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--- |
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tags: |
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- mteb |
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- sentence-transfomres |
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- transformers |
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model-index: |
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- name: bge-large-en |
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results: |
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- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_counterfactual |
|
name: MTEB AmazonCounterfactualClassification (en) |
|
config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
|
metrics: |
|
- type: accuracy |
|
value: 76.94029850746269 |
|
- type: ap |
|
value: 40.00228964744091 |
|
- type: f1 |
|
value: 70.86088267934595 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_polarity |
|
name: MTEB AmazonPolarityClassification |
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config: default |
|
split: test |
|
revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
|
metrics: |
|
- type: accuracy |
|
value: 91.93745 |
|
- type: ap |
|
value: 88.24758534667426 |
|
- type: f1 |
|
value: 91.91033034217591 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (en) |
|
config: en |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 46.158 |
|
- type: f1 |
|
value: 45.78935185074774 |
|
- 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: 39.972 |
|
- type: map_at_10 |
|
value: 54.874 |
|
- type: map_at_100 |
|
value: 55.53399999999999 |
|
- type: map_at_1000 |
|
value: 55.539 |
|
- type: map_at_3 |
|
value: 51.031000000000006 |
|
- type: map_at_5 |
|
value: 53.342999999999996 |
|
- type: mrr_at_1 |
|
value: 40.541 |
|
- type: mrr_at_10 |
|
value: 55.096000000000004 |
|
- type: mrr_at_100 |
|
value: 55.75599999999999 |
|
- type: mrr_at_1000 |
|
value: 55.761 |
|
- type: mrr_at_3 |
|
value: 51.221000000000004 |
|
- type: mrr_at_5 |
|
value: 53.568000000000005 |
|
- type: ndcg_at_1 |
|
value: 39.972 |
|
- type: ndcg_at_10 |
|
value: 62.456999999999994 |
|
- type: ndcg_at_100 |
|
value: 65.262 |
|
- type: ndcg_at_1000 |
|
value: 65.389 |
|
- type: ndcg_at_3 |
|
value: 54.673 |
|
- type: ndcg_at_5 |
|
value: 58.80499999999999 |
|
- type: precision_at_1 |
|
value: 39.972 |
|
- type: precision_at_10 |
|
value: 8.634 |
|
- type: precision_at_100 |
|
value: 0.9860000000000001 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 21.740000000000002 |
|
- type: precision_at_5 |
|
value: 15.036 |
|
- type: recall_at_1 |
|
value: 39.972 |
|
- type: recall_at_10 |
|
value: 86.344 |
|
- type: recall_at_100 |
|
value: 98.578 |
|
- type: recall_at_1000 |
|
value: 99.57300000000001 |
|
- type: recall_at_3 |
|
value: 65.22 |
|
- type: recall_at_5 |
|
value: 75.178 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-p2p |
|
name: MTEB ArxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
|
metrics: |
|
- type: v_measure |
|
value: 48.94652870403906 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/arxiv-clustering-s2s |
|
name: MTEB ArxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
|
metrics: |
|
- type: v_measure |
|
value: 43.17257160340209 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/askubuntudupquestions-reranking |
|
name: MTEB AskUbuntuDupQuestions |
|
config: default |
|
split: test |
