| --- |
| language: |
| - en |
| library_name: sentence-transformers |
| license: mit |
| pipeline_tag: sentence-similarity |
| tags: |
| - feature-extraction |
| - mteb |
| - sentence-similarity |
| - sentence-transformers |
|
|
| model-index: |
| - name: GIST-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: 75.26865671641791 |
| - type: ap |
| value: 38.25623793370476 |
| - type: f1 |
| value: 69.26434651320257 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 93.232225 |
| - type: ap |
| value: 89.97936072879344 |
| - type: f1 |
| value: 93.22122653806187 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 49.715999999999994 |
| - type: f1 |
| value: 49.169789920136076 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 34.922 |
| - type: map_at_10 |
| value: 50.524 |
| - type: map_at_100 |
| value: 51.247 |
| - type: map_at_1000 |
| value: 51.249 |
| - type: map_at_3 |
| value: 45.887 |
| - type: map_at_5 |
| value: 48.592999999999996 |
| - type: mrr_at_1 |
| value: 34.922 |
| - type: mrr_at_10 |
| value: 50.382000000000005 |
| - type: mrr_at_100 |
| value: 51.104000000000006 |
| - type: mrr_at_1000 |
| value: 51.105999999999995 |
| - type: mrr_at_3 |
| value: 45.733000000000004 |
| - type: mrr_at_5 |
| value: 48.428 |
| - type: ndcg_at_1 |
| value: 34.922 |
| - type: ndcg_at_10 |
| value: 59.12 |
| - type: ndcg_at_100 |
| value: 62.083999999999996 |
| - type: ndcg_at_1000 |
| value: 62.137 |
| - type: ndcg_at_3 |
| value: 49.616 |
| - type: ndcg_at_5 |
| value: 54.501 |
| - type: precision_at_1 |
| value: 34.922 |
| - type: precision_at_10 |
| value: 8.649 |
| - type: precision_at_100 |
| value: 0.991 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 20.152 |
| - type: precision_at_5 |
| value: 14.466999999999999 |
| - type: recall_at_1 |
| value: 34.922 |
| - type: recall_at_10 |
| value: 86.48599999999999 |
| - type: recall_at_100 |
| value: 99.14699999999999 |
| - type: recall_at_1000 |
| value: 99.57300000000001 |
| - type: recall_at_3 |
| value: 60.455000000000005 |
| - type: recall_at_5 |
| value: 72.333 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 47.623282347623714 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 39.86487843524932 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 62.3290291318171 |
| - type: mrr |
| value: 75.2379853141626 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.52002953574285 |
| - type: cos_sim_spearman |
| value: 86.98752423842483 |
| - type: euclidean_pearson |
| value: 86.89442688314197 |
| - type: euclidean_spearman |
| value: 86.88631711307471 |
| - type: manhattan_pearson |
| value: 87.03723618507175 |
| - type: manhattan_spearman |
| value: 86.76041062975224 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 86.64935064935065 |
| - type: f1 |
| value: 86.61903824934998 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 39.21904455377494 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 35.43342755570654 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 31.843 |
| - type: map_at_10 |
| value: 43.379 |
| - type: map_at_100 |
| value: 44.946999999999996 |
| - type: map_at_1000 |
| value: 45.078 |
| - type: map_at_3 |
| value: 39.598 |
| - type: map_at_5 |
| value: 41.746 |
| - type: mrr_at_1 |
| value: 39.199 |
| - type: mrr_at_10 |
| value: 49.672 |
| - type: mrr_at_100 |
| value: 50.321000000000005 |
| - type: mrr_at_1000 |
| value: 50.365 |
| - type: mrr_at_3 |
| value: 46.805 |
| - type: mrr_at_5 |
| value: 48.579 |
| - type: ndcg_at_1 |
| value: 39.199 |
| - type: ndcg_at_10 |
| value: 50.163999999999994 |
| - type: ndcg_at_100 |
| value: 55.418 |
| - type: ndcg_at_1000 |
| value: 57.353 |
| - type: ndcg_at_3 |
| value: 44.716 |
| - type: ndcg_at_5 |
| value: 47.268 |
| - type: precision_at_1 |
| value: 39.199 |
| - type: precision_at_10 |
| value: 9.757 |
| - type: precision_at_100 |
| value: 1.552 |
| - type: precision_at_1000 |
| value: 0.20500000000000002 |
| - type: precision_at_3 |
| value: 21.602 |
| - type: precision_at_5 |
| value: 15.479000000000001 |
| - type: recall_at_1 |
| value: 31.843 |
| - type: recall_at_10 |
| value: 62.743 |
| - type: recall_at_100 |
| value: 84.78099999999999 |
| - type: recall_at_1000 |
| value: 96.86099999999999 |
| - type: recall_at_3 |
| value: 46.927 |
| - type: recall_at_5 |
| value: 54.355 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 29.321 |
| - type: map_at_10 |
| value: 39.062999999999995 |
| - type: map_at_100 |
| value: 40.403 |
| - type: map_at_1000 |
| value: 40.534 |
| - type: map_at_3 |
| value: 36.367 |
| - type: map_at_5 |
| value: 37.756 |
| - type: mrr_at_1 |
| value: 35.987 |
| - type: mrr_at_10 |
| value: 44.708999999999996 |
| - type: mrr_at_100 |
| value: 45.394 |
| - type: mrr_at_1000 |
| value: 45.436 |
| - type: mrr_at_3 |
| value: 42.463 |
| - type: mrr_at_5 |
| value: 43.663000000000004 |
| - type: ndcg_at_1 |
| value: 35.987 |
| - type: ndcg_at_10 |
| value: 44.585 |
| - type: ndcg_at_100 |
| value: 49.297999999999995 |
| - type: ndcg_at_1000 |
| value: 51.315 |
| - type: ndcg_at_3 |
| value: 40.569 |
| - type: ndcg_at_5 |
| value: 42.197 |
| - type: precision_at_1 |
| value: 35.987 |
| - type: precision_at_10 |
| value: 8.369 |
| - type: precision_at_100 |
| value: 1.366 |
| - type: precision_at_1000 |
| value: 0.184 |
| - type: precision_at_3 |
| value: 19.427 |
| - type: precision_at_5 |
| value: 13.58 |
| - type: recall_at_1 |
| value: 29.321 |
| - type: recall_at_10 |
| value: 54.333 |
| - type: recall_at_100 |
| value: 74.178 |
| - type: recall_at_1000 |
| value: 86.732 |
| - type: recall_at_3 |
| value: 42.46 |
| - type: recall_at_5 |
| value: 47.089999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 38.811 |
| - type: map_at_10 |
| value: 51.114000000000004 |
| - type: map_at_100 |
| value: 52.22 |
| - type: map_at_1000 |
| value: 52.275000000000006 |
| - type: map_at_3 |
| value: 47.644999999999996 |
| - type: map_at_5 |
| value: 49.675000000000004 |
| - type: mrr_at_1 |
| value: 44.389 |
| - type: mrr_at_10 |
| value: 54.459 |
| - type: mrr_at_100 |
| value: 55.208999999999996 |
| - type: mrr_at_1000 |
| value: 55.239000000000004 |
| - type: mrr_at_3 |
| value: 51.954 |
| - type: mrr_at_5 |
| value: 53.571999999999996 |
| - type: ndcg_at_1 |
