|
--- |
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library_name: sentence-transformers |
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pipeline_tag: feature-extraction |
|
tags: |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
|
- transformers |
|
- transformers.js |
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model-index: |
|
- name: epoch_0_model |
|
results: |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
|
split: test |
|
revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 76.8507462686567 |
|
- type: ap |
|
value: 40.592189159090495 |
|
- type: f1 |
|
value: 71.01634655512476 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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config: default |
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split: test |
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 91.51892500000001 |
|
- type: ap |
|
value: 88.50346762975335 |
|
- type: f1 |
|
value: 91.50342077459624 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 47.364 |
|
- type: f1 |
|
value: 46.72708080922794 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
|
name: MTEB ArguAna |
|
config: default |
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split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.178 |
|
- type: map_at_10 |
|
value: 40.244 |
|
- type: map_at_100 |
|
value: 41.321999999999996 |
|
- type: map_at_1000 |
|
value: 41.331 |
|
- type: map_at_3 |
|
value: 35.016999999999996 |
|
- type: map_at_5 |
|
value: 37.99 |
|
- type: mrr_at_1 |
|
value: 25.605 |
|
- type: mrr_at_10 |
|
value: 40.422000000000004 |
|
- type: mrr_at_100 |
|
value: 41.507 |
|
- type: mrr_at_1000 |
|
value: 41.516 |
|
- type: mrr_at_3 |
|
value: 35.23 |
|
- type: mrr_at_5 |
|
value: 38.15 |
|
- type: ndcg_at_1 |
|
value: 25.178 |
|
- type: ndcg_at_10 |
|
value: 49.258 |
|
- type: ndcg_at_100 |
|
value: 53.776 |
|
- type: ndcg_at_1000 |
|
value: 53.995000000000005 |
|
- type: ndcg_at_3 |
|
value: 38.429 |
|
- type: ndcg_at_5 |
|
value: 43.803 |
|
- type: precision_at_1 |
|
value: 25.178 |
|
- type: precision_at_10 |
|
value: 7.831 |
|
- type: precision_at_100 |
|
value: 0.979 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 16.121 |
|
- type: precision_at_5 |
|
value: 12.29 |
|
- type: recall_at_1 |
|
value: 25.178 |
|
- type: recall_at_10 |
|
value: 78.307 |
|
- type: recall_at_100 |
|
value: 97.866 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 48.364000000000004 |
|
- type: recall_at_5 |
|
value: 61.451 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 45.93034494751465 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 36.64579480054327 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
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config: default |
|
split: test |
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 60.601310529222054 |
|
- type: mrr |
|
value: 75.04484896451656 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 88.57797718095814 |
|
- type: cos_sim_spearman |
|
value: 86.47064499110101 |
|
- type: euclidean_pearson |
|
value: 87.4559602783142 |
|
- type: euclidean_spearman |
|
value: 86.47064499110101 |
|
- type: manhattan_pearson |
|
value: 87.7232764230245 |
|
- type: manhattan_spearman |
|
value: 86.91222131777742 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 84.5422077922078 |
|
- type: f1 |
|
value: 84.47657456950589 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 38.48953561974464 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 32.75995857510105 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackAndroidRetrieval |
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config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.008000000000003 |
|
- type: map_at_10 |
|
value: 39.51 |
|
- type: map_at_100 |
|
value: 40.841 |
|
- type: map_at_1000 |
|
value: 40.973 |
|
- type: map_at_3 |
|
value: 36.248999999999995 |
|
- type: map_at_5 |
|
value: 38.096999999999994 |
|
- type: mrr_at_1 |
|
value: 36.481 |
|
- type: mrr_at_10 |
|
value: 44.818000000000005 |
|
- type: mrr_at_100 |
|
value: 45.64 |
|
- type: mrr_at_1000 |
|
value: 45.687 |
|
- type: mrr_at_3 |
|
value: 42.036 |
|
- type: mrr_at_5 |
|
value: 43.782 |
|
- type: ndcg_at_1 |
|
value: 36.481 |
|
- type: ndcg_at_10 |
|
value: 45.152 |
|
- type: ndcg_at_100 |
|
value: 50.449 |
|
- type: ndcg_at_1000 |
|
value: 52.76499999999999 |
|
- type: ndcg_at_3 |
|
value: 40.161 |
|
- type: ndcg_at_5 |
|
value: 42.577999999999996 |
|
- type: precision_at_1 |
|
value: 36.481 |
|
- type: precision_at_10 |
|
value: 8.369 |
|
- type: precision_at_100 |
|
value: 1.373 |
|
- type: precision_at_1000 |
|
value: 0.186 |
|
- type: precision_at_3 |
|
value: 18.693 |
|
- type: precision_at_5 |
|
value: 13.533999999999999 |
|
- type: recall_at_1 |
|
value: 30.008000000000003 |
|
- type: recall_at_10 |
|
value: 56.108999999999995 |
|
- type: recall_at_100 |
|
value: 78.55499999999999 |
|
- type: recall_at_1000 |
|
value: 93.659 |
|
- type: recall_at_3 |
|
value: 41.754999999999995 |
|
- type: recall_at_5 |
|
value: 48.296 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.262 |
|
- type: map_at_10 |
|
value: 40.139 |
|
- type: map_at_100 |
|
value: 41.394 |
|
- type: map_at_1000 |
|
value: 41.526 |
|
- type: map_at_3 |
|
value: 37.155 |
|
- type: map_at_5 |
|
value: 38.785 |
|
- type: mrr_at_1 |
|
value: 38.153 |
|
- type: mrr_at_10 |
|
value: 46.369 |
|
- type: mrr_at_100 |
|
value: 47.072 |
|
- type: mrr_at_1000 |
|
value: 47.111999999999995 |
|
- type: mrr_at_3 |
|
value: 44.268 |
|
- type: mrr_at_5 |
|
value: 45.389 |
|
- type: ndcg_at_1 |
|
value: 38.153 |
|
- type: ndcg_at_10 |
|
value: 45.925 |
|
- type: ndcg_at_100 |
|
value: 50.394000000000005 |
|
- type: ndcg_at_1000 |
|
value: 52.37500000000001 |
|
- type: ndcg_at_3 |
|
value: 41.754000000000005 |
|
- type: ndcg_at_5 |
|
value: 43.574 |
|
- type: precision_at_1 |
|
value: 38.153 |
|
- type: precision_at_10 |
|
value: 8.796 |
|
- type: precision_at_100 |
|
value: 1.432 |
|
- type: precision_at_1000 |
|
value: 0.189 |
|
- type: precision_at_3 |
|
value: 20.318 |
|
- type: precision_at_5 |
|
value: 14.395 |
|
- type: recall_at_1 |
|
value: 30.262 |
|
- type: recall_at_10 |
|
value: 55.72200000000001 |
|
- type: recall_at_100 |
|
value: 74.97500000000001 |
|
- type: recall_at_1000 |
|
value: 87.342 |
|
- type: recall_at_3 |
|
value: 43.129 |
|
- type: recall_at_5 |
|
value: 48.336 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.951 |
|
- type: map_at_10 |
|
value: 51.248000000000005 |
|
- type: map_at_100 |
|
value: 52.188 |
|
- type: map_at_1000 |
|
value: 52.247 |
|
- type: map_at_3 |
|
value: 48.211 |
|
- type: map_at_5 |
|
value: 49.797000000000004 |
|
- type: mrr_at_1 |
|
value: 45.329 |
|
- type: mrr_at_10 |
|
value: 54.749 |
|
- type: mrr_at_100 |
|
value: 55.367999999999995 |
|
- type: mrr_at_1000 |
|
value: 55.400000000000006 |
|
- type: mrr_at_3 |
|
value: 52.382 |
|
- type: mrr_at_5 |
|
value: 53.649 |
|
- type: ndcg_at_1 |
|
value: 45.329 |
|
- type: ndcg_at_10 |
|
value: 56.847 |
|
- type: ndcg_at_100 |
|
value: 60.738 |
|
- type: ndcg_at_1000 |
|
value: 61.976 |
|
- type: ndcg_at_3 |
|
value: 51.59 |
|
- type: ndcg_at_5 |
|
value: 53.915 |
|
- type: precision_at_1 |
|
value: 45.329 |