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
|
metrics: |
|
- type: map |
|
value: 63.97867370559182 |
|
- type: mrr |
|
value: 77.00820032537484 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/biosses-sts |
|
name: MTEB BIOSSES |
|
config: default |
|
split: test |
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.00986015960616 |
|
- type: cos_sim_spearman |
|
value: 80.36387933827882 |
|
- type: euclidean_pearson |
|
value: 80.32305287257296 |
|
- type: euclidean_spearman |
|
value: 82.0524720308763 |
|
- type: manhattan_pearson |
|
value: 80.19847473906454 |
|
- type: manhattan_spearman |
|
value: 81.87957652506985 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/banking77 |
|
name: MTEB Banking77Classification |
|
config: default |
|
split: test |
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
|
metrics: |
|
- type: accuracy |
|
value: 88.00000000000001 |
|
- type: f1 |
|
value: 87.99039027511853 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-p2p |
|
name: MTEB BiorxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
|
metrics: |
|
- type: v_measure |
|
value: 41.36932844640705 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/biorxiv-clustering-s2s |
|
name: MTEB BiorxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
|
metrics: |
|
- type: v_measure |
|
value: 38.34983239611985 |
|
- 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: 32.257999999999996 |
|
- type: map_at_10 |
|
value: 42.937 |
|
- type: map_at_100 |
|
value: 44.406 |
|
- type: map_at_1000 |
|
value: 44.536 |
|
- type: map_at_3 |
|
value: 39.22 |
|
- type: map_at_5 |
|
value: 41.458 |
|
- type: mrr_at_1 |
|
value: 38.769999999999996 |
|
- type: mrr_at_10 |
|
value: 48.701 |
|
- type: mrr_at_100 |
|
value: 49.431000000000004 |
|
- type: mrr_at_1000 |
|
value: 49.476 |
|
- type: mrr_at_3 |
|
value: 45.875 |
|
- type: mrr_at_5 |
|
value: 47.67 |
|
- type: ndcg_at_1 |
|
value: 38.769999999999996 |
|
- type: ndcg_at_10 |
|
value: 49.35 |
|
- type: ndcg_at_100 |
|
value: 54.618 |
|
- type: ndcg_at_1000 |
|
value: 56.655 |
|
- type: ndcg_at_3 |
|
value: 43.826 |
|
- type: ndcg_at_5 |
|
value: 46.72 |
|
- type: precision_at_1 |
|
value: 38.769999999999996 |
|
- type: precision_at_10 |
|
value: 9.328 |
|
- type: precision_at_100 |
|
value: 1.484 |
|
- type: precision_at_1000 |
|
value: 0.196 |
|
- type: precision_at_3 |
|
value: 20.649 |
|
- type: precision_at_5 |
|
value: 15.25 |
|
- type: recall_at_1 |
|
value: 32.257999999999996 |
|
- type: recall_at_10 |
|
value: 61.849 |
|
- type: recall_at_100 |
|
value: 83.70400000000001 |
|
- type: recall_at_1000 |
|
value: 96.344 |
|
- type: recall_at_3 |
|
value: 46.037 |
|
- type: recall_at_5 |
|
value: 53.724000000000004 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 32.979 |
|
- type: map_at_10 |
|
value: 43.376999999999995 |
|
- type: map_at_100 |
|
value: 44.667 |
|
- type: map_at_1000 |
|
value: 44.794 |
|
- type: map_at_3 |
|
value: 40.461999999999996 |
|
- type: map_at_5 |
|
value: 42.138 |
|
- type: mrr_at_1 |
|
value: 41.146 |
|
- type: mrr_at_10 |
|
value: 49.575 |
|
- type: mrr_at_100 |
|
value: 50.187000000000005 |
|
- type: mrr_at_1000 |
|
value: 50.231 |
|
- type: mrr_at_3 |
|
value: 47.601 |
|
- type: mrr_at_5 |
|
value: 48.786 |
|
- type: ndcg_at_1 |
|
value: 41.146 |
|
- type: ndcg_at_10 |
|
value: 48.957 |
|
- type: ndcg_at_100 |
|
value: 53.296 |
|
- type: ndcg_at_1000 |
|
value: 55.254000000000005 |
|
- type: ndcg_at_3 |
|