| value: 44.389 |
| - type: ndcg_at_10 |
| value: 56.979 |
| - type: ndcg_at_100 |
| value: 61.266 |
| - type: ndcg_at_1000 |
| value: 62.315 |
| - type: ndcg_at_3 |
| value: 51.342 |
| - type: ndcg_at_5 |
| value: 54.33 |
| - type: precision_at_1 |
| value: 44.389 |
| - type: precision_at_10 |
| value: 9.26 |
| - type: precision_at_100 |
| value: 1.226 |
| - type: precision_at_1000 |
| value: 0.136 |
| - type: precision_at_3 |
| value: 22.926 |
| - type: precision_at_5 |
| value: 15.987000000000002 |
| - type: recall_at_1 |
| value: 38.811 |
| - type: recall_at_10 |
| value: 70.841 |
| - type: recall_at_100 |
| value: 89.218 |
| - type: recall_at_1000 |
| value: 96.482 |
| - type: recall_at_3 |
| value: 56.123999999999995 |
| - type: recall_at_5 |
| value: 63.322 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.378 |
| - type: map_at_10 |
| value: 34.311 |
| - type: map_at_100 |
| value: 35.399 |
| - type: map_at_1000 |
| value: 35.482 |
| - type: map_at_3 |
| value: 31.917 |
| - type: map_at_5 |
| value: 33.275 |
| - type: mrr_at_1 |
| value: 27.683999999999997 |
| - type: mrr_at_10 |
| value: 36.575 |
| - type: mrr_at_100 |
| value: 37.492 |
| - type: mrr_at_1000 |
| value: 37.556 |
| - type: mrr_at_3 |
| value: 34.35 |
| - type: mrr_at_5 |
| value: 35.525 |
| - type: ndcg_at_1 |
| value: 27.683999999999997 |
| - type: ndcg_at_10 |
| value: 39.247 |
| - type: ndcg_at_100 |
| value: 44.424 |
| - type: ndcg_at_1000 |
| value: 46.478 |
| - type: ndcg_at_3 |
| value: 34.684 |
| - type: ndcg_at_5 |
| value: 36.886 |
| - type: precision_at_1 |
| value: 27.683999999999997 |
| - type: precision_at_10 |
| value: 5.989 |
| - type: precision_at_100 |
| value: 0.899 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 14.84 |
| - type: precision_at_5 |
| value: 10.215 |
| - type: recall_at_1 |
| value: 25.378 |
| - type: recall_at_10 |
| value: 52.195 |
| - type: recall_at_100 |
| value: 75.764 |
| - type: recall_at_1000 |
| value: 91.012 |
| - type: recall_at_3 |
| value: 39.885999999999996 |
| - type: recall_at_5 |
| value: 45.279 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 17.326 |
| - type: map_at_10 |
| value: 25.247000000000003 |
| - type: map_at_100 |
| value: 26.473000000000003 |
| - type: map_at_1000 |
| value: 26.579000000000004 |
| - type: map_at_3 |
| value: 22.466 |
| - type: map_at_5 |
| value: 24.113 |
| - type: mrr_at_1 |
| value: 21.393 |
| - type: mrr_at_10 |
| value: 30.187 |
| - type: mrr_at_100 |
| value: 31.089 |
| - type: mrr_at_1000 |
| value: 31.15 |
| - type: mrr_at_3 |
| value: 27.279999999999998 |
| - type: mrr_at_5 |
| value: 29.127 |
| - type: ndcg_at_1 |
| value: 21.393 |
| - type: ndcg_at_10 |
| value: 30.668 |
| - type: ndcg_at_100 |
| value: 36.543 |
| - type: ndcg_at_1000 |
| value: 39.181 |
| - type: ndcg_at_3 |
| value: 25.552000000000003 |
| - type: ndcg_at_5 |
| value: 28.176000000000002 |
| - type: precision_at_1 |
| value: 21.393 |
| - type: precision_at_10 |
| value: 5.784000000000001 |
| - type: precision_at_100 |
| value: 1.001 |
| - type: precision_at_1000 |
| value: 0.136 |
| - type: precision_at_3 |
| value: 12.231 |
| - type: precision_at_5 |
| value: 9.179 |
| - type: recall_at_1 |
| value: 17.326 |
| - type: recall_at_10 |
| value: 42.415000000000006 |
| - type: recall_at_100 |
| value: 68.605 |
| - type: recall_at_1000 |
| value: 87.694 |
| - type: recall_at_3 |
| value: 28.343 |
| - type: recall_at_5 |
| value: 35.086 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 29.069 |
| - type: map_at_10 |
| value: 40.027 |
| - type: map_at_100 |
| value: 41.308 |
| - type: map_at_1000 |
| value: 41.412 |
| - type: map_at_3 |
| value: 36.864000000000004 |
| - type: map_at_5 |
| value: 38.641999999999996 |
| - type: mrr_at_1 |
| value: 35.707 |
| - type: mrr_at_10 |
| value: 45.527 |
| - type: mrr_at_100 |
| value: 46.348 |
| - type: mrr_at_1000 |
| value: 46.392 |
| - type: mrr_at_3 |
| value: 43.086 |
| - type: mrr_at_5 |
| value: 44.645 |
| - type: ndcg_at_1 |
| value: 35.707 |
| - type: ndcg_at_10 |
| value: 46.117000000000004 |
| - type: ndcg_at_100 |
| value: 51.468 |
| - type: ndcg_at_1000 |
| value: 53.412000000000006 |
| - type: ndcg_at_3 |
| value: 41.224 |
| - type: ndcg_at_5 |
| value: 43.637 |
| - type: precision_at_1 |
| value: 35.707 |
| - type: precision_at_10 |
| value: 8.459999999999999 |
| - type: precision_at_100 |
| value: 1.2970000000000002 |
| - type: precision_at_1000 |
| value: 0.165 |
| - type: precision_at_3 |
| value: 19.731 |
| - type: precision_at_5 |
| value: 14.013 |
| - type: recall_at_1 |
| value: 29.069 |
| - type: recall_at_10 |
| value: 58.343999999999994 |
| - type: recall_at_100 |
| value: 81.296 |
| - type: recall_at_1000 |
| value: 93.974 |
| - type: recall_at_3 |
| value: 44.7 |
| - type: recall_at_5 |
| value: 50.88700000000001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.905 |
| - type: map_at_10 |
| value: 33.983000000000004 |
| - type: map_at_100 |
| value: 35.372 |
| - type: map_at_1000 |
| value: 35.487 |
| - type: map_at_3 |
| value: 30.902 |
| - type: map_at_5 |
| value: 32.505 |
| - type: mrr_at_1 |
| value: 29.794999999999998 |
| - type: mrr_at_10 |
| value: 39.28 |
| - type: mrr_at_100 |
| value: 40.215 |
| - type: mrr_at_1000 |
| value: 40.276 |
| - type: mrr_at_3 |
| value: 36.701 |
| - type: mrr_at_5 |
| value: 38.105 |
| - type: ndcg_at_1 |
| value: 29.794999999999998 |
| - type: ndcg_at_10 |
| value: 40.041 |
| - type: ndcg_at_100 |
| value: 45.884 |
| - type: ndcg_at_1000 |
| value: 48.271 |
| - type: ndcg_at_3 |
| value: 34.931 |
| - type: ndcg_at_5 |
| value: 37.044 |
| - type: precision_at_1 |
| value: 29.794999999999998 |
| - type: precision_at_10 |
| value: 7.546 |
| - type: precision_at_100 |
| value: 1.216 |
| - type: precision_at_1000 |
| value: 0.158 |
| - type: precision_at_3 |
| value: 16.933 |
| - type: precision_at_5 |
| value: 12.1 |
| - type: recall_at_1 |
| value: 23.905 |
| - type: recall_at_10 |
| value: 52.945 |
| - type: recall_at_100 |
| value: 77.551 |
| - type: recall_at_1000 |
| value: 93.793 |
| - type: recall_at_3 |
| value: 38.364 |
| - type: recall_at_5 |
| value: 44.044 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.24441666666667 |
| - type: map_at_10 |
| value: 34.4595 |