|
- type: precision_at_10 |
|
value: 8.959 |
|
- type: precision_at_100 |
|
value: 1.187 |
|
- type: precision_at_1000 |
|
value: 0.134 |
|
- type: precision_at_3 |
|
value: 22.612 |
|
- type: precision_at_5 |
|
value: 15.273 |
|
- type: recall_at_1 |
|
value: 39.951 |
|
- type: recall_at_10 |
|
value: 70.053 |
|
- type: recall_at_100 |
|
value: 86.996 |
|
- type: recall_at_1000 |
|
value: 95.707 |
|
- type: recall_at_3 |
|
value: 56.032000000000004 |
|
- type: recall_at_5 |
|
value: 61.629999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.566 |
|
- type: map_at_10 |
|
value: 33.207 |
|
- type: map_at_100 |
|
value: 34.166000000000004 |
|
- type: map_at_1000 |
|
value: 34.245 |
|
- type: map_at_3 |
|
value: 30.94 |
|
- type: map_at_5 |
|
value: 32.01 |
|
- type: mrr_at_1 |
|
value: 27.345000000000002 |
|
- type: mrr_at_10 |
|
value: 35.193000000000005 |
|
- type: mrr_at_100 |
|
value: 35.965 |
|
- type: mrr_at_1000 |
|
value: 36.028999999999996 |
|
- type: mrr_at_3 |
|
value: 32.806000000000004 |
|
- type: mrr_at_5 |
|
value: 34.021 |
|
- type: ndcg_at_1 |
|
value: 27.345000000000002 |
|
- type: ndcg_at_10 |
|
value: 37.891999999999996 |
|
- type: ndcg_at_100 |
|
value: 42.664 |
|
- type: ndcg_at_1000 |
|
value: 44.757000000000005 |
|
- type: ndcg_at_3 |
|
value: 33.123000000000005 |
|
- type: ndcg_at_5 |
|
value: 35.035 |
|
- type: precision_at_1 |
|
value: 27.345000000000002 |
|
- type: precision_at_10 |
|
value: 5.763 |
|
- type: precision_at_100 |
|
value: 0.859 |
|
- type: precision_at_1000 |
|
value: 0.108 |
|
- type: precision_at_3 |
|
value: 13.71 |
|
- type: precision_at_5 |
|
value: 9.401 |
|
- type: recall_at_1 |
|
value: 25.566 |
|
- type: recall_at_10 |
|
value: 50.563 |
|
- type: recall_at_100 |
|
value: 72.86399999999999 |
|
- type: recall_at_1000 |
|
value: 88.68599999999999 |
|
- type: recall_at_3 |
|
value: 37.43 |
|
- type: recall_at_5 |
|
value: 41.894999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.663 |
|
- type: map_at_10 |
|
value: 23.552 |
|
- type: map_at_100 |
|
value: 24.538 |
|
- type: map_at_1000 |
|
value: 24.661 |
|
- type: map_at_3 |
|
value: 21.085 |
|
- type: map_at_5 |
|
value: 22.391 |
|
- type: mrr_at_1 |
|
value: 20.025000000000002 |
|
- type: mrr_at_10 |
|
value: 27.643 |
|
- type: mrr_at_100 |
|
value: 28.499999999999996 |
|
- type: mrr_at_1000 |
|
value: 28.582 |
|
- type: mrr_at_3 |
|
value: 25.083 |
|
- type: mrr_at_5 |
|
value: 26.544 |
|
- type: ndcg_at_1 |
|
value: 20.025000000000002 |
|
- type: ndcg_at_10 |
|
value: 28.272000000000002 |
|
- type: ndcg_at_100 |
|
value: 33.353 |
|
- type: ndcg_at_1000 |
|
value: 36.454 |
|
- type: ndcg_at_3 |
|
value: 23.579 |
|
- type: ndcg_at_5 |
|
value: 25.685000000000002 |
|
- type: precision_at_1 |
|
value: 20.025000000000002 |
|
- type: precision_at_10 |
|
value: 5.187 |
|
- type: precision_at_100 |
|
value: 0.897 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 10.987 |
|
- type: precision_at_5 |
|
value: 8.06 |
|
- type: recall_at_1 |
|
value: 16.663 |
|
- type: recall_at_10 |
|
value: 38.808 |
|
- type: recall_at_100 |
|
value: 61.305 |
|
- type: recall_at_1000 |
|
value: 83.571 |
|
- type: recall_at_3 |
|
value: 25.907999999999998 |
|
- type: recall_at_5 |
|
value: 31.214 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.695999999999998 |
|
- type: map_at_10 |
|
value: 37.018 |
|
- type: map_at_100 |
|
value: 38.263000000000005 |
|
- type: map_at_1000 |
|
value: 38.371 |
|
- type: map_at_3 |
|
value: 34.226 |
|
- type: map_at_5 |
|
value: 35.809999999999995 |
|
- type: mrr_at_1 |
|
value: 32.916000000000004 |
|
- type: mrr_at_10 |
|
value: 42.067 |
|
- type: mrr_at_100 |
|
value: 42.925000000000004 |
|
- type: mrr_at_1000 |
|
value: 42.978 |
|
- type: mrr_at_3 |
|
value: 39.637 |
|
- type: mrr_at_5 |
|
value: 41.134 |
|
- type: ndcg_at_1 |
|
value: 32.916000000000004 |
|
- type: ndcg_at_10 |
|
value: 42.539 |
|
- type: ndcg_at_100 |
|
value: 47.873 |
|
- type: ndcg_at_1000 |
|
value: 50.08200000000001 |
|
- type: ndcg_at_3 |
|
value: 37.852999999999994 |
|
- type: ndcg_at_5 |
|
value: 40.201 |
|
- type: precision_at_1 |
|
value: 32.916000000000004 |
|
- type: precision_at_10 |
|
value: 7.5840000000000005 |
|
- type: precision_at_100 |
|
value: 1.199 |
|
- type: precision_at_1000 |
|
value: 0.155 |
|
- type: precision_at_3 |
|
value: 17.485 |
|
- type: precision_at_5 |
|
value: 12.512 |
|
- type: recall_at_1 |
|
value: 27.695999999999998 |
|
- type: recall_at_10 |
|
value: 53.638 |
|
- type: recall_at_100 |
|
value: 76.116 |
|
- type: recall_at_1000 |
|
value: 91.069 |
|
- type: recall_at_3 |
|
value: 41.13 |
|
- type: recall_at_5 |
|
value: 46.872 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.108 |
|
- type: map_at_10 |
|
value: 33.372 |
|
- type: map_at_100 |
|
value: 34.656 |
|
- type: map_at_1000 |
|
value: 34.768 |
|
- type: map_at_3 |
|
value: 30.830999999999996 |
|
- type: map_at_5 |
|
value: 32.204 |
|
- type: mrr_at_1 |
|
value: 29.110000000000003 |
|
- type: mrr_at_10 |
|
value: 37.979 |
|
- type: mrr_at_100 |
|
value: 38.933 |
|
- type: mrr_at_1000 |
|
value: 38.988 |
|
- type: mrr_at_3 |
|
value: 35.731 |
|
- type: mrr_at_5 |
|
value: 36.963 |
|
- type: ndcg_at_1 |
|
value: 29.110000000000003 |
|
- type: ndcg_at_10 |
|
value: 38.635000000000005 |
|
- type: ndcg_at_100 |
|
value: 44.324999999999996 |
|
- type: ndcg_at_1000 |
|
value: 46.747 |
|
- type: ndcg_at_3 |
|
value: 34.37 |
|
- type: ndcg_at_5 |
|
value: 36.228 |
|
- type: precision_at_1 |
|
value: 29.110000000000003 |
|
- type: precision_at_10 |
|
value: 6.963 |
|
- type: precision_at_100 |
|
value: 1.146 |
|
- type: precision_at_1000 |
|
value: 0.152 |
|
- type: precision_at_3 |
|
value: 16.400000000000002 |
|
- type: precision_at_5 |
|
value: 11.552999999999999 |
|
- type: recall_at_1 |
|
value: 24.108 |
|
- type: recall_at_10 |
|
value: 49.597 |
|
- type: recall_at_100 |
|
value: 73.88900000000001 |
|
- type: recall_at_1000 |
|
value: 90.62400000000001 |
|
- type: recall_at_3 |
|
value: 37.662 |
|
- type: recall_at_5 |
|
value: 42.565 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.00791666666667 |
|
- type: map_at_10 |
|
value: 33.287749999999996 |
|
- type: map_at_100 |
|
value: 34.41141666666667 |
|
- type: map_at_1000 |
|
value: 34.52583333333333 |
|
- type: map_at_3 |
|
value: 30.734416666666668 |
|
- type: map_at_5 |
|
value: 32.137166666666666 |
|
- type: mrr_at_1 |
|
value: 29.305666666666664 |
|
- type: mrr_at_10 |
|
value: 37.22966666666666 |
|
- type: mrr_at_100 |
|
value: 38.066583333333334 |
|
- type: mrr_at_1000 |
|
value: 38.12616666666667 |
|
- type: mrr_at_3 |
|
value: 34.92275 |
|
- type: mrr_at_5 |
|
value: 36.23333333333334 |
|
- type: ndcg_at_1 |
|
value: 29.305666666666664 |
|
- type: ndcg_at_10 |
|
value: 38.25533333333333 |
|
- type: ndcg_at_100 |
|
value: 43.25266666666666 |
|
- type: ndcg_at_1000 |
|
value: 45.63583333333334 |
|
- type: ndcg_at_3 |
|
value: 33.777166666666666 |
|
- type: ndcg_at_5 |
|
value: 35.85 |
|
- type: precision_at_1 |
|
value: 29.305666666666664 |
|
- type: precision_at_10 |
|
value: 6.596416666666667 |
|
- type: precision_at_100 |
|
value: 1.0784166666666668 |
|
- type: precision_at_1000 |
|
value: 0.14666666666666664 |
|
- type: precision_at_3 |
|
value: 15.31075 |
|
- type: precision_at_5 |