value: 45.235 |
|
- type: ndcg_at_5 |
|
value: 47.014 |
|
- type: precision_at_1 |
|
value: 41.146 |
|
- type: precision_at_10 |
|
value: 9.107999999999999 |
|
- type: precision_at_100 |
|
value: 1.481 |
|
- type: precision_at_1000 |
|
value: 0.193 |
|
- type: precision_at_3 |
|
value: 21.783 |
|
- type: precision_at_5 |
|
value: 15.274 |
|
- type: recall_at_1 |
|
value: 32.979 |
|
- type: recall_at_10 |
|
value: 58.167 |
|
- type: recall_at_100 |
|
value: 76.374 |
|
- type: recall_at_1000 |
|
value: 88.836 |
|
- type: recall_at_3 |
|
value: 46.838 |
|
- type: recall_at_5 |
|
value: 52.006 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGamingRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.326 |
|
- type: map_at_10 |
|
value: 53.468 |
|
- type: map_at_100 |
|
value: 54.454 |
|
- type: map_at_1000 |
|
value: 54.508 |
|
- type: map_at_3 |
|
value: 50.12799999999999 |
|
- type: map_at_5 |
|
value: 51.991 |
|
- type: mrr_at_1 |
|
value: 46.394999999999996 |
|
- type: mrr_at_10 |
|
value: 57.016999999999996 |
|
- type: mrr_at_100 |
|
value: 57.67099999999999 |
|
- type: mrr_at_1000 |
|
value: 57.699999999999996 |
|
- type: mrr_at_3 |
|
value: 54.65 |
|
- type: mrr_at_5 |
|
value: 56.101 |
|
- type: ndcg_at_1 |
|
value: 46.394999999999996 |
|
- type: ndcg_at_10 |
|
value: 59.507 |
|
- type: ndcg_at_100 |
|
value: 63.31099999999999 |
|
- type: ndcg_at_1000 |
|
value: 64.388 |
|
- type: ndcg_at_3 |
|
value: 54.04600000000001 |
|
- type: ndcg_at_5 |
|
value: 56.723 |
|
- type: precision_at_1 |
|
value: 46.394999999999996 |
|
- type: precision_at_10 |
|
value: 9.567 |
|
- type: precision_at_100 |
|
value: 1.234 |
|
- type: precision_at_1000 |
|
value: 0.13699999999999998 |
|
- type: precision_at_3 |
|
value: 24.117 |
|
- type: precision_at_5 |
|
value: 16.426 |
|
- type: recall_at_1 |
|
value: 40.326 |
|
- type: recall_at_10 |
|
value: 73.763 |
|
- type: recall_at_100 |
|
value: 89.927 |
|
- type: recall_at_1000 |
|
value: 97.509 |
|
- type: recall_at_3 |
|
value: 59.34 |
|
- type: recall_at_5 |
|
value: 65.915 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.661 |
|
- type: map_at_10 |
|
value: 35.522 |
|
- type: map_at_100 |
|
value: 36.619 |
|
- type: map_at_1000 |
|
value: 36.693999999999996 |
|
- type: map_at_3 |
|
value: 33.154 |
|
- type: map_at_5 |
|
value: 34.353 |
|
- type: mrr_at_1 |
|
value: 28.362 |
|
- type: mrr_at_10 |
|
value: 37.403999999999996 |
|
- type: mrr_at_100 |
|
value: 38.374 |
|
- type: mrr_at_1000 |
|
value: 38.428000000000004 |
|
- type: mrr_at_3 |
|
value: 35.235 |
|
- type: mrr_at_5 |
|
value: 36.269 |
|
- type: ndcg_at_1 |
|
value: 28.362 |
|
- type: ndcg_at_10 |
|
value: 40.431 |
|
- type: ndcg_at_100 |
|
value: 45.745999999999995 |
|
- type: ndcg_at_1000 |
|
value: 47.493 |
|
- type: ndcg_at_3 |
|
value: 35.733 |
|
- type: ndcg_at_5 |
|
value: 37.722 |
|
- type: precision_at_1 |
|
value: 28.362 |
|
- type: precision_at_10 |
|
value: 6.101999999999999 |
|
- type: precision_at_100 |
|
value: 0.922 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_3 |
|
value: 15.140999999999998 |
|
- type: precision_at_5 |
|
value: 10.305 |
|
- type: recall_at_1 |
|
value: 26.661 |
|
- type: recall_at_10 |
|
value: 53.675 |
|
- type: recall_at_100 |
|
value: 77.891 |
|
- type: recall_at_1000 |
|
value: 90.72 |
|
- type: recall_at_3 |
|
value: 40.751 |
|
- type: recall_at_5 |
|
value: 45.517 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 18.886 |
|
- type: map_at_10 |
|