| - type: map_at_100 |
| value: 35.699999999999996 |
| - type: map_at_1000 |
| value: 35.8155 |
| - type: map_at_3 |
| value: 31.608333333333338 |
| - type: map_at_5 |
| value: 33.189416666666666 |
| - type: mrr_at_1 |
| value: 29.825250000000004 |
| - type: mrr_at_10 |
| value: 38.60875 |
| - type: mrr_at_100 |
| value: 39.46575 |
| - type: mrr_at_1000 |
| value: 39.52458333333333 |
| - type: mrr_at_3 |
| value: 36.145166666666675 |
| - type: mrr_at_5 |
| value: 37.57625 |
| - type: ndcg_at_1 |
| value: 29.825250000000004 |
| - type: ndcg_at_10 |
| value: 39.88741666666667 |
| - type: ndcg_at_100 |
| value: 45.17966666666667 |
| - type: ndcg_at_1000 |
| value: 47.440583333333336 |
| - type: ndcg_at_3 |
| value: 35.04591666666666 |
| - type: ndcg_at_5 |
| value: 37.32025 |
| - type: precision_at_1 |
| value: 29.825250000000004 |
| - type: precision_at_10 |
| value: 7.07225 |
| - type: precision_at_100 |
| value: 1.1462499999999998 |
| - type: precision_at_1000 |
| value: 0.15325 |
| - type: precision_at_3 |
| value: 16.18375 |
| - type: precision_at_5 |
| value: 11.526833333333334 |
| - type: recall_at_1 |
| value: 25.24441666666667 |
| - type: recall_at_10 |
| value: 51.744916666666676 |
| - type: recall_at_100 |
| value: 75.04574999999998 |
| - type: recall_at_1000 |
| value: 90.65558333333334 |
| - type: recall_at_3 |
| value: 38.28349999999999 |
| - type: recall_at_5 |
| value: 44.16591666666667 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.237000000000002 |
| - type: map_at_10 |
| value: 30.667 |
| - type: map_at_100 |
| value: 31.592 |
| - type: map_at_1000 |
| value: 31.688 |
| - type: map_at_3 |
| value: 28.810999999999996 |
| - type: map_at_5 |
| value: 29.788999999999998 |
| - type: mrr_at_1 |
| value: 26.840000000000003 |
| - type: mrr_at_10 |
| value: 33.305 |
| - type: mrr_at_100 |
| value: 34.089000000000006 |
| - type: mrr_at_1000 |
| value: 34.159 |
| - type: mrr_at_3 |
| value: 31.518 |
| - type: mrr_at_5 |
| value: 32.469 |
| - type: ndcg_at_1 |
| value: 26.840000000000003 |
| - type: ndcg_at_10 |
| value: 34.541 |
| - type: ndcg_at_100 |
| value: 39.206 |
| - type: ndcg_at_1000 |
| value: 41.592 |
| - type: ndcg_at_3 |
| value: 31.005 |
| - type: ndcg_at_5 |
| value: 32.554 |
| - type: precision_at_1 |
| value: 26.840000000000003 |
| - type: precision_at_10 |
| value: 5.3069999999999995 |
| - type: precision_at_100 |
| value: 0.8340000000000001 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 13.292000000000002 |
| - type: precision_at_5 |
| value: 9.049 |
| - type: recall_at_1 |
| value: 24.237000000000002 |
| - type: recall_at_10 |
| value: 43.862 |
| - type: recall_at_100 |
| value: 65.352 |
| - type: recall_at_1000 |
| value: 82.704 |
| - type: recall_at_3 |
| value: 34.009 |
| - type: recall_at_5 |
| value: 37.878 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 16.482 |
| - type: map_at_10 |
| value: 23.249 |
| - type: map_at_100 |
| value: 24.388 |
| - type: map_at_1000 |
| value: 24.519 |
| - type: map_at_3 |
| value: 20.971 |
| - type: map_at_5 |
| value: 22.192 |
| - type: mrr_at_1 |
| value: 19.993 |
| - type: mrr_at_10 |
| value: 26.985 |
| - type: mrr_at_100 |
| value: 27.975 |
| - type: mrr_at_1000 |
| value: 28.052 |
| - type: mrr_at_3 |
| value: 24.954 |
| - type: mrr_at_5 |
| value: 26.070999999999998 |
| - type: ndcg_at_1 |
| value: 19.993 |
| - type: ndcg_at_10 |
| value: 27.656 |
| - type: ndcg_at_100 |
| value: 33.256 |
| - type: ndcg_at_1000 |
| value: 36.275 |
| - type: ndcg_at_3 |
| value: 23.644000000000002 |
| - type: ndcg_at_5 |
| value: 25.466 |
| - type: precision_at_1 |
| value: 19.993 |
| - type: precision_at_10 |
| value: 5.093 |
| - type: precision_at_100 |
| value: 0.932 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 11.149000000000001 |
| - type: precision_at_5 |
| value: 8.149000000000001 |
| - type: recall_at_1 |
| value: 16.482 |
| - type: recall_at_10 |
| value: 37.141999999999996 |
| - type: recall_at_100 |
| value: 62.696 |
| - type: recall_at_1000 |
| value: 84.333 |
| - type: recall_at_3 |
| value: 26.031 |
| - type: recall_at_5 |
| value: 30.660999999999998 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.887999999999998 |
| - type: map_at_10 |
| value: 34.101 |
| - type: map_at_100 |
| value: 35.27 |
| - type: map_at_1000 |
| value: 35.370000000000005 |
| - type: map_at_3 |
| value: 31.283 |
| - type: map_at_5 |
| value: 32.72 |
| - type: mrr_at_1 |
| value: 29.011 |
| - type: mrr_at_10 |
| value: 38.004 |
| - type: mrr_at_100 |
| value: 38.879000000000005 |
| - type: mrr_at_1000 |
| value: 38.938 |
| - type: mrr_at_3 |
| value: 35.571999999999996 |
| - type: mrr_at_5 |
| value: 36.789 |
| - type: ndcg_at_1 |
| value: 29.011 |
| - type: ndcg_at_10 |
| value: 39.586 |
| - type: ndcg_at_100 |
| value: 44.939 |
| - type: ndcg_at_1000 |
| value: 47.236 |
| - type: ndcg_at_3 |
| value: 34.4 |
| - type: ndcg_at_5 |
| value: 36.519 |
| - type: precision_at_1 |
| value: 29.011 |
| - type: precision_at_10 |
| value: 6.763 |
| - type: precision_at_100 |
| value: 1.059 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 15.609 |
| - type: precision_at_5 |
| value: 10.896 |
| - type: recall_at_1 |
| value: 24.887999999999998 |
| - type: recall_at_10 |
| value: 52.42 |
| - type: recall_at_100 |
| value: 75.803 |
| - type: recall_at_1000 |
| value: 91.725 |
| - type: recall_at_3 |
| value: 38.080999999999996 |
| - type: recall_at_5 |
| value: 43.47 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.953 |
| - type: map_at_10 |
| value: 32.649 |
| - type: map_at_100 |
| value: 34.181 |
| - type: map_at_1000 |
| value: 34.398 |
| - type: map_at_3 |
| value: 29.567 |
| - type: map_at_5 |
| value: 31.263 |
| - type: mrr_at_1 |
| value: 29.051 |
| - type: mrr_at_10 |
| value: 37.419999999999995 |
| - type: mrr_at_100 |
| value: 38.396 |
| - type: mrr_at_1000 |
| value: 38.458 |
| - type: mrr_at_3 |
| value: 34.782999999999994 |
| - type: mrr_at_5 |
| value: 36.254999999999995 |
| - type: ndcg_at_1 |
| value: 29.051 |
| - type: ndcg_at_10 |
| value: 38.595 |
| - type: ndcg_at_100 |
| value: 44.6 |
| - type: ndcg_at_1000 |
| value: 47.158 |
| - type: ndcg_at_3 |
| value: 33.56 |
| - type: ndcg_at_5 |
| value: 35.870000000000005 |
| - type: precision_at_1 |
