| --- |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - feature-extraction |
| - sentence-similarity |
| - mteb |
| - transformers.js |
| model-index: |
| - name: modernbert-embed-large |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 76.7910447761194 |
| - type: ap |
| value: 39.79562424828666 |
| - type: f1 |
| value: 70.69575548517653 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 94.19505000000001 |
| - type: ap |
| value: 91.75071069741077 |
| - type: f1 |
| value: 94.19151001437368 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 47.664 |
| - type: f1 |
| value: 46.932904638602466 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| metrics: |
| - type: map_at_1 |
| value: 25.178 |
| - type: map_at_10 |
| value: 41.088 |
| - type: map_at_100 |
| value: 42.143 |
| - type: map_at_1000 |
| value: 42.152 |
| - type: map_at_20 |
| value: 41.946 |
| - type: map_at_3 |
| value: 36.048 |
| - type: map_at_5 |
| value: 38.619 |
| - type: mrr_at_1 |
| value: 25.533 |
| - type: mrr_at_10 |
| value: 41.238 |
| - type: mrr_at_100 |
| value: 42.293 |
| - type: mrr_at_1000 |
| value: 42.302 |
| - type: mrr_at_20 |
| value: 42.096000000000004 |
| - type: mrr_at_3 |
| value: 36.260999999999996 |
| - type: mrr_at_5 |
| value: 38.797 |
| - type: ndcg_at_1 |
| value: 25.178 |
| - type: ndcg_at_10 |
| value: 50.352 |
| - type: ndcg_at_100 |
| value: 54.583000000000006 |
| - type: ndcg_at_1000 |
| value: 54.797 |
| - type: ndcg_at_20 |
| value: 53.36 |
| - type: ndcg_at_3 |
| value: 39.781 |
| - type: ndcg_at_5 |
| value: 44.412 |
| - type: precision_at_1 |
| value: 25.178 |
| - type: precision_at_10 |
| value: 8.016 |
| - type: precision_at_100 |
| value: 0.98 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_20 |
| value: 4.591 |
| - type: precision_at_3 |
| value: 16.88 |
| - type: precision_at_5 |
| value: 12.376 |
| - type: recall_at_1 |
| value: 25.178 |
| - type: recall_at_10 |
| value: 80.156 |
| - type: recall_at_100 |
| value: 98.009 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_20 |
| value: 91.821 |
| - type: recall_at_3 |
| value: 50.63999999999999 |
| - type: recall_at_5 |
| value: 61.878 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 47.800803622189214 |
| - type: v_measures |
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| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 39.46617889287484 |
| - type: v_measures |
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| - task: |
| type: Reranking |
| dataset: |
| type: None |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 63.213756562834234 |
| - type: mrr |
| value: 76.76493866244557 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.90977143141957 |
| - type: cos_sim_spearman |
| value: 87.47729443431557 |
| - type: euclidean_pearson |
| value: 86.45663786393041 |
| - type: euclidean_spearman |
| value: 86.31461733951959 |
| - type: manhattan_pearson |
| value: 85.94280510342506 |
| - type: manhattan_spearman |
| value: 85.61158927235539 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 86.17857142857144 |
| - type: f1 |
| value: 86.14192410600847 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 39.466770895318334 |
| - type: v_measures |
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| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 34.668146108668715 |
| - type: v_measures |
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| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: f46a197baaae43b4f621051089b82a364682dfeb |
| metrics: |
| - type: map_at_1 |
| value: 34.394999999999996 |
| - type: map_at_10 |
| value: 45.882 |
| - type: map_at_100 |
| value: 47.4 |
| - type: map_at_1000 |
| value: 47.509 |
| - type: map_at_20 |
| value: 46.822 |
| - type: map_at_3 |
| value: 42.408 |
| - type: map_at_5 |
| value: 44.586 |
| - type: mrr_at_1 |
| value: 41.202 |
| - type: mrr_at_10 |
| value: 51.134 |
| - type: mrr_at_100 |
| value: 51.943 |
| - type: mrr_at_1000 |
| value: 51.986 |
| - type: mrr_at_20 |
| value: 51.717 |
| - type: mrr_at_3 |
| value: 48.784 |
| - type: mrr_at_5 |
| value: 50.336000000000006 |
| - type: ndcg_at_1 |
| value: 41.202 |
| - type: ndcg_at_10 |
| value: 51.842999999999996 |
| - type: ndcg_at_100 |
| value: 57.177 |
| - type: ndcg_at_1000 |
| value: 58.89 |
| - type: ndcg_at_20 |
| value: 54.357 |
| - type: ndcg_at_3 |
| value: 47.286 |
| - type: ndcg_at_5 |
| value: 49.829 |
| - type: precision_at_1 |
| value: 41.202 |
| - type: precision_at_10 |
| value: 9.585 |
| - type: precision_at_100 |
| value: 1.5150000000000001 |
| - type: precision_at_1000 |
| value: 0.194 |
| - type: precision_at_20 |
| value: 5.808 |
| - type: precision_at_3 |
| value: 22.508 |
| - type: precision_at_5 |
| value: 16.366 |
| - type: recall_at_1 |
| value: 34.394999999999996 |
| - type: recall_at_10 |
| value: 63.17 |
| - type: recall_at_100 |
| value: 84.867 |
| - type: recall_at_1000 |
| value: 95.733 |
| - type: recall_at_20 |
| value: 72.011 |
| - type: recall_at_3 |
| value: 49.966 |
| - type: recall_at_5 |
| value: 56.802 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
| metrics: |
| - type: map_at_1 |
| value: 34.324 |
| - type: map_at_10 |
| value: 46.123 |
| - type: map_at_100 |
| value: 47.455999999999996 |
| - type: map_at_1000 |
| value: 47.576 |
| - type: map_at_20 |
| value: 46.851 |
| - type: map_at_3 |
| value: 42.945 |
| - type: map_at_5 |
| value: 44.751000000000005 |
| - type: mrr_at_1 |
| value: 43.248 |
| - type: mrr_at_10 |
| value: 52.544000000000004 |
| - type: mrr_at_100 |
| value: 53.102000000000004 |
| - type: mrr_at_1000 |
| value: 53.138 |
| - type: mrr_at_20 |
| value: 52.861000000000004 |
| - type: mrr_at_3 |
| value: 50.37199999999999 |
| - type: mrr_at_5 |
| value: 51.712 |
| - type: ndcg_at_1 |
| value: 43.248 |
| - type: ndcg_at_10 |
| value: 52.235 |
| - type: ndcg_at_100 |
| value: 56.355 |
| - type: ndcg_at_1000 |
| value: 58.053 |
| - type: ndcg_at_20 |
| value: 53.849000000000004 |
| - type: ndcg_at_3 |
| value: 48.208 |
| - type: ndcg_at_5 |
| value: 50.134 |
| - type: precision_at_1 |
| value: 43.248 |
| - type: precision_at_10 |
| value: 9.917 |
| - type: precision_at_100 |
| value: 1.532 |
| - type: precision_at_1000 |
| value: 0.198 |
| - type: precision_at_20 |
| value: 5.779999999999999 |
| - type: precision_at_3 |
| value: 23.588 |
| - type: precision_at_5 |
| value: 16.586000000000002 |
| - type: recall_at_1 |
| value: 34.324 |
| - type: recall_at_10 |
| value: 62.56 |
| - type: recall_at_100 |
| value: 79.745 |
| - type: recall_at_1000 |
| value: 90.082 |
| - type: recall_at_20 |
| value: 68.367 |
| - type: recall_at_3 |
| value: 50.171 |
| - type: recall_at_5 |
| value: 55.889 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
| metrics: |
| - type: map_at_1 |
| value: 44.143 |
| - type: map_at_10 |
| value: 56.53 |
| - type: map_at_100 |
| value: 57.48799999999999 |
| - type: map_at_1000 |
| value: 57.535000000000004 |
| - type: map_at_20 |
| value: 57.152 |
| - type: map_at_3 |
| value: 53.382 |
| - type: map_at_5 |
| value: 55.156000000000006 |
| - type: mrr_at_1 |
| value: 50.09400000000001 |
| - type: mrr_at_10 |
| value: 59.819 |
| - type: mrr_at_100 |
| value: 60.431000000000004 |
| - type: mrr_at_1000 |
| value: 60.455000000000005 |
| - type: mrr_at_20 |
| value: 60.251999999999995 |
| - type: mrr_at_3 |
| value: 57.544 |
| - type: mrr_at_5 |
| value: 58.904999999999994 |
| - type: ndcg_at_1 |
| value: 50.09400000000001 |
| - type: ndcg_at_10 |
| value: 62.141999999999996 |
| - type: ndcg_at_100 |
| value: 65.755 |
| - type: ndcg_at_1000 |
| value: 66.674 |
| - type: ndcg_at_20 |
| value: 63.92400000000001 |
| - type: ndcg_at_3 |
| value: 56.986000000000004 |
| - type: ndcg_at_5 |
| value: 59.519999999999996 |
| - type: precision_at_1 |
| value: 50.09400000000001 |
| - type: precision_at_10 |
| value: 9.743 |
| - type: precision_at_100 |
| value: 1.246 |
| - type: precision_at_1000 |
| value: 0.136 |
| - type: precision_at_20 |
| value: 5.439 |
| - type: precision_at_3 |
| value: 25.119999999999997 |
| - type: precision_at_5 |
| value: 17.052999999999997 |
| - type: recall_at_1 |
| value: 44.143 |
| - type: recall_at_10 |
| value: 75.372 |
| - type: recall_at_100 |
| value: 90.602 |
| - type: recall_at_1000 |
| value: 97.043 |
| - type: recall_at_20 |
