--- language: - en library_name: sentence-transformers license: mit pipeline_tag: sentence-similarity tags: - feature-extraction - mteb - sentence-similarity - sentence-transformers - transformers model-index: - name: NoInstruct-small-Embedding-v0 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 75.76119402985074 - type: ap value: 39.03628777559392 - type: f1 value: 69.85860402259618 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.29920000000001 - type: ap value: 90.03479490717608 - type: f1 value: 93.28554395248467 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.98799999999999 - type: f1 value: 49.46151232451642 - task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics: - type: map_at_1 value: 31.935000000000002 - type: map_at_10 value: 48.791000000000004 - type: map_at_100 value: 49.619 - type: map_at_1000 value: 49.623 - type: map_at_3 value: 44.334 - type: map_at_5 value: 46.908 - type: mrr_at_1 value: 32.93 - type: mrr_at_10 value: 49.158 - type: mrr_at_100 value: 50.00599999999999 - type: mrr_at_1000 value: 50.01 - type: mrr_at_3 value: 44.618 - type: mrr_at_5 value: 47.325 - type: ndcg_at_1 value: 31.935000000000002 - type: ndcg_at_10 value: 57.593 - type: ndcg_at_100 value: 60.841 - type: ndcg_at_1000 value: 60.924 - type: ndcg_at_3 value: 48.416 - type: ndcg_at_5 value: 53.05 - type: precision_at_1 value: 31.935000000000002 - type: precision_at_10 value: 8.549 - type: precision_at_100 value: 0.9900000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 20.081 - type: precision_at_5 value: 14.296000000000001 - type: recall_at_1 value: 31.935000000000002 - type: recall_at_10 value: 85.491 - type: recall_at_100 value: 99.004 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 60.242 - type: recall_at_5 value: 71.479 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 47.78438534940855 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 40.12916178519471 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 62.125361608299855 - type: mrr value: 74.92525172580574 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 88.64322910336641 - type: cos_sim_spearman value: 87.20138453306345 - type: euclidean_pearson value: 87.08547818178234 - type: euclidean_spearman value: 87.17066094143931 - type: manhattan_pearson value: 87.30053110771618 - type: manhattan_spearman value: 86.86824441211934 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 86.3961038961039 - type: f1 value: 86.3669961645295 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 39.40291404289857 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 35.102356817746816 - task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics: - type: map_at_1 value: 31.013 - type: map_at_10 value: 42.681999999999995 - type: map_at_100 value: 44.24 - type: map_at_1000 value: 44.372 - type: map_at_3 value: 39.181 - type: map_at_5 value: 41.071999999999996 - type: mrr_at_1 value: 38.196999999999996 - type: mrr_at_10 value: 48.604 - type: mrr_at_100 value: 49.315 - type: mrr_at_1000 value: 49.363 - type: mrr_at_3 value: 45.756 - type: mrr_at_5 value: 47.43 - type: ndcg_at_1 value: 38.196999999999996 - type: ndcg_at_10 value: 49.344 - type: ndcg_at_100 value: 54.662 - type: ndcg_at_1000 value: 56.665 - type: ndcg_at_3 value: 44.146 - type: ndcg_at_5 value: 46.514 - type: precision_at_1 value: 38.196999999999996 - type: precision_at_10 value: 9.571 - type: precision_at_100 value: 1.542 - type: precision_at_1000 value: 0.202 - type: precision_at_3 value: 21.364 - type: precision_at_5 value: 15.336 - type: recall_at_1 value: 31.013 - type: recall_at_10 value: 61.934999999999995 - type: recall_at_100 value: 83.923 - type: recall_at_1000 value: 96.601 - type: recall_at_3 value: 46.86 - type: recall_at_5 value: 53.620000000000005 - task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics: - type: map_at_1 value: 29.84 - type: map_at_10 value: 39.335 - type: map_at_100 value: 40.647 - type: map_at_1000 value: 40.778 - type: map_at_3 value: 36.556 - type: map_at_5 value: 38.048 - type: mrr_at_1 value: 36.815 - type: mrr_at_10 value: 45.175 - type: mrr_at_100 value: 45.907 - type: mrr_at_1000 value: 45.946999999999996 - type: mrr_at_3 value: 42.909000000000006 - type: mrr_at_5 value: 44.227 - type: ndcg_at_1 value: 36.815 - type: ndcg_at_10 value: 44.783 - type: ndcg_at_100 value: 49.551 - type: ndcg_at_1000 value: 51.612 - type: ndcg_at_3 value: 40.697 - type: ndcg_at_5 value: 42.558 - type: precision_at_1 value: 36.815 - type: precision_at_10 value: 8.363 - type: precision_at_100 value: 1.385 - type: precision_at_1000 value: 0.186 - type: precision_at_3 value: 19.342000000000002 - type: precision_at_5 value: 13.706999999999999 - type: recall_at_1 value: 29.84 - type: recall_at_10 value: 54.164 - type: recall_at_100 value: 74.36 - type: recall_at_1000 value: 87.484 - type: recall_at_3 value: 42.306 - type: recall_at_5 value: 47.371 - task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics: - type: map_at_1 value: 39.231 - type: map_at_10 value: 51.44800000000001 - type: map_at_100 value: 52.574 - type: map_at_1000 value: 52.629999999999995 - type: map_at_3 value: 48.077 - type: map_at_5 value: 50.019000000000005 - type: mrr_at_1 value: 44.89 - type: mrr_at_10 value: 54.803000000000004 - type: mrr_at_100 value: 55.556000000000004 - type: mrr_at_1000 value: 55.584 - type: mrr_at_3 value: 52.32 - type: mrr_at_5 value: 53.846000000000004 - type: ndcg_at_1 value: 44.89 - type: ndcg_at_10 value: 57.228 - type: ndcg_at_100 value: 61.57 - type: ndcg_at_1000 value: 62.613 - type: ndcg_at_3 value: 51.727000000000004 - type: ndcg_at_5 value: 54.496 - type: precision_at_1 value: 44.89 - type: precision_at_10 value: 9.266 - type: precision_at_100 value: 1.2309999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 23.051 - type: precision_at_5 value: 15.987000000000002 - type: recall_at_1 value: 39.231 - type: recall_at_10 value: 70.82000000000001 - type: recall_at_100 value: 89.446 - type: recall_at_1000 value: 96.665 - type: recall_at_3 value: 56.40500000000001 - type: recall_at_5 value: 62.993 - task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics: - type: map_at_1 value: 25.296000000000003 - type: map_at_10 value: 34.021 - type: map_at_100 value: 35.158 - type: map_at_1000 value: 35.233 - type: map_at_3 value: 31.424999999999997 - type: map_at_5 value: 33.046 - type: mrr_at_1 value: 27.232 - type: mrr_at_10 value: 36.103 - type: mrr_at_100 value: 37.076 - type: mrr_at_1000 value: 37.135 - type: mrr_at_3 value: 33.635 - type: mrr_at_5 value: 35.211 - type: ndcg_at_1 value: 27.232 - type: ndcg_at_10 value: 38.878 - type: ndcg_at_100 value: 44.284 - type: ndcg_at_1000 value: 46.268 - type: ndcg_at_3 value: 33.94 - type: ndcg_at_5 value: 36.687 - type: precision_at_1 value: 27.232 - type: precision_at_10 value: 5.921 - type: precision_at_100 value: 0.907 - type: precision_at_1000 value: 0.11199999999999999 - type: precision_at_3 value: 14.426 - type: precision_at_5 value: 10.215 - type: recall_at_1 value: 25.296000000000003 - type: recall_at_10 value: 51.708 - type: recall_at_100 value: 76.36699999999999 - type: recall_at_1000 value: 91.306 - type: recall_at_3 value: 38.651 - type: recall_at_5 value: 45.201 - task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics: - type: map_at_1 value: 16.24 - type: map_at_10 value: 24.696 - type: map_at_100 value: 25.945 - type: map_at_1000 value: 26.069 - type: map_at_3 value: 22.542 - type: map_at_5 value: 23.526 - type: mrr_at_1 value: 20.149 - type: mrr_at_10 value: 29.584 - type: mrr_at_100 value: 30.548 - type: mrr_at_1000 value: 30.618000000000002 - type: mrr_at_3 value: 27.301 - type: mrr_at_5 value: 28.563 - type: ndcg_at_1 value: 20.149 - type: ndcg_at_10 value: 30.029 - type: ndcg_at_100 value: 35.812 - type: ndcg_at_1000 value: 38.755 - type: ndcg_at_3 value: 26.008 - type: ndcg_at_5 value: 27.517000000000003 - type: precision_at_1 value: 20.149 - type: precision_at_10 value: 5.647 - type: precision_at_100 value: 0.968 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 12.934999999999999 - type: precision_at_5 value: 8.955 - type: recall_at_1 value: 16.24 - type: recall_at_10 value: 41.464 - type: recall_at_100 value: 66.781 - type: recall_at_1000 value: 87.85300000000001 - type: recall_at_3 value: 29.822 - type: recall_at_5 value: 34.096 - task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics: - type: map_at_1 value: 29.044999999999998 - type: map_at_10 value: 39.568999999999996 - type: map_at_100 value: 40.831 - type: map_at_1000 value: 40.948 - type: map_at_3 value: 36.495 - type: map_at_5 value: 38.21 - type: mrr_at_1 value: 35.611 - type: mrr_at_10 value: 45.175 - type: mrr_at_100 value: 45.974 - type: mrr_at_1000 value: 46.025 - type: mrr_at_3 value: 42.765 - type: mrr_at_5 value: 44.151 - type: ndcg_at_1 value: 35.611 - type: ndcg_at_10 value: 45.556999999999995 - type: ndcg_at_100 value: 50.86000000000001 - type: ndcg_at_1000 value: 52.983000000000004 - type: ndcg_at_3 value: 40.881 - type: ndcg_at_5 value: 43.035000000000004 - type: precision_at_1 value: 35.611 - type: precision_at_10 value: 8.306 - type: precision_at_100 value: 1.276 - type: precision_at_1000 value: 0.165 - type: precision_at_3 value: 19.57 - type: precision_at_5 value: 13.725000000000001 - type: recall_at_1 value: 29.044999999999998 - type: recall_at_10 value: 57.513999999999996 - type: recall_at_100 value: 80.152 - type: recall_at_1000 value: 93.982 - type: recall_at_3 value: 44.121 - type: recall_at_5 value: 50.007000000000005 - task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics: - type: map_at_1 value: 22.349 - type: map_at_10 value: 33.434000000000005 - type: map_at_100 value: 34.8 - type: map_at_1000 value: 34.919 - type: map_at_3 value: 30.348000000000003 - type: map_at_5 value: 31.917 - type: mrr_at_1 value: 28.195999999999998 - type: mrr_at_10 value: 38.557 - type: mrr_at_100 value: 39.550999999999995 - type: mrr_at_1000 value: 39.607 - type: mrr_at_3 value: 36.035000000000004 - type: mrr_at_5 value: 37.364999999999995 - type: ndcg_at_1 value: 28.195999999999998 - type: ndcg_at_10 value: 39.656000000000006 - type: ndcg_at_100 value: 45.507999999999996 - type: ndcg_at_1000 value: 47.848 - type: ndcg_at_3 value: 34.609 - type: ndcg_at_5 value: 36.65 - type: precision_at_1 value: 28.195999999999998 - type: precision_at_10 value: 7.534000000000001 - type: precision_at_100 value: 1.217 - type: precision_at_1000 value: 0.158 - type: precision_at_3 value: 17.085 - type: precision_at_5 value: 12.169 - type: recall_at_1 value: 22.349 - type: recall_at_10 value: 53.127 - type: recall_at_100 value: 77.884 - type: recall_at_1000 value: 93.705 - type: recall_at_3 value: 38.611000000000004 - type: recall_at_5 value: 44.182 - task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 25.215749999999996 - type: map_at_10 value: 34.332750000000004 - type: map_at_100 value: 35.58683333333333 - type: map_at_1000 value: 35.70458333333333 - type: map_at_3 value: 31.55441666666667 - type: map_at_5 value: 33.100833333333334 - type: mrr_at_1 value: 29.697250000000004 - type: mrr_at_10 value: 38.372249999999994 - type: mrr_at_100 value: 39.26708333333334 - type: mrr_at_1000 value: 39.3265 - type: mrr_at_3 value: 35.946083333333334 - type: mrr_at_5 value: 37.336999999999996 - type: ndcg_at_1 value: 29.697250000000004 - type: ndcg_at_10 value: 39.64575 - type: ndcg_at_100 value: 44.996833333333335 - type: ndcg_at_1000 value: 47.314499999999995 - type: ndcg_at_3 value: 34.93383333333334 - type: ndcg_at_5 value: 37.15291666666667 - type: precision_at_1 value: 29.697250000000004 - type: precision_at_10 value: 6.98825 - type: precision_at_100 value: 1.138 - type: precision_at_1000 value: 0.15283333333333332 - type: precision_at_3 value: 16.115583333333333 - type: precision_at_5 value: 11.460916666666666 - type: recall_at_1 value: 25.215749999999996 - type: recall_at_10 value: 51.261250000000004 - type: recall_at_100 value: 74.67258333333334 - type: recall_at_1000 value: 90.72033333333334 - type: recall_at_3 value: 38.1795 - type: recall_at_5 value: 43.90658333333334 - task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics: - type: map_at_1 value: 24.352 - type: map_at_10 value: 30.576999999999998 - type: map_at_100 value: 31.545 - type: map_at_1000 value: 31.642 - type: map_at_3 value: 28.605000000000004 - type: map_at_5 value: 29.828 - type: mrr_at_1 value: 26.994 - type: mrr_at_10 value: 33.151 - type: mrr_at_100 value: 33.973 - type: mrr_at_1000 value: 34.044999999999995 - type: mrr_at_3 value: 31.135 - type: mrr_at_5 value: 32.262 - type: ndcg_at_1 value: 26.994 - type: ndcg_at_10 value: 34.307 - type: ndcg_at_100 value: 39.079 - type: ndcg_at_1000 value: 41.548 - type: ndcg_at_3 value: 30.581000000000003 - type: ndcg_at_5 value: 32.541 - type: precision_at_1 value: 26.994 - type: precision_at_10 value: 5.244999999999999 - type: precision_at_100 value: 0.831 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 12.781 - type: precision_at_5 value: 9.017999999999999 - type: recall_at_1 value: 24.352 - type: recall_at_10 value: 43.126999999999995 - type: recall_at_100 value: 64.845 - type: recall_at_1000 value: 83.244 - type: recall_at_3 value: 33.308 - type: recall_at_5 value: 37.984 - task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics: - type: map_at_1 value: 16.592000000000002 - type: map_at_10 value: 23.29 - type: map_at_100 value: 24.423000000000002 - type: map_at_1000 value: 24.554000000000002 - type: map_at_3 value: 20.958 - type: map_at_5 value: 22.267 - type: mrr_at_1 value: 20.061999999999998 - type: mrr_at_10 value: 26.973999999999997 - type: mrr_at_100 value: 27.944999999999997 - type: mrr_at_1000 value: 28.023999999999997 - type: mrr_at_3 value: 24.839 - type: mrr_at_5 value: 26.033 - type: ndcg_at_1 value: 20.061999999999998 - type: ndcg_at_10 value: 27.682000000000002 - type: ndcg_at_100 value: 33.196 - type: ndcg_at_1000 value: 36.246 - type: ndcg_at_3 value: 23.559 - type: ndcg_at_5 value: 25.507 - type: precision_at_1 value: 20.061999999999998 - type: precision_at_10 value: 5.086 - type: precision_at_100 value: 0.9249999999999999 - type: precision_at_1000 value: 0.136 - type: precision_at_3 value: 11.046 - type: precision_at_5 value: 8.149000000000001 - type: recall_at_1 value: 16.592000000000002 - type: recall_at_10 value: 37.181999999999995 - type: recall_at_100 value: 62.224999999999994 - type: recall_at_1000 value: 84.072 - type: recall_at_3 value: 25.776 - type: recall_at_5 value: 30.680000000000003 - task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics: - type: map_at_1 value: 26.035999999999998 - type: map_at_10 value: 34.447 - type: map_at_100 value: 35.697 - type: map_at_1000 value: 35.802 - type: map_at_3 value: 31.64 - type: map_at_5 value: 33.056999999999995 - type: mrr_at_1 value: 29.851 - type: mrr_at_10 value: 38.143 - type: mrr_at_100 value: 39.113 - type: mrr_at_1000 value: 39.175 - type: mrr_at_3 value: 35.665 - type: mrr_at_5 value: 36.901 - type: ndcg_at_1 value: 29.851 - type: ndcg_at_10 value: 39.554 - type: ndcg_at_100 value: 45.091 - type: ndcg_at_1000 value: 47.504000000000005 - type: ndcg_at_3 value: 34.414 - type: ndcg_at_5 value: 36.508 - type: precision_at_1 value: 29.851 - type: precision_at_10 value: 6.614000000000001 - type: precision_at_100 value: 1.051 - type: precision_at_1000 value: 0.13699999999999998 - type: precision_at_3 value: 15.329999999999998 - type: precision_at_5 value: 10.671999999999999 - type: recall_at_1 value: 26.035999999999998 - type: recall_at_10 value: 51.396 - type: recall_at_100 value: 75.09 - type: recall_at_1000 value: 91.904 - type: recall_at_3 value: 37.378 - type: recall_at_5 value: 42.69 - task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics: - type: map_at_1 value: 23.211000000000002 - type: map_at_10 value: 32.231 - type: map_at_100 value: 33.772999999999996 - type: map_at_1000 value: 33.982 - type: map_at_3 value: 29.128 - type: map_at_5 value: 31.002999999999997 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 36.388 - type: mrr_at_100 value: 37.384 - type: mrr_at_1000 value: 37.44 - type: mrr_at_3 value: 33.762 - type: mrr_at_5 value: 35.234 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 38.043 - type: ndcg_at_100 value: 44.21 - type: ndcg_at_1000 value: 46.748 - type: ndcg_at_3 value: 32.981 - type: ndcg_at_5 value: 35.58 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 7.352 - type: precision_at_100 value: 1.5 - type: precision_at_1000 value: 0.23700000000000002 - type: precision_at_3 value: 15.613 - type: precision_at_5 value: 11.501999999999999 - type: recall_at_1 value: 23.211000000000002 - type: recall_at_10 value: 49.851 - type: recall_at_100 value: 77.596 - type: recall_at_1000 value: 93.683 - type: recall_at_3 value: 35.403 - type: recall_at_5 value: 42.485 - task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics: - type: map_at_1 value: 19.384 - type: map_at_10 value: 26.262999999999998 - type: map_at_100 value: 27.409 - type: map_at_1000 value: 27.526 - type: map_at_3 value: 23.698 - type: map_at_5 value: 25.217 - type: mrr_at_1 value: 20.702 - type: mrr_at_10 value: 27.810000000000002 - type: mrr_at_100 value: 28.863 - type: mrr_at_1000 value: 28.955 - type: mrr_at_3 value: 25.230999999999998 - type: mrr_at_5 value: 26.821 - type: ndcg_at_1 value: 20.702 - type: ndcg_at_10 value: 30.688 - type: ndcg_at_100 value: 36.138999999999996 - type: ndcg_at_1000 value: 38.984 - type: ndcg_at_3 value: 25.663000000000004 - type: ndcg_at_5 value: 28.242 - type: precision_at_1 value: 20.702 - type: precision_at_10 value: 4.954 - type: precision_at_100 value: 0.823 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 10.844 - type: precision_at_5 value: 8.096 - type: recall_at_1 value: 19.384 - type: recall_at_10 value: 42.847 - type: recall_at_100 value: 67.402 - type: recall_at_1000 value: 88.145 - type: recall_at_3 value: 29.513 - type: recall_at_5 value: 35.57 - task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics: - type: map_at_1 value: 14.915000000000001 - type: map_at_10 value: 25.846999999999998 - type: map_at_100 value: 27.741 - type: map_at_1000 value: 27.921000000000003 - type: map_at_3 value: 21.718 - type: map_at_5 value: 23.948 - type: mrr_at_1 value: 33.941 - type: mrr_at_10 value: 46.897 - type: mrr_at_100 value: 47.63 - type: mrr_at_1000 value: 47.658 - type: mrr_at_3 value: 43.919999999999995 - type: mrr_at_5 value: 45.783 - type: ndcg_at_1 value: 33.941 - type: ndcg_at_10 value: 35.202 - type: ndcg_at_100 value: 42.132 - type: ndcg_at_1000 value: 45.190999999999995 - type: ndcg_at_3 value: 29.68 - type: ndcg_at_5 value: 31.631999999999998 - type: precision_at_1 value: 33.941 - type: precision_at_10 value: 10.906 - type: precision_at_100 value: 1.8339999999999999 - type: precision_at_1000 value: 0.241 - type: precision_at_3 value: 22.606 - type: precision_at_5 value: 17.081 - type: recall_at_1 value: 14.915000000000001 - type: recall_at_10 value: 40.737 - type: recall_at_100 value: 64.42 - type: recall_at_1000 value: 81.435 - type: recall_at_3 value: 26.767000000000003 - type: recall_at_5 value: 32.895 - task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics: - type: map_at_1 value: 8.665000000000001 - type: map_at_10 value: 19.087 - type: map_at_100 value: 26.555 - type: map_at_1000 value: 28.105999999999998 - type: map_at_3 value: 13.858999999999998 - type: map_at_5 value: 16.083 - type: mrr_at_1 value: 68.5 - type: mrr_at_10 value: 76.725 - type: mrr_at_100 value: 76.974 - type: mrr_at_1000 value: 76.981 - type: mrr_at_3 value: 75.583 - type: mrr_at_5 value: 76.208 - type: ndcg_at_1 value: 55.875 - type: ndcg_at_10 value: 41.018 - type: ndcg_at_100 value: 44.982 - type: ndcg_at_1000 value: 52.43 - type: ndcg_at_3 value: 46.534 - type: ndcg_at_5 value: 43.083 - type: precision_at_1 value: 68.5 - type: precision_at_10 value: 32.35 - type: precision_at_100 value: 10.078 - type: precision_at_1000 value: 1.957 - type: precision_at_3 value: 50.083 - type: precision_at_5 value: 41.3 - type: recall_at_1 value: 8.665000000000001 - type: recall_at_10 value: 24.596999999999998 - type: recall_at_100 value: 50.612 - type: recall_at_1000 value: 74.24 - type: recall_at_3 value: 15.337 - type: recall_at_5 value: 18.796 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 55.06500000000001 - type: f1 value: 49.827367590822035 - task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics: - type: map_at_1 value: 76.059 - type: map_at_10 value: 83.625 - type: map_at_100 value: 83.845 - type: map_at_1000 value: 83.858 - type: map_at_3 value: 82.67099999999999 - type: map_at_5 value: 83.223 - type: mrr_at_1 value: 82.013 - type: mrr_at_10 value: 88.44800000000001 - type: mrr_at_100 value: 88.535 - type: mrr_at_1000 value: 88.537 - type: mrr_at_3 value: 87.854 - type: mrr_at_5 value: 88.221 - type: ndcg_at_1 value: 82.013 - type: ndcg_at_10 value: 87.128 - type: ndcg_at_100 value: 87.922 - type: ndcg_at_1000 value: 88.166 - type: ndcg_at_3 value: 85.648 - type: ndcg_at_5 value: 86.366 - type: precision_at_1 value: 82.013 - type: precision_at_10 