|
value: 10.830916666666667 |
|
- type: recall_at_1 |
|
value: 25.00791666666667 |
|
- type: recall_at_10 |
|
value: 49.10933333333333 |
|
- type: recall_at_100 |
|
value: 71.09216666666667 |
|
- type: recall_at_1000 |
|
value: 87.77725000000001 |
|
- type: recall_at_3 |
|
value: 36.660916666666665 |
|
- type: recall_at_5 |
|
value: 41.94149999999999 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.521 |
|
- type: map_at_10 |
|
value: 30.043 |
|
- type: map_at_100 |
|
value: 30.936000000000003 |
|
- type: map_at_1000 |
|
value: 31.022 |
|
- type: map_at_3 |
|
value: 27.926000000000002 |
|
- type: map_at_5 |
|
value: 29.076999999999998 |
|
- type: mrr_at_1 |
|
value: 26.227 |
|
- type: mrr_at_10 |
|
value: 32.822 |
|
- type: mrr_at_100 |
|
value: 33.61 |
|
- type: mrr_at_1000 |
|
value: 33.672000000000004 |
|
- type: mrr_at_3 |
|
value: 30.776999999999997 |
|
- type: mrr_at_5 |
|
value: 31.866 |
|
- type: ndcg_at_1 |
|
value: 26.227 |
|
- type: ndcg_at_10 |
|
value: 34.041 |
|
- type: ndcg_at_100 |
|
value: 38.394 |
|
- type: ndcg_at_1000 |
|
value: 40.732 |
|
- type: ndcg_at_3 |
|
value: 30.037999999999997 |
|
- type: ndcg_at_5 |
|
value: 31.845000000000002 |
|
- type: precision_at_1 |
|
value: 26.227 |
|
- type: precision_at_10 |
|
value: 5.244999999999999 |
|
- type: precision_at_100 |
|
value: 0.808 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_3 |
|
value: 12.679000000000002 |
|
- type: precision_at_5 |
|
value: 8.773 |
|
- type: recall_at_1 |
|
value: 23.521 |
|
- type: recall_at_10 |
|
value: 43.633 |
|
- type: recall_at_100 |
|
value: 63.126000000000005 |
|
- type: recall_at_1000 |
|
value: 80.765 |
|
- type: recall_at_3 |
|
value: 32.614 |
|
- type: recall_at_5 |
|
value: 37.15 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.236 |
|
- type: map_at_10 |
|
value: 22.898 |
|
- type: map_at_100 |
|
value: 23.878 |
|
- type: map_at_1000 |
|
value: 24.009 |
|
- type: map_at_3 |
|
value: 20.87 |
|
- type: map_at_5 |
|
value: 22.025 |
|
- type: mrr_at_1 |
|
value: 19.339000000000002 |
|
- type: mrr_at_10 |
|
value: 26.382 |
|
- type: mrr_at_100 |
|
value: 27.245 |
|
- type: mrr_at_1000 |
|
value: 27.33 |
|
- type: mrr_at_3 |
|
value: 24.386 |
|
- type: mrr_at_5 |
|
value: 25.496000000000002 |
|
- type: ndcg_at_1 |
|
value: 19.339000000000002 |
|
- type: ndcg_at_10 |
|
value: 27.139999999999997 |
|
- type: ndcg_at_100 |
|
value: 31.944 |
|
- type: ndcg_at_1000 |
|
value: 35.077999999999996 |
|
- type: ndcg_at_3 |
|
value: 23.424 |
|
- type: ndcg_at_5 |
|
value: 25.188 |
|
- type: precision_at_1 |
|
value: 19.339000000000002 |
|
- type: precision_at_10 |
|
value: 4.8309999999999995 |
|
- type: precision_at_100 |
|
value: 0.845 |
|
- type: precision_at_1000 |
|
value: 0.128 |
|
- type: precision_at_3 |
|
value: 10.874 |
|
- type: precision_at_5 |
|
value: 7.825 |
|
- type: recall_at_1 |
|
value: 16.236 |
|
- type: recall_at_10 |
|
value: 36.513 |
|
- type: recall_at_100 |
|
value: 57.999 |
|
- type: recall_at_1000 |
|
value: 80.512 |
|
- type: recall_at_3 |
|
value: 26.179999999999996 |
|
- type: recall_at_5 |
|
value: 30.712 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.11 |
|
- type: map_at_10 |
|
value: 31.566 |
|
- type: map_at_100 |
|
value: 32.647 |
|
- type: map_at_1000 |
|
value: 32.753 |
|
- type: map_at_3 |
|
value: 29.24 |
|
- type: map_at_5 |
|
value: 30.564999999999998 |
|
- type: mrr_at_1 |
|
value: 28.265 |
|
- type: mrr_at_10 |
|
value: 35.504000000000005 |
|
- type: mrr_at_100 |
|
value: 36.436 |
|
- type: mrr_at_1000 |
|
value: 36.503 |
|
- type: mrr_at_3 |
|
value: 33.349000000000004 |
|
- type: mrr_at_5 |
|
value: 34.622 |
|
- type: ndcg_at_1 |
|
value: 28.265 |
|
- type: ndcg_at_10 |
|
value: 36.192 |
|
- type: ndcg_at_100 |
|
value: 41.388000000000005 |
|
- type: ndcg_at_1000 |
|
value: 43.948 |
|
- type: ndcg_at_3 |
|
value: 31.959 |
|
- type: ndcg_at_5 |
|
value: 33.998 |
|
- type: precision_at_1 |
|
value: 28.265 |
|
- type: precision_at_10 |
|
value: 5.989 |
|
- type: precision_at_100 |
|
value: 0.9650000000000001 |
|
- type: precision_at_1000 |
|
value: 0.13 |
|
- type: precision_at_3 |
|
value: 14.335 |
|
- type: precision_at_5 |
|
value: 10.112 |
|
- type: recall_at_1 |
|
value: 24.11 |
|
- type: recall_at_10 |
|
value: 46.418 |
|
- type: recall_at_100 |
|
value: 69.314 |
|
- type: recall_at_1000 |
|
value: 87.397 |
|
- type: recall_at_3 |
|
value: 34.724 |
|
- type: recall_at_5 |
|
value: 39.925 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.091 |
|
- type: map_at_10 |
|
value: 29.948999999999998 |
|
- type: map_at_100 |
|
value: 31.502000000000002 |
|
- type: map_at_1000 |
|
value: 31.713 |
|
- type: map_at_3 |
|
value: 27.464 |
|
- type: map_at_5 |
|
value: 28.968 |
|
- type: mrr_at_1 |
|
value: 26.482 |
|
- type: mrr_at_10 |
|
value: 34.009 |
|
- type: mrr_at_100 |
|
value: 35.081 |
|
- type: mrr_at_1000 |
|
value: 35.138000000000005 |
|
- type: mrr_at_3 |
|
value: 31.785000000000004 |
|
- type: mrr_at_5 |
|
value: 33.178999999999995 |
|
- type: ndcg_at_1 |
|
value: 26.482 |
|
- type: ndcg_at_10 |
|
value: 35.008 |
|
- type: ndcg_at_100 |
|
value: 41.272999999999996 |
|
- type: ndcg_at_1000 |
|
value: 43.972 |
|
- type: ndcg_at_3 |
|
value: 30.804 |
|
- type: ndcg_at_5 |
|
value: 33.046 |
|
- type: precision_at_1 |
|
value: 26.482 |
|
- type: precision_at_10 |
|
value: 6.462 |
|
- type: precision_at_100 |
|
value: 1.431 |
|
- type: precision_at_1000 |
|
value: 0.22899999999999998 |
|
- type: precision_at_3 |
|
value: 14.360999999999999 |
|
- type: precision_at_5 |
|
value: 10.474 |
|
- type: recall_at_1 |
|
value: 22.091 |
|
- type: recall_at_10 |
|
value: 45.125 |
|
- type: recall_at_100 |
|
value: 72.313 |
|
- type: recall_at_1000 |
|
value: 89.503 |
|
- type: recall_at_3 |
|
value: 33.158 |
|
- type: recall_at_5 |
|
value: 39.086999999999996 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 19.883 |
|
- type: map_at_10 |
|
value: 26.951000000000004 |
|
- type: map_at_100 |
|
value: 27.927999999999997 |
|
- type: map_at_1000 |
|
value: 28.022000000000002 |
|
- type: map_at_3 |
|
value: 24.616 |
|
- type: map_at_5 |
|
value: 25.917 |
|
- type: mrr_at_1 |
|
value: 21.996 |
|
- type: mrr_at_10 |
|
value: 29.221000000000004 |
|
- type: mrr_at_100 |
|
value: 30.024 |
|
- type: mrr_at_1000 |
|
value: 30.095 |
|
- type: mrr_at_3 |
|
value: 26.833000000000002 |
|
- type: mrr_at_5 |
|
value: 28.155 |
|
- type: ndcg_at_1 |
|
value: 21.996 |
|
- type: ndcg_at_10 |
|
value: 31.421 |
|
- type: ndcg_at_100 |
|
value: 36.237 |
|
- type: ndcg_at_1000 |
|
value: 38.744 |
|
- type: ndcg_at_3 |
|
value: 26.671 |
|
- type: ndcg_at_5 |
|
value: 28.907 |
|
- type: precision_at_1 |
|
value: 21.996 |
|
- type: precision_at_10 |
|
value: 5.009 |
|
- type: precision_at_100 |
|
value: 0.799 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 11.275 |
|
- type: precision_at_5 |
|
value: 8.059 |
|
- type: recall_at_1 |
|
value: 19.883 |
|
- type: recall_at_10 |
|
value: 43.132999999999996 |
|
- type: recall_at_100 |
|
value: 65.654 |
|
- type: recall_at_1000 |
|
value: 84.492 |
|
- type: recall_at_3 |
|
value: 30.209000000000003 |
|
- type: recall_at_5 |
|
value: 35.616 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.756 |
|
- type: map_at_10 |
|
value: 30.378 |
|