| value: 81.83500000000001 |
| - type: recall_at_3 |
| value: 61.607 |
| - type: recall_at_5 |
| value: 67.755 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: 5003b3064772da1887988e05400cf3806fe491f2 |
| metrics: |
| - type: map_at_1 |
| value: 26.621 |
| - type: map_at_10 |
| value: 35.865 |
| - type: map_at_100 |
| value: 36.93 |
| - type: map_at_1000 |
| value: 37.008 |
| - type: map_at_20 |
| value: 36.509 |
| - type: map_at_3 |
| value: 33.532000000000004 |
| - type: map_at_5 |
| value: 34.745 |
| - type: mrr_at_1 |
| value: 28.588 |
| - type: mrr_at_10 |
| value: 37.828 |
| - type: mrr_at_100 |
| value: 38.779 |
| - type: mrr_at_1000 |
| value: 38.834 |
| - type: mrr_at_20 |
| value: 38.419 |
| - type: mrr_at_3 |
| value: 35.725 |
| - type: mrr_at_5 |
| value: 36.803999999999995 |
| - type: ndcg_at_1 |
| value: 28.588 |
| - type: ndcg_at_10 |
| value: 40.983999999999995 |
| - type: ndcg_at_100 |
| value: 46.117000000000004 |
| - type: ndcg_at_1000 |
| value: 47.959 |
| - type: ndcg_at_20 |
| value: 43.22 |
| - type: ndcg_at_3 |
| value: 36.455 |
| - type: ndcg_at_5 |
| value: 38.393 |
| - type: precision_at_1 |
| value: 28.588 |
| - type: precision_at_10 |
| value: 6.282 |
| - type: precision_at_100 |
| value: 0.927 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_20 |
| value: 3.689 |
| - type: precision_at_3 |
| value: 15.744 |
| - type: precision_at_5 |
| value: 10.644 |
| - type: recall_at_1 |
| value: 26.621 |
| - type: recall_at_10 |
| value: 54.80199999999999 |
| - type: recall_at_100 |
| value: 78.171 |
| - type: recall_at_1000 |
| value: 91.786 |
| - type: recall_at_20 |
| value: 63.195 |
| - type: recall_at_3 |
| value: 42.164 |
| - type: recall_at_5 |
| value: 46.936 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| metrics: |
| - type: map_at_1 |
| value: 18.619 |
| - type: map_at_10 |
| value: 27.577 |
| - type: map_at_100 |
| value: 28.717 |
| - type: map_at_1000 |
| value: 28.835 |
| - type: map_at_20 |
| value: 28.18 |
| - type: map_at_3 |
| value: 24.462999999999997 |
| - type: map_at_5 |
| value: 26.230999999999998 |
| - type: mrr_at_1 |
| value: 22.886 |
| - type: mrr_at_10 |
| value: 32.089 |
| - type: mrr_at_100 |
| value: 32.998 |
| - type: mrr_at_1000 |
| value: 33.06 |
| - type: mrr_at_20 |
| value: 32.633 |
| - type: mrr_at_3 |
| value: 29.125 |
| - type: mrr_at_5 |
| value: 30.792 |
| - type: ndcg_at_1 |
| value: 22.886 |
| - type: ndcg_at_10 |
| value: 33.343 |
| - type: ndcg_at_100 |
| value: 38.735 |
| - type: ndcg_at_1000 |
| value: 41.393 |
| - type: ndcg_at_20 |
| value: 35.455 |
| - type: ndcg_at_3 |
| value: 27.575 |
| - type: ndcg_at_5 |
| value: 30.361 |
| - type: precision_at_1 |
| value: 22.886 |
| - type: precision_at_10 |
| value: 6.256 |
| - type: precision_at_100 |
| value: 1.03 |
| - type: precision_at_1000 |
| value: 0.13899999999999998 |
| - type: precision_at_20 |
| value: 3.7130000000000005 |
| - type: precision_at_3 |
| value: 13.267000000000001 |
| - type: precision_at_5 |
| value: 9.851 |
| - type: recall_at_1 |
| value: 18.619 |
| - type: recall_at_10 |
| value: 46.478 |
| - type: recall_at_100 |
| value: 69.614 |
| - type: recall_at_1000 |
| value: 88.331 |
| - type: recall_at_20 |
| value: 54.254000000000005 |
| - type: recall_at_3 |
| value: 30.897999999999996 |
| - type: recall_at_5 |
| value: 37.785000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
| metrics: |
| - type: map_at_1 |
| value: 31.087999999999997 |
| - type: map_at_10 |
| value: 41.668 |
| - type: map_at_100 |
| value: 42.983 |
| - type: map_at_1000 |
| value: 43.081 |
| - type: map_at_20 |
| value: 42.373 |
| - type: map_at_3 |
| value: 38.481 |
| - type: map_at_5 |
| value: 40.196 |
| - type: mrr_at_1 |
| value: 37.824999999999996 |
| - type: mrr_at_10 |
| value: 47.471999999999994 |
| - type: mrr_at_100 |
| value: 48.311 |
| - type: mrr_at_1000 |
| value: 48.351 |
| - type: mrr_at_20 |
| value: 47.981 |
| - type: mrr_at_3 |
| value: 45.074999999999996 |
| - type: mrr_at_5 |
| value: 46.37 |
| - type: ndcg_at_1 |
| value: 37.824999999999996 |
| - type: ndcg_at_10 |
| value: 47.63 |
| - type: ndcg_at_100 |
| value: 52.979 |
| - type: ndcg_at_1000 |
| value: 54.771 |
| - type: ndcg_at_20 |
| value: 49.733 |
| - type: ndcg_at_3 |
| value: 42.657000000000004 |
| - type: ndcg_at_5 |
| value: 44.878 |
| - type: precision_at_1 |
| value: 37.824999999999996 |
| - type: precision_at_10 |
| value: 8.527 |
| - type: precision_at_100 |
| value: 1.303 |
| - type: precision_at_1000 |
| value: 0.16199999999999998 |
| - type: precision_at_20 |
| value: 4.966 |
| - type: precision_at_3 |
| value: 19.955000000000002 |
| - type: precision_at_5 |
| value: 14.033000000000001 |
| - type: recall_at_1 |
| value: 31.087999999999997 |
| - type: recall_at_10 |
| value: 59.585 |
| - type: recall_at_100 |
| value: 81.625 |
| - type: recall_at_1000 |
| value: 93.297 |
| - type: recall_at_20 |
| value: 66.813 |
| - type: recall_at_3 |
| value: 45.492 |
| - type: recall_at_5 |
| value: 51.283 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
| metrics: |
| - type: map_at_1 |
| value: 28.756999999999998 |
| - type: map_at_10 |
| value: 40.275 |
| - type: map_at_100 |
| value: 41.655 |
| - type: map_at_1000 |
| value: 41.752 |
| - type: map_at_20 |
| value: 41.118 |
| - type: map_at_3 |
| value: 36.815 |
| - type: map_at_5 |
| value: 38.662 |
| - type: mrr_at_1 |
| value: 35.502 |
| - type: mrr_at_10 |
| value: 45.818 |
| - type: mrr_at_100 |
| value: 46.704 |
| - type: mrr_at_1000 |
| value: 46.745999999999995 |
| - type: mrr_at_20 |
| value: 46.387 |
| - type: mrr_at_3 |
| value: 43.322 |
| - type: mrr_at_5 |
| value: 44.675 |
| - type: ndcg_at_1 |
| value: 35.502 |
| - type: ndcg_at_10 |
| value: 46.658 |
| - type: ndcg_at_100 |
| value: 52.097 |
| - type: ndcg_at_1000 |
| value: 53.928 |
| - type: ndcg_at_20 |
| value: 49.134 |
| - type: ndcg_at_3 |
| value: 41.234 |
| - type: ndcg_at_5 |
| value: 43.579 |
| - type: precision_at_1 |
| value: 35.502 |
| - type: precision_at_10 |
| value: 8.652999999999999 |
| - type: precision_at_100 |
| value: 1.306 |
| - type: precision_at_1000 |
| value: 0.163 |
| - type: precision_at_20 |
| value: 5.086 |
| - type: precision_at_3 |
| value: 19.825 |
| - type: precision_at_5 |
| value: 13.995 |
| - type: recall_at_1 |
| value: 28.756999999999998 |
| - type: recall_at_10 |
| value: 59.79 |
| - type: recall_at_100 |
| value: 82.597 |
| - type: recall_at_1000 |
| value: 94.663 |
| - type: recall_at_20 |
| value: 68.74 |
| - type: recall_at_3 |
| value: 44.736 |
| - type: recall_at_5 |
| value: 51.047 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| metrics: |
| - type: map_at_1 |
| value: 28.592666666666673 |
| - type: map_at_10 |
| value: 38.50391666666667 |
| - type: map_at_100 |
| value: 39.719166666666666 |
| - type: map_at_1000 |
| value: 39.82683333333334 |
| - type: map_at_20 |
| value: 39.18608333333333 |
| - type: map_at_3 |
| value: 35.561833333333325 |
| - type: map_at_5 |
| value: 37.181000000000004 |
| - type: mrr_at_1 |
| value: 33.67625 |
| - type: mrr_at_10 |
| value: 42.727 |
| - type: mrr_at_100 |
| value: 43.55041666666667 |
| - type: mrr_at_1000 |
| value: 43.60058333333334 |
| - type: mrr_at_20 |
| value: 43.21508333333333 |
| - type: mrr_at_3 |
| value: 40.32983333333334 |
| - type: mrr_at_5 |
| value: 41.699333333333335 |
| - type: ndcg_at_1 |
| value: 33.67625 |
| - type: ndcg_at_10 |
| value: 44.064416666666666 |
| - type: ndcg_at_100 |
| value: 49.085 |
| - type: ndcg_at_1000 |
| value: 51.09325 |
| - type: ndcg_at_20 |
| value: 46.07716666666666 |
| - type: ndcg_at_3 |
| value: 39.22225 |
| - type: ndcg_at_5 |
| value: 41.47508333333333 |
| - type: precision_at_1 |
| value: 33.67625 |
| - type: precision_at_10 |
| value: 7.689916666666667 |
| - type: precision_at_100 |
| value: 1.1995833333333334 |
| - type: precision_at_1000 |
| value: 0.15541666666666665 |
| - type: precision_at_20 |
| value: 4.515500000000001 |
| - type: precision_at_3 |
| value: 18.07241666666667 |
| - type: precision_at_5 |
| value: 12.732833333333332 |
| - type: recall_at_1 |
| value: 28.592666666666673 |
| - type: recall_at_10 |
| value: 56.15700000000001 |
| - type: recall_at_100 |
| value: 77.97075000000001 |
| - type: recall_at_1000 |
| value: 91.73058333333333 |
| - type: recall_at_20 |
| value: 63.49649999999999 |