value: 10.32 - type: precision_at_100 value: 1.093 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 32.408 - type: precision_at_5 value: 19.973 - type: recall_at_1 value: 76.059 - type: recall_at_10 value: 93.229 - type: recall_at_100 value: 96.387 - type: recall_at_1000 value: 97.916 - type: recall_at_3 value: 89.025 - type: recall_at_5 value: 90.96300000000001 - task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics: - type: map_at_1 value: 20.479 - type: map_at_10 value: 33.109 - type: map_at_100 value: 34.803 - type: map_at_1000 value: 35.003 - type: map_at_3 value: 28.967 - type: map_at_5 value: 31.385 - type: mrr_at_1 value: 40.278000000000006 - type: mrr_at_10 value: 48.929 - type: mrr_at_100 value: 49.655 - type: mrr_at_1000 value: 49.691 - type: mrr_at_3 value: 46.605000000000004 - type: mrr_at_5 value: 48.056 - type: ndcg_at_1 value: 40.278000000000006 - type: ndcg_at_10 value: 40.649 - type: ndcg_at_100 value: 47.027 - type: ndcg_at_1000 value: 50.249 - type: ndcg_at_3 value: 37.364000000000004 - type: ndcg_at_5 value: 38.494 - type: precision_at_1 value: 40.278000000000006 - type: precision_at_10 value: 11.327 - type: precision_at_100 value: 1.802 - type: precision_at_1000 value: 0.23700000000000002 - type: precision_at_3 value: 25.102999999999998 - type: precision_at_5 value: 18.457 - type: recall_at_1 value: 20.479 - type: recall_at_10 value: 46.594 - type: recall_at_100 value: 71.101 - type: recall_at_1000 value: 90.31099999999999 - type: recall_at_3 value: 33.378 - type: recall_at_5 value: 39.587 - task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics: - type: map_at_1 value: 36.59 - type: map_at_10 value: 58.178 - type: map_at_100 value: 59.095 - type: map_at_1000 value: 59.16400000000001 - type: map_at_3 value: 54.907 - type: map_at_5 value: 56.89999999999999 - type: mrr_at_1 value: 73.18 - type: mrr_at_10 value: 79.935 - type: mrr_at_100 value: 80.16799999999999 - type: mrr_at_1000 value: 80.17800000000001 - type: mrr_at_3 value: 78.776 - type: mrr_at_5 value: 79.522 - type: ndcg_at_1 value: 73.18 - type: ndcg_at_10 value: 66.538 - type: ndcg_at_100 value: 69.78 - type: ndcg_at_1000 value: 71.102 - type: ndcg_at_3 value: 61.739 - type: ndcg_at_5 value: 64.35600000000001 - type: precision_at_1 value: 73.18 - type: precision_at_10 value: 14.035 - type: precision_at_100 value: 1.657 - type: precision_at_1000 value: 0.183 - type: precision_at_3 value: 39.684999999999995 - type: precision_at_5 value: 25.885 - type: recall_at_1 value: 36.59 - type: recall_at_10 value: 70.176 - type: recall_at_100 value: 82.836 - type: recall_at_1000 value: 91.526 - type: recall_at_3 value: 59.526999999999994 - type: recall_at_5 value: 64.713 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 90.1472 - type: ap value: 85.73994227076815 - type: f1 value: 90.1271700788608 - task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics: - type: map_at_1 value: 21.689 - type: map_at_10 value: 33.518 - type: map_at_100 value: 34.715 - type: map_at_1000 value: 34.766000000000005 - type: map_at_3 value: 29.781000000000002 - type: map_at_5 value: 31.838 - type: mrr_at_1 value: 22.249 - type: mrr_at_10 value: 34.085 - type: mrr_at_100 value: 35.223 - type: mrr_at_1000 value: 35.266999999999996 - type: mrr_at_3 value: 30.398999999999997 - type: mrr_at_5 value: 32.437 - type: ndcg_at_1 value: 22.249 - type: ndcg_at_10 value: 40.227000000000004 - type: ndcg_at_100 value: 45.961999999999996 - type: ndcg_at_1000 value: 47.248000000000005 - type: ndcg_at_3 value: 32.566 - type: ndcg_at_5 value: 36.229 - type: precision_at_1 value: 22.249 - type: precision_at_10 value: 6.358 - type: precision_at_100 value: 0.923 - type: precision_at_1000 value: 0.10300000000000001 - type: precision_at_3 value: 13.83 - type: precision_at_5 value: 10.145999999999999 - type: recall_at_1 value: 21.689 - type: recall_at_10 value: 60.92999999999999 - type: recall_at_100 value: 87.40599999999999 - type: recall_at_1000 value: 97.283 - type: recall_at_3 value: 40.01 - type: recall_at_5 value: 48.776 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 95.28727770177838 - type: f1 value: 95.02577308660041 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.5736434108527 - type: f1 value: 61.2451202054398 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 76.01210490921318 - type: f1 value: 73.70188053982473 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 79.33422999327504 - type: f1 value: 79.48369022509658 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.70891567267726 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 32.15203494451706 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 31.919517862194173 - type: mrr value: 33.15466289140483 - task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics: - type: map_at_1 value: 5.992 - type: map_at_10 value: 13.197000000000001 - type: map_at_100 value: 16.907 - type: map_at_1000 value: 18.44 - type: map_at_3 value: 9.631 - type: map_at_5 value: 11.243 - type: mrr_at_1 value: 44.272 - type: mrr_at_10 value: 53.321 - type: mrr_at_100 value: 53.903 - type: mrr_at_1000 value: 53.952999999999996 - type: mrr_at_3 value: 51.393 - type: mrr_at_5 value: 52.708999999999996 - type: ndcg_at_1 value: 42.415000000000006 - type: ndcg_at_10 value: 34.921 - type: ndcg_at_100 value: 32.384 - type: ndcg_at_1000 value: 41.260000000000005 - type: ndcg_at_3 value: 40.186 - type: ndcg_at_5 value: 37.89 - type: precision_at_1 value: 44.272 - type: precision_at_10 value: 26.006 - type: precision_at_100 value: 8.44 - type: precision_at_1000 value: 2.136 - type: precision_at_3 value: 37.977 - type: precision_at_5 value: 32.755 - type: recall_at_1 value: 5.992 - type: recall_at_10 value: 17.01 - type: recall_at_100 value: 33.080999999999996 - type: recall_at_1000 value: 65.054 - type: recall_at_3 value: 10.528 - type: recall_at_5 value: 13.233 - task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics: - type: map_at_1 value: 28.871999999999996 - type: map_at_10 value: 43.286 - type: map_at_100 value: 44.432 - type: map_at_1000 value: 44.464999999999996 - type: map_at_3 value: 38.856 - type: map_at_5 value: 41.514 - type: mrr_at_1 value: 32.619 - type: mrr_at_10 value: 45.75 - type: mrr_at_100 value: 46.622 - type: mrr_at_1000 value: 46.646 - type: mrr_at_3 value: 41.985 - type: mrr_at_5 value: 44.277 - type: ndcg_at_1 value: 32.59 - type: ndcg_at_10 value: 50.895999999999994 - type: ndcg_at_100 value: 55.711999999999996 - type: ndcg_at_1000 value: 56.48800000000001 - type: ndcg_at_3 value: 42.504999999999995 - type: ndcg_at_5 value: 46.969 - type: precision_at_1 value: 32.59 - type: precision_at_10 value: 8.543000000000001 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 19.448 - type: precision_at_5 value: 14.218 - type: recall_at_1 value: 28.871999999999996 - type: recall_at_10 value: 71.748 - type: recall_at_100 value: 92.55499999999999 - type: recall_at_1000 value: 98.327 - type: recall_at_3 value: 49.944 - type: recall_at_5 value: 60.291 - task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 metrics: - type: map_at_1 value: 70.664 - type: map_at_10 value: 84.681 - type: map_at_100 value: 85.289 - type: map_at_1000 value: 85.306 - type: map_at_3 value: 81.719 - type: map_at_5 value: 83.601 - type: mrr_at_1 value: 81.35 - type: mrr_at_10 value: 87.591 - type: mrr_at_100 value: 87.691 - type: mrr_at_1000 value: 87.693 - type: mrr_at_3 value: 86.675 - type: mrr_at_5 value: 87.29299999999999 - type: ndcg_at_1 value: 81.33 - type: ndcg_at_10 value: 88.411 - type: ndcg_at_100 value: 89.579 - type: ndcg_at_1000 value: 89.687 - type: ndcg_at_3 value: 85.613 - type: ndcg_at_5 value: 87.17 - type: precision_at_1 value: 81.33 - type: precision_at_10 value: 13.422 - type: precision_at_100 value: 1.5270000000000001 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.463 - type: precision_at_5 value: 24.646 - type: recall_at_1 value: 70.664 - type: recall_at_10 value: 95.54 - type: recall_at_100 value: 99.496 - type: recall_at_1000 value: 99.978 - type: recall_at_3 value: 87.481 - type: recall_at_5 value: 91.88499999999999 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 55.40341814991112 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 metrics: - type: v_measure value: 61.231318481346655 - task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 metrics: - type: map_at_1 value: 4.833 - type: map_at_10 value: 13.149 - type: map_at_100 value: 