--- datasets: - allenai/c4 language: - en license: apache-2.0 model-index: - name: gte-large-en-v1.5 results: - dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics: - type: accuracy value: 73.01492537313432 - type: ap value: 35.05341696659522 - type: f1 value: 66.71270310883853 task: type: Classification - dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics: - type: accuracy value: 93.97189999999999 - type: ap value: 90.5952493948908 - type: f1 value: 93.95848137716877 task: type: Classification - dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics: - type: accuracy value: 54.196 - type: f1 value: 53.80122334012787 task: type: Classification - dataset: config: default name: MTEB ArguAna revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics: - type: map_at_1 value: 47.297 - type: map_at_10 value: 64.303 - type: map_at_100 value: 64.541 - type: map_at_1000 value: 64.541 - type: map_at_3 value: 60.728 - type: map_at_5 value: 63.114000000000004 - type: mrr_at_1 value: 48.435 - type: mrr_at_10 value: 64.657 - type: mrr_at_100 value: 64.901 - type: mrr_at_1000 value: 64.901 - type: mrr_at_3 value: 61.06 - type: mrr_at_5 value: 63.514 - type: ndcg_at_1 value: 47.297 - type: ndcg_at_10 value: 72.107 - type: ndcg_at_100 value: 72.963 - type: ndcg_at_1000 value: 72.963 - type: ndcg_at_3 value: 65.063 - type: ndcg_at_5 value: 69.352 - type: precision_at_1 value: 47.297 - type: precision_at_10 value: 9.623 - type: precision_at_100 value: 0.996 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 25.865 - type: precision_at_5 value: 17.596 - type: recall_at_1 value: 47.297 - type: recall_at_10 value: 96.23 - type: recall_at_100 value: 99.644 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 77.596 - type: recall_at_5 value: 87.98 task: type: Retrieval - dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics: - type: v_measure value: 48.467787861077475 task: type: Clustering - dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics: - type: v_measure value: 43.39198391914257 task: type: Clustering - dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics: - type: map value: 63.12794820591384 - type: mrr value: 75.9331442641692 task: type: Reranking - dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics: - type: cos_sim_pearson value: 87.85062993863319 - type: cos_sim_spearman value: 85.39049989733459 - type: euclidean_pearson value: 86.00222680278333 - type: euclidean_spearman value: 85.45556162077396 - type: manhattan_pearson value: 85.88769871785621 - type: manhattan_spearman value: 85.11760211290839 task: type: STS - dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics: - type: accuracy value: 87.32792207792208 - type: f1 value: 87.29132945999555 task: type: Classification - dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics: - type: v_measure value: 40.5779328301945 task: type: Clustering - dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics: - type: v_measure value: 37.94425623865118 task: type: Clustering - dataset: config: default name: MTEB CQADupstackAndroidRetrieval revision: f46a197baaae43b4f621051089b82a364682dfeb split: test type: mteb/cqadupstack-android metrics: - type: map_at_1 value: 32.978 - type: map_at_10 value: 44.45 - type: map_at_100 value: 46.19 - type: map_at_1000 value: 46.303 - type: map_at_3 value: 40.849000000000004 - type: map_at_5 value: 42.55 - type: mrr_at_1 value: 40.629 - type: mrr_at_10 value: 50.848000000000006 - type: mrr_at_100 value: 51.669 - type: mrr_at_1000 value: 51.705 - type: mrr_at_3 value: 47.997 - type: mrr_at_5 value: 49.506 - type: ndcg_at_1 value: 40.629 - type: ndcg_at_10 value: 51.102000000000004 - type: ndcg_at_100 value: 57.159000000000006 - type: ndcg_at_1000 value: 58.669000000000004 - type: ndcg_at_3 value: 45.738 - type: ndcg_at_5 value: 47.632999999999996 - type: precision_at_1 value: 40.629 - type: precision_at_10 value: 9.700000000000001 - type: precision_at_100 value: 1.5970000000000002 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.698 - type: precision_at_5 value: 15.393 - type: recall_at_1 value: 32.978 - type: recall_at_10 value: 63.711 - type: recall_at_100 value: 88.39399999999999 - type: recall_at_1000 value: 97.513 - type: recall_at_3 value: 48.025 - type: recall_at_5 value: 53.52 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackEnglishRetrieval revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 split: test type: mteb/cqadupstack-english metrics: - type: map_at_1 value: 30.767 - type: map_at_10 value: 42.195 - type: map_at_100 value: 43.541999999999994 - type: map_at_1000 value: 43.673 - type: map_at_3 value: 38.561 - type: map_at_5 value: 40.532000000000004 - type: mrr_at_1 value: 38.79 - type: mrr_at_10 value: 48.021 - type: mrr_at_100 value: 48.735 - type: mrr_at_1000 value: 48.776 - type: mrr_at_3 value: 45.594 - type: mrr_at_5 value: 46.986 - type: ndcg_at_1 value: 38.79 - type: ndcg_at_10 value: 48.468 - type: ndcg_at_100 value: 53.037 - type: ndcg_at_1000 value: 55.001999999999995 - type: ndcg_at_3 value: 43.409 - type: ndcg_at_5 value: 45.654 - type: precision_at_1 value: 38.79 - type: precision_at_10 value: 9.452 - type: precision_at_100 value: 1.518 - type: precision_at_1000 value: 0.201 - type: precision_at_3 value: 21.21 - type: precision_at_5 value: 15.171999999999999 - type: recall_at_1 value: 30.767 - type: recall_at_10 value: 60.118 - type: recall_at_100 value: 79.271 - type: recall_at_1000 value: 91.43299999999999 - type: recall_at_3 value: 45.36 - type: recall_at_5 value: 51.705 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGamingRetrieval revision: 4885aa143210c98657558c04aaf3dc47cfb54340 split: test type: mteb/cqadupstack-gaming metrics: - type: map_at_1 value: 40.007 - type: map_at_10 value: 53.529 - type: map_at_100 value: 54.602 - type: map_at_1000 value: 54.647 - type: map_at_3 value: 49.951 - type: map_at_5 value: 52.066 - type: mrr_at_1 value: 45.705 - type: mrr_at_10 value: 56.745000000000005 - type: mrr_at_100 value: 57.43899999999999 - type: mrr_at_1000 value: 57.462999999999994 - type: mrr_at_3 value: 54.25299999999999 - type: mrr_at_5 value: 55.842000000000006 - type: ndcg_at_1 value: 45.705 - type: ndcg_at_10 value: 59.809 - type: ndcg_at_100 value: 63.837999999999994 - type: ndcg_at_1000 value: 64.729 - type: ndcg_at_3 value: 53.994 - type: ndcg_at_5 value: 57.028 - type: precision_at_1 value: 45.705 - type: precision_at_10 value: 9.762 - type: precision_at_100 value: 1.275 - type: precision_at_1000 value: 0.13899999999999998 - type: precision_at_3 value: 24.368000000000002 - type: precision_at_5 value: 16.84 - type: recall_at_1 value: 40.007 - type: recall_at_10 value: 75.017 - type: recall_at_100 value: 91.99000000000001 - type: recall_at_1000 value: 98.265 - type: recall_at_3 value: 59.704 - type: recall_at_5 value: 67.109 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackGisRetrieval revision: 5003b3064772da1887988e05400cf3806fe491f2 split: test type: mteb/cqadupstack-gis metrics: - type: map_at_1 value: 26.639000000000003 - type: map_at_10 value: 35.926 - type: map_at_100 value: 37.126999999999995 - type: map_at_1000 value: 37.202 - type: map_at_3 value: 32.989000000000004 - type: map_at_5 value: 34.465 - type: mrr_at_1 value: 28.475 - type: mrr_at_10 value: 37.7 - type: mrr_at_100 value: 38.753 - type: mrr_at_1000 value: 38.807 - type: mrr_at_3 value: 35.066 - type: mrr_at_5 value: 36.512 - type: ndcg_at_1 value: 28.475 - type: ndcg_at_10 value: 41.245 - type: ndcg_at_100 value: 46.814 - type: ndcg_at_1000 value: 48.571 - type: ndcg_at_3 value: 35.528999999999996 - type: ndcg_at_5 value: 38.066 - type: precision_at_1 value: 28.475 - type: precision_at_10 value: 6.497 - type: precision_at_100 value: 0.9650000000000001 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 15.065999999999999 - type: precision_at_5 value: 10.599 - type: recall_at_1 value: 26.639000000000003 - type: recall_at_10 value: 55.759 - type: recall_at_100 value: 80.913 - type: recall_at_1000 value: 93.929 - type: recall_at_3 value: 40.454 - type: recall_at_5 value: 46.439 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackMathematicaRetrieval revision: 90fceea13679c63fe563ded68f3b6f06e50061de split: test type: mteb/cqadupstack-mathematica metrics: - type: map_at_1 value: 15.767999999999999 - type: map_at_10 value: 24.811 - type: map_at_100 value: 26.064999999999998 - type: map_at_1000 value: 26.186999999999998 - type: map_at_3 value: 21.736 - type: map_at_5 value: 23.283 - type: mrr_at_1 value: 19.527 - type: mrr_at_10 value: 29.179 - type: mrr_at_100 value: 30.153999999999996 - type: mrr_at_1000 value: 30.215999999999998 - type: mrr_at_3 value: 26.223000000000003 - type: mrr_at_5 value: 27.733999999999998 - type: ndcg_at_1 value: 19.527 - type: ndcg_at_10 value: 30.786 - type: ndcg_at_100 value: 36.644 - type: ndcg_at_1000 value: 39.440999999999995 - type: ndcg_at_3 value: 24.958 - type: ndcg_at_5 value: 27.392 - type: precision_at_1 value: 19.527 - type: precision_at_10 value: 5.995 - type: precision_at_100 value: 1.03 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 12.520999999999999 - type: precision_at_5 value: 9.129 - type: recall_at_1 value: 15.767999999999999 - type: recall_at_10 value: 44.824000000000005 - type: recall_at_100 value: 70.186 - type: recall_at_1000 value: 89.934 - type: recall_at_3 value: 28.607 - type: recall_at_5 value: 34.836 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackPhysicsRetrieval revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 split: test type: mteb/cqadupstack-physics metrics: - type: map_at_1 value: 31.952 - type: map_at_10 value: 44.438 - type: map_at_100 value: 45.778 - type: map_at_1000 value: 45.883 - type: map_at_3 value: 41.044000000000004 - type: map_at_5 value: 42.986000000000004 - type: mrr_at_1 value: 39.172000000000004 - type: mrr_at_10 value: 49.76 - type: mrr_at_100 value: 50.583999999999996 - type: mrr_at_1000 value: 50.621 - type: mrr_at_3 value: 47.353 - type: mrr_at_5 value: 48.739 - type: ndcg_at_1 value: 39.172000000000004 - type: ndcg_at_10 value: 50.760000000000005 - type: ndcg_at_100 value: 56.084 - type: ndcg_at_1000 value: 57.865 - type: ndcg_at_3 value: 45.663 - type: ndcg_at_5 value: 48.178 - type: precision_at_1 value: 39.172000000000004 - type: precision_at_10 value: 9.22 - type: precision_at_100 value: 1.387 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 21.976000000000003 - type: precision_at_5 value: 15.457 - type: recall_at_1 value: 31.952 - type: recall_at_10 value: 63.900999999999996 - type: recall_at_100 value: 85.676 - type: recall_at_1000 value: 97.03699999999999 - type: recall_at_3 value: 49.781 - type: recall_at_5 value: 56.330000000000005 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackProgrammersRetrieval revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 split: test type: mteb/cqadupstack-programmers metrics: - type: map_at_1 value: 25.332 - type: map_at_10 value: 36.874 - type: map_at_100 value: 38.340999999999994 - type: map_at_1000 value: 38.452 - type: map_at_3 value: 33.068 - type: map_at_5 value: 35.324 - type: mrr_at_1 value: 30.822 - type: mrr_at_10 value: 41.641 - type: mrr_at_100 value: 42.519 - type: mrr_at_1000 value: 42.573 - type: mrr_at_3 value: 38.413000000000004 - type: mrr_at_5 value: 40.542 - type: ndcg_at_1 value: 30.822 - type: ndcg_at_10 value: 43.414 - type: ndcg_at_100 value: 49.196 - type: ndcg_at_1000 value: 51.237 - type: ndcg_at_3 value: 37.230000000000004 - type: ndcg_at_5 value: 40.405 - type: precision_at_1 value: 30.822 - type: precision_at_10 value: 8.379 - type: precision_at_100 value: 1.315 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 18.417 - type: precision_at_5 value: 13.744 - type: recall_at_1 value: 25.332 - type: recall_at_10 value: 57.774 - type: recall_at_100 value: 82.071 - type: recall_at_1000 value: 95.60600000000001 - type: recall_at_3 value: 40.722 - type: recall_at_5 value: 48.754999999999995 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackRetrieval revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: mteb/cqadupstack metrics: - type: map_at_1 value: 25.91033333333334 - type: map_at_10 value: 36.23225000000001 - type: map_at_100 value: 37.55766666666667 - type: map_at_1000 value: 37.672583333333336 - type: map_at_3 value: 32.95666666666667 - type: map_at_5 value: 34.73375 - type: mrr_at_1 value: 30.634 - type: mrr_at_10 value: 40.19449999999999 - type: mrr_at_100 value: 41.099250000000005 - type: mrr_at_1000 value: 41.15091666666667 - type: mrr_at_3 value: 37.4615 - type: mrr_at_5 value: 39.00216666666667 - type: ndcg_at_1 value: 30.634 - type: ndcg_at_10 value: 42.162166666666664 - type: ndcg_at_100 value: 47.60708333333333 - type: ndcg_at_1000 value: 49.68616666666666 - type: ndcg_at_3 value: 36.60316666666666 - type: ndcg_at_5 value: 39.15616666666668 - type: precision_at_1 value: 30.634 - type: precision_at_10 value: 7.6193333333333335 - type: precision_at_100 value: 1.2198333333333333 - type: precision_at_1000 value: 0.15975000000000003 - type: precision_at_3 value: 17.087 - type: precision_at_5 value: 12.298333333333334 - type: recall_at_1 value: 25.91033333333334 - type: recall_at_10 value: 55.67300000000001 - type: recall_at_100 value: 79.20608333333334 - type: recall_at_1000 value: 93.34866666666667 - type: recall_at_3 value: 40.34858333333333 - type: recall_at_5 value: 46.834083333333325 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackStatsRetrieval revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a split: test type: mteb/cqadupstack-stats metrics: - type: map_at_1 value: 25.006 - type: map_at_10 value: 32.177 - type: map_at_100 value: 33.324999999999996 - type: map_at_1000 value: 33.419 - type: map_at_3 value: 29.952 - type: map_at_5 value: 31.095 - type: mrr_at_1 value: 28.066999999999997 - type: mrr_at_10 value: 34.995 - type: mrr_at_100 value: 35.978 - type: mrr_at_1000 value: 36.042 - type: mrr_at_3 value: 33.103 - type: mrr_at_5 value: 34.001 - type: ndcg_at_1 value: 28.066999999999997 - type: ndcg_at_10 value: 36.481 - type: ndcg_at_100 value: 42.022999999999996 - type: ndcg_at_1000 value: 44.377 - type: ndcg_at_3 value: 32.394 - type: ndcg_at_5 value: 34.108 - type: precision_at_1 value: 28.066999999999997 - type: precision_at_10 value: 5.736 - type: precision_at_100 value: 0.9259999999999999 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 13.804 - type: precision_at_5 value: 9.508999999999999 - type: recall_at_1 value: 25.006 - type: recall_at_10 value: 46.972 - type: recall_at_100 value: 72.138 - type: recall_at_1000 value: 89.479 - type: recall_at_3 value: 35.793 - type: recall_at_5 value: 39.947 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackTexRetrieval revision: 46989137a86843e03a6195de44b09deda022eec7 split: test type: mteb/cqadupstack-tex metrics: - type: map_at_1 value: 16.07 - type: map_at_10 value: 24.447 - type: map_at_100 value: 25.685999999999996 - type: map_at_1000 value: 25.813999999999997 - type: map_at_3 value: 21.634 - type: map_at_5 value: 23.133 - type: mrr_at_1 value: 19.580000000000002 - type: mrr_at_10 value: 28.127999999999997 - type: mrr_at_100 value: 29.119 - type: mrr_at_1000 value: 29.192 - type: mrr_at_3 value: 25.509999999999998 - type: mrr_at_5 value: 26.878 - type: ndcg_at_1 value: 19.580000000000002 - type: ndcg_at_10 value: 29.804000000000002 - type: ndcg_at_100 value: 35.555 - type: ndcg_at_1000 value: 38.421 - type: ndcg_at_3 value: 24.654999999999998 - type: ndcg_at_5 value: 26.881 - type: precision_at_1 value: 19.580000000000002 - type: precision_at_10 value: 5.736 - type: precision_at_100 value: 1.005 - type: precision_at_1000 value: 0.145 - type: precision_at_3 value: 12.033000000000001 - type: precision_at_5 value: 8.871 - type: recall_at_1 value: 16.07 - type: recall_at_10 value: 42.364000000000004 - type: recall_at_100 value: 68.01899999999999 - type: recall_at_1000 value: 88.122 - type: recall_at_3 value: 27.846 - type: recall_at_5 value: 33.638 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackUnixRetrieval revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 split: test type: mteb/cqadupstack-unix metrics: - type: map_at_1 value: 26.365 - type: map_at_10 value: 36.591 - type: map_at_100 value: 37.730000000000004 - type: map_at_1000 value: 37.84 - type: map_at_3 value: 33.403 - type: map_at_5 value: 35.272999999999996 - type: mrr_at_1 value: 30.503999999999998 - type: mrr_at_10 value: 39.940999999999995 - type: mrr_at_100 value: 40.818 - type: mrr_at_1000 value: 40.876000000000005 - type: mrr_at_3 value: 37.065 - type: mrr_at_5 value: 38.814 - type: ndcg_at_1 value: 30.503999999999998 - type: ndcg_at_10 value: 42.185 - type: ndcg_at_100 value: 47.416000000000004 - type: ndcg_at_1000 value: 49.705 - type: ndcg_at_3 value: 36.568 - type: ndcg_at_5 value: 39.416000000000004 - type: precision_at_1 value: 30.503999999999998 - type: precision_at_10 value: 7.276000000000001 - type: precision_at_100 value: 1.118 - type: precision_at_1000 value: 0.14300000000000002 - type: precision_at_3 value: 16.729 - type: precision_at_5 value: 12.107999999999999 - type: recall_at_1 value: 26.365 - type: recall_at_10 value: 55.616 - type: recall_at_100 value: 78.129 - type: recall_at_1000 value: 93.95599999999999 - type: recall_at_3 value: 40.686 - type: recall_at_5 value: 47.668 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWebmastersRetrieval revision: 160c094312a0e1facb97e55eeddb698c0abe3571 split: test type: mteb/cqadupstack-webmasters metrics: - type: map_at_1 value: 22.750999999999998 - type: map_at_10 value: 33.446 - type: map_at_100 value: 35.235 - type: map_at_1000 value: 35.478 - type: map_at_3 value: 29.358 - type: map_at_5 value: 31.525 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 37.694 - type: mrr_at_100 value: 38.732 - type: mrr_at_1000 value: 38.779 - type: mrr_at_3 value: 34.223 - type: mrr_at_5 value: 36.08 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 40.557 - type: ndcg_at_100 value: 46.605999999999995 - type: ndcg_at_1000 value: 48.917 - type: ndcg_at_3 value: 33.677 - type: ndcg_at_5 value: 36.85 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 8.3 - type: precision_at_100 value: 1.6260000000000001 - type: precision_at_1000 value: 0.253 - type: precision_at_3 value: 16.008 - type: precision_at_5 value: 12.292 - type: recall_at_1 value: 22.750999999999998 - type: recall_at_10 value: 55.643 - type: recall_at_100 value: 82.151 - type: recall_at_1000 value: 95.963 - type: recall_at_3 value: 36.623 - type: recall_at_5 value: 44.708 task: type: Retrieval - dataset: config: default name: MTEB CQADupstackWordpressRetrieval revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: mteb/cqadupstack-wordpress metrics: - type: map_at_1 value: 17.288999999999998 - type: map_at_10 value: 25.903 - type: map_at_100 value: 27.071 - type: map_at_1000 value: 27.173000000000002 - type: map_at_3 value: 22.935 - type: map_at_5 value: 24.573 - type: mrr_at_1 value: 18.669 - type: mrr_at_10 value: 27.682000000000002 - type: mrr_at_100 value: 28.691 - type: mrr_at_1000 value: 28.761 - type: mrr_at_3 value: 24.738 - type: mrr_at_5 value: 26.392 - type: ndcg_at_1 value: 18.669 - type: ndcg_at_10 value: 31.335 - type: ndcg_at_100 value: 36.913000000000004 - type: ndcg_at_1000 value: 39.300000000000004 - type: ndcg_at_3 value: 25.423000000000002 - type: ndcg_at_5 value: 28.262999999999998 - type: precision_at_1 value: 18.669 - type: precision_at_10 value: 5.379 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.11900000000000001 - type: precision_at_3 value: 11.214 - type: precision_at_5 value: 8.466 - type: recall_at_1 value: 17.288999999999998 - type: recall_at_10 value: 46.377 - type: recall_at_100 value: 71.53500000000001 - type: recall_at_1000 value: 88.947 - type: recall_at_3 value: 30.581999999999997 - type: recall_at_5 value: 37.354 task: type: Retrieval - dataset: config: default name: MTEB ClimateFEVER revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 split: test type: mteb/climate-fever metrics: - type: map_at_1 value: 21.795 - type: map_at_10 value: 37.614999999999995 - type: map_at_100 value: 40.037 - type: map_at_1000 value: 40.184999999999995 - type: map_at_3 value: 32.221 - type: map_at_5 value: 35.154999999999994 - type: mrr_at_1 value: 50.358000000000004 - type: mrr_at_10 value: 62.129 - type: mrr_at_100 value: 62.613 - type: mrr_at_1000 value: 62.62 - type: mrr_at_3 value: 59.272999999999996 - type: mrr_at_5 value: 61.138999999999996 - type: ndcg_at_1 value: 50.358000000000004 - type: ndcg_at_10 value: 48.362 - type: ndcg_at_100 value: 55.932 - type: ndcg_at_1000 value: 58.062999999999995 - type: ndcg_at_3 value: 42.111 - type: ndcg_at_5 value: 44.063 - type: precision_at_1 value: 50.358000000000004 - type: precision_at_10 value: 14.677999999999999 - type: precision_at_100 value: 2.2950000000000004 - type: precision_at_1000 value: 0.271 - type: precision_at_3 value: 31.77 - type: precision_at_5 value: 23.375 - type: recall_at_1 value: 21.795 - type: recall_at_10 value: 53.846000000000004 - type: recall_at_100 value: 78.952 - type: recall_at_1000 value: 90.41900000000001 - type: recall_at_3 value: 37.257 - type: recall_at_5 value: 44.661 task: type: Retrieval - dataset: config: default name: MTEB DBPedia revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 split: test type: mteb/dbpedia metrics: - type: map_at_1 value: 9.728 - type: map_at_10 value: 22.691 - type: map_at_100 value: 31.734 - type: map_at_1000 value: 33.464 - type: map_at_3 value: 16.273 - type: map_at_5 value: 19.016 - type: mrr_at_1 value: 73.25 - type: mrr_at_10 value: 80.782 - type: mrr_at_100 value: 81.01899999999999 - type: mrr_at_1000 value: 81.021 - type: mrr_at_3 value: 79.583 - type: mrr_at_5 value: 80.146 - type: ndcg_at_1 value: 59.62499999999999 - type: ndcg_at_10 value: 46.304 - type: ndcg_at_100 value: 51.23 - type: ndcg_at_1000 value: 58.048 - type: ndcg_at_3 value: 51.541000000000004 - type: ndcg_at_5 value: 48.635 - type: precision_at_1 value: 73.25 - type: precision_at_10 value: 36.375 - type: precision_at_100 value: 11.53 - type: precision_at_1000 value: 2.23 - type: precision_at_3 value: 55.583000000000006 - type: precision_at_5 value: 47.15 - type: recall_at_1 value: 9.728 - type: recall_at_10 value: 28.793999999999997 - type: recall_at_100 value: 57.885 - type: recall_at_1000 value: 78.759 - type: recall_at_3 value: 17.79 - type: recall_at_5 value: 21.733 task: type: Retrieval - dataset: config: default name: MTEB EmotionClassification revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 split: test type: mteb/emotion metrics: - type: accuracy value: 46.775 - type: f1 value: 41.89794273264891 task: type: Classification - dataset: config: default name: MTEB FEVER revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 split: test type: mteb/fever metrics: - type: map_at_1 value: 85.378 - type: map_at_10 value: 91.51 - type: map_at_100 value: 91.666 - type: map_at_1000 value: 91.676 - type: map_at_3 value: 90.757 - type: map_at_5 value: 91.277 - type: mrr_at_1 value: 91.839 - type: mrr_at_10 value: 95.49 - type: mrr_at_100 value: 95.493 - type: mrr_at_1000 value: 95.493 - type: mrr_at_3 value: 95.345 - type: mrr_at_5 value: 95.47200000000001 - type: ndcg_at_1 value: 91.839 - type: ndcg_at_10 value: 93.806 - type: ndcg_at_100 value: 94.255 - type: ndcg_at_1000 value: 94.399 - type: ndcg_at_3 value: 93.027 - type: ndcg_at_5 value: 93.51 - type: precision_at_1 value: 91.839 - type: precision_at_10 value: 10.93 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 34.873 - type: precision_at_5 value: 21.44 - type: recall_at_1 value: 85.378 - type: recall_at_10 value: 96.814 - type: recall_at_100 value: 98.386 - type: recall_at_1000 value: 99.21600000000001 - type: recall_at_3 value: 94.643 - type: recall_at_5 value: 95.976 task: type: Retrieval - dataset: config: default name: MTEB FiQA2018 revision: 27a168819829fe9bcd655c2df245fb19452e8e06 split: test type: mteb/fiqa metrics: - type: map_at_1 value: 32.190000000000005 - type: map_at_10 value: 53.605000000000004 - type: map_at_100 value: 55.550999999999995 - type: map_at_1000 value: 55.665 - type: map_at_3 value: 46.62 - type: map_at_5 value: 50.517999999999994 - type: mrr_at_1 value: 60.34 - type: mrr_at_10 value: 70.775 - type: mrr_at_100 value: 71.238 - type: mrr_at_1000 value: 71.244 - type: mrr_at_3 value: 68.72399999999999 - type: mrr_at_5 value: 69.959 - type: ndcg_at_1 value: 60.34 - type: ndcg_at_10 value: 63.226000000000006 - type: ndcg_at_100 value: 68.60300000000001 - type: ndcg_at_1000 value: 69.901 - type: ndcg_at_3 value: 58.048 - type: ndcg_at_5 value: 59.789 - type: precision_at_1 value: 60.34 - type: precision_at_10 value: 17.130000000000003 - type: precision_at_100 value: 2.29 - type: precision_at_1000 value: 0.256 - type: precision_at_3 value: 38.323 - type: precision_at_5 value: 27.87 - type: recall_at_1 value: 32.190000000000005 - type: recall_at_10 value: 73.041 - type: recall_at_100 value: 91.31 - type: recall_at_1000 value: 98.104 - type: recall_at_3 value: 53.70399999999999 - type: recall_at_5 value: 62.358999999999995 task: type: Retrieval - dataset: config: default name: MTEB HotpotQA revision: ab518f4d6fcca38d87c25209f94beba119d02014 split: test type: mteb/hotpotqa metrics: - type: map_at_1 value: 43.511 - type: map_at_10 value: 58.15 - type: map_at_100 value: 58.95399999999999 - type: map_at_1000 value: 59.018 - type: map_at_3 value: 55.31700000000001 - type: map_at_5 value: 57.04900000000001 - type: mrr_at_1 value: 87.022 - type: mrr_at_10 value: 91.32000000000001 - type: mrr_at_100 value: 91.401 - type: mrr_at_1000 value: 91.403 - type: mrr_at_3 value: 90.77 - type: mrr_at_5 value: 91.156 - type: ndcg_at_1 value: 87.022 - type: ndcg_at_10 value: 68.183 - type: ndcg_at_100 value: 70.781 - type: ndcg_at_1000 value: 72.009 - type: ndcg_at_3 value: 64.334 - type: ndcg_at_5 value: 66.449 - type: precision_at_1 value: 87.022 - type: precision_at_10 value: 13.406 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.17099999999999999 - type: precision_at_3 value: 39.023 - type: precision_at_5 value: 25.080000000000002 - type: recall_at_1 value: 43.511 - type: recall_at_10 value: 67.02900000000001 - type: recall_at_100 value: 77.11 - type: recall_at_1000 value: 85.294 - type: recall_at_3 value: 58.535000000000004 - type: recall_at_5 value: 62.70099999999999 task: type: Retrieval - dataset: config: default name: MTEB ImdbClassification revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics: - type: accuracy value: 92.0996 - type: ap value: 87.86206089096373 - type: f1 value: 92.07554547510763 task: type: Classification - dataset: config: default name: MTEB MSMARCO revision: c5a29a104738b98a9e76336939199e264163d4a0 split: dev type: mteb/msmarco metrics: - type: map_at_1 value: 23.179 - type: map_at_10 value: 35.86 - type: map_at_100 value: 37.025999999999996 - type: map_at_1000 value: 37.068 - type: map_at_3 value: 31.921 - type: map_at_5 value: 34.172000000000004 - type: mrr_at_1 value: 23.926 - type: mrr_at_10 value: 36.525999999999996 - type: mrr_at_100 value: 37.627 - type: mrr_at_1000 value: 37.665 - type: mrr_at_3 value: 32.653 - type: mrr_at_5 value: 34.897 - type: ndcg_at_1 value: 23.910999999999998 - type: ndcg_at_10 value: 42.927 - type: ndcg_at_100 value: 48.464 - type: ndcg_at_1000 value: 49.533 - type: ndcg_at_3 value: 34.910000000000004 - type: ndcg_at_5 value: 38.937 - type: precision_at_1 value: 23.910999999999998 - type: precision_at_10 value: 6.758 - type: precision_at_100 value: 0.9520000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.838000000000001 - type: precision_at_5 value: 10.934000000000001 - type: recall_at_1 value: 23.179 - type: recall_at_10 value: 64.622 - type: recall_at_100 value: 90.135 - type: recall_at_1000 value: 98.301 - type: recall_at_3 value: 42.836999999999996 - type: recall_at_5 value: 52.512 task: type: Retrieval - dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics: - type: accuracy value: 96.59598723210215 - type: f1 value: 96.41913500001952 task: type: Classification - dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics: - type: accuracy value: 82.89557683538533 - type: f1 value: 63.379319722356264 task: type: Classification - dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics: - type: accuracy value: 78.93745796906524 - type: f1 value: 75.71616541785902 task: type: Classification - dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics: - type: accuracy value: 81.41223940820443 - type: f1 value: 81.2877893719078 task: type: Classification - dataset: config: default name: MTEB MedrxivClusteringP2P revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics: - type: v_measure value: 35.03682528325662 task: type: Clustering - dataset: config: default name: MTEB MedrxivClusteringS2S revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics: - type: v_measure value: 32.942529406124 task: type: Clustering - dataset: config: default name: MTEB MindSmallReranking revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 split: test type: mteb/mind_small metrics: - type: map value: 31.459949660460317 - type: mrr value: 32.70509582031616 task: type: Reranking - dataset: config: default name: MTEB NFCorpus revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 split: test type: mteb/nfcorpus metrics: - type: map_at_1 value: 6.497 - type: map_at_10 value: 13.843 - type: map_at_100 value: 17.713 - type: map_at_1000 value: 19.241 - type: map_at_3 value: 10.096 - type: map_at_5 value: 11.85 - type: mrr_at_1 value: 48.916 - type: mrr_at_10 value: 57.764 - type: mrr_at_100 value: 58.251 - type: mrr_at_1000 value: 58.282999999999994 - type: mrr_at_3 value: 55.623999999999995 - type: mrr_at_5 value: 57.018 - type: ndcg_at_1 value: 46.594 - type: ndcg_at_10 value: 36.945 - type: ndcg_at_100 value: 34.06 - type: ndcg_at_1000 value: 43.05 - type: ndcg_at_3 value: 41.738 - type: ndcg_at_5 value: 39.330999999999996 - type: precision_at_1 value: 48.916 - type: precision_at_10 value: 27.43 - type: precision_at_100 value: 8.616 - type: precision_at_1000 value: 2.155 - type: precision_at_3 value: 39.112 - type: precision_at_5 value: 33.808 - type: recall_at_1 value: 6.497 - type: recall_at_10 value: 18.163 - type: recall_at_100 value: 34.566 - type: recall_at_1000 value: 67.15 - type: recall_at_3 value: 11.100999999999999 - type: recall_at_5 value: 14.205000000000002 task: type: Retrieval - dataset: config: default name: MTEB NQ revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 split: test type: mteb/nq metrics: - type: map_at_1 value: 31.916 - type: map_at_10 value: 48.123 - type: map_at_100 value: 49.103 - type: map_at_1000 value: 49.131 - type: map_at_3 value: 43.711 - type: map_at_5 value: 46.323 - type: mrr_at_1 value: 36.181999999999995 - type: mrr_at_10 value: 50.617999999999995 - type: mrr_at_100 value: 51.329 - type: mrr_at_1000 value: 51.348000000000006 - type: mrr_at_3 value: 47.010999999999996 - type: mrr_at_5 value: 49.175000000000004 - type: ndcg_at_1 value: 36.181999999999995 - type: ndcg_at_10 value: 56.077999999999996 - type: ndcg_at_100 value: 60.037 - type: ndcg_at_1000 value: 60.63499999999999 - type: ndcg_at_3 value: 47.859 - type: ndcg_at_5 value: 52.178999999999995 - type: precision_at_1 value: 36.181999999999995 - type: precision_at_10 value: 9.284 - type: precision_at_100 value: 1.149 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 22.006999999999998 - type: precision_at_5 value: 15.695 - type: recall_at_1 value: 31.916 - type: recall_at_10 value: 77.771 - type: recall_at_100 value: 94.602 - type: recall_at_1000 value: 98.967 - type: recall_at_3 value: 56.528 - type: recall_at_5 value: 66.527 task: type: Retrieval - dataset: config: default name: MTEB QuoraRetrieval revision: None split: test type: mteb/quora metrics: - type: map_at_1 value: 71.486 - type: map_at_10 value: 85.978 - type: map_at_100 value: 86.587 - type: map_at_1000 value: 86.598 - type: map_at_3 value: 83.04899999999999 - type: map_at_5 value: 84.857 - type: mrr_at_1 value: 82.32000000000001 - type: mrr_at_10 value: 88.64 - type: mrr_at_100 value: 88.702 - type: mrr_at_1000 value: 88.702 - type: mrr_at_3 value: 87.735 - type: mrr_at_5 value: 88.36 - type: ndcg_at_1 value: 82.34 - type: ndcg_at_10 value: 89.67 - type: ndcg_at_100 value: 90.642 - type: ndcg_at_1000 value: 90.688 - type: ndcg_at_3 value: 86.932 - type: ndcg_at_5 value: 88.408 - type: precision_at_1 value: 82.34 - type: precision_at_10 value: 13.675999999999998 - type: precision_at_100 value: 1.544 