| - type: recall_at_3 |
| value: 42.612833333333334 |
| - type: recall_at_5 |
| value: 48.44591666666667 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
| metrics: |
| - type: map_at_1 |
| value: 25.381999999999998 |
| - type: map_at_10 |
| value: 33.311 |
| - type: map_at_100 |
| value: 34.171 |
| - type: map_at_1000 |
| value: 34.254 |
| - type: map_at_20 |
| value: 33.732 |
| - type: map_at_3 |
| value: 31.025999999999996 |
| - type: map_at_5 |
| value: 32.253 |
| - type: mrr_at_1 |
| value: 28.221 |
| - type: mrr_at_10 |
| value: 36.132999999999996 |
| - type: mrr_at_100 |
| value: 36.848 |
| - type: mrr_at_1000 |
| value: 36.902 |
| - type: mrr_at_20 |
| value: 36.497 |
| - type: mrr_at_3 |
| value: 33.947 |
| - type: mrr_at_5 |
| value: 35.174 |
| - type: ndcg_at_1 |
| value: 28.221 |
| - type: ndcg_at_10 |
| value: 37.882 |
| - type: ndcg_at_100 |
| value: 42.283 |
| - type: ndcg_at_1000 |
| value: 44.458 |
| - type: ndcg_at_20 |
| value: 39.268 |
| - type: ndcg_at_3 |
| value: 33.611999999999995 |
| - type: ndcg_at_5 |
| value: 35.583 |
| - type: precision_at_1 |
| value: 28.221 |
| - type: precision_at_10 |
| value: 6.043 |
| - type: precision_at_100 |
| value: 0.8909999999999999 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_20 |
| value: 3.405 |
| - type: precision_at_3 |
| value: 14.673 |
| - type: precision_at_5 |
| value: 10.152999999999999 |
| - type: recall_at_1 |
| value: 25.381999999999998 |
| - type: recall_at_10 |
| value: 48.980000000000004 |
| - type: recall_at_100 |
| value: 69.625 |
| - type: recall_at_1000 |
| value: 85.946 |
| - type: recall_at_20 |
| value: 54.041 |
| - type: recall_at_3 |
| value: 37.077 |
| - type: recall_at_5 |
| value: 42.097 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: 46989137a86843e03a6195de44b09deda022eec7 |
| metrics: |
| - type: map_at_1 |
| value: 18.186 |
| - type: map_at_10 |
| value: 26.450000000000003 |
| - type: map_at_100 |
| value: 27.62 |
| - type: map_at_1000 |
| value: 27.746 |
| - type: map_at_20 |
| value: 27.105 |
| - type: map_at_3 |
| value: 23.982999999999997 |
| - type: map_at_5 |
| value: 25.306 |
| - type: mrr_at_1 |
| value: 22.092 |
| - type: mrr_at_10 |
| value: 30.326999999999998 |
| - type: mrr_at_100 |
| value: 31.322 |
| - type: mrr_at_1000 |
| value: 31.394 |
| - type: mrr_at_20 |
| value: 30.923000000000002 |
| - type: mrr_at_3 |
| value: 28.063 |
| - type: mrr_at_5 |
| value: 29.284 |
| - type: ndcg_at_1 |
| value: 22.092 |
| - type: ndcg_at_10 |
| value: 31.418000000000003 |
| - type: ndcg_at_100 |
| value: 36.924 |
| - type: ndcg_at_1000 |
| value: 39.645 |
| - type: ndcg_at_20 |
| value: 33.597 |
| - type: ndcg_at_3 |
| value: 27.045 |
| - type: ndcg_at_5 |
| value: 28.971999999999998 |
| - type: precision_at_1 |
| value: 22.092 |
| - type: precision_at_10 |
| value: 5.785 |
| - type: precision_at_100 |
| value: 0.989 |
| - type: precision_at_1000 |
| value: 0.13999999999999999 |
| - type: precision_at_20 |
| value: 3.517 |
| - type: precision_at_3 |
| value: 12.985 |
| - type: precision_at_5 |
| value: 9.291 |
| - type: recall_at_1 |
| value: 18.186 |
| - type: recall_at_10 |
| value: 42.443 |
| - type: recall_at_100 |
| value: 66.964 |
| - type: recall_at_1000 |
| value: 86.005 |
| - type: recall_at_20 |
| value: 50.52799999999999 |
| - type: recall_at_3 |
| value: 30.095 |
| - type: recall_at_5 |
| value: 35.148 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
| metrics: |
| - type: map_at_1 |
| value: 31.049 |
| - type: map_at_10 |
| value: 40.217000000000006 |
| - type: map_at_100 |
| value: 41.345 |
| - type: map_at_1000 |
| value: 41.447 |
| - type: map_at_20 |
| value: 40.818 |
| - type: map_at_3 |
| value: 37.413999999999994 |
| - type: map_at_5 |
| value: 39.001000000000005 |
| - type: mrr_at_1 |
| value: 36.474000000000004 |
| - type: mrr_at_10 |
| value: 44.655 |
| - type: mrr_at_100 |
| value: 45.399 |
| - type: mrr_at_1000 |
| value: 45.454 |
| - type: mrr_at_20 |
| value: 45.011 |
| - type: mrr_at_3 |
| value: 42.226 |
| - type: mrr_at_5 |
| value: 43.653999999999996 |
| - type: ndcg_at_1 |
| value: 36.474000000000004 |
| - type: ndcg_at_10 |
| value: 45.509 |
| - type: ndcg_at_100 |
| value: 50.571 |
| - type: ndcg_at_1000 |
| value: 52.605999999999995 |
| - type: ndcg_at_20 |
| value: 47.275 |
| - type: ndcg_at_3 |
| value: 40.766000000000005 |
| - type: ndcg_at_5 |
| value: 42.979 |
| - type: precision_at_1 |
| value: 36.474000000000004 |
| - type: precision_at_10 |
| value: 7.509 |
| - type: precision_at_100 |
| value: 1.1320000000000001 |
| - type: precision_at_1000 |
| value: 0.14100000000000001 |
| - type: precision_at_20 |
| value: 4.3 |
| - type: precision_at_3 |
| value: 18.315 |
| - type: precision_at_5 |
| value: 12.705 |
| - type: recall_at_1 |
| value: 31.049 |
| - type: recall_at_10 |
| value: 57.135999999999996 |
| - type: recall_at_100 |
| value: 79.196 |
| - type: recall_at_1000 |
| value: 93.002 |
| - type: recall_at_20 |
| value: 63.416 |
| - type: recall_at_3 |
| value: 43.893 |
| - type: recall_at_5 |
| value: 49.675999999999995 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
| metrics: |
| - type: map_at_1 |
| value: 27.929 |
| - type: map_at_10 |
| value: 36.897000000000006 |
| - type: map_at_100 |
| value: 38.635000000000005 |
| - type: map_at_1000 |
| value: 38.842 |
| - type: map_at_20 |
| value: 37.814 |
| - type: map_at_3 |
| value: 33.522 |
| - type: map_at_5 |
| value: 35.128 |
| - type: mrr_at_1 |
| value: 33.399 |
| - type: mrr_at_10 |
| value: 41.817 |
| - type: mrr_at_100 |
| value: 42.797000000000004 |
| - type: mrr_at_1000 |
| value: 42.842999999999996 |
| - type: mrr_at_20 |
| value: 42.381 |
| - type: mrr_at_3 |
| value: 38.999 |
| - type: mrr_at_5 |
| value: 40.57 |
| - type: ndcg_at_1 |
| value: 33.399 |
| - type: ndcg_at_10 |
| value: 43.134 |
| - type: ndcg_at_100 |
| value: 49.009 |
| - type: ndcg_at_1000 |
| value: 51.199 |
| - type: ndcg_at_20 |
| value: 45.391999999999996 |
| - type: ndcg_at_3 |
| value: 37.645 |
| - type: ndcg_at_5 |
| value: 39.940999999999995 |
| - type: precision_at_1 |
| value: 33.399 |
| - type: precision_at_10 |
| value: 8.36 |
| - type: precision_at_100 |
| value: 1.646 |
| - type: precision_at_1000 |
| value: 0.244 |
| - type: precision_at_20 |
| value: 5.257 |
| - type: precision_at_3 |
| value: 17.457 |
| - type: precision_at_5 |
| value: 12.727 |
| - type: recall_at_1 |
| value: 27.929 |
| - type: recall_at_10 |
| value: 54.822 |
| - type: recall_at_100 |
| value: 80.63900000000001 |
| - type: recall_at_1000 |
| value: 94.382 |
| - type: recall_at_20 |
| value: 63.432 |
| - type: recall_at_3 |
| value: 39.291 |
| - type: recall_at_5 |
| value: 45.385999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 22.619 |
| - type: map_at_10 |
| value: 31.252000000000002 |
| - type: map_at_100 |
| value: 32.23 |
| - type: map_at_1000 |
| value: 32.336999999999996 |
| - type: map_at_20 |
| value: 31.758999999999997 |
| - type: map_at_3 |
| value: 28.771 |
| - type: map_at_5 |
| value: 30.157 |
| - type: mrr_at_1 |
| value: 24.584 |
| - type: mrr_at_10 |
| value: 33.088 |
| - type: mrr_at_100 |
| value: 33.971000000000004 |
| - type: mrr_at_1000 |
| value: 34.044000000000004 |
| - type: mrr_at_20 |
| value: 33.519 |
| - type: mrr_at_3 |
| value: 30.775999999999996 |
| - type: mrr_at_5 |
| value: 32.116 |
| - type: ndcg_at_1 |
| value: 24.584 |
| - type: ndcg_at_10 |
| value: 35.995 |
| - type: ndcg_at_100 |
| value: 41.018 |
| - type: ndcg_at_1000 |
| value: 43.543 |
| - type: ndcg_at_20 |
| value: 37.722 |
| - type: ndcg_at_3 |
| value: 31.197999999999997 |
| - type: ndcg_at_5 |
| value: 33.532000000000004 |
| - type: precision_at_1 |
| value: 24.584 |
| - type: precision_at_10 |
| value: 5.619 |
| - type: precision_at_100 |
| value: 0.878 |
| - type: precision_at_1000 |
| value: 0.121 |
| - type: precision_at_20 |
| value: 3.2259999999999995 |
| - type: precision_at_3 |
| value: 13.431999999999999 |
| - type: precision_at_5 |
| value: 9.39 |
| - type: recall_at_1 |
| value: 22.619 |
| - type: recall_at_10 |
| value: 48.746 |
| - type: recall_at_100 |
| value: 72.004 |
| - type: recall_at_1000 |
| value: 90.497 |
| - type: recall_at_20 |
| value: 55.326 |
| - type: recall_at_3 |
| value: 35.964 |
| - type: recall_at_5 |
| value: 41.547 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