- type: map_at_100 |
|
value: 32.537 |
|
- type: map_at_1000 |
|
value: 32.717 |
|
- type: map_at_3 |
|
value: 25.599 |
|
- type: map_at_5 |
|
value: 28.372999999999998 |
|
- type: mrr_at_1 |
|
value: 41.303 |
|
- type: mrr_at_10 |
|
value: 53.483999999999995 |
|
- type: mrr_at_100 |
|
value: 54.106 |
|
- type: mrr_at_1000 |
|
value: 54.127 |
|
- type: mrr_at_3 |
|
value: 50.315 |
|
- type: mrr_at_5 |
|
value: 52.396 |
|
- type: ndcg_at_1 |
|
value: 41.303 |
|
- type: ndcg_at_10 |
|
value: 40.503 |
|
- type: ndcg_at_100 |
|
value: 47.821000000000005 |
|
- type: ndcg_at_1000 |
|
value: 50.788 |
|
- type: ndcg_at_3 |
|
value: 34.364 |
|
- type: ndcg_at_5 |
|
value: 36.818 |
|
- type: precision_at_1 |
|
value: 41.303 |
|
- type: precision_at_10 |
|
value: 12.463000000000001 |
|
- type: precision_at_100 |
|
value: 2.037 |
|
- type: precision_at_1000 |
|
value: 0.26 |
|
- type: precision_at_3 |
|
value: 25.798 |
|
- type: precision_at_5 |
|
value: 19.896 |
|
- type: recall_at_1 |
|
value: 17.756 |
|
- type: recall_at_10 |
|
value: 46.102 |
|
- type: recall_at_100 |
|
value: 70.819 |
|
- type: recall_at_1000 |
|
value: 87.21799999999999 |
|
- type: recall_at_3 |
|
value: 30.646 |
|
- type: recall_at_5 |
|
value: 38.022 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.033 |
|
- type: map_at_10 |
|
value: 20.584 |
|
- type: map_at_100 |
|
value: 29.518 |
|
- type: map_at_1000 |
|
value: 31.186000000000003 |
|
- type: map_at_3 |
|
value: 14.468 |
|
- type: map_at_5 |
|
value: 17.177 |
|
- type: mrr_at_1 |
|
value: 69.75 |
|
- type: mrr_at_10 |
|
value: 77.025 |
|
- type: mrr_at_100 |
|
value: 77.36699999999999 |
|
- type: mrr_at_1000 |
|
value: 77.373 |
|
- type: mrr_at_3 |
|
value: 75.583 |
|
- type: mrr_at_5 |
|
value: 76.396 |
|
- type: ndcg_at_1 |
|
value: 58.5 |
|
- type: ndcg_at_10 |
|
value: 45.033 |
|
- type: ndcg_at_100 |
|
value: 49.071 |
|
- type: ndcg_at_1000 |
|
value: 56.056 |
|
- type: ndcg_at_3 |
|
value: 49.936 |
|
- type: ndcg_at_5 |
|
value: 47.471999999999994 |
|
- type: precision_at_1 |
|
value: 69.75 |
|
- type: precision_at_10 |
|
value: 35.775 |
|
- type: precision_at_100 |
|
value: 11.594999999999999 |
|
- type: precision_at_1000 |
|
value: 2.062 |
|
- type: precision_at_3 |
|
value: 52.5 |
|
- type: precision_at_5 |
|
value: 45.300000000000004 |
|
- type: recall_at_1 |
|
value: 9.033 |
|
- type: recall_at_10 |
|
value: 26.596999999999998 |
|
- type: recall_at_100 |
|
value: 54.607000000000006 |
|
- type: recall_at_1000 |
|
value: 76.961 |
|
- type: recall_at_3 |
|
value: 15.754999999999999 |
|
- type: recall_at_5 |
|
value: 20.033 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 48.345000000000006 |
|
- type: f1 |
|
value: 43.4514918068706 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.29100000000001 |
|
- type: map_at_10 |
|
value: 81.059 |
|
- type: map_at_100 |
|
value: 81.341 |
|
- type: map_at_1000 |
|
value: 81.355 |
|
- type: map_at_3 |
|
value: 79.74799999999999 |
|
- type: map_at_5 |
|
value: 80.612 |
|
- type: mrr_at_1 |
|
value: 76.40299999999999 |
|
- type: mrr_at_10 |
|
value: 84.615 |
|
- type: mrr_at_100 |
|
value: 84.745 |
|
- type: mrr_at_1000 |
|
value: 84.748 |
|
- type: mrr_at_3 |
|
value: 83.776 |
|
- type: mrr_at_5 |
|
value: 84.343 |
|
- type: ndcg_at_1 |
|
value: 76.40299999999999 |
|
- type: ndcg_at_10 |
|
value: 84.981 |
|
- type: ndcg_at_100 |
|
value: 86.00999999999999 |
|
- type: ndcg_at_1000 |
|
value: 86.252 |
|
- type: ndcg_at_3 |
|
value: 82.97 |
|
- type: ndcg_at_5 |
|
value: 84.152 |
|
- type: precision_at_1 |
|
value: 76.40299999999999 |
|
- type: precision_at_10 |
|
value: 10.446 |
|
- type: precision_at_100 |
|
value: 1.1199999999999999 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 32.147999999999996 |
|
- type: precision_at_5 |
|
value: 20.135 |
|
- type: recall_at_1 |
|
value: 71.29100000000001 |
|
- type: recall_at_10 |
|
value: 93.232 |
|
- type: recall_at_100 |
|
value: 97.363 |
|
- type: recall_at_1000 |
|
value: 98.905 |
|
- type: recall_at_3 |
|
value: 87.893 |
|
- type: recall_at_5 |
|
value: 90.804 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.667 |
|
- type: map_at_10 |
|
value: 30.853 |
|
- type: map_at_100 |
|
value: 32.494 |
|
- type: map_at_1000 |
|
value: 32.677 |
|
- type: map_at_3 |
|
value: 26.91 |
|
- type: map_at_5 |
|
value: 29.099000000000004 |
|
- type: mrr_at_1 |
|
value: 37.191 |
|
- type: mrr_at_10 |
|
value: 46.171 |
|
- type: mrr_at_100 |
|
value: 47.056 |
|
- type: mrr_at_1000 |
|
value: 47.099000000000004 |
|
- type: mrr_at_3 |
|
value: 44.059 |
|
- type: mrr_at_5 |
|
value: 45.147 |
|
- type: ndcg_at_1 |
|
value: 37.191 |
|
- type: ndcg_at_10 |
|
value: 38.437 |
|
- type: ndcg_at_100 |
|
value: 44.62 |
|
- type: ndcg_at_1000 |
|
value: 47.795 |
|
- type: ndcg_at_3 |
|
value: 35.003 |
|
- type: ndcg_at_5 |
|
value: 36.006 |
|
- type: precision_at_1 |
|
value: 37.191 |
|
- type: precision_at_10 |
|
value: 10.586 |
|
- type: precision_at_100 |
|
value: 1.688 |
|
- type: precision_at_1000 |
|
value: 0.22699999999999998 |
|
- type: precision_at_3 |
|
value: 23.302 |
|
- type: precision_at_5 |
|
value: 17.006 |
|
- type: recall_at_1 |
|
value: 18.667 |
|
- type: recall_at_10 |
|
value: 45.367000000000004 |
|
- type: recall_at_100 |
|
value: 68.207 |
|
- type: recall_at_1000 |
|
value: 87.072 |
|
- type: recall_at_3 |
|
value: 32.129000000000005 |
|
- type: recall_at_5 |
|
value: 37.719 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 39.494 |
|
- type: map_at_10 |
|
value: 66.223 |
|
- type: map_at_100 |
|
value: 67.062 |
|
- type: map_at_1000 |
|
value: 67.11500000000001 |
|
- type: map_at_3 |
|
value: 62.867 |
|
- type: map_at_5 |
|
value: 64.994 |
|
- type: mrr_at_1 |
|
value: 78.987 |
|
- type: mrr_at_10 |
|
value: 84.585 |
|
- type: mrr_at_100 |
|
value: 84.773 |
|
- type: mrr_at_1000 |
|
value: 84.77900000000001 |
|
- type: mrr_at_3 |
|
value: 83.592 |
|
- type: mrr_at_5 |
|
value: 84.235 |
|
- type: ndcg_at_1 |
|
value: 78.987 |
|
- type: ndcg_at_10 |
|
value: 73.64 |
|
- type: ndcg_at_100 |
|
value: 76.519 |
|
- type: ndcg_at_1000 |
|
value: 77.51 |
|
- type: ndcg_at_3 |
|
value: 68.893 |
|
- type: ndcg_at_5 |
|
value: 71.585 |
|
- type: precision_at_1 |
|
value: 78.987 |
|
- type: precision_at_10 |
|
value: 15.529000000000002 |
|
- type: precision_at_100 |
|
value: 1.7770000000000001 |
|
- type: precision_at_1000 |
|
value: 0.191 |
|
- type: precision_at_3 |
|
value: 44.808 |
|
- type: precision_at_5 |
|
value: 29.006999999999998 |
|
- type: recall_at_1 |
|
value: 39.494 |
|
- type: recall_at_10 |
|
value: 77.643 |
|
- type: recall_at_100 |
|
value: 88.825 |
|
- type: recall_at_1000 |
|
value: 95.321 |
|
- type: recall_at_3 |
|
value: 67.211 |
|
- type: recall_at_5 |
|
value: 72.519 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 85.55959999999999 |
|
- type: ap |
|
value: 80.7246500384617 |
|
- type: f1 |
|
value: 85.52336485065454 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.631 |
|
- type: map_at_10 |
|
value: 36.264 |
|
- type: map_at_100 |
|
value: 37.428 |
|
- type: map_at_1000 |
|
value: 37.472 |
|
- type: map_at_3 |
|
value: 32.537 |
|
- type: map_at_5 |
|
value: 34.746 |
|
- type: mrr_at_1 |
|
value: 24.312 |
|
- type: mrr_at_10 |
|
value: 36.858000000000004 |
|
- type: mrr_at_100 |
|