| value: 29.051 |
| - type: precision_at_10 |
| value: 7.53 |
| - type: precision_at_100 |
| value: 1.538 |
| - type: precision_at_1000 |
| value: 0.24 |
| - type: precision_at_3 |
| value: 15.744 |
| - type: precision_at_5 |
| value: 11.542 |
| - type: recall_at_1 |
| value: 23.953 |
| - type: recall_at_10 |
| value: 50.08200000000001 |
| - type: recall_at_100 |
| value: 77.364 |
| - type: recall_at_1000 |
| value: 93.57799999999999 |
| - type: recall_at_3 |
| value: 35.432 |
| - type: recall_at_5 |
| value: 41.875 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 17.72 |
| - type: map_at_10 |
| value: 25.724000000000004 |
| - type: map_at_100 |
| value: 26.846999999999998 |
| - type: map_at_1000 |
| value: 26.964 |
| - type: map_at_3 |
| value: 22.909 |
| - type: map_at_5 |
| value: 24.596999999999998 |
| - type: mrr_at_1 |
| value: 18.854000000000003 |
| - type: mrr_at_10 |
| value: 27.182000000000002 |
| - type: mrr_at_100 |
| value: 28.182000000000002 |
| - type: mrr_at_1000 |
| value: 28.274 |
| - type: mrr_at_3 |
| value: 24.276 |
| - type: mrr_at_5 |
| value: 26.115 |
| - type: ndcg_at_1 |
| value: 18.854000000000003 |
| - type: ndcg_at_10 |
| value: 30.470000000000002 |
| - type: ndcg_at_100 |
| value: 35.854 |
| - type: ndcg_at_1000 |
| value: 38.701 |
| - type: ndcg_at_3 |
| value: 24.924 |
| - type: ndcg_at_5 |
| value: 27.895999999999997 |
| - type: precision_at_1 |
| value: 18.854000000000003 |
| - type: precision_at_10 |
| value: 5.009 |
| - type: precision_at_100 |
| value: 0.835 |
| - type: precision_at_1000 |
| value: 0.117 |
| - type: precision_at_3 |
| value: 10.721 |
| - type: precision_at_5 |
| value: 8.133 |
| - type: recall_at_1 |
| value: 17.72 |
| - type: recall_at_10 |
| value: 43.617 |
| - type: recall_at_100 |
| value: 67.941 |
| - type: recall_at_1000 |
| value: 88.979 |
| - type: recall_at_3 |
| value: 29.044999999999998 |
| - type: recall_at_5 |
| value: 36.044 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 13.427 |
| - type: map_at_10 |
| value: 22.935 |
| - type: map_at_100 |
| value: 24.808 |
| - type: map_at_1000 |
| value: 24.994 |
| - type: map_at_3 |
| value: 19.533 |
| - type: map_at_5 |
| value: 21.261 |
| - type: mrr_at_1 |
| value: 30.945 |
| - type: mrr_at_10 |
| value: 43.242000000000004 |
| - type: mrr_at_100 |
| value: 44.013999999999996 |
| - type: mrr_at_1000 |
| value: 44.048 |
| - type: mrr_at_3 |
| value: 40.109 |
| - type: mrr_at_5 |
| value: 42.059999999999995 |
| - type: ndcg_at_1 |
| value: 30.945 |
| - type: ndcg_at_10 |
| value: 31.828 |
| - type: ndcg_at_100 |
| value: 38.801 |
| - type: ndcg_at_1000 |
| value: 42.126999999999995 |
| - type: ndcg_at_3 |
| value: 26.922 |
| - type: ndcg_at_5 |
| value: 28.483999999999998 |
| - type: precision_at_1 |
| value: 30.945 |
| - type: precision_at_10 |
| value: 9.844 |
| - type: precision_at_100 |
| value: 1.7309999999999999 |
| - type: precision_at_1000 |
| value: 0.23500000000000001 |
| - type: precision_at_3 |
| value: 20.477999999999998 |
| - type: precision_at_5 |
| value: 15.27 |
| - type: recall_at_1 |
| value: 13.427 |
| - type: recall_at_10 |
| value: 37.141000000000005 |
| - type: recall_at_100 |
| value: 61.007 |
| - type: recall_at_1000 |
| value: 79.742 |
| - type: recall_at_3 |
| value: 24.431 |
| - type: recall_at_5 |
| value: 29.725 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 9.122 |
| - type: map_at_10 |
| value: 18.799 |
| - type: map_at_100 |
| value: 25.724999999999998 |
| - type: map_at_1000 |
| value: 27.205000000000002 |
| - type: map_at_3 |
| value: 14.194999999999999 |
| - type: map_at_5 |
| value: 16.225 |
| - type: mrr_at_1 |
| value: 68.0 |
| - type: mrr_at_10 |
| value: 76.035 |
| - type: mrr_at_100 |
| value: 76.292 |
| - type: mrr_at_1000 |
| value: 76.297 |
| - type: mrr_at_3 |
| value: 74.458 |
| - type: mrr_at_5 |
| value: 75.558 |
| - type: ndcg_at_1 |
| value: 56.00000000000001 |
| - type: ndcg_at_10 |
| value: 39.761 |
| - type: ndcg_at_100 |
| value: 43.736999999999995 |
| - type: ndcg_at_1000 |
| value: 51.146 |
| - type: ndcg_at_3 |
| value: 45.921 |
| - type: ndcg_at_5 |
| value: 42.756 |
| - type: precision_at_1 |
| value: 68.0 |
| - type: precision_at_10 |
| value: 30.275000000000002 |
| - type: precision_at_100 |
| value: 9.343 |
| - type: precision_at_1000 |
| value: 1.8270000000000002 |
| - type: precision_at_3 |
| value: 49.167 |
| - type: precision_at_5 |
| value: 40.699999999999996 |
| - type: recall_at_1 |
| value: 9.122 |
| - type: recall_at_10 |
| value: 23.669999999999998 |
| - type: recall_at_100 |
| value: 48.719 |
| - type: recall_at_1000 |
| value: 72.033 |
| - type: recall_at_3 |
| value: 15.498999999999999 |
| - type: recall_at_5 |
| value: 18.657 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 55.885000000000005 |
| - type: f1 |
| value: 50.70726446938571 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 75.709 |
| - type: map_at_10 |
| value: 83.345 |
| - type: map_at_100 |
| value: 83.557 |
| - type: map_at_1000 |
| value: 83.572 |
| - type: map_at_3 |
| value: 82.425 |
| - type: map_at_5 |
| value: 83.013 |
| - type: mrr_at_1 |
| value: 81.593 |
| - type: mrr_at_10 |
| value: 88.331 |
| - type: mrr_at_100 |
| value: 88.408 |
| - type: mrr_at_1000 |
| value: 88.41 |
| - type: mrr_at_3 |
| value: 87.714 |
| - type: mrr_at_5 |
| value: 88.122 |
| - type: ndcg_at_1 |
| value: 81.593 |
| - type: ndcg_at_10 |
| value: 86.925 |
| - type: ndcg_at_100 |
| value: 87.67 |
| - type: ndcg_at_1000 |
| value: 87.924 |
| - type: ndcg_at_3 |
| value: 85.5 |
| - type: ndcg_at_5 |
| value: 86.283 |
| - type: precision_at_1 |
| value: 81.593 |
| - type: precision_at_10 |
| value: 10.264 |
| - type: precision_at_100 |
| value: 1.084 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 32.388 |
| - type: precision_at_5 |
| value: 19.991 |
| - type: recall_at_1 |
| value: 75.709 |
| - type: recall_at_10 |
| value: 93.107 |
| - type: recall_at_100 |
| value: 96.024 |
| - type: recall_at_1000 |
| value: 97.603 |
| - type: recall_at_3 |
| value: 89.08500000000001 |
| - type: recall_at_5 |
| value: 91.15299999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 19.121 |
| - type: map_at_10 |
| value: 31.78 |
| - type: map_at_100 |
| value: 33.497 |
| - type: map_at_1000 |