| metrics: |
| - type: map_at_1 |
| value: 16.493 |
| - type: map_at_10 |
| value: 28.988999999999997 |
| - type: map_at_100 |
| value: 30.964999999999996 |
| - type: map_at_1000 |
| value: 31.142999999999997 |
| - type: map_at_20 |
| value: 30.103 |
| - type: map_at_3 |
| value: 24.006 |
| - type: map_at_5 |
| value: 26.535999999999998 |
| - type: mrr_at_1 |
| value: 37.915 |
| - type: mrr_at_10 |
| value: 50.736000000000004 |
| - type: mrr_at_100 |
| value: 51.361999999999995 |
| - type: mrr_at_1000 |
| value: 51.388999999999996 |
| - type: mrr_at_20 |
| value: 51.148 |
| - type: mrr_at_3 |
| value: 47.589999999999996 |
| - type: mrr_at_5 |
| value: 49.55 |
| - type: ndcg_at_1 |
| value: 37.915 |
| - type: ndcg_at_10 |
| value: 39.139 |
| - type: ndcg_at_100 |
| value: 45.993 |
| - type: ndcg_at_1000 |
| value: 48.861 |
| - type: ndcg_at_20 |
| value: 41.923 |
| - type: ndcg_at_3 |
| value: 32.491 |
| - type: ndcg_at_5 |
| value: 34.775 |
| - type: precision_at_1 |
| value: 37.915 |
| - type: precision_at_10 |
| value: 12.293 |
| - type: precision_at_100 |
| value: 1.9709999999999999 |
| - type: precision_at_1000 |
| value: 0.251 |
| - type: precision_at_20 |
| value: 7.3389999999999995 |
| - type: precision_at_3 |
| value: 24.407999999999998 |
| - type: precision_at_5 |
| value: 18.775 |
| - type: recall_at_1 |
| value: 16.493 |
| - type: recall_at_10 |
| value: 45.904 |
| - type: recall_at_100 |
| value: 69.037 |
| - type: recall_at_1000 |
| value: 84.815 |
| - type: recall_at_20 |
| value: 53.657 |
| - type: recall_at_3 |
| value: 29.629 |
| - type: recall_at_5 |
| value: 36.325 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
| metrics: |
| - type: map_at_1 |
| value: 9.180000000000001 |
| - type: map_at_10 |
| value: 20.714 |
| - type: map_at_100 |
| value: 28.801 |
| - type: map_at_1000 |
| value: 30.43 |
| - type: map_at_20 |
| value: 23.673 |
| - type: map_at_3 |
| value: 14.551 |
| - type: map_at_5 |
| value: 17.067 |
| - type: mrr_at_1 |
| value: 68.25 |
| - type: mrr_at_10 |
| value: 75.83 |
| - type: mrr_at_100 |
| value: 76.225 |
| - type: mrr_at_1000 |
| value: 76.232 |
| - type: mrr_at_20 |
| value: 76.14 |
| - type: mrr_at_3 |
| value: 74.375 |
| - type: mrr_at_5 |
| value: 75.225 |
| - type: ndcg_at_1 |
| value: 56.99999999999999 |
| - type: ndcg_at_10 |
| value: 43.071 |
| - type: ndcg_at_100 |
| value: 47.189 |
| - type: ndcg_at_1000 |
| value: 54.125 |
| - type: ndcg_at_20 |
| value: 42.111 |
| - type: ndcg_at_3 |
| value: 47.67 |
| - type: ndcg_at_5 |
| value: 44.983000000000004 |
| - type: precision_at_1 |
| value: 68.25 |
| - type: precision_at_10 |
| value: 34.599999999999994 |
| - type: precision_at_100 |
| value: 10.8 |
| - type: precision_at_1000 |
| value: 2.12 |
| - type: precision_at_20 |
| value: 25.7 |
| - type: precision_at_3 |
| value: 51.417 |
| - type: precision_at_5 |
| value: 43.85 |
| - type: recall_at_1 |
| value: 9.180000000000001 |
| - type: recall_at_10 |
| value: 26.212000000000003 |
| - type: recall_at_100 |
| value: 52.443 |
| - type: recall_at_1000 |
| value: 73.939 |
| - type: recall_at_20 |
| value: 33.101 |
| - type: recall_at_3 |
| value: 15.787999999999998 |
| - type: recall_at_5 |
| value: 19.691 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 49.625 |
| - type: f1 |
| value: 44.48944228050152 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
| metrics: |
| - type: map_at_1 |
| value: 76.773 |
| - type: map_at_10 |
| value: 85.175 |
| - type: map_at_100 |
| value: 85.353 |
| - type: map_at_1000 |
| value: 85.36500000000001 |
| - type: map_at_20 |
| value: 85.271 |
| - type: map_at_3 |
| value: 84.261 |
| - type: map_at_5 |
| value: 84.899 |
| - type: mrr_at_1 |
| value: 82.853 |
| - type: mrr_at_10 |
| value: 90.02 |
| - type: mrr_at_100 |
| value: 90.048 |
| - type: mrr_at_1000 |
| value: 90.048 |
| - type: mrr_at_20 |
| value: 90.039 |
| - type: mrr_at_3 |
| value: 89.51599999999999 |
| - type: mrr_at_5 |
| value: 89.92099999999999 |
| - type: ndcg_at_1 |
| value: 82.853 |
| - type: ndcg_at_10 |
| value: 88.75999999999999 |
| - type: ndcg_at_100 |
| value: 89.347 |
| - type: ndcg_at_1000 |
| value: 89.547 |
| - type: ndcg_at_20 |
| value: 88.994 |
| - type: ndcg_at_3 |
| value: 87.481 |
| - type: ndcg_at_5 |
| value: 88.31700000000001 |
| - type: precision_at_1 |
| value: 82.853 |
| - type: precision_at_10 |
| value: 10.519 |
| - type: precision_at_100 |
| value: 1.1039999999999999 |
| - type: precision_at_1000 |
| value: 0.11399999999999999 |
| - type: precision_at_20 |
| value: 5.341 |
| - type: precision_at_3 |
| value: 33.323 |
| - type: precision_at_5 |
| value: 20.596999999999998 |
| - type: recall_at_1 |
| value: 76.773 |
| - type: recall_at_10 |
| value: 94.95700000000001 |
| - type: recall_at_100 |
| value: 97.167 |
| - type: recall_at_1000 |
| value: 98.354 |
| - type: recall_at_20 |
| value: 95.71 |
| - type: recall_at_3 |
| value: 91.47999999999999 |
| - type: recall_at_5 |
| value: 93.658 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
| metrics: |
| - type: map_at_1 |
| value: 21.629 |
| - type: map_at_10 |
| value: 36.394 |
| - type: map_at_100 |
| value: 38.308 |
| - type: map_at_1000 |
| value: 38.478 |
| - type: map_at_20 |
| value: 37.425999999999995 |
| - type: map_at_3 |
| value: 31.971 |
| - type: map_at_5 |
| value: 34.5 |
| - type: mrr_at_1 |
| value: 44.599 |
| - type: mrr_at_10 |
| value: 53.369 |
| - type: mrr_at_100 |
| value: 54.06999999999999 |
| - type: mrr_at_1000 |
| value: 54.114 |
| - type: mrr_at_20 |
| value: 53.754999999999995 |
| - type: mrr_at_3 |
| value: 51.415 |
| - type: mrr_at_5 |
| value: 52.479 |
| - type: ndcg_at_1 |
| value: 44.599 |
| - type: ndcg_at_10 |
| value: 44.425 |
| - type: ndcg_at_100 |
| value: 51.036 |
| - type: ndcg_at_1000 |
| value: 53.806 |
| - type: ndcg_at_20 |
| value: 46.934 |
| - type: ndcg_at_3 |
| value: 41.287 |
| - type: ndcg_at_5 |
| value: 42.143 |
| - type: precision_at_1 |
| value: 44.599 |
| - type: precision_at_10 |
| value: 12.222 |
| - type: precision_at_100 |
| value: 1.91 |
| - type: precision_at_1000 |
| value: 0.24 |
| - type: precision_at_20 |
| value: 7.176 |
| - type: precision_at_3 |
| value: 28.086 |
| - type: precision_at_5 |
| value: 20.369999999999997 |
| - type: recall_at_1 |
| value: 21.629 |
| - type: recall_at_10 |
| value: 51.168 |
| - type: recall_at_100 |
| value: 75.32600000000001 |
| - type: recall_at_1000 |
| value: 91.766 |
| - type: recall_at_20 |
| value: 58.923 |
| - type: recall_at_3 |
| value: 37.364999999999995 |
| - type: recall_at_5 |
| value: 43.322 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
| metrics: |
| - type: map_at_1 |
| value: 42.336 |
| - type: map_at_10 |
| value: 59.602999999999994 |
| - type: map_at_100 |
| value: 60.367000000000004 |
| - type: map_at_1000 |
| value: 60.428000000000004 |
| - type: map_at_20 |
| value: 60.068 |
| - type: map_at_3 |
| value: 56.842000000000006 |
| - type: map_at_5 |
| value: 58.669000000000004 |
| - type: mrr_at_1 |
| value: 84.673 |
| - type: mrr_at_10 |
| value: 88.713 |
| - type: mrr_at_100 |
| value: 88.852 |
| - type: mrr_at_1000 |
| value: 88.857 |
| - type: mrr_at_20 |
| value: 88.806 |
| - type: mrr_at_3 |
| value: 88.202 |
| - type: mrr_at_5 |
| value: 88.522 |
| - type: ndcg_at_1 |
| value: 84.673 |
| - type: ndcg_at_10 |
| value: 68.67 |
| - type: ndcg_at_100 |
| value: 71.277 |
| - type: ndcg_at_1000 |
| value: 72.47 |
| - type: ndcg_at_20 |
| value: 69.797 |
| - type: ndcg_at_3 |
| value: 64.971 |
| - type: ndcg_at_5 |
| value: 67.16 |
| - type: precision_at_1 |
| value: 84.673 |
| - type: precision_at_10 |
| value: 13.66 |
| - type: precision_at_100 |
| value: 1.5699999999999998 |
| - type: precision_at_1000 |
| value: 0.173 |
| - type: precision_at_20 |
| value: 7.19 |
| - type: precision_at_3 |
| value: 40.135 |
| - type: precision_at_5 |
| value: 25.81 |
| - type: recall_at_1 |
| value: 42.336 |
| - type: recall_at_10 |
| value: 68.298 |
| - type: recall_at_100 |
| value: 78.494 |
| - type: recall_at_1000 |
| value: 86.435 |
| - type: recall_at_20 |
| value: 71.904 |
| - type: recall_at_3 |
| value: 60.202999999999996 |
| - type: recall_at_5 |
| value: 64.524 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 89.0388 |
| - type: ap |
| value: 84.768407855227 |
| - type: f1 |