value: 37.966 |
|
- type: mrr_at_1000 |
|
value: 38.004 |
|
- type: mrr_at_3 |
|
value: 33.188 |
|
- type: mrr_at_5 |
|
value: 35.367 |
|
- type: ndcg_at_1 |
|
value: 24.312 |
|
- type: ndcg_at_10 |
|
value: 43.126999999999995 |
|
- type: ndcg_at_100 |
|
value: 48.642 |
|
- type: ndcg_at_1000 |
|
value: 49.741 |
|
- type: ndcg_at_3 |
|
value: 35.589 |
|
- type: ndcg_at_5 |
|
value: 39.515 |
|
- type: precision_at_1 |
|
value: 24.312 |
|
- type: precision_at_10 |
|
value: 6.699 |
|
- type: precision_at_100 |
|
value: 0.9450000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 15.153 |
|
- type: precision_at_5 |
|
value: 11.065999999999999 |
|
- type: recall_at_1 |
|
value: 23.631 |
|
- type: recall_at_10 |
|
value: 64.145 |
|
- type: recall_at_100 |
|
value: 89.41 |
|
- type: recall_at_1000 |
|
value: 97.83500000000001 |
|
- type: recall_at_3 |
|
value: 43.769000000000005 |
|
- type: recall_at_5 |
|
value: 53.169 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 93.4108527131783 |
|
- type: f1 |
|
value: 93.1415880261038 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 77.24806201550388 |
|
- type: f1 |
|
value: 60.531916308197175 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.71553463349024 |
|
- type: f1 |
|
value: 71.70753174900791 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 77.79757901815736 |
|
- type: f1 |
|
value: 77.83719850433258 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 33.74193296622113 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 30.64257594108566 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.811018518883625 |
|
- type: mrr |
|
value: 31.910376577445003 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.409 |
|
- type: map_at_10 |
|
value: 13.093 |
|
- type: map_at_100 |
|
value: 16.256999999999998 |
|
- type: map_at_1000 |
|
value: 17.617 |
|
- type: map_at_3 |
|
value: 9.555 |
|
- type: map_at_5 |
|
value: 11.428 |
|
- type: mrr_at_1 |
|
value: 45.201 |
|
- type: mrr_at_10 |
|
value: 54.179 |
|
- type: mrr_at_100 |
|
value: 54.812000000000005 |
|
- type: mrr_at_1000 |
|
value: 54.840999999999994 |
|
- type: mrr_at_3 |
|
value: 51.909000000000006 |
|
- type: mrr_at_5 |
|
value: 53.519000000000005 |
|
- type: ndcg_at_1 |
|
value: 43.189 |
|
- type: ndcg_at_10 |
|
value: 35.028 |
|
- type: ndcg_at_100 |
|
value: 31.226 |
|
- type: ndcg_at_1000 |
|
value: 39.678000000000004 |
|
- type: ndcg_at_3 |
|
value: 40.596 |
|
- type: ndcg_at_5 |
|
value: 38.75 |
|
- type: precision_at_1 |
|
value: 44.582 |
|
- type: precision_at_10 |
|
value: 25.974999999999998 |
|
- type: precision_at_100 |
|
value: 7.793 |
|
- type: precision_at_1000 |
|
value: 2.036 |
|
- type: precision_at_3 |
|
value: 38.493 |
|
- type: precision_at_5 |
|
value: 33.994 |
|
- type: recall_at_1 |
|
value: 5.409 |
|
- type: recall_at_10 |
|
value: 16.875999999999998 |
|
- type: recall_at_100 |
|
value: 30.316 |
|
- type: recall_at_1000 |
|
value: 60.891 |
|
- type: recall_at_3 |
|
value: 10.688 |
|
- type: recall_at_5 |
|
value: 13.832 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 36.375 |
|
- type: map_at_10 |
|
value: 51.991 |
|
- type: map_at_100 |
|
value: 52.91400000000001 |
|
- type: map_at_1000 |
|
value: 52.93600000000001 |
|
- type: map_at_3 |
|
value: 48.014 |
|
- type: map_at_5 |
|
value: 50.381 |
|
- type: mrr_at_1 |
|
value: 40.759 |
|
- type: mrr_at_10 |
|
value: 54.617000000000004 |
|
- type: mrr_at_100 |
|
value: 55.301 |
|
- type: mrr_at_1000 |
|
value: 55.315000000000005 |
|
- type: mrr_at_3 |
|
value: 51.516 |
|
- type: mrr_at_5 |
|
value: 53.435 |
|
- type: ndcg_at_1 |
|
value: 40.759 |
|
- type: ndcg_at_10 |
|
value: 59.384 |
|
- type: ndcg_at_100 |
|
value: 63.157 |
|
- type: ndcg_at_1000 |
|
value: 63.654999999999994 |
|
- type: ndcg_at_3 |
|
value: 52.114000000000004 |
|
- type: ndcg_at_5 |
|
value: 55.986000000000004 |
|
- type: precision_at_1 |
|
value: 40.759 |
|
- type: precision_at_10 |
|
value: 9.411999999999999 |
|
- type: precision_at_100 |
|
value: 1.153 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 23.329 |
|
- type: precision_at_5 |
|
value: 16.256999999999998 |
|
- type: recall_at_1 |
|
value: 36.375 |
|
- type: recall_at_10 |
|
value: 79.053 |
|
- type: recall_at_100 |
|
value: 95.167 |
|
- type: recall_at_1000 |
|
value: 98.82 |
|
- type: recall_at_3 |
|
value: 60.475 |
|
- type: recall_at_5 |
|
value: 69.327 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 70.256 |
|
- type: map_at_10 |
|
value: 83.8 |
|
- type: map_at_100 |
|
value: 84.425 |
|
- type: map_at_1000 |
|
value: 84.444 |
|
- type: map_at_3 |
|
value: 80.906 |
|
- type: map_at_5 |
|
value: 82.717 |
|
- type: mrr_at_1 |
|
value: 80.97999999999999 |
|
- type: mrr_at_10 |
|
value: 87.161 |
|
- type: mrr_at_100 |
|
value: 87.262 |
|
- type: mrr_at_1000 |
|
value: 87.263 |
|
- type: mrr_at_3 |
|
value: 86.175 |
|
- type: mrr_at_5 |
|
value: 86.848 |
|
- type: ndcg_at_1 |
|
value: 80.97999999999999 |
|
- type: ndcg_at_10 |
|
value: 87.697 |
|
- type: ndcg_at_100 |
|
value: 88.959 |
|
- type: ndcg_at_1000 |
|
value: 89.09899999999999 |
|
- type: ndcg_at_3 |
|
value: 84.83800000000001 |
|
- type: ndcg_at_5 |
|
value: 86.401 |
|
- type: precision_at_1 |
|
value: 80.97999999999999 |
|
- type: precision_at_10 |
|
value: 13.261000000000001 |
|
- type: precision_at_100 |
|
value: 1.5150000000000001 |
|
- type: precision_at_1000 |
|
value: 0.156 |
|
- type: precision_at_3 |
|
value: 37.01 |
|
- type: precision_at_5 |
|
value: 24.298000000000002 |
|
- type: recall_at_1 |
|
value: 70.256 |
|
- type: recall_at_10 |
|
value: 94.935 |
|
- type: recall_at_100 |
|
value: 99.274 |
|
- type: recall_at_1000 |
|
value: 99.928 |
|
- type: recall_at_3 |
|
value: 86.602 |
|
- type: recall_at_5 |
|
value: 91.133 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 56.322692497613104 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 61.895813503775074 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 4.338 |
|
- type: map_at_10 |
|
value: 10.767 |
|
- type: map_at_100 |
|
value: 12.537999999999998 |
|
- type: map_at_1000 |
|
value: 12.803999999999998 |
|
- type: map_at_3 |
|
value: 7.788 |
|
- type: map_at_5 |
|
value: 9.302000000000001 |
|
- type: mrr_at_1 |
|
value: 21.4 |
|
- type: mrr_at_10 |
|
value: 31.637999999999998 |
|
- type: mrr_at_100 |
|
value: 32.688 |
|
- type: mrr_at_1000 |
|
value: 32.756 |
|
- type: mrr_at_3 |
|
value: 28.433000000000003 |
|
- type: mrr_at_5 |
|
value: 30.178 |
|
- type: ndcg_at_1 |
|
value: 21.4 |
|
- type: ndcg_at_10 |
|
value: 18.293 |
|
- type: ndcg_at_100 |
|
value: 25.274 |
|
- type: ndcg_at_1000 |
|
value: 30.284 |
|
- type: ndcg_at_3 |
|
value: 17.391000000000002 |
|
- type: ndcg_at_5 |
|
value: 15.146999999999998 |
|
- type: precision_at_1 |
|
value: 21.4 |
|
- type: precision_at_10 |
|
value: 9.48 |
|
- type: precision_at_100 |
|
value: 1.949 |
|
- type: precision_at_1000 |
|
value: 0.316 |
|
- type: precision_at_3 |
|
value: 16.167 |
|
- type: precision_at_5 |
|
value: 13.22 |
|
- type: recall_at_1 |
|
value: 4.338 |
|
- type: recall_at_10 |
|
value: 19.213 |
|
- type: recall_at_100 |
|
value: 39.562999999999995 |
|
- type: recall_at_1000 |
|
value: 64.08 |
|
- type: recall_at_3 |
|
value: 9.828000000000001 |
|
- type: recall_at_5 |
|
