--- library_name: transformers tags: - gte - mteb license: apache-2.0 language: - en model-index: - name: gte-large-en-v1.5 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 73.01492537313432 - type: ap value: 35.05341696659522 - type: f1 value: 66.71270310883853 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.97189999999999 - type: ap value: 90.5952493948908 - type: f1 value: 93.95848137716877 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 54.196 - type: f1 value: 53.80122334012787 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a 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: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.467787861077475 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.39198391914257 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 63.12794820591384 - type: mrr value: 75.9331442641692 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a 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: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.32792207792208 - type: f1 value: 87.29132945999555 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 40.5779328301945 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 37.94425623865118 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb 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: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 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: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 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: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 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: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de 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: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 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: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 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: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 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: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a 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: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 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: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 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: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 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: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 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: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 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: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 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: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 46.775 - type: f1 value: 41.89794273264891 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 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: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 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: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 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: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.0996 - type: ap value: 87.86206089096373 - type: f1 value: 92.07554547510763 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 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: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 96.59598723210215 - type: f1 value: 96.41913500001952 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 82.89557683538533 - type: f1 value: 63.379319722356264 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 78.93745796906524 - type: f1 value: 75.71616541785902 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 81.41223940820443 - type: f1 value: 81.2877893719078 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 35.03682528325662 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.942529406124 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.459949660460317 - type: mrr value: 32.70509582031616 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 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: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 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: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None 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: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 59.75758437908334 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 68.03497914092789 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None 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: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 82.39274129958557 - type: cos_sim_spearman value: 79.78021235170053 - 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type: max_f1 value: 76.43554032918746 --- # gte-large-en-v1.5 We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**. The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU). The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)). We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct). - **Developed by:** Institute for Intelligent Computing, Alibaba Group - **Model type:** Text Embeddings - **Paper:** Coming soon. ### Model list | Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo | |:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: | |[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| English | 7720 | 32768 | 4096 | 67.34 | 87.57 | |[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 | |[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 | ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer input_texts = [ "what is the capital of China?", "how to implement quick sort in python?", "Beijing", "sorting algorithms" ] model_path = 'Alibaba-NLP/gte-large-en-v1.5' tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModel.from_pretrained(model_path, trust_remote_code=True) # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` **It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/test-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).** Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = ['That is a happy person', 'That is a very happy person'] model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True) embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ## Training Details ### Training Data - Masked language modeling (MLM): `c4-en` - Weak-supervised contrastive (WSC) pre-training: GTE pre-training data - Supervised contrastive fine-tuning: GTE fine-tuning data ### Training Procedure To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training. The entire training process is as follows: - MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000 - MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000 - MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000 - WSC: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000 - Fine-tuning: TODO ## Evaluation ### MTEB The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2). | Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 | | [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 | | [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 | | [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 | ### LOCO | Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 | | [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 | | [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 | ## Citation [TODO] **BibTeX:** [More Information Needed] **APA:** [More Information Needed]