| value: 89.00848365810504 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| metrics: |
| - type: map_at_1 |
| value: 22.676 |
| - type: map_at_10 |
| value: 35.476 |
| - type: map_at_100 |
| value: 36.669000000000004 |
| - type: map_at_1000 |
| value: 36.714999999999996 |
| - type: map_at_20 |
| value: 36.253 |
| - type: map_at_3 |
| value: 31.430000000000003 |
| - type: map_at_5 |
| value: 33.891 |
| - type: mrr_at_1 |
| value: 23.281 |
| - type: mrr_at_10 |
| value: 35.994 |
| - type: mrr_at_100 |
| value: 37.128 |
| - type: mrr_at_1000 |
| value: 37.169000000000004 |
| - type: mrr_at_20 |
| value: 36.735 |
| - type: mrr_at_3 |
| value: 32.025 |
| - type: mrr_at_5 |
| value: 34.43 |
| - type: ndcg_at_1 |
| value: 23.281 |
| - type: ndcg_at_10 |
| value: 42.548 |
| - type: ndcg_at_100 |
| value: 48.138999999999996 |
| - type: ndcg_at_1000 |
| value: 49.26 |
| - type: ndcg_at_20 |
| value: 45.29 |
| - type: ndcg_at_3 |
| value: 34.414 |
| - type: ndcg_at_5 |
| value: 38.775999999999996 |
| - type: precision_at_1 |
| value: 23.281 |
| - type: precision_at_10 |
| value: 6.721000000000001 |
| - type: precision_at_100 |
| value: 0.9490000000000001 |
| - type: precision_at_1000 |
| value: 0.105 |
| - type: precision_at_20 |
| value: 3.93 |
| - type: precision_at_3 |
| value: 14.67 |
| - type: precision_at_5 |
| value: 11.003 |
| - type: recall_at_1 |
| value: 22.676 |
| - type: recall_at_10 |
| value: 64.33 |
| - type: recall_at_100 |
| value: 89.836 |
| - type: recall_at_1000 |
| value: 98.346 |
| - type: recall_at_20 |
| value: 74.958 |
| - type: recall_at_3 |
| value: 42.437000000000005 |
| - type: recall_at_5 |
| value: 52.89 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.26493388052896 |
| - type: f1 |
| value: 93.09322316606121 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 79.26356589147285 |
| - type: f1 |
| value: 62.91191113045691 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 75.4034969737727 |
| - type: f1 |
| value: 73.26712703676112 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 78.55749831876263 |
| - type: f1 |
| value: 78.59077417507389 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 34.39782367001404 |
| - type: v_measures |
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| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 31.630415762081864 |
| - type: v_measures |
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| - task: |
| type: Reranking |
| dataset: |
| type: None |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.989924085485676 |
| - type: mrr |
| value: 31.985114880107695 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| metrics: |
| - type: map_at_1 |
| value: 5.771 |
| - type: map_at_10 |
| value: 13.008000000000001 |
| - type: map_at_100 |
| value: 16.125999999999998 |
| - type: map_at_1000 |
| value: 17.482 |
| - type: map_at_20 |
| value: 14.324 |
| - type: map_at_3 |
| value: 9.69 |
| - type: map_at_5 |
| value: 11.174000000000001 |
| - type: mrr_at_1 |
| value: 45.201 |
| - type: mrr_at_10 |
| value: 53.989 |
| - type: mrr_at_100 |
| value: 54.50899999999999 |
| - type: mrr_at_1000 |
| value: 54.551 |
| - type: mrr_at_20 |
| value: 54.247 |
| - type: mrr_at_3 |
| value: 52.373999999999995 |
| - type: mrr_at_5 |
| value: 53.225 |
| - type: ndcg_at_1 |
| value: 43.808 |
| - type: ndcg_at_10 |
| value: 34.757 |
| - type: ndcg_at_100 |
| value: 31.174000000000003 |
| - type: ndcg_at_1000 |
| value: 39.607 |
| - type: ndcg_at_20 |
| value: 32.151999999999994 |
| - type: ndcg_at_3 |
| value: 40.458 |
| - type: ndcg_at_5 |
| value: 38.06 |
| - type: precision_at_1 |
| value: 45.201 |
| - type: precision_at_10 |
| value: 25.728 |
| - type: precision_at_100 |
| value: 7.82 |
| - type: precision_at_1000 |
| value: 2.032 |
| - type: precision_at_20 |
| value: 18.793000000000003 |
| - type: precision_at_3 |
| value: 38.080000000000005 |
| - type: precision_at_5 |
| value: 32.879000000000005 |
| - type: recall_at_1 |
| value: 5.771 |
| - type: recall_at_10 |
| value: 16.567 |
| - type: recall_at_100 |
| value: 30.447999999999997 |
| - type: recall_at_1000 |
| value: 60.941 |
| - type: recall_at_20 |
| value: 20.092 |
| - type: recall_at_3 |
| value: 10.928 |
| - type: recall_at_5 |
| value: 13.235 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
| metrics: |
| - type: map_at_1 |
| value: 40.716 |
| - type: map_at_10 |
| value: 56.599999999999994 |
| - type: map_at_100 |
| value: 57.389 |
| - type: map_at_1000 |
| value: 57.408 |
| - type: map_at_20 |
| value: 57.154 |
| - type: map_at_3 |
| value: 52.577 |
| - type: map_at_5 |
| value: 55.076 |
| - type: mrr_at_1 |
| value: 45.655 |
| - type: mrr_at_10 |
| value: 59.014 |
| - type: mrr_at_100 |
| value: 59.568 |
| - type: mrr_at_1000 |
| value: 59.580999999999996 |
| - type: mrr_at_20 |
| value: 59.41499999999999 |
| - type: mrr_at_3 |
| value: 55.88999999999999 |
| - type: mrr_at_5 |
| value: 57.879999999999995 |
| - type: ndcg_at_1 |
| value: 45.626 |
| - type: ndcg_at_10 |
| value: 63.778 |
| - type: ndcg_at_100 |
| value: 66.905 |
| - type: ndcg_at_1000 |
| value: 67.322 |
| - type: ndcg_at_20 |
| value: 65.521 |
| - type: ndcg_at_3 |
| value: 56.494 |
| - type: ndcg_at_5 |
| value: 60.553999999999995 |
| - type: precision_at_1 |
| value: 45.626 |
| - type: precision_at_10 |
| value: 9.942 |
| - type: precision_at_100 |
| value: 1.169 |
| - type: precision_at_1000 |
| value: 0.121 |
| - type: precision_at_20 |
| value: 5.390000000000001 |
| - type: precision_at_3 |
| value: 25.135 |
| - type: precision_at_5 |
| value: 17.451 |
| - type: recall_at_1 |
| value: 40.716 |
| - type: recall_at_10 |
| value: 82.998 |
| - type: recall_at_100 |
| value: 96.236 |
| - type: recall_at_1000 |
| value: 99.31400000000001 |
| - type: recall_at_20 |
| value: 89.402 |
| - type: recall_at_3 |
| value: 64.47699999999999 |
| - type: recall_at_5 |
| value: 73.774 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
| metrics: |
| - type: map_at_1 |
| value: 71.679 |
| - type: map_at_10 |
| value: 85.63 |
| - type: map_at_100 |
| value: 86.24000000000001 |
| - type: map_at_1000 |
| value: 86.25500000000001 |
| - type: map_at_20 |
| value: 86.03 |
| - type: map_at_3 |
| value: 82.712 |
| - type: map_at_5 |
| value: 84.59400000000001 |
| - type: mrr_at_1 |
| value: 82.58 |
| - type: mrr_at_10 |
| value: 88.459 |
| - type: mrr_at_100 |
| value: 88.544 |
| - type: mrr_at_1000 |
| value: 88.545 |
| - type: mrr_at_20 |
| value: 88.521 |
| - type: mrr_at_3 |
| value: 87.548 |
| - type: mrr_at_5 |
| value: 88.19 |
| - type: ndcg_at_1 |
| value: 82.57 |
| - type: ndcg_at_10 |
| value: 89.205 |
| - type: ndcg_at_100 |
| value: 90.316 |
| - type: ndcg_at_1000 |
| value: 90.4 |
| - type: ndcg_at_20 |
| value: 89.802 |
| - type: ndcg_at_3 |
| value: 86.5 |
| - type: ndcg_at_5 |
| value: 88.06 |
| - type: precision_at_1 |
| value: 82.57 |
| - type: precision_at_10 |
| value: 13.511000000000001 |
| - type: precision_at_100 |
| value: 1.532 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_20 |
| value: 7.1499999999999995 |
| - type: precision_at_3 |
| value: 37.82 |
| - type: precision_at_5 |
| value: 24.892 |
| - type: recall_at_1 |
| value: 71.679 |
| - type: recall_at_10 |
| value: 95.926 |
| - type: recall_at_100 |
| value: 99.653 |
| - type: recall_at_1000 |
| value: 99.99 |
| - type: recall_at_20 |
| value: 97.81 |
| - type: recall_at_3 |
| value: 88.124 |
| - type: recall_at_5 |
| value: 92.535 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 58.980204279295776 |
| - type: v_measures |
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0.54260081746137] |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
| metrics: |
| - type: v_measure |
| value: 64.68385650734866 |
| - type: v_measures |
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| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
| metrics: |
| - type: map_at_1 |
| value: 4.1930000000000005 |
| - type: map_at_10 |
| value: 10.993 |
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| value: 12.821 |
| - type: map_at_1000 |
| value: 13.094 |
| - type: map_at_20 |
| value: 11.899999999999999 |
| - type: map_at_3 |
| value: 7.753 |
| - type: map_at_5 |
| value: 9.479 |
| - type: mrr_at_1 |
| value: 20.7 |
| - type: mrr_at_10 |
| value: 31.776 |
| - type: mrr_at_100 |
| value: 32.863 |