15.578 - type: map_at_1000 value: 15.963 - type: map_at_3 value: 9.269 - type: map_at_5 value: 11.182 - type: mrr_at_1 value: 23.9 - type: mrr_at_10 value: 35.978 - type: mrr_at_100 value: 37.076 - type: mrr_at_1000 value: 37.126 - type: mrr_at_3 value: 32.333 - type: mrr_at_5 value: 34.413 - type: ndcg_at_1 value: 23.9 - type: ndcg_at_10 value: 21.823 - type: ndcg_at_100 value: 30.833 - type: ndcg_at_1000 value: 36.991 - type: ndcg_at_3 value: 20.465 - type: ndcg_at_5 value: 17.965999999999998 - type: precision_at_1 value: 23.9 - type: precision_at_10 value: 11.49 - type: precision_at_100 value: 2.444 - type: precision_at_1000 value: 0.392 - type: precision_at_3 value: 19.3 - type: precision_at_5 value: 15.959999999999999 - type: recall_at_1 value: 4.833 - type: recall_at_10 value: 23.294999999999998 - type: recall_at_100 value: 49.63 - type: recall_at_1000 value: 79.49199999999999 - type: recall_at_3 value: 11.732 - type: recall_at_5 value: 16.167 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: 20a6d6f312dd54037fe07a32d58e5e168867909d metrics: - type: cos_sim_pearson value: 85.62938108735759 - type: cos_sim_spearman value: 80.30777094408789 - type: euclidean_pearson value: 82.94516686659536 - type: euclidean_spearman value: 80.34489663248169 - type: manhattan_pearson value: 82.85830094736245 - type: manhattan_spearman value: 80.24902623215449 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - 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task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics: - type: v_measure value: 62.045718843534736 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 36.6501777041092 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 52.963913408053955 - type: mrr value: 53.87972423818012 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 30.44195730764998 - type: cos_sim_spearman value: 30.59626288679397 - 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NoInstruct small Embedding v0

*NoInstruct Embedding: Asymmetric Pooling is All You Need* This model has improved retrieval performance compared to the [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) model. One of the things that the `GIST` family of models fell short on is the performance on retrieval tasks. We propose a method that produces improved retrieval performance while maintaining independence on crafting arbitrary instructions, a trending paradigm in embedding models for retrieval tasks, when encoding a query. Technical details of the model will be published shortly. # Usage ```Python from typing import Union import torch import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer model = AutoModel.from_pretrained("avsolatorio/NoInstruct-small-Embedding-v0") tokenizer = AutoTokenizer.from_pretrained("avsolatorio/NoInstruct-small-Embedding-v0") def get_embedding(text: Union[str, list[str]], mode: str = "sentence"): model.eval() assert mode in ("query", "sentence"), f"mode={mode} was passed but only `query` and `sentence` are the supported modes." if isinstance(text, str): text = [text] inp = tokenizer(text, return_tensors="pt", padding=True, truncation=True) with torch.no_grad(): output = model(**inp) # The model is optimized to use the mean pooling for queries, # while the sentence / document embedding uses the [CLS] representation. if mode == "query": vectors = output.last_hidden_state * inp["attention_mask"].unsqueeze(2) vectors = vectors.sum(dim=1) / inp["attention_mask"].sum(dim=-1).view(-1, 1) else: vectors = output.last_hidden_state[:, 0, :] return vectors texts = [ "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.", "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.", "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes" ] # Compute embeddings embeddings = get_embedding(texts, mode="sentence") # Compute cosine-similarity for each pair of sentences scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) print(scores.cpu().numpy()) # Test the retrieval performance. query = get_embedding("Which sentence talks about concept on jobs?", mode="query") scores = F.cosine_similarity(query, embeddings, dim=-1) print(scores.cpu().numpy()) ``` Support for the Sentence Transformers library will follow soon.