| value: 33.696 |
| - type: map_at_3 |
| value: 27.893 |
| - type: map_at_5 |
| value: 30.087000000000003 |
| - type: mrr_at_1 |
| value: 38.272 |
| - type: mrr_at_10 |
| value: 47.176 |
| - type: mrr_at_100 |
| value: 48.002 |
| - type: mrr_at_1000 |
| value: 48.044 |
| - type: mrr_at_3 |
| value: 45.086999999999996 |
| - type: mrr_at_5 |
| value: 46.337 |
| - type: ndcg_at_1 |
| value: 38.272 |
| - type: ndcg_at_10 |
| value: 39.145 |
| - type: ndcg_at_100 |
| value: 45.696999999999996 |
| - type: ndcg_at_1000 |
| value: 49.0 |
| - type: ndcg_at_3 |
| value: 36.148 |
| - type: ndcg_at_5 |
| value: 37.023 |
| - type: precision_at_1 |
| value: 38.272 |
| - type: precision_at_10 |
| value: 11.065 |
| - type: precision_at_100 |
| value: 1.7840000000000003 |
| - type: precision_at_1000 |
| value: 0.23600000000000002 |
| - type: precision_at_3 |
| value: 24.587999999999997 |
| - type: precision_at_5 |
| value: 18.056 |
| - type: recall_at_1 |
| value: 19.121 |
| - type: recall_at_10 |
| value: 44.857 |
| - type: recall_at_100 |
| value: 69.774 |
| - type: recall_at_1000 |
| value: 89.645 |
| - type: recall_at_3 |
| value: 32.588 |
| - type: recall_at_5 |
| value: 37.939 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 36.428 |
| - type: map_at_10 |
| value: 56.891999999999996 |
| - type: map_at_100 |
| value: 57.82899999999999 |
| - type: map_at_1000 |
| value: 57.896 |
| - type: map_at_3 |
| value: 53.762 |
| - type: map_at_5 |
| value: 55.718 |
| - type: mrr_at_1 |
| value: 72.856 |
| - type: mrr_at_10 |
| value: 79.245 |
| - type: mrr_at_100 |
| value: 79.515 |
| - type: mrr_at_1000 |
| value: 79.525 |
| - type: mrr_at_3 |
| value: 78.143 |
| - type: mrr_at_5 |
| value: 78.822 |
| - type: ndcg_at_1 |
| value: 72.856 |
| - type: ndcg_at_10 |
| value: 65.204 |
| - type: ndcg_at_100 |
| value: 68.552 |
| - type: ndcg_at_1000 |
| value: 69.902 |
| - type: ndcg_at_3 |
| value: 60.632 |
| - type: ndcg_at_5 |
| value: 63.161 |
| - type: precision_at_1 |
| value: 72.856 |
| - type: precision_at_10 |
| value: 13.65 |
| - type: precision_at_100 |
| value: 1.6260000000000001 |
| - type: precision_at_1000 |
| value: 0.181 |
| - type: precision_at_3 |
| value: 38.753 |
| - type: precision_at_5 |
| value: 25.251 |
| - type: recall_at_1 |
| value: 36.428 |
| - type: recall_at_10 |
| value: 68.25099999999999 |
| - type: recall_at_100 |
| value: 81.317 |
| - type: recall_at_1000 |
| value: 90.27 |
| - type: recall_at_3 |
| value: 58.13 |
| - type: recall_at_5 |
| value: 63.126000000000005 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 89.4868 |
| - type: ap |
| value: 84.88319192880247 |
| - type: f1 |
| value: 89.46144458052846 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 21.282999999999998 |
| - type: map_at_10 |
| value: 33.045 |
| - type: map_at_100 |
| value: 34.238 |
| - type: map_at_1000 |
| value: 34.29 |
| - type: map_at_3 |
| value: 29.305999999999997 |
| - type: map_at_5 |
| value: 31.391000000000002 |
| - type: mrr_at_1 |
| value: 21.92 |
| - type: mrr_at_10 |
| value: 33.649 |
| - type: mrr_at_100 |
| value: 34.791 |
| - type: mrr_at_1000 |
| value: 34.837 |
| - type: mrr_at_3 |
| value: 30.0 |
| - type: mrr_at_5 |
| value: 32.039 |
| - type: ndcg_at_1 |
| value: 21.92 |
| - type: ndcg_at_10 |
| value: 39.729 |
| - type: ndcg_at_100 |
| value: 45.484 |
| - type: ndcg_at_1000 |
| value: 46.817 |
| - type: ndcg_at_3 |
| value: 32.084 |
| - type: ndcg_at_5 |
| value: 35.789 |
| - type: precision_at_1 |
| value: 21.92 |
| - type: precision_at_10 |
| value: 6.297 |
| - type: precision_at_100 |
| value: 0.918 |
| - type: precision_at_1000 |
| value: 0.10300000000000001 |
| - type: precision_at_3 |
| value: 13.639000000000001 |
| - type: precision_at_5 |
| value: 10.054 |
| - type: recall_at_1 |
| value: 21.282999999999998 |
| - type: recall_at_10 |
| value: 60.343999999999994 |
| - type: recall_at_100 |
| value: 86.981 |
| - type: recall_at_1000 |
| value: 97.205 |
| - type: recall_at_3 |
| value: 39.452999999999996 |
| - type: recall_at_5 |
| value: 48.333 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 95.47879616963064 |
| - type: f1 |
| value: 95.21800589958251 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 79.09256725946192 |
| - type: f1 |
| value: 60.554043889452515 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 75.53463349024882 |
| - type: f1 |
| value: 73.14418495756476 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 79.22663080026899 |
| - type: f1 |
| value: 79.331456217501 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 34.50316010430136 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 32.15612040042282 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 32.36227552557184 |
| - type: mrr |
| value: 33.57901344209811 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.6610000000000005 |
| - type: map_at_10 |
| value: 12.992 |
| - type: map_at_100 |
| value: 16.756999999999998 |
| - type: map_at_1000 |
| value: 18.25 |
| - type: map_at_3 |
| value: 9.471 |
| - type: map_at_5 |
| value: 11.116 |
| - type: mrr_at_1 |
| value: 43.653 |
| - type: mrr_at_10 |
| value: 53.388999999999996 |
| - type: mrr_at_100 |
| value: 53.982 |
| - type: mrr_at_1000 |
| value: 54.033 |
| - type: mrr_at_3 |
| value: 51.858000000000004 |
| - type: mrr_at_5 |
| value: 53.019000000000005 |
| - type: ndcg_at_1 |
| value: 41.641 |
| - type: ndcg_at_10 |
| value: 34.691 |
| - type: ndcg_at_100 |
| value: 32.305 |
| - type: ndcg_at_1000 |
| value: 41.132999999999996 |
| - type: ndcg_at_3 |
| value: 40.614 |
| - type: ndcg_at_5 |
| value: 38.456 |
| - type: precision_at_1 |
| value: 43.344 |
| - type: precision_at_10 |
| value: 25.881999999999998 |
| - type: precision_at_100 |
| value: 8.483 |
| - type: precision_at_1000 |
| value: 2.131 |
| - type: precision_at_3 |
| value: 38.803 |
| - type: precision_at_5 |
| value: 33.87 |
| - type: recall_at_1 |
| value: 5.6610000000000005 |
| - type: recall_at_10 |
| value: 16.826 |
| - type: recall_at_100 |
| value: 32.939 |
| - type: recall_at_1000 |
| value: 65.161 |
| - type: recall_at_3 |
| value: 10.756 |
| - type: recall_at_5 |
| value: 13.331000000000001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 26.692 |
| - type: map_at_10 |