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.24 - type: precision_at_5 value: 25.068 - type: recall_at_1 value: 71.486 - type: recall_at_10 value: 96.844 - type: recall_at_100 value: 99.843 - type: recall_at_1000 value: 99.996 - type: recall_at_3 value: 88.92099999999999 - type: recall_at_5 value: 93.215 task: type: Retrieval - dataset: config: default name: MTEB RedditClustering revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics: - type: v_measure value: 59.75758437908334 task: type: Clustering - dataset: config: default name: MTEB RedditClusteringP2P revision: 282350215ef01743dc01b456c7f5241fa8937f16 split: test type: mteb/reddit-clustering-p2p metrics: - type: v_measure value: 68.03497914092789 task: type: Clustering - dataset: config: default name: MTEB SCIDOCS revision: None split: test type: mteb/scidocs metrics: - type: map_at_1 value: 5.808 - type: map_at_10 value: 16.059 - type: map_at_100 value: 19.048000000000002 - type: map_at_1000 value: 19.43 - type: map_at_3 value: 10.953 - type: map_at_5 value: 13.363 - type: mrr_at_1 value: 28.7 - type: mrr_at_10 value: 42.436 - type: mrr_at_100 value: 43.599 - type: mrr_at_1000 value: 43.62 - type: mrr_at_3 value: 38.45 - type: mrr_at_5 value: 40.89 - type: ndcg_at_1 value: 28.7 - type: ndcg_at_10 value: 26.346000000000004 - type: ndcg_at_100 value: 36.758 - type: ndcg_at_1000 value: 42.113 - type: ndcg_at_3 value: 24.254 - type: ndcg_at_5 value: 21.506 - type: precision_at_1 value: 28.7 - type: precision_at_10 value: 13.969999999999999 - type: precision_at_100 value: 2.881 - type: precision_at_1000 value: 0.414 - type: precision_at_3 value: 22.933 - type: precision_at_5 value: 19.220000000000002 - type: recall_at_1 value: 5.808 - type: recall_at_10 value: 28.310000000000002 - type: recall_at_100 value: 58.475 - type: recall_at_1000 value: 84.072 - type: recall_at_3 value: 13.957 - type: recall_at_5 value: 19.515 task: type: Retrieval - dataset: config: default name: MTEB SICK-R revision: a6ea5a8cab320b040a23452cc28066d9beae2cee split: test type: mteb/sickr-sts metrics: - type: cos_sim_pearson value: 82.39274129958557 - type: cos_sim_spearman value: 79.78021235170053 - type: euclidean_pearson value: 79.35335401300166 - type: euclidean_spearman value: 79.7271870968275 - type: manhattan_pearson value: 79.35256263340601 - type: manhattan_spearman value: 79.76036386976321 task: type: STS - dataset: config: default name: MTEB STS12 revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics: - type: cos_sim_pearson value: 83.99130429246708 - type: cos_sim_spearman value: 73.88322811171203 - type: euclidean_pearson value: 80.7569419170376 - type: euclidean_spearman value: 73.82542155409597 - type: manhattan_pearson value: 80.79468183847625 - type: manhattan_spearman value: 73.87027144047784 task: type: STS - dataset: config: default name: MTEB STS13 revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics: - type: cos_sim_pearson value: 84.88548789489907 - type: cos_sim_spearman value: 85.07535893847255 - type: euclidean_pearson value: 84.6637222061494 - type: euclidean_spearman value: 85.14200626702456 - type: manhattan_pearson value: 84.75327892344734 - type: manhattan_spearman value: 85.24406181838596 task: type: STS - dataset: config: default name: MTEB STS14 revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics: - type: cos_sim_pearson value: 82.88140039325008 - type: cos_sim_spearman value: 79.61211268112362 - type: euclidean_pearson value: 81.29639728816458 - type: euclidean_spearman value: 79.51284578041442 - type: manhattan_pearson value: 81.3381797137111 - type: manhattan_spearman value: 79.55683684039808 task: type: STS - dataset: config: default name: MTEB STS15 revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics: - type: cos_sim_pearson value: 85.16716737270485 - type: cos_sim_spearman value: 86.14823841857738 - type: euclidean_pearson value: 85.36325733440725 - type: euclidean_spearman value: 86.04919691402029 - type: manhattan_pearson value: 85.3147511385052 - type: manhattan_spearman value: 86.00676205857764 task: type: STS - dataset: config: default name: MTEB STS16 revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics: - type: cos_sim_pearson value: 80.34266645861588 - type: cos_sim_spearman value: 81.59914035005882 - type: euclidean_pearson value: 81.15053076245988 - type: euclidean_spearman value: 81.52776915798489 - type: manhattan_pearson value: 81.1819647418673 - type: manhattan_spearman value: 81.57479527353556 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: 89.38263326821439 - type: cos_sim_spearman value: 89.10946308202642 - type: euclidean_pearson value: 88.87831312540068 - type: euclidean_spearman value: 89.03615865973664 - type: manhattan_pearson value: 88.79835539970384 - type: manhattan_spearman value: 88.9766156339753 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: 70.1574915581685 - type: cos_sim_spearman value: 70.59144980004054 - type: euclidean_pearson value: 71.43246306918755 - type: euclidean_spearman value: 70.5544189562984 - type: manhattan_pearson value: 71.4071414609503 - type: manhattan_spearman value: 70.31799126163712 task: type: STS - dataset: config: default name: MTEB STSBenchmark revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics: - type: cos_sim_pearson value: 83.36215796635351 - type: cos_sim_spearman value: 83.07276756467208 - type: euclidean_pearson value: 83.06690453635584 - type: euclidean_spearman value: 82.9635366303289 - type: manhattan_pearson value: 83.04994049700815 - type: manhattan_spearman value: 82.98120125356036 task: type: STS - dataset: config: default name: MTEB SciDocsRR revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab split: test type: mteb/scidocs-reranking metrics: - type: map value: 86.92530011616722 - type: mrr value: 96.21826793395421 task: type: Reranking - dataset: config: default name: MTEB SciFact revision: 0228b52cf27578f30900b9e5271d331663a030d7 split: test type: mteb/scifact metrics: - type: map_at_1 value: 65.75 - type: map_at_10 value: 77.701 - type: map_at_100 value: 78.005 - type: map_at_1000 value: 78.006 - type: map_at_3 value: 75.48 - type: map_at_5 value: 76.927 - type: mrr_at_1 value: 68.333 - type: mrr_at_10 value: 78.511 - type: mrr_at_100 value: 78.704 - type: mrr_at_1000 value: 78.704 - type: mrr_at_3 value: 77 - type: mrr_at_5 value: 78.083 - type: ndcg_at_1 value: 68.333 - type: ndcg_at_10 value: 82.42699999999999 - type: ndcg_at_100 value: 83.486 - type: ndcg_at_1000 value: 83.511 - type: ndcg_at_3 value: 