| - type: mrr_at_1000 |
| value: 32.921 |
| - type: mrr_at_20 |
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| - type: mrr_at_3 |
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| - type: mrr_at_5 |
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| - type: ndcg_at_1 |
| value: 20.7 |
| - type: ndcg_at_10 |
| value: 18.602 |
| - type: ndcg_at_100 |
| value: 26.063 |
| - type: ndcg_at_1000 |
| value: 30.988 |
| - type: ndcg_at_20 |
| value: 21.124000000000002 |
| - type: ndcg_at_3 |
| value: 17.538999999999998 |
| - type: ndcg_at_5 |
| value: 15.604999999999999 |
| - type: precision_at_1 |
| value: 20.7 |
| - type: precision_at_10 |
| value: 9.69 |
| - type: precision_at_100 |
| value: 2.051 |
| - type: precision_at_1000 |
| value: 0.32299999999999995 |
| - type: precision_at_20 |
| value: 6.3 |
| - type: precision_at_3 |
| value: 16.567 |
| - type: precision_at_5 |
| value: 13.96 |
| - type: recall_at_1 |
| value: 4.1930000000000005 |
| - type: recall_at_10 |
| value: 19.618 |
| - type: recall_at_100 |
| value: 41.643 |
| - type: recall_at_1000 |
| value: 65.693 |
| - type: recall_at_20 |
| value: 25.562 |
| - type: recall_at_3 |
| value: 10.062999999999999 |
| - type: recall_at_5 |
| value: 14.127999999999998 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.46613174654865 |
| - type: cos_sim_spearman |
| value: 80.3049357832415 |
| - type: euclidean_pearson |
| value: 81.26631332583317 |
| - type: euclidean_spearman |
| value: 80.3154745166346 |
| - type: manhattan_pearson |
| value: 81.14703159845031 |
| - type: manhattan_spearman |
| value: 80.20912001232311 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.54049067032975 |
| - type: cos_sim_spearman |
| value: 80.96545866938635 |
| - type: euclidean_pearson |
| value: 83.96265705630466 |
| - type: euclidean_spearman |
| value: 79.93146623957664 |
| - type: manhattan_pearson |
| value: 83.90680327172007 |
| - type: manhattan_spearman |
| value: 79.9387741861374 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.88551701212096 |
| - type: cos_sim_spearman |
| value: 87.86522961782607 |
| - type: euclidean_pearson |
| value: 87.36290945594213 |
| - type: euclidean_spearman |
| value: 87.83062393537139 |
| - type: manhattan_pearson |
| value: 87.32544594269082 |
| - type: manhattan_spearman |
| value: 87.81556963071229 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.30880458174929 |
| - type: cos_sim_spearman |
| value: 83.80166079353091 |
| - type: euclidean_pearson |
| value: 85.32128873266257 |
| - type: euclidean_spearman |
| value: 83.86251092262333 |
| - type: manhattan_pearson |
| value: 85.2712567451151 |
| - type: manhattan_spearman |
| value: 83.80950203378747 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.26254668067915 |
| - type: cos_sim_spearman |
| value: 88.58702965856746 |
| - type: euclidean_pearson |
| value: 87.9969808017743 |
| - type: euclidean_spearman |
| value: 88.48082129802832 |
| - type: manhattan_pearson |
| value: 88.005385920726 |
| - type: manhattan_spearman |
| value: 88.48824252319064 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.9048844772477 |
| - type: cos_sim_spearman |
| value: 86.81864160521327 |
| - type: euclidean_pearson |
| value: 86.28264402848413 |
| - type: euclidean_spearman |
| value: 86.78000025418731 |
| - type: manhattan_pearson |
| value: 86.2441248990138 |
| - type: manhattan_spearman |
| value: 86.75021285222047 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS17 (en-en) |
| config: en-en |
| split: test |
| revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.489340312079 |
| - type: cos_sim_spearman |
| value: 87.98810146323362 |
| - type: euclidean_pearson |
| value: 89.17657344753519 |
| - type: euclidean_spearman |
| value: 88.96877394433339 |
| - type: manhattan_pearson |
| value: 89.17489837230771 |
| - type: manhattan_spearman |
| value: 88.87394331518345 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STS22 (en) |
| config: en |
| split: test |
| revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
| metrics: |
| - type: cos_sim_pearson |
| value: 63.020191114515576 |
| - type: cos_sim_spearman |
| value: 66.81821028889179 |
| - type: euclidean_pearson |
| value: 66.11102477309004 |
| - type: euclidean_spearman |
| value: 66.59000262767655 |
| - type: manhattan_pearson |
| value: 66.0319349852117 |
| - type: manhattan_spearman |
| value: 66.51366211903893 |
| - task: |
| type: STS |
| dataset: |
| type: None |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.05763458617234 |
| - type: cos_sim_spearman |
| value: 87.40353901525121 |
| - type: euclidean_pearson |
| value: 87.43632331678887 |
| - type: euclidean_spearman |
| value: 87.58631222421829 |
| - type: manhattan_pearson |
| value: 87.40408795218912 |
| - type: manhattan_spearman |
| value: 87.55530395433567 |
| - task: |
| type: Reranking |
| dataset: |
| type: None |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 83.40728647106346 |
| - type: mrr |
| value: 95.39606725881237 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| metrics: |
| - type: map_at_1 |
| value: 55.344 |
| - type: map_at_10 |
| value: 66.467 |
| - type: map_at_100 |
| value: 66.841 |
| - type: map_at_1000 |
| value: 66.86800000000001 |
| - type: map_at_20 |
| value: 66.728 |
| - type: map_at_3 |
| value: 62.888 |
| - type: map_at_5 |
| value: 65.10000000000001 |
| - type: mrr_at_1 |
| value: 58.333 |
| - type: mrr_at_10 |
| value: 67.471 |
| - type: mrr_at_100 |
| value: 67.75 |
| - type: mrr_at_1000 |
| value: 67.778 |
| - type: mrr_at_20 |
| value: 67.649 |
| - type: mrr_at_3 |
| value: 64.72200000000001 |
| - type: mrr_at_5 |
| value: 66.539 |
| - type: ndcg_at_1 |
| value: 58.333 |
| - type: ndcg_at_10 |
| value: 71.707 |
| - type: ndcg_at_100 |
| value: 73.301 |
| - type: ndcg_at_1000 |
| value: 74.053 |
| - type: ndcg_at_20 |
| value: 72.482 |
| - type: ndcg_at_3 |
| value: 65.561 |
| - type: ndcg_at_5 |
| value: 69.017 |
| - type: precision_at_1 |
| value: 58.333 |
| - type: precision_at_10 |
| value: 9.866999999999999 |
| - type: precision_at_100 |
| value: 1.0699999999999998 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_20 |
| value: 5.1 |
| - type: precision_at_3 |
| value: 25.778000000000002 |
| - type: precision_at_5 |
| value: 17.533 |
| - type: recall_at_1 |
| value: 55.344 |
| - type: recall_at_10 |
| value: 86.76700000000001 |
| - type: recall_at_100 |
| value: 94.0 |
| - type: recall_at_1000 |
| value: 100.0 |
| - type: recall_at_20 |
| value: 89.60000000000001 |
| - type: recall_at_3 |
| value: 70.406 |
| - type: recall_at_5 |
| value: 79.106 |
| - task: |
| type: PairClassification |
| dataset: |
| type: None |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.71089108910891 |
| - type: cos_sim_ap |
| value: 91.82444380538519 |
| - type: cos_sim_f1 |
| value: 85.34525583705911 |
| - type: cos_sim_precision |
| value: 84.79763079960513 |
| - type: cos_sim_recall |
| value: 85.9 |
| - type: dot_accuracy |
| value: 99.56039603960396 |
| - type: dot_ap |
| value: 84.71022538609428 |
| - type: dot_f1 |
| value: 76.18100447538538 |
| - type: dot_precision |
| value: 75.76656775469831 |
| - type: dot_recall |
| value: 76.6 |
| - type: euclidean_accuracy |
| value: 99.7 |
| - type: euclidean_ap |
| value: 91.68317023504792 |
| - type: euclidean_f1 |
| value: 84.65712876171682 |
| - type: euclidean_precision |
| value: 83.54430379746836 |
| - type: euclidean_recall |
| value: 85.8 |
| - type: manhattan_accuracy |
| value: 99.69900990099009 |
| - type: manhattan_ap |
| value: 91.5749511659937 |
| - type: manhattan_f1 |
| value: 84.6989141164857 |
| - type: manhattan_precision |
| value: 83.62573099415205 |
| - type: manhattan_recall |
| value: 85.8 |
| - type: max_accuracy |
| value: 99.71089108910891 |
| - type: max_ap |
| value: 91.82444380538519 |
| - type: max_f1 |
| value: 85.34525583705911 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 69.36504474977566 |
| - type: v_measures |
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0.6816924973160465] |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 34.72911995025639 |
| - type: v_measures |
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| - task: |
| type: Reranking |
| dataset: |
| type: None |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 52.975020393803675 |
