.03893866106175 - type: manhattan_pearson value: 84.22733643205514 - type: manhattan_spearman value: 84.06504411263612 - task: type: Reranking dataset: name: MTEB SciDocsRR type: mteb/scidocs-reranking config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 79.75608022582414 - type: mrr value: 94.0947732369301 - task: type: Retrieval dataset: name: MTEB SciFact type: scifact config: default split: test revision: None metrics: - type: map_at_1 value: 50.161 - type: map_at_10 value: 59.458999999999996 - type: map_at_100 value: 60.156 - type: map_at_1000 value: 60.194 - type: map_at_3 value: 56.45400000000001 - type: map_at_5 value: 58.165 - type: mrr_at_1 value: 53.333 - type: mrr_at_10 value: 61.050000000000004 - type: mrr_at_100 value: 61.586 - type: mrr_at_1000 value: 61.624 - type: mrr_at_3 value: 58.889 - type: mrr_at_5 value: 60.122 - type: ndcg_at_1 value: 53.333 - type: ndcg_at_10 value: 63.888999999999996 - type: ndcg_at_100 value: 66.963 - type: ndcg_at_1000 value: 68.062 - type: ndcg_at_3 value: 59.01 - type: ndcg_at_5 value: 61.373999999999995 - type: precision_at_1 value: 53.333 - type: precision_at_10 value: 8.633000000000001 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 23.111 - type: precision_at_5 value: 15.467 - type: recall_at_1 value: 50.161 - type: recall_at_10 value: 75.922 - type: recall_at_100 value: 90.0 - type: recall_at_1000 value: 98.667 - type: recall_at_3 value: 62.90599999999999 - type: recall_at_5 value: 68.828 - task: type: PairClassification dataset: name: MTEB SprintDuplicateQuestions type: mteb/sprintduplicatequestions-pairclassification config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.81188118811882 - type: cos_sim_ap value: 95.11619225962413 - type: cos_sim_f1 value: 90.35840484603736 - type: cos_sim_precision value: 91.23343527013252 - type: cos_sim_recall value: 89.5 - type: dot_accuracy value: 99.81188118811882 - type: dot_ap value: 95.11619225962413 - type: dot_f1 value: 90.35840484603736 - type: dot_precision value: 91.23343527013252 - type: dot_recall value: 89.5 - type: euclidean_accuracy value: 99.81188118811882 - type: euclidean_ap value: 95.11619225962413 - type: euclidean_f1 value: 90.35840484603736 - type: euclidean_precision value: 91.23343527013252 - type: euclidean_recall value: 89.5 - type: manhattan_accuracy value: 99.80891089108911 - type: manhattan_ap value: 95.07294266220966 - type: manhattan_f1 value: 90.21794221996959 - type: manhattan_precision value: 91.46968139773895 - type: manhattan_recall value: 89.0 - type: max_accuracy value: 99.81188118811882 - type: max_ap value: 95.11619225962413 - type: max_f1 value: 90.35840484603736 - task: type: Clustering dataset: name: MTEB StackExchangeClustering type: mteb/stackexchange-clustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 55.3481874105239 - task: type: Clustering dataset: name: MTEB StackExchangeClusteringP2P type: mteb/stackexchange-clustering-p2p config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 34.421291695525 - task: type: Reranking dataset: name: MTEB StackOverflowDupQuestions type: mteb/stackoverflowdupquestions-reranking config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 49.98746633276634 - type: mrr value: 50.63143249724133 - task: type: Summarization dataset: name: MTEB SummEval type: mteb/summeval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.009961979844036 - type: cos_sim_spearman value: 30.558416108881044 - type: dot_pearson value: 31.009964941134253 - type: dot_spearman value: 30.545760761761393 - task: type: Retrieval dataset: name: MTEB TRECCOVID type: trec-covid config: default split: test revision: None metrics: - type: map_at_1 value: 0.207 - type: map_at_10 value: 1.6 - type: map_at_100 value: 8.594 - type: map_at_1000 value: 20.213 - type: map_at_3 value: 0.585 - type: map_at_5 value: 0.9039999999999999 - type: mrr_at_1 value: 78.0 - type: mrr_at_10 value: 87.4 - type: mrr_at_100 value: 87.4 - type: mrr_at_1000 value: 87.4 - type: mrr_at_3 value: 86.667 - type: mrr_at_5 value: 87.06700000000001 - type: ndcg_at_1 value: 73.0 - type: ndcg_at_10 value: 65.18 - type: ndcg_at_100 value: 49.631 - type: ndcg_at_1000 value: 43.498999999999995 - type: ndcg_at_3 value: 71.83800000000001 - type: ndcg_at_5 value: 69.271 - type: precision_at_1 value: 78.0 - type: precision_at_10 value: 69.19999999999999 - type: precision_at_100 value: 50.980000000000004 - type: precision_at_1000 value: 19.426 - type: precision_at_3 value: 77.333 - type: precision_at_5 value: 74.0 - type: recall_at_1 value: 0.207 - type: recall_at_10 value: 1.822 - type: recall_at_100 value: 11.849 - type: recall_at_1000 value: 40.492 - type: recall_at_3 value: 0.622 - type: recall_at_5 value: 0.9809999999999999 - task: type: Retrieval dataset: name: MTEB Touche2020 type: webis-touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 2.001 - type: map_at_10 value: 10.376000000000001 - type: map_at_100 value: 16.936999999999998 - type: map_at_1000 value: 18.615000000000002 - type: map_at_3 value: 5.335999999999999 - type: map_at_5 value: 7.374 - type: mrr_at_1 value: 20.408 - type: mrr_at_10 value: 38.29 - type: mrr_at_100 value: 39.33 - type: mrr_at_1000 value: 39.347 - type: mrr_at_3 value: 32.993 - type: mrr_at_5 value: 36.973 - type: ndcg_at_1 value: 17.347 - type: ndcg_at_10 value: 23.515 - type: ndcg_at_100 value: 37.457 - type: ndcg_at_1000 value: 49.439 - type: ndcg_at_3 value: 22.762999999999998 - type: ndcg_at_5 value: 22.622 - type: precision_at_1 value: 20.408 - type: precision_at_10 value: 22.448999999999998 - type: precision_at_100 value: 8.184 - type: precision_at_1000 value: 1.608 - type: precision_at_3 value: 25.85 - type: precision_at_5 value: 25.306 - type: recall_at_1 value: 2.001 - type: recall_at_10 value: 17.422 - type: recall_at_100 value: 51.532999999999994 - type: recall_at_1000 value: 87.466 - type: recall_at_3 value: 6.861000000000001 - type: recall_at_5 value: 10.502 - task: type: Classification dataset: name: MTEB ToxicConversationsClassification type: mteb/toxic_conversations_50k config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 71.54419999999999 - type: ap value: 14.372170450843907 - type: f1 value: 54.94420257390529 - task: type: Classification dataset: name: MTEB TweetSentimentExtractionClassification type: mteb/tweet_sentiment_extraction config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 59.402942840973395 - type: f1 value: 59.4166538875571 - task: type: Clustering dataset: name: MTEB TwentyNewsgroupsClustering type: mteb/twentynewsgroups-clustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 41.569064336457906 - task: type: PairClassification dataset: name: MTEB TwitterSemEval2015 type: mteb/twittersemeval2015-pairclassification config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 85.31322644096085 - type: cos_sim_ap value: 72.14518894837381 - type: cos_sim_f1 value: 66.67489813557229 - type: cos_sim_precision value: 62.65954977953121 - type: cos_sim_recall value: 71.2401055408971 - type: dot_accuracy value: 85.31322644096085 - type: dot_ap value: 72.14521480685293 - type: dot_f1 value: 66.67489813557229 - type: dot_precision value: 62.65954977953121 - type: dot_recall value: 71.2401055408971 - type: euclidean_accuracy value: 85.31322644096085 - type: euclidean_ap value: 72.14520820485349 - type: euclidean_f1 value: 66.67489813557229 - type: euclidean_precision value: 62.65954977953121 - type: euclidean_recall value: 71.2401055408971 - type: manhattan_accuracy value: 85.21785778148656 - type: manhattan_ap value: 72.01177147657364 - type: manhattan_f1 value: 66.62594673833374 - type: manhattan_precision value: 62.0336669699727 - type: manhattan_recall value: 71.95250659630607 - type: max_accuracy value: 85.31322644096085 - type: max_ap value: 72.14521480685293 - type: max_f1 value: 66.67489813557229 - task: type: PairClassification dataset: name: MTEB TwitterURLCorpus type: mteb/twitterurlcorpus-pairclassification config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 89.12756626693057 - type: cos_sim_ap value: 86.05430786440826 - type: cos_sim_f1 value: 78.27759692216631 - type: cos_sim_precision value: 75.33466248931929 - type: cos_sim_recall value: 81.45980905451185 - type: dot_accuracy value: 89.12950673341872 - type: dot_ap value: 86.05431161145492 - type: dot_f1 value: 78.27759692216631 - type: dot_precision value: 75.33466248931929 - type: dot_recall value: 81.45980905451185 - type: euclidean_accuracy value: 89.12756626693057 - type: euclidean_ap value: 86.05431303247397 - type: euclidean_f1 value: 78.27759692216631 - type: euclidean_precision value: 75.33466248931929 - type: euclidean_recall value: 81.45980905451185 - type: manhattan_accuracy value: 89.04994760740482 - type: manhattan_ap value: 86.00860610892074 - type: manhattan_f1 value: 78.1846776005392 - type: manhattan_precision value: 76.10438839480975 - type: manhattan_recall value: 80.3818909762858 - type: max_accuracy value: 89.12950673341872 - type: max_ap value: 86.05431303247397 - type: max_f1 value: 78.27759692216631 --- # djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF This model was converted to GGUF format from [`jinaai/jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF --hf-file jina-embeddings-v2-small-en-q5_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF --hf-file jina-embeddings-v2-small-en-q5_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF --hf-file jina-embeddings-v2-small-en-q5_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo djuna/jina-embeddings-v2-small-en-Q5_K_M-GGUF --hf-file jina-embeddings-v2-small-en-q5_k_m.gguf -c 2048 ```