value: 13.383000000000001 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.42568163642142 |
|
- type: cos_sim_spearman |
|
value: 78.5797159641342 |
|
- type: euclidean_pearson |
|
value: 80.22151260811604 |
|
- type: euclidean_spearman |
|
value: 78.5797151953878 |
|
- type: manhattan_pearson |
|
value: 80.21224215864788 |
|
- type: manhattan_spearman |
|
value: 78.55641478381344 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 85.44020710812569 |
|
- type: cos_sim_spearman |
|
value: 78.91631735081286 |
|
- type: euclidean_pearson |
|
value: 81.64188964182102 |
|
- type: euclidean_spearman |
|
value: 78.91633286881678 |
|
- type: manhattan_pearson |
|
value: 81.69294748512496 |
|
- type: manhattan_spearman |
|
value: 78.93438558002656 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.27165426412311 |
|
- type: cos_sim_spearman |
|
value: 85.40429140249618 |
|
- type: euclidean_pearson |
|
value: 84.7509580724893 |
|
- type: euclidean_spearman |
|
value: 85.40429140249618 |
|
- type: manhattan_pearson |
|
value: 84.76488289321308 |
|
- type: manhattan_spearman |
|
value: 85.4256793698708 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.138851760732 |
|
- type: cos_sim_spearman |
|
value: 81.64101363896586 |
|
- type: euclidean_pearson |
|
value: 82.55165038934942 |
|
- type: euclidean_spearman |
|
value: 81.64105257080502 |
|
- type: manhattan_pearson |
|
value: 82.52802949883335 |
|
- type: manhattan_spearman |
|
value: 81.61255430718158 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.0654695484029 |
|
- type: cos_sim_spearman |
|
value: 87.20408521902229 |
|
- type: euclidean_pearson |
|
value: 86.8110651362115 |
|
- type: euclidean_spearman |
|
value: 87.20408521902229 |
|
- type: manhattan_pearson |
|
value: 86.77984656478691 |
|
- type: manhattan_spearman |
|
value: 87.1719947099227 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.77823915496512 |
|
- type: cos_sim_spearman |
|
value: 85.43566325729779 |
|
- type: euclidean_pearson |
|
value: 84.5396956658821 |
|
- type: euclidean_spearman |
|
value: 85.43566325729779 |
|
- type: manhattan_pearson |
|
value: 84.5665398848169 |
|
- type: manhattan_spearman |
|
value: 85.44375870303232 |
|
- 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: 87.20030208471798 |
|
- type: cos_sim_spearman |
|
value: 87.20485505076539 |
|
- type: euclidean_pearson |
|
value: 88.10588324368722 |
|
- type: euclidean_spearman |
|
value: 87.20485505076539 |
|
- type: manhattan_pearson |
|
value: 87.92324770415183 |
|
- type: manhattan_spearman |
|
value: 87.0571314561877 |
|
- 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: 63.06093161604453 |
|
- type: cos_sim_spearman |
|
value: 64.2163140357722 |
|
- type: euclidean_pearson |
|
value: 65.27589680994006 |
|
- type: euclidean_spearman |
|
value: 64.2163140357722 |
|
- type: manhattan_pearson |
|
value: 65.45904383711101 |
|
- type: manhattan_spearman |
|
value: 64.55404716679305 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 84.32976164578706 |
|
- type: cos_sim_spearman |
|
value: 85.54302197678368 |
|
- type: euclidean_pearson |
|
value: 85.26307149193056 |
|
- type: euclidean_spearman |
|
value: 85.54302197678368 |
|
- type: manhattan_pearson |
|
value: 85.26647282029371 |
|
- type: manhattan_spearman |
|
value: 85.5316135265568 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 81.44675968318754 |
|
- type: mrr |
|
value: 94.92741826075158 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 56.34400000000001 |
|
- type: map_at_10 |
|
value: 65.927 |
|
- type: map_at_100 |
|
value: 66.431 |
|
- type: map_at_1000 |
|
value: 66.461 |
|
- type: map_at_3 |
|
value: 63.529 |
|
- type: map_at_5 |
|
value: 64.818 |
|
- type: mrr_at_1 |
|
value: 59.333000000000006 |
|
- type: mrr_at_10 |
|
value: 67.54599999999999 |
|
- type: mrr_at_100 |
|
value: 67.892 |
|
- type: mrr_at_1000 |
|
value: 67.917 |
|
- type: mrr_at_3 |
|
value: 65.778 |
|
- type: mrr_at_5 |
|
value: 66.794 |
|
- type: ndcg_at_1 |
|
value: 59.333000000000006 |
|
- type: ndcg_at_10 |
|
value: 70.5 |
|
- type: ndcg_at_100 |
|
value: 72.688 |
|
- type: ndcg_at_1000 |
|
value: 73.483 |
|
- type: ndcg_at_3 |
|
value: 66.338 |
|
- type: ndcg_at_5 |
|
value: 68.265 |
|
- type: precision_at_1 |
|
value: 59.333000000000006 |
|
- type: precision_at_10 |
|
value: 9.3 |
|
- type: precision_at_100 |
|
value: 1.053 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 25.889 |
|
- type: precision_at_5 |
|
value: 16.866999999999997 |
|
- type: recall_at_1 |
|
value: 56.34400000000001 |
|
- type: recall_at_10 |
|
value: 82.789 |
|
- type: recall_at_100 |
|
value: 92.767 |
|
- type: recall_at_1000 |
|
value: 99 |
|
- type: recall_at_3 |
|
value: 71.64399999999999 |
|
- type: recall_at_5 |
|
value: 76.322 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.75742574257426 |
|
- type: cos_sim_ap |
|
value: 93.52081548447406 |
|
- type: cos_sim_f1 |
|
value: 87.33850129198966 |
|
- type: cos_sim_precision |
|
value: 90.37433155080214 |
|
- type: cos_sim_recall |
|
value: 84.5 |
|
- type: dot_accuracy |
|
value: 99.75742574257426 |
|
- type: dot_ap |
|
value: 93.52081548447406 |
|
- type: dot_f1 |
|
value: 87.33850129198966 |
|
- type: dot_precision |
|
value: 90.37433155080214 |
|
- type: dot_recall |
|
value: 84.5 |
|
- type: euclidean_accuracy |
|
value: 99.75742574257426 |
|
- type: euclidean_ap |
|
value: 93.52081548447406 |
|
- type: euclidean_f1 |
|
value: 87.33850129198966 |
|
- type: euclidean_precision |
|
value: 90.37433155080214 |
|
- type: euclidean_recall |
|
value: 84.5 |
|
- type: manhattan_accuracy |
|
value: 99.75841584158415 |
|
- type: manhattan_ap |
|
value: 93.4975678585854 |
|
- type: manhattan_f1 |
|
value: 87.26708074534162 |
|
- type: manhattan_precision |
|
value: 90.45064377682404 |
|
- type: manhattan_recall |
|
value: 84.3 |
|
- type: max_accuracy |
|
value: 99.75841584158415 |
|
- type: max_ap |
|
value: 93.52081548447406 |
|
- type: max_f1 |
|
value: 87.33850129198966 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 64.31437036686651 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 33.25569319007206 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 49.90474939720706 |
|
- type: mrr |
|
value: 50.568115503777264 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 29.866828641244712 |
|
- type: cos_sim_spearman |
|
value: 30.077555055873866 |
|
- type: dot_pearson |
|
value: 29.866832988572266 |
|
- type: dot_spearman |
|
value: 30.077555055873866 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.232 |
|
- type: map_at_10 |
|
value: 2.094 |
|
- type: map_at_100 |
|
value: 11.971 |
|
- type: map_at_1000 |
|
value: 28.158 |
|
- type: map_at_3 |
|
value: 0.688 |
|
- type: map_at_5 |
|
value: 1.114 |
|
- type: mrr_at_1 |
|
value: 88 |
|
- type: mrr_at_10 |
|
value: 93.4 |
|
- type: mrr_at_100 |
|
value: 93.4 |
|
- type: mrr_at_1000 |
|
value: 93.4 |
|
- type: mrr_at_3 |
|
value: 93 |
|
- type: mrr_at_5 |
|
value: 93.4 |
|
- type: ndcg_at_1 |
|
value: 84 |
|
- type: ndcg_at_10 |
|
value: 79.923 |
|
- type: ndcg_at_100 |
|
value: 61.17 |
|
- type: ndcg_at_1000 |
|
value: 53.03 |
|
- type: ndcg_at_3 |
|
value: 84.592 |
|
- type: ndcg_at_5 |
|
value: 82.821 |
|
- type: precision_at_1 |
|
value: 88 |
|
- type: precision_at_10 |
|
value: 85 |