| - type: mrr |
| value: 53.87404772515067 |
| - task: |
| type: Summarization |
| dataset: |
| type: None |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.205065693047615 |
| - type: cos_sim_spearman |
| value: 28.307951294409406 |
| - type: dot_pearson |
| value: 29.15581947828465 |
| - type: dot_spearman |
| value: 28.222470759389505 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
| metrics: |
| - type: map_at_1 |
| value: 0.249 |
| - type: map_at_10 |
| value: 2.243 |
| - type: map_at_100 |
| value: 13.791 |
| - type: map_at_1000 |
| value: 32.539 |
| - type: map_at_20 |
| value: 4.112 |
| - type: map_at_3 |
| value: 0.7060000000000001 |
| - type: map_at_5 |
| value: 1.1860000000000002 |
| - type: mrr_at_1 |
| value: 96.0 |
| - type: mrr_at_10 |
| value: 98.0 |
| - type: mrr_at_100 |
| value: 98.0 |
| - type: mrr_at_1000 |
| value: 98.0 |
| - type: mrr_at_20 |
| value: 98.0 |
| - type: mrr_at_3 |
| value: 98.0 |
| - type: mrr_at_5 |
| value: 98.0 |
| - type: ndcg_at_1 |
| value: 92.0 |
| - type: ndcg_at_10 |
| value: 86.083 |
| - type: ndcg_at_100 |
| value: 66.471 |
| - type: ndcg_at_1000 |
| value: 57.31699999999999 |
| - type: ndcg_at_20 |
| value: 82.783 |
| - type: ndcg_at_3 |
| value: 88.805 |
| - type: ndcg_at_5 |
| value: 88.96 |
| - type: precision_at_1 |
| value: 96.0 |
| - type: precision_at_10 |
| value: 91.2 |
| - type: precision_at_100 |
| value: 68.16 |
| - type: precision_at_1000 |
| value: 25.290000000000003 |
| - type: precision_at_20 |
| value: 86.9 |
| - type: precision_at_3 |
| value: 94.0 |
| - type: precision_at_5 |
| value: 94.39999999999999 |
| - type: recall_at_1 |
| value: 0.249 |
| - type: recall_at_10 |
| value: 2.3800000000000003 |
| - type: recall_at_100 |
| value: 16.45 |
| - type: recall_at_1000 |
| value: 53.1 |
| - type: recall_at_20 |
| value: 4.4670000000000005 |
| - type: recall_at_3 |
| value: 0.734 |
| - type: recall_at_5 |
| value: 1.246 |
| - task: |
| type: Retrieval |
| dataset: |
| type: None |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
| metrics: |
| - type: map_at_1 |
| value: 3.2520000000000002 |
| - type: map_at_10 |
| value: 11.805 |
| - type: map_at_100 |
| value: 18.749 |
| - type: map_at_1000 |
| value: 20.416999999999998 |
| - type: map_at_20 |
| value: 14.685 |
| - type: map_at_3 |
| value: 6.6739999999999995 |
| - type: map_at_5 |
| value: 8.863 |
| - type: mrr_at_1 |
| value: 42.857 |
| - type: mrr_at_10 |
| value: 57.635999999999996 |
| - type: mrr_at_100 |
| value: 58.034 |
| - type: mrr_at_1000 |
| value: 58.048 |
| - type: mrr_at_20 |
| value: 57.979 |
| - type: mrr_at_3 |
| value: 54.422000000000004 |
| - type: mrr_at_5 |
| value: 56.15599999999999 |
| - type: ndcg_at_1 |
| value: 39.796 |
| - type: ndcg_at_10 |
| value: 30.263 |
| - type: ndcg_at_100 |
| value: 40.825 |
| - type: ndcg_at_1000 |
| value: 52.447 |
| - type: ndcg_at_20 |
| value: 30.453000000000003 |
| - type: ndcg_at_3 |
| value: 35.086 |
| - type: ndcg_at_5 |
| value: 31.947 |
| - type: precision_at_1 |
| value: 42.857 |
| - type: precision_at_10 |
| value: 26.327 |
| - type: precision_at_100 |
| value: 8.041 |
| - type: precision_at_1000 |
| value: 1.582 |
| - type: precision_at_20 |
| value: 19.592000000000002 |
| - type: precision_at_3 |
| value: 36.054 |
| - type: precision_at_5 |
| value: 31.019999999999996 |
| - type: recall_at_1 |
| value: 3.2520000000000002 |
| - type: recall_at_10 |
| value: 18.471 |
| - type: recall_at_100 |
| value: 49.08 |
| - type: recall_at_1000 |
| value: 84.733 |
| - type: recall_at_20 |
| value: 26.389000000000003 |
| - type: recall_at_3 |
| value: 8.051 |
| - type: recall_at_5 |
| value: 11.672 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| metrics: |
| - type: accuracy |
| value: 68.10546875 |
| - type: ap |
| value: 12.899352291322325 |
| - type: f1 |
| value: 52.14484661172115 |
| - task: |
| type: Classification |
| dataset: |
| type: None |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 62.323146576117715 |
| - type: f1 |
| value: 62.6518883448989 |
| - task: |
| type: Clustering |
| dataset: |
| type: None |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 51.261957327618525 |
| - type: v_measures |
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| - task: |
| type: PairClassification |
| dataset: |
| type: None |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 87.09542826488645 |
| - type: cos_sim_ap |
| value: 77.72170475021885 |
| - type: cos_sim_f1 |
| value: 70.67669172932331 |
| - type: cos_sim_precision |
| value: 64.5238614077141 |
| - type: cos_sim_recall |
| value: 78.12664907651715 |
| - type: dot_accuracy |
| value: 83.96614412588663 |
| - type: dot_ap |
| value: 68.08590796036842 |
| - type: dot_f1 |
| value: 63.934426229508205 |
| - type: dot_precision |
| value: 58.854860186418115 |
| - type: dot_recall |
| value: 69.9736147757256 |
| - type: euclidean_accuracy |
| value: 87.20271800679502 |
| - type: euclidean_ap |
| value: 77.87533191263717 |
| - type: euclidean_f1 |
| value: 70.92216475337455 |
| - type: euclidean_precision |
| value: 67.94778825235677 |
| - type: euclidean_recall |
| value: 74.1688654353562 |
| - type: manhattan_accuracy |
| value: 87.20867854801216 |
| - type: manhattan_ap |
| value: 77.84249032925085 |
| - type: manhattan_f1 |
| value: 71.11665626949471 |
| - type: manhattan_precision |
| value: 67.45562130177515 |
| - type: manhattan_recall |
| value: 75.19788918205805 |
| - type: max_accuracy |
| value: 87.20867854801216 |
| - type: max_ap |
| value: 77.87533191263717 |
| - type: max_f1 |
| value: 71.11665626949471 |
| - task: |
| type: PairClassification |
| dataset: |
| type: None |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.22070865836147 |
| - type: cos_sim_ap |
| value: 86.38617271379728 |
| - type: cos_sim_f1 |
| value: 78.946594085626 |
| - type: cos_sim_precision |
| value: 75.5774647887324 |
| - type: cos_sim_recall |
| value: 82.63012011087157 |
| - type: dot_accuracy |
| value: 87.16963558039352 |
| - type: dot_ap |
| value: 82.0965358395614 |
| - type: dot_f1 |
| value: 75.00997859138575 |
| - type: dot_precision |
| value: 70.93541966920596 |
| - type: dot_recall |
| value: 79.58115183246073 |
| - type: euclidean_accuracy |
| value: 89.14891139830016 |
| - type: euclidean_ap |
| value: 86.28000880804873 |
| - type: euclidean_f1 |
| value: 78.7341306347746 |
| - type: euclidean_precision |
| value: 75.40706280397546 |
| - type: euclidean_recall |
| value: 82.36834000615954 |
| - type: manhattan_accuracy |
| value: 89.15279233127644 |
| - type: manhattan_ap |
| value: 86.25024653784152 |
| - type: manhattan_f1 |
| value: 78.72760457406788 |
| - type: manhattan_precision |
| value: 76.25369795800563 |
| - type: manhattan_recall |
| value: 81.36741607637819 |
| - type: max_accuracy |
| value: 89.22070865836147 |
| - type: max_ap |
| value: 86.38617271379728 |
| - type: max_f1 |
| value: 78.946594085626 |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - answerdotai/ModernBERT-large |
| - lightonai/modernbert-embed-large-unsupervised |
| --- |
| |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/609bbe2f4932693ca2009d6a/049agfQyt8JORvpeYOyDi.jpeg" width="500" height="auto"> |
|
|
| # ModernBERT-embed-large |
|
|
| ModernBERT-embed-large is an embedding model trained from [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large), bringing the new advances of ModernBERT to embeddings! |
|
|
| Indeed, ModernBERT is a base model trained for Masked Language Modeling and can not directly be used to perform tasks such as retrieval without further fine-tuning. |
|
|
| ModernBERT-embed-large is fine-tuned on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets and also supports Matryoshka Representation Learning dimensions of 256 to reduce memory with minimal performance loss. |
|
|
| ## Performance |
|
|
|
|
|
|
| | Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | |
| |-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------| |
| | nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | 53.01 | 81.94 | 30.4 | |
| | modernbert-embed-base | 768 | 62.62 | 74.31 | 44.98 | 83.96 | 56.42 | 52.89 | 81.78 | **31.39** | |
| | modernbert-embed-large | 1024 | **63,84** | **75.03** | **46.04** | **85.31** | **57.64** | **54.36** | **83.80** | 28.31 | |
| | nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 | |
| | modernbert-embed-base | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 | |
| | modernbert-embed-large | 256 | 62.43 | 73.60 | 44.59 | 84.89 | 57.08 | 51.72 | 83.46 | 29.03 | |
|
|
|
|
| ## Usage |
|
|
| You can use these models directly with the latest transformers release and requires installing `transformers>=4.48.0`: |
|
|
| ```bash |
| pip install transformers>=4.48.0 |
| ``` |
|
|
| Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes). |
|
|
| Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient. |
|
|
| ### Sentence Transformers |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("lightonai/modernbert-embed-large") |
| |
| query_embeddings = model.encode([ |
| "search_query: What is TSNE?", |
| "search_query: Who is Laurens van der Maaten?", |
| ]) |
| doc_embeddings = model.encode([ |
| "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten", |
| ]) |
| print(query_embeddings.shape, doc_embeddings.shape) |
| # (2, 1024) (1, 1024) |
| |
| similarities = model.similarity(query_embeddings, doc_embeddings) |
| print(similarities) |
| # tensor([[0.6518], |
| # [0.4237]]) |
| ``` |
|
|
| <details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary> |
|
|
| In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model. |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("lightonai/modernbert-embed-large", truncate_dim=256) |
| |
| query_embeddings = model.encode([ |
| "search_query: What is TSNE?", |
| "search_query: Who is Laurens van der Maaten?", |
| ]) |
| doc_embeddings = model.encode([ |
| "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten", |
| ]) |
| print(query_embeddings.shape, doc_embeddings.shape) |
| # (2, 256) (1, 256) |
| |
| similarities = model.similarity(query_embeddings, doc_embeddings) |
| print(similarities) |
| # tensor([[0.6835], |
| # [0.3982]]) |
| ``` |
|
|
| Note the small differences compared to the full 1024-dimensional similarities. |
|
|
| </details> |
|
|
| ### Transformers |
|
|
| ```python |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoTokenizer, AutoModel |
| |
| |
| def mean_pooling(model_output, attention_mask): |
| token_embeddings = model_output[0] |
| input_mask_expanded = ( |
| attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
| ) |
| return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( |
| input_mask_expanded.sum(1), min=1e-9 |
| ) |
| |
| |
| queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"] |
| documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"] |
| |
| tokenizer = AutoTokenizer.from_pretrained("lightonai/modernbert-embed-large") |
| model = AutoModel.from_pretrained("lightonai/modernbert-embed-large") |
| |
| encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") |
| encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt") |
| |
| with torch.no_grad(): |
| queries_outputs = model(**encoded_queries) |
| documents_outputs = model(**encoded_documents) |
| |
| query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"]) |
| query_embeddings = F.normalize(query_embeddings, p=2, dim=1) |
| doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"]) |
| doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1) |
| print(query_embeddings.shape, doc_embeddings.shape) |
| # torch.Size([2, 1024]) torch.Size([1, 1024]) |
| |
| similarities = query_embeddings @ doc_embeddings.T |
| print(similarities) |
| # tensor([[0.6518], |
| # [0.4237]]) |
| ``` |
|
|
| <details><summary>Click to see Transformers usage with Matryoshka Truncation</summary> |
|
|
| In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization. |
|
|
| ```python |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoTokenizer, AutoModel |
| |
| |
| def mean_pooling(model_output, attention_mask): |
| token_embeddings = model_output[0] |
| input_mask_expanded = ( |
| attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
| ) |
| return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( |
| input_mask_expanded.sum(1), min=1e-9 |
| ) |
| |
| |
| queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"] |
| documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"] |
| |
| tokenizer = AutoTokenizer.from_pretrained(".") |
| model = AutoModel.from_pretrained(".") |
| truncate_dim = 256 |
| |
| encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt") |
| encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt") |
| |
| with torch.no_grad(): |
| queries_outputs = model(**encoded_queries) |
| documents_outputs = model(**encoded_documents) |
| |
| query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"]) |
| query_embeddings = query_embeddings[:, :truncate_dim] |
| query_embeddings = F.normalize(query_embeddings, p=2, dim=1) |
| doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"]) |
| doc_embeddings = doc_embeddings[:, :truncate_dim] |
| doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1) |
| print(query_embeddings.shape, doc_embeddings.shape) |
| # torch.Size([2, 256]) torch.Size([1, 256]) |
| |
| similarities = query_embeddings @ doc_embeddings.T |
| print(similarities) |
| # tensor([[0.6835], |
| # [0.3982]]) |
| ``` |
|
|
| Note the small differences compared to the full 1024-dimensional similarities. |
|
|
| </details> |
|
|
| ### Transformers.js |
|
|
| If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: |
| ```bash |
| npm i @huggingface/transformers |
| ``` |
|
|
| Then, you can compute embeddings as follows: |
|
|
| ```javascript |
| import { pipeline, matmul } from '@huggingface/transformers'; |
| |
| // Create a feature extraction pipeline |
| const extractor = await pipeline( |
| "feature-extraction", |
| "lightonai/modernbert-embed-large", |
| { dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16" |
| ); |
| |
| // Embed queries and documents |
| const query_embeddings = await extractor([ |
| "search_query: What is TSNE?", |
| "search_query: Who is Laurens van der Maaten?", |
| ], { pooling: "mean", normalize: true }, |
| ); |
| const doc_embeddings = await extractor([ |
| "search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten", |
| ], { pooling: "mean", normalize: true }, |
| ); |
| |
| // Compute similarity scores |
| const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0)); |
| console.log(similarities.tolist()); |
| ``` |
|
|
|
|
| ## Training |
| We train ModernBERT-embed-large using a multi-stage training pipeline. Starting from the pretrained [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) model, |
| the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles. |
|
|
| In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage. |
|
|
| For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1). |
|
|
| Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) |
|
|
| ## Acknowledgment |
| We wanted to thank [Zach Nussbaum](https://huggingface.co/zpn) from [Nomic AI](https://huggingface.co/nomic-ai) for building and sharing the Nomic Embed recipe and tools and its support during the training of this model! |
|
|
| The training has been run on Orange Business Cloud Avenue infrastructure. |
|
|
| ## Citation |
|
|
| If you find the model, dataset, or training code useful, please considering citing ModernBERT as well as Nomic Embed: |
| ```bibtex |
| @misc{modernbert, |
| title={Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference}, |
| author={Benjamin Warner and Antoine Chaffin and Benjamin Clavié and Orion Weller and Oskar Hallström and Said Taghadouini and Alexis Gallagher and Raja Biswas and Faisal Ladhak and Tom Aarsen and Nathan Cooper and Griffin Adams and Jeremy Howard and Iacopo Poli}, |
| year={2024}, |
| eprint={2412.13663}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2412.13663}, |
| } |
| ``` |
|
|
| ```bibtex |
| @misc{nussbaum2024nomic, |
| title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, |
| author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar}, |
| year={2024}, |
| eprint={2402.01613}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
| And if you want to cite this fine-tuning in particular, please use: |
| ```bibtex |
| @misc{ModernBERT-embed-large, |
| title={ModernBERT-embed-large}, |
| author={Chaffin, Antoine}, |
| url={https://huggingface.co/lightonai/modernbert-embed-large}, |
| year={2025} |
| } |
| ``` |