78.96300000000001 - type: ndcg_at_5 value: 81.028 - type: precision_at_1 value: 68.333 - type: precision_at_10 value: 10.667 - type: precision_at_100 value: 1.127 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 31.333 - type: precision_at_5 value: 20.133000000000003 - type: recall_at_1 value: 65.75 - type: recall_at_10 value: 95.578 - type: recall_at_100 value: 99.833 - type: recall_at_1000 value: 100 - type: recall_at_3 value: 86.506 - type: recall_at_5 value: 91.75 task: type: Retrieval - dataset: config: default name: MTEB SprintDuplicateQuestions revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics: - type: cos_sim_accuracy value: 99.75247524752476 - type: cos_sim_ap value: 94.16065078045173 - type: cos_sim_f1 value: 87.22986247544205 - type: cos_sim_precision value: 85.71428571428571 - type: cos_sim_recall value: 88.8 - type: dot_accuracy value: 99.74554455445545 - type: dot_ap value: 93.90633887037264 - type: dot_f1 value: 86.9873417721519 - type: dot_precision value: 88.1025641025641 - type: dot_recall value: 85.9 - type: euclidean_accuracy value: 99.75247524752476 - type: euclidean_ap value: 94.17466319018055 - type: euclidean_f1 value: 87.3405299313052 - type: euclidean_precision value: 85.74181117533719 - type: euclidean_recall value: 89 - type: manhattan_accuracy value: 99.75445544554455 - type: manhattan_ap value: 94.27688371923577 - type: manhattan_f1 value: 87.74002954209749 - type: manhattan_precision value: 86.42095053346266 - type: manhattan_recall value: 89.1 - type: max_accuracy value: 99.75445544554455 - type: max_ap value: 94.27688371923577 - type: max_f1 value: 87.74002954209749 task: type: PairClassification - dataset: config: default name: MTEB StackExchangeClustering revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics: - type: v_measure value: 71.26500637517056 task: type: Clustering - dataset: config: default name: MTEB StackExchangeClusteringP2P revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics: - type: v_measure value: 39.17507906280528 task: type: Clustering - dataset: config: default name: MTEB StackOverflowDupQuestions revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics: - type: map value: 52.4848744828509 - type: mrr value: 53.33678168236992 task: type: Reranking - dataset: config: default name: MTEB SummEval revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c split: test type: mteb/summeval metrics: - type: cos_sim_pearson value: 30.599864323827887 - type: cos_sim_spearman value: 30.91116204665598 - type: dot_pearson value: 30.82637894269936 - type: dot_spearman value: 30.957573868416066 task: type: Summarization - dataset: config: default name: MTEB TRECCOVID revision: None split: test type: mteb/trec-covid metrics: - type: map_at_1 value: 0.23600000000000002 - type: map_at_10 value: 1.892 - type: map_at_100 value: 11.586 - type: map_at_1000 value: 27.761999999999997 - type: map_at_3 value: 0.653 - type: map_at_5 value: 1.028 - type: mrr_at_1 value: 88 - type: mrr_at_10 value: 94 - type: mrr_at_100 value: 94 - type: mrr_at_1000 value: 94 - type: mrr_at_3 value: 94 - type: mrr_at_5 value: 94 - type: ndcg_at_1 value: 82 - type: ndcg_at_10 value: 77.48899999999999 - type: ndcg_at_100 value: 60.141 - type: ndcg_at_1000 value: 54.228 - type: ndcg_at_3 value: 82.358 - type: ndcg_at_5 value: 80.449 - type: precision_at_1 value: 88 - type: precision_at_10 value: 82.19999999999999 - type: precision_at_100 value: 61.760000000000005 - type: precision_at_1000 value: 23.684 - type: precision_at_3 value: 88 - type: precision_at_5 value: 85.6 - type: recall_at_1 value: 0.23600000000000002 - type: recall_at_10 value: 2.117 - type: recall_at_100 value: 14.985000000000001 - type: recall_at_1000 value: 51.107 - type: recall_at_3 value: 0.688 - type: recall_at_5 value: 1.1039999999999999 task: type: Retrieval - dataset: config: default name: MTEB Touche2020 revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f split: test type: mteb/touche2020 metrics: - type: map_at_1 value: 2.3040000000000003 - type: map_at_10 value: 9.025 - type: map_at_100 value: 15.312999999999999 - type: map_at_1000 value: 16.954 - type: map_at_3 value: 4.981 - type: map_at_5 value: 6.32 - type: mrr_at_1 value: 24.490000000000002 - type: mrr_at_10 value: 39.835 - type: mrr_at_100 value: 40.8 - type: mrr_at_1000 value: 40.8 - type: mrr_at_3 value: 35.034 - type: mrr_at_5 value: 37.687 - type: ndcg_at_1 value: 22.448999999999998 - type: ndcg_at_10 value: 22.545 - type: ndcg_at_100 value: 35.931999999999995 - type: ndcg_at_1000 value: 47.665 - type: ndcg_at_3 value: 23.311 - type: ndcg_at_5 value: 22.421 - type: precision_at_1 value: 24.490000000000002 - type: precision_at_10 value: 20.408 - type: precision_at_100 value: 7.815999999999999 - type: precision_at_1000 value: 1.553 - type: precision_at_3 value: 25.169999999999998 - type: precision_at_5 value: 23.265 - type: recall_at_1 value: 2.3040000000000003 - type: recall_at_10 value: 15.693999999999999 - type: recall_at_100 value: 48.917 - type: recall_at_1000 value: 84.964 - type: recall_at_3 value: 6.026 - type: recall_at_5 value: 9.066 task: type: Retrieval - dataset: config: default name: MTEB ToxicConversationsClassification revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c split: test type: mteb/toxic_conversations_50k metrics: - type: accuracy value: 82.6074 - type: ap value: 23.187467098602013 - type: f1 value: 65.36829506379657 task: type: Classification - dataset: config: default name: MTEB TweetSentimentExtractionClassification revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics: - type: accuracy value: 63.16355404640635 - type: f1 value: 63.534725639863346 task: type: Classification - dataset: config: default name: MTEB TwentyNewsgroupsClustering revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics: - type: v_measure value: 50.91004094411276 task: type: Clustering - dataset: config: default name: MTEB TwitterSemEval2015 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics: - type: cos_sim_accuracy value: 86.55301901412649 - type: cos_sim_ap value: 75.25312618556728 - type: cos_sim_f1 value: 68.76561719140429 - type: cos_sim_precision value: 65.3061224489796 - type: cos_sim_recall value: 72.61213720316623 - type: dot_accuracy value: 86.29671574178936 - type: dot_ap value: 75.11910195501207 - type: dot_f1 value: 68.44048376830045 - type: dot_precision value: 66.12546125461255 - type: dot_recall value: 70.92348284960423 - type: euclidean_accuracy value: 86.5828217202122 - type: euclidean_ap