|
- type: precision_at_100 |
|
value: 63.019999999999996 |
|
- type: precision_at_1000 |
|
value: 23.554 |
|
- type: precision_at_3 |
|
value: 89.333 |
|
- type: precision_at_5 |
|
value: 87.2 |
|
- type: recall_at_1 |
|
value: 0.232 |
|
- type: recall_at_10 |
|
value: 2.255 |
|
- type: recall_at_100 |
|
value: 14.823 |
|
- type: recall_at_1000 |
|
value: 49.456 |
|
- type: recall_at_3 |
|
value: 0.718 |
|
- type: recall_at_5 |
|
value: 1.175 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.547 |
|
- type: map_at_10 |
|
value: 11.375 |
|
- type: map_at_100 |
|
value: 18.194 |
|
- type: map_at_1000 |
|
value: 19.749 |
|
- type: map_at_3 |
|
value: 5.825 |
|
- type: map_at_5 |
|
value: 8.581 |
|
- type: mrr_at_1 |
|
value: 32.653 |
|
- type: mrr_at_10 |
|
value: 51.32 |
|
- type: mrr_at_100 |
|
value: 51.747 |
|
- type: mrr_at_1000 |
|
value: 51.747 |
|
- type: mrr_at_3 |
|
value: 47.278999999999996 |
|
- type: mrr_at_5 |
|
value: 48.605 |
|
- type: ndcg_at_1 |
|
value: 29.592000000000002 |
|
- type: ndcg_at_10 |
|
value: 28.151 |
|
- type: ndcg_at_100 |
|
value: 39.438 |
|
- type: ndcg_at_1000 |
|
value: 50.769 |
|
- type: ndcg_at_3 |
|
value: 30.758999999999997 |
|
- type: ndcg_at_5 |
|
value: 30.366 |
|
- type: precision_at_1 |
|
value: 32.653 |
|
- type: precision_at_10 |
|
value: 25.714 |
|
- type: precision_at_100 |
|
value: 8.041 |
|
- type: precision_at_1000 |
|
value: 1.555 |
|
- type: precision_at_3 |
|
value: 33.333 |
|
- type: precision_at_5 |
|
value: 31.837 |
|
- type: recall_at_1 |
|
value: 2.547 |
|
- type: recall_at_10 |
|
value: 18.19 |
|
- type: recall_at_100 |
|
value: 49.538 |
|
- type: recall_at_1000 |
|
value: 83.86 |
|
- type: recall_at_3 |
|
value: 7.329 |
|
- type: recall_at_5 |
|
value: 11.532 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 71.4952 |
|
- type: ap |
|
value: 14.793362635531409 |
|
- type: f1 |
|
value: 55.204635551516915 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 61.5365025466893 |
|
- type: f1 |
|
value: 61.81742556334845 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 49.05531070301185 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.51725576682364 |
|
- type: cos_sim_ap |
|
value: 75.2292304265163 |
|
- type: cos_sim_f1 |
|
value: 69.54022988505749 |
|
- type: cos_sim_precision |
|
value: 63.65629110039457 |
|
- type: cos_sim_recall |
|
value: 76.62269129287598 |
|
- type: dot_accuracy |
|
value: 86.51725576682364 |
|
- type: dot_ap |
|
value: 75.22922386081054 |
|
- type: dot_f1 |
|
value: 69.54022988505749 |
|
- type: dot_precision |
|
value: 63.65629110039457 |
|
- type: dot_recall |
|
value: 76.62269129287598 |
|
- type: euclidean_accuracy |
|
value: 86.51725576682364 |
|
- type: euclidean_ap |
|
value: 75.22925730473472 |
|
- type: euclidean_f1 |
|
value: 69.54022988505749 |
|
- type: euclidean_precision |
|
value: 63.65629110039457 |
|
- type: euclidean_recall |
|
value: 76.62269129287598 |
|
- type: manhattan_accuracy |
|
value: 86.52321630804077 |
|
- type: manhattan_ap |
|
value: 75.20608115037336 |
|
- type: manhattan_f1 |
|
value: 69.60000000000001 |
|
- type: manhattan_precision |
|
value: 64.37219730941705 |
|
- type: manhattan_recall |
|
value: 75.75197889182058 |
|
- type: max_accuracy |
|
value: 86.52321630804077 |
|
- type: max_ap |
|
value: 75.22925730473472 |
|
- type: max_f1 |
|
value: 69.60000000000001 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 89.34877944657896 |
|
- type: cos_sim_ap |
|
value: 86.71257569277373 |
|
- type: cos_sim_f1 |
|
value: 79.10386355986088 |
|
- type: cos_sim_precision |
|
value: 76.91468470434214 |
|
- type: cos_sim_recall |
|
value: 81.4213119802895 |
|
- type: dot_accuracy |
|
value: 89.34877944657896 |
|
- type: dot_ap |
|
value: 86.71257133133368 |
|
- type: dot_f1 |
|
value: 79.10386355986088 |
|
- type: dot_precision |
|
value: 76.91468470434214 |
|
- type: dot_recall |
|
value: 81.4213119802895 |
|
- type: euclidean_accuracy |
|
value: 89.34877944657896 |
|
- type: euclidean_ap |
|
value: 86.71257651501476 |
|
- type: euclidean_f1 |
|
value: 79.10386355986088 |
|
- type: euclidean_precision |
|
value: 76.91468470434214 |
|
- type: euclidean_recall |
|
value: 81.4213119802895 |
|
- type: manhattan_accuracy |
|
value: 89.35848177901967 |
|
- type: manhattan_ap |
|
value: 86.69330615469126 |
|
- type: manhattan_f1 |
|
value: 79.13867741453949 |
|
- type: manhattan_precision |
|
value: 76.78881807647741 |
|
- type: manhattan_recall |
|
value: 81.63689559593472 |
|
- type: max_accuracy |
|
value: 89.35848177901967 |
|
- type: max_ap |
|
value: 86.71257651501476 |
|
- type: max_f1 |
|
value: 79.13867741453949 |
|
license: apache-2.0 |
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language: |
|
- en |
|
--- |
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|
|
|
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# nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder |
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|
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`nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. |
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|
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| Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data | |
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| :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- | |
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| nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ | |
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| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ | |
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| text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ | |
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| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ | |
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|
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## Hosted Inference API |
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|
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The easiest way to get started with Nomic Embed is through the Nomic Embedding API. |
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|
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Generating embeddings with the `nomic` Python client is as easy as |
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|
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```python |
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from nomic import embed |
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|
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output = embed.text( |
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texts=['Nomic Embedding API', '#keepAIOpen'], |
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model='nomic-embed-text-v1', |
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task_type='search_document' |
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) |
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|
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print(output) |
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``` |
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|