| value: 41.065000000000005 |
| - type: map_at_100 |
| value: 42.235 |
| - type: map_at_1000 |
| value: 42.27 |
| - type: map_at_3 |
| value: 36.635 |
| - type: map_at_5 |
| value: 39.219 |
| - type: mrr_at_1 |
| value: 30.214000000000002 |
| - type: mrr_at_10 |
| value: 43.443 |
| - type: mrr_at_100 |
| value: 44.326 |
| - type: mrr_at_1000 |
| value: 44.352000000000004 |
| - type: mrr_at_3 |
| value: 39.623999999999995 |
| - type: mrr_at_5 |
| value: 41.898 |
| - type: ndcg_at_1 |
| value: 30.214000000000002 |
| - type: ndcg_at_10 |
| value: 48.692 |
| - type: ndcg_at_100 |
| value: 53.671 |
| - type: ndcg_at_1000 |
| value: 54.522000000000006 |
| - type: ndcg_at_3 |
| value: 40.245 |
| - type: ndcg_at_5 |
| value: 44.580999999999996 |
| - type: precision_at_1 |
| value: 30.214000000000002 |
| - type: precision_at_10 |
| value: 8.3 |
| - type: precision_at_100 |
| value: 1.1079999999999999 |
| - type: precision_at_1000 |
| value: 0.11900000000000001 |
| - type: precision_at_3 |
| value: 18.521 |
| - type: precision_at_5 |
| value: 13.627 |
| - type: recall_at_1 |
| value: 26.692 |
| - type: recall_at_10 |
| value: 69.699 |
| - type: recall_at_100 |
| value: 91.425 |
| - type: recall_at_1000 |
| value: 97.78099999999999 |
| - type: recall_at_3 |
| value: 47.711 |
| - type: recall_at_5 |
| value: 57.643 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 70.962 |
| - type: map_at_10 |
| value: 84.772 |
| - type: map_at_100 |
| value: 85.402 |
| - type: map_at_1000 |
| value: 85.418 |
| - type: map_at_3 |
| value: 81.89 |
| - type: map_at_5 |
| value: 83.685 |
| - type: mrr_at_1 |
| value: 81.67 |
| - type: mrr_at_10 |
| value: 87.681 |
| - type: mrr_at_100 |
| value: 87.792 |
| - type: mrr_at_1000 |
| value: 87.79299999999999 |
| - type: mrr_at_3 |
| value: 86.803 |
| - type: mrr_at_5 |
| value: 87.392 |
| - type: ndcg_at_1 |
| value: 81.69 |
| - type: ndcg_at_10 |
| value: 88.429 |
| - type: ndcg_at_100 |
| value: 89.66 |
| - type: ndcg_at_1000 |
| value: 89.762 |
| - type: ndcg_at_3 |
| value: 85.75 |
| - type: ndcg_at_5 |
| value: 87.20700000000001 |
| - type: precision_at_1 |
| value: 81.69 |
| - type: precision_at_10 |
| value: 13.395000000000001 |
| - type: precision_at_100 |
| value: 1.528 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 37.507000000000005 |
| - type: precision_at_5 |
| value: 24.614 |
| - type: recall_at_1 |
| value: 70.962 |
| - type: recall_at_10 |
| value: 95.339 |
| - type: recall_at_100 |
| value: 99.543 |
| - type: recall_at_1000 |
| value: 99.984 |
| - type: recall_at_3 |
| value: 87.54899999999999 |
| - type: recall_at_5 |
| value: 91.726 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 55.506631779239555 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 60.63731341848479 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.852 |
| - type: map_at_10 |
| value: 13.175 |
| - type: map_at_100 |
| value: 15.623999999999999 |
| - type: map_at_1000 |
| value: 16.002 |
| - type: map_at_3 |
| value: 9.103 |
| - type: map_at_5 |
| value: 11.068999999999999 |
| - type: mrr_at_1 |
| value: 23.9 |
| - type: mrr_at_10 |
| value: 35.847 |
| - type: mrr_at_100 |
| value: 36.968 |
| - type: mrr_at_1000 |
| value: 37.018 |
| - type: mrr_at_3 |
| value: 32.300000000000004 |
| - type: mrr_at_5 |
| value: 34.14 |
| - type: ndcg_at_1 |
| value: 23.9 |
| - type: ndcg_at_10 |
| value: 21.889 |
| - type: ndcg_at_100 |
| value: 30.903000000000002 |
| - type: ndcg_at_1000 |
| value: 36.992000000000004 |
| - type: ndcg_at_3 |
| value: 20.274 |
| - type: ndcg_at_5 |
| value: 17.773 |
| - type: precision_at_1 |
| value: 23.9 |
| - type: precision_at_10 |
| value: 11.61 |
| - type: precision_at_100 |
| value: 2.4539999999999997 |
| - type: precision_at_1000 |
| value: 0.391 |
| - type: precision_at_3 |
| value: 19.133 |
| - type: precision_at_5 |
| value: 15.740000000000002 |
| - type: recall_at_1 |
| value: 4.852 |
| - type: recall_at_10 |
| value: 23.507 |
| - type: recall_at_100 |
| value: 49.775000000000006 |
| - type: recall_at_1000 |
| value: 79.308 |
| - type: recall_at_3 |
| value: 11.637 |
| - type: recall_at_5 |
| value: 15.947 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.03345827446948 |
| - type: cos_sim_spearman |
| value: 80.53174518259549 |
| - type: euclidean_pearson |
| value: 83.44538971660883 |
| - type: euclidean_spearman |
| value: 80.57344324098692 |
| - type: manhattan_pearson |
| value: 83.36528808195459 |
| - type: manhattan_spearman |
| value: 80.48931287157902 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.21363088257881 |
| - type: cos_sim_spearman |
| value: 75.56589127055523 |
| - type: euclidean_pearson |
| value: 82.32868324521908 |
| - type: euclidean_spearman |
| value: 75.31928550664554 |
| - type: manhattan_pearson |
| value: 82.31332875713211 |
| - type: manhattan_spearman |
| value: 75.35376322099196 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.09085593258487 |
| - type: cos_sim_spearman |
| value: 86.26355088415221 |
| - type: euclidean_pearson |
| value: 85.49646115361156 |
| - type: euclidean_spearman |
| value: 86.20652472228703 |
| - type: manhattan_pearson |
| value: 85.44084081123815 |
| - type: manhattan_spearman |
| value: 86.1162623448951 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.68250248349368 |
| - type: cos_sim_spearman |
| value: 82.29883673695083 |
| - type: euclidean_pearson |
| value: 84.17633035446019 |
| - type: euclidean_spearman |
| value: 82.19990511264791 |
| - type: manhattan_pearson |
| value: 84.17408410692279 |
| - type: manhattan_spearman |
| value: 82.249873895981 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.31878760045024 |
| - type: cos_sim_spearman |
| value: 88.7364409031183 |
| - type: euclidean_pearson |
| value: 88.230537618603 |
| - type: euclidean_spearman |
| value: 88.76484309646318 |
| - type: manhattan_pearson |
| value: 88.17689071136469 |
| - type: manhattan_spearman |
| value: 88.72809249037928 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.41078559110638 |
| - type: cos_sim_spearman |
| value: 85.27439135411049 |
| - type: euclidean_pearson |
| value: 84.5333571592088 |
| - type: euclidean_spearman |
| value: 85.25645460575957 |
| - type: manhattan_pearson |
| value: 84.38428921610226 |