value: 75.22986344900924 - type: euclidean_f1 value: 68.81267797449549 - type: euclidean_precision value: 64.8238861674831 - type: euclidean_recall value: 73.3245382585752 - type: manhattan_accuracy value: 86.61262442629791 - type: manhattan_ap value: 75.24401608557328 - type: manhattan_f1 value: 68.80473982483257 - type: manhattan_precision value: 67.21187720181177 - type: manhattan_recall value: 70.47493403693932 - type: max_accuracy value: 86.61262442629791 - type: max_ap value: 75.25312618556728 - type: max_f1 value: 68.81267797449549 task: type: PairClassification - dataset: config: default name: MTEB TwitterURLCorpus revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics: - type: cos_sim_accuracy value: 88.10688089416696 - type: cos_sim_ap value: 84.17862178779863 - type: cos_sim_f1 value: 76.17305208781748 - type: cos_sim_precision value: 71.31246641590543 - type: cos_sim_recall value: 81.74468740375731 - type: dot_accuracy value: 88.1844995536927 - type: dot_ap value: 84.33816725235876 - type: dot_f1 value: 76.43554032918746 - type: dot_precision value: 74.01557767200346 - type: dot_recall value: 79.0190945488143 - type: euclidean_accuracy value: 88.07001203089223 - type: euclidean_ap value: 84.12267000814985 - type: euclidean_f1 value: 76.12232600180778 - type: euclidean_precision value: 74.50604541433205 - type: euclidean_recall value: 77.81028641823221 - type: manhattan_accuracy value: 88.06419063142779 - type: manhattan_ap value: 84.11648917164187 - type: manhattan_f1 value: 76.20579953925474 - type: manhattan_precision value: 72.56772755762935 - type: manhattan_recall value: 80.22790267939637 - type: max_accuracy value: 88.1844995536927 - type: max_ap value: 84.33816725235876 - type: max_f1 value: 76.43554032918746 task: type: PairClassification tags: - gte - mteb - sentence-similarity - onnx - teradata --- ***See Disclaimer below*** ---- # A Teradata Vantage compatible Embeddings Model # Alibaba-NLP/gte-large-en-v1.5 ## Overview of this Model An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings. - 434.14M params (Sizes in ONNX format - "fp32": 1664.98MB, "int8": 424.0MB, "uint8": 424.0MB) - 8192 maximum input tokens - 1024 dimensions of output vector - Licence: apache-2.0. The released models can be used for commercial purposes free of charge. - Reference to Original Model: https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5 ## Quickstart: Deploying this Model in Teradata Vantage We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. **Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed. To get started, clone the pre-converted model directly from the Teradata HuggingFace repository. ```python import teradataml as tdml import getpass from huggingface_hub import hf_hub_download model_name = "gte-large-en-v1.5" number_dimensions_output = 1024 model_file_name = "model.onnx" # Step 1: Download Model from Teradata HuggingFace Page hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./") hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./") # Step 2: Create Connection to Vantage tdml.create_context(host = input('enter your hostname'), username=input('enter your username'), password = getpass.getpass("enter your password")) # Step 3: Load Models into Vantage # a) Embedding model tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = f"onnx/{model_file_name}", table_name = 'embeddings_models' ) # b) Tokenizer tdml.save_byom(model_id = model_name, # must be unique in the models table model_file = 'tokenizer.json', table_name = 'embeddings_tokenizers') # Step 4: Test ONNXEmbeddings Function # Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. # If it has got a different name, just rename it in a subquery/CTE. input_table = "emails.emails" embeddings_query = f""" SELECT * from mldb.ONNXEmbeddings( on {input_table} as InputTable on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION using Accumulate('id', 'txt') ModelOutputTensor('sentence_embedding') EnableMemoryCheck('false') OutputFormat('FLOAT32({number_dimensions_output})') OverwriteCachedModel('true') ) a """ DF_embeddings = tdml.DataFrame.from_query(embeddings_query) DF_embeddings ``` ## What Can I Do with the Embeddings? Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples: - **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb) - **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb) - **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb) ## Deep Dive into Model Conversion to ONNX **The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format. ### Part 1. Importing and Converting Model using optimum We start by importing the pre-trained [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) model from Hugging Face. To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage We are generating ONNX files for multiple different precisions: fp32, int8, uint8 You can find the detailed conversion steps in the file [convert.py](./convert.py) ### Part 2. Running the model in Python with onnxruntime & compare results Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results. If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database. ```python import onnxruntime as rt from sentence_transformers.util import cos_sim from sentence_transformers import SentenceTransformer import transformers sentences_1 = 'How is the weather today?' sentences_2 = 'What is the current weather like today?' # Calculate ONNX result tokenizer = transformers.AutoTokenizer.from_pretrained("Alibaba-NLP/gte-large-en-v1.5") predef_sess = rt.InferenceSession("onnx/model.onnx") enc1 = tokenizer(sentences_1) embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids], "attention_mask": [enc1.attention_mask]}) enc2 = tokenizer(sentences_2) embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids], "attention_mask": [enc2.attention_mask]}) # Calculate embeddings with SentenceTransformer model = SentenceTransformer(model_id, trust_remote_code=True) embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True) embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True) # Compare results print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0]))) print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer))) ``` You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py) ----- DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata. To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations. While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws. You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.