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For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text) |
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|
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## Data Visualization |
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Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data! |
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[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample) |
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|
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## Training Details |
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|
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We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048), |
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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. |
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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. |
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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). |
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|
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Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) |
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## Usage |
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|
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Note `nomic-embed-text` requires prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`. |
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For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries. |
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|
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### Sentence Transformers |
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```python |
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from sentence_transformers import SentenceTransformer |
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|
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True) |
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sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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|
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### Transformers |
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|
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```python |
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import torch |
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import torch.nn.functional as F |
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from transformers import AutoTokenizer, AutoModel |
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|
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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|
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sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] |
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|
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tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
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model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True) |
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model.eval() |
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|
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
|
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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|
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embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
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embeddings = F.normalize(embeddings, p=2, dim=1) |
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print(embeddings) |
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``` |
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|
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The model natively supports scaling of the sequence length past 2048 tokens. To do so, |
|
|
|
```diff |
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- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
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+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192) |
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|
|
|
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- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True) |
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+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2) |
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``` |
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|
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### Transformers.js |
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|
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```js |
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import { pipeline } from '@xenova/transformers'; |
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|
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// Create a feature extraction pipeline |
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const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', { |
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quantized: false, // Comment out this line to use the quantized version |
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}); |
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|
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// Compute sentence embeddings |
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const texts = ['What is TSNE?', 'Who is Laurens van der Maaten?']; |
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const embeddings = await extractor(texts, { pooling: 'mean', normalize: true }); |
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console.log(embeddings); |
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``` |
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|
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# Join the Nomic Community |
|
|
|
- Nomic: [https://nomic.ai](https://nomic.ai) |
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- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8) |
|
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) |
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|
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# Citation |
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|
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If you find the model, dataset, or training code useful, please cite our work |
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|
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```bibtex |
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@misc{nussbaum2024nomic, |
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title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, |
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author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar}, |
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year={2024}, |
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eprint={2402.01613}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
|
``` |