| - type: manhattan_spearman |
| value: 85.07796040798796 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts17-crosslingual-sts |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.82374132382576 |
| - type: cos_sim_spearman |
| value: 89.02101343562433 |
| - type: euclidean_pearson |
| value: 89.50729765458932 |
| - type: euclidean_spearman |
| value: 89.04184772869253 |
| - type: manhattan_pearson |
| value: 89.51737904059856 |
| - type: manhattan_spearman |
| value: 89.12925950440676 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts22-crosslingual-sts |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
| metrics: |
| - type: cos_sim_pearson |
| value: 67.56051823873482 |
| - type: cos_sim_spearman |
| value: 68.50988748185463 |
| - type: euclidean_pearson |
| value: 69.16524346147456 |
| - type: euclidean_spearman |
| value: 68.61859952449579 |
| - type: manhattan_pearson |
| value: 69.10618915706995 |
| - type: manhattan_spearman |
| value: 68.36401769459522 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.4159693872625 |
| - type: cos_sim_spearman |
| value: 87.07819121764247 |
| - type: euclidean_pearson |
| value: 87.03013260863153 |
| - type: euclidean_spearman |
| value: 87.06547293631309 |
| - type: manhattan_pearson |
| value: 86.8129744446062 |
| - type: manhattan_spearman |
| value: 86.88494096335627 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 86.47758088996575 |
| - type: mrr |
| value: 96.17891458577733 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 57.538999999999994 |
| - type: map_at_10 |
| value: 66.562 |
| - type: map_at_100 |
| value: 67.254 |
| - type: map_at_1000 |
| value: 67.284 |
| - type: map_at_3 |
| value: 63.722 |
| - type: map_at_5 |
| value: 65.422 |
| - type: mrr_at_1 |
| value: 60.0 |
| - type: mrr_at_10 |
| value: 67.354 |
| - type: mrr_at_100 |
| value: 67.908 |
| - type: mrr_at_1000 |
| value: 67.93299999999999 |
| - type: mrr_at_3 |
| value: 65.056 |
| - type: mrr_at_5 |
| value: 66.43900000000001 |
| - type: ndcg_at_1 |
| value: 60.0 |
| - type: ndcg_at_10 |
| value: 70.858 |
| - type: ndcg_at_100 |
| value: 73.67099999999999 |
| - type: ndcg_at_1000 |
| value: 74.26700000000001 |
| - type: ndcg_at_3 |
| value: 65.911 |
| - type: ndcg_at_5 |
| value: 68.42200000000001 |
| - type: precision_at_1 |
| value: 60.0 |
| - type: precision_at_10 |
| value: 9.4 |
| - type: precision_at_100 |
| value: 1.083 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 25.444 |
| - type: precision_at_5 |
| value: 17.0 |
| - type: recall_at_1 |
| value: 57.538999999999994 |
| - type: recall_at_10 |
| value: 83.233 |
| - type: recall_at_100 |
| value: 95.667 |
| - type: recall_at_1000 |
| value: 100.0 |
| - type: recall_at_3 |
| value: 69.883 |
| - type: recall_at_5 |
| value: 76.19399999999999 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.82574257425742 |
| - type: cos_sim_ap |
| value: 95.78722833053911 |
| - type: cos_sim_f1 |
| value: 90.94650205761316 |
| - type: cos_sim_precision |
| value: 93.64406779661016 |
| - type: cos_sim_recall |
| value: 88.4 |
| - type: dot_accuracy |
| value: 99.83366336633664 |
| - type: dot_ap |
| value: 95.89733601612964 |
| - type: dot_f1 |
| value: 91.41981613891727 |
| - type: dot_precision |
| value: 93.42379958246346 |
| - type: dot_recall |
| value: 89.5 |
| - type: euclidean_accuracy |
| value: 99.82574257425742 |
| - type: euclidean_ap |
| value: 95.75227035138846 |
| - type: euclidean_f1 |
| value: 90.96509240246407 |
| - type: euclidean_precision |
| value: 93.45991561181435 |
| - type: euclidean_recall |
| value: 88.6 |
| - type: manhattan_accuracy |
| value: 99.82574257425742 |
| - type: manhattan_ap |
| value: 95.76278266220176 |
| - type: manhattan_f1 |
| value: 91.08409321175279 |
| - type: manhattan_precision |
| value: 92.29979466119097 |
| - type: manhattan_recall |
| value: 89.9 |
| - type: max_accuracy |
| value: 99.83366336633664 |
| - type: max_ap |
| value: 95.89733601612964 |
| - type: max_f1 |
| value: 91.41981613891727 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 61.905425988638605 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 36.159589881679736 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 53.0605499476397 |
| - type: mrr |
| value: 53.91594516594517 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.202718009067 |
| - type: cos_sim_spearman |
| value: 31.136199912366987 |
| - type: dot_pearson |
| value: 30.66329011927951 |
| - type: dot_spearman |
| value: 30.107664909625107 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.209 |
| - type: map_at_10 |
| value: 1.712 |
| - type: map_at_100 |
| value: 9.464 |
| - type: map_at_1000 |
| value: 23.437 |
| - type: map_at_3 |
| value: 0.609 |
| - type: map_at_5 |
| value: 0.9440000000000001 |
| - type: mrr_at_1 |
| value: 78.0 |
| - type: mrr_at_10 |
| value: 86.833 |
| - type: mrr_at_100 |
| value: 86.833 |
| - type: mrr_at_1000 |
| value: 86.833 |
| - type: mrr_at_3 |
| value: 85.333 |
| - type: mrr_at_5 |
| value: 86.833 |
| - type: ndcg_at_1 |
| value: 74.0 |
| - type: ndcg_at_10 |
| value: 69.14 |
| - type: ndcg_at_100 |
| value: 53.047999999999995 |
| - type: ndcg_at_1000 |
| value: 48.577 |
| - type: ndcg_at_3 |
| value: 75.592 |
| - type: ndcg_at_5 |
| value: 72.509 |
| - type: precision_at_1 |
| value: 78.0 |
| - type: precision_at_10 |
| value: 73.0 |
| - type: precision_at_100 |
| value: 54.44 |
| - type: precision_at_1000 |
| value: 21.326 |
| - type: precision_at_3 |
| value: 80.667 |
| - type: precision_at_5 |
| value: 77.2 |
| - type: recall_at_1 |
| value: 0.209 |
| - type: recall_at_10 |
| value: 1.932 |
| - type: recall_at_100 |
| value: 13.211999999999998 |
| - type: recall_at_1000 |
| value: 45.774 |
| - type: recall_at_3 |
| value: 0.644 |
| - type: recall_at_5 |
| value: 1.0290000000000001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.609 |
| - type: map_at_10 |
| value: 8.334999999999999 |
| - type: map_at_100 |
| value: 14.604000000000001 |
| - type: map_at_1000 |
| value: 16.177 |
| - type: map_at_3 |
| value: 4.87 |
| - type: map_at_5 |
| value: 6.3149999999999995 |
| - type: mrr_at_1 |
| value: 32.653 |
| - type: mrr_at_10 |
| value: 45.047 |
| - type: mrr_at_100 |
| value: 45.808 |
| - type: mrr_at_1000 |
| value: 45.808 |
| - type: mrr_at_3 |
| value: 41.497 |
| - type: mrr_at_5 |
| value: 43.231 |
| - type: ndcg_at_1 |
| value: 30.612000000000002 |
| - type: ndcg_at_10 |
| value: 21.193 |
| - type: ndcg_at_100 |
| value: 34.97 |
| - type: ndcg_at_1000 |
| value: 46.69 |
| - type: ndcg_at_3 |
| value: 24.823 |
| - type: ndcg_at_5 |
| value: 22.872999999999998 |
| - type: precision_at_1 |
| value: 32.653 |
| - type: precision_at_10 |
| value: 17.959 |
| - type: precision_at_100 |
| value: 7.4079999999999995 |
| - type: precision_at_1000 |
| value: 1.537 |
| - type: precision_at_3 |
| value: 25.85 |
| - type: precision_at_5 |
| value: 22.448999999999998 |
| - type: recall_at_1 |
| value: 2.609 |
| - type: recall_at_10 |
| value: 13.63 |
| - type: recall_at_100 |
| value: 47.014 |
| - type: recall_at_1000 |
| value: 83.176 |
| - type: recall_at_3 |
| value: 5.925 |
| - type: recall_at_5 |
| value: 8.574 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 72.80239999999999 |
| - type: ap |
| value: 15.497911013214791 |
| - type: f1 |
| value: 56.258411577947285 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 61.00452744765139 |
| - type: f1 |
| value: 61.42228624410908 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 50.00516915962345 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 85.62317458425225 |
| - type: cos_sim_ap |
| value: 72.95115658063823 |
| - type: cos_sim_f1 |
| value: 66.78976523344764 |
| - type: cos_sim_precision |
| value: 66.77215189873418 |
| - type: cos_sim_recall |
| value: 66.80738786279683 |
| - type: dot_accuracy |
| value: 85.62317458425225 |
| - type: dot_ap |
| value: 73.10385271517778 |
| - type: dot_f1 |
| value: 66.94853829427399 |
| - type: dot_precision |
| value: 61.74242424242424 |
| - type: dot_recall |
| value: 73.11345646437995 |
| - type: euclidean_accuracy |
| value: 85.65893783155511 |
| - type: euclidean_ap |
| value: 72.87428208473992 |
| - type: euclidean_f1 |
| value: 66.70919994896005 |
| - type: euclidean_precision |
| value: 64.5910551025451 |
| - type: euclidean_recall |
| value: 68.97097625329816 |
| - type: manhattan_accuracy |
| value: 85.59933241938367 |
| - type: manhattan_ap |
| value: 72.67282695064966 |
| - type: manhattan_f1 |
| value: 66.67537215983286 |
| - type: manhattan_precision |
| value: 66.00310237849017 |
| - type: manhattan_recall |
| value: 67.36147757255937 |
| - type: max_accuracy |
| value: 85.65893783155511 |
| - type: max_ap |
| value: 73.10385271517778 |
| - type: max_f1 |
| value: 66.94853829427399 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.69096130709822 |
| - type: cos_sim_ap |
| value: 85.30326978668063 |
| - type: cos_sim_f1 |
| value: 77.747088683189 |
| - type: cos_sim_precision |
| value: 75.4491451753115 |
| - type: cos_sim_recall |
| value: 80.189405605174 |
| - type: dot_accuracy |
| value: 88.43870066363954 |
| - type: dot_ap |
| value: 84.62999949222983 |
| - type: dot_f1 |
| value: 77.3074661963551 |
| - type: dot_precision |
| value: 73.93871239808828 |
| - type: dot_recall |
| value: 80.99784416384355 |
| - type: euclidean_accuracy |
| value: 88.70066363953894 |
| - type: euclidean_ap |
| value: 85.34184508966621 |
| - type: euclidean_f1 |
| value: 77.76871756856931 |
| - type: euclidean_precision |
| value: 74.97855917667239 |
| - type: euclidean_recall |
| value: 80.77456113335386 |
| - type: manhattan_accuracy |
| value: 88.68319944114566 |
| - type: manhattan_ap |
| value: 85.3026464242333 |
| - type: manhattan_f1 |
| value: 77.66561049296294 |
| - type: manhattan_precision |
| value: 74.4665818849795 |
| - type: manhattan_recall |
| value: 81.15183246073299 |
| - type: max_accuracy |
| value: 88.70066363953894 |
| - type: max_ap |
| value: 85.34184508966621 |
| - type: max_f1 |
| value: 77.76871756856931 |
| --- |
| <h1 align="center">GIST small Embedding v0</h1> |
|
|
| *GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* |
|
|
| The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task). |
|
|
| The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions. |
|
|
| Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829) |
|
|
|
|
| # Data |
|
|
| The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available: |
|
|
| - Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets) |
| - Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb |
|
|
| The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`). |
|
|
| The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741). |
|
|
| The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some. |
|
|
| The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance. |
|
|
| # Usage |
|
|
| The model can be easily loaded using the Sentence Transformers library. |
|
|
| ```Python |
| import torch.nn.functional as F |
| from sentence_transformers import SentenceTransformer |
| |
| revision = None # Replace with the specific revision to ensure reproducibility if the model is updated. |
| |
| model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision) |
| |
| 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 = model.encode(texts, convert_to_tensor=True) |
| |
| # Compute cosine-similarity for each pair of sentences |
| scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1) |
| |
| print(scores.cpu().numpy()) |
| ``` |
|
|
| # Training Parameters |
|
|
| Below are the training parameters used to fine-tune the model: |
|
|
| ``` |
| Epochs = 40 |
| Warmup ratio = 0.1 |
| Learning rate = 5e-6 |
| Batch size = 16 |
| Checkpoint step = 102000 |
| Contrastive loss temperature = 0.01 |
| ``` |
|
|
|
|
| # Evaluation |
|
|
| The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite. |
|
|
|
|
| # Citation |
|
|
| Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗 |
|
|
| ``` |
| @article{solatorio2024gistembed, |
| title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, |
| author={Aivin V. Solatorio}, |
| journal={arXiv preprint arXiv:2402.16829}, |
| year={2024}, |
| URL={https://arxiv.org/abs/2402.16829} |
| eprint={2402.16829}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
|
|
| # Acknowledgements |
|
|
| This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444. |
|
|
| The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. |