value: 27.288 |
|
- type: map_at_100 |
|
value: 28.327999999999996 |
|
- type: map_at_1000 |
|
value: 28.438999999999997 |
|
- type: map_at_3 |
|
value: 24.453 |
|
- type: map_at_5 |
|
value: 25.959 |
|
- type: mrr_at_1 |
|
value: 23.134 |
|
- type: mrr_at_10 |
|
value: 32.004 |
|
- type: mrr_at_100 |
|
value: 32.789 |
|
- type: mrr_at_1000 |
|
value: 32.857 |
|
- type: mrr_at_3 |
|
value: 29.084 |
|
- type: mrr_at_5 |
|
value: 30.614 |
|
- type: ndcg_at_1 |
|
value: 23.134 |
|
- type: ndcg_at_10 |
|
value: 32.852 |
|
- type: ndcg_at_100 |
|
value: 37.972 |
|
- type: ndcg_at_1000 |
|
value: 40.656 |
|
- type: ndcg_at_3 |
|
value: 27.435 |
|
- type: ndcg_at_5 |
|
value: 29.823 |
|
- type: precision_at_1 |
|
value: 23.134 |
|
- type: precision_at_10 |
|
value: 6.032 |
|
- type: precision_at_100 |
|
value: 0.9950000000000001 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 13.017999999999999 |
|
- type: precision_at_5 |
|
value: 9.501999999999999 |
|
- type: recall_at_1 |
|
value: 18.886 |
|
- type: recall_at_10 |
|
value: 45.34 |
|
- type: recall_at_100 |
|
value: 67.947 |
|
- type: recall_at_1000 |
|
value: 86.924 |
|
- type: recall_at_3 |
|
value: 30.535 |
|
- type: recall_at_5 |
|
value: 36.451 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.994999999999997 |
|
- type: map_at_10 |
|
value: 40.04 |
|
- type: map_at_100 |
|
value: 41.435 |
|
- type: map_at_1000 |
|
value: 41.537 |
|
- type: map_at_3 |
|
value: 37.091 |
|
- type: map_at_5 |
|
value: 38.802 |
|
- type: mrr_at_1 |
|
value: 35.034 |
|
- type: mrr_at_10 |
|
value: 45.411 |
|
- type: mrr_at_100 |
|
value: 46.226 |
|
- type: mrr_at_1000 |
|
value: 46.27 |
|
- type: mrr_at_3 |
|
value: 43.086 |
|
- type: mrr_at_5 |
|
value: 44.452999999999996 |
|
- type: ndcg_at_1 |
|
value: 35.034 |
|
- type: ndcg_at_10 |
|
value: 46.076 |
|
- type: ndcg_at_100 |
|
value: 51.483000000000004 |
|
- type: ndcg_at_1000 |
|
value: 53.433 |
|
- type: ndcg_at_3 |
|
value: 41.304 |
|
- type: ndcg_at_5 |
|
value: 43.641999999999996 |
|
- type: precision_at_1 |
|
value: 35.034 |
|
- type: precision_at_10 |
|
value: 8.258000000000001 |
|
- type: precision_at_100 |
|
value: 1.268 |
|
- type: precision_at_1000 |
|
value: 0.161 |
|
- type: precision_at_3 |
|
value: 19.57 |
|
- type: precision_at_5 |
|
value: 13.782 |
|
- type: recall_at_1 |
|
value: 28.994999999999997 |
|
- type: recall_at_10 |
|
value: 58.538000000000004 |
|
- type: recall_at_100 |
|
value: 80.72399999999999 |
|
- type: recall_at_1000 |
|
value: 93.462 |
|
- type: recall_at_3 |
|
value: 45.199 |
|
- type: recall_at_5 |
|
value: 51.237 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.795 |
|
- type: map_at_10 |
|
value: 34.935 |
|
- type: map_at_100 |
|
value: 36.306 |
|
- type: map_at_1000 |
|
value: 36.417 |
|
- type: map_at_3 |
|
value: 31.831 |
|
- type: map_at_5 |
|
value: 33.626 |
|
- type: mrr_at_1 |
|
value: 30.479 |
|
- type: mrr_at_10 |
|
value: 40.225 |
|
- type: mrr_at_100 |
|
value: 41.055 |
|
- type: mrr_at_1000 |
|
value: 41.114 |
|
- type: mrr_at_3 |
|
value: 37.538 |
|
- type: mrr_at_5 |
|
value: 39.073 |
|
- type: ndcg_at_1 |
|
value: 30.479 |
|
- type: ndcg_at_10 |
|
value: 40.949999999999996 |
|
- type: ndcg_at_100 |
|
value: 46.525 |
|
- type: ndcg_at_1000 |
|
value: 48.892 |
|
- type: ndcg_at_3 |
|
value: 35.79 |
|
- type: ndcg_at_5 |
|
value: 38.237 |
|
- type: precision_at_1 |
|
value: 30.479 |
|
- type: precision_at_10 |
|
value: 7.6259999999999994 |
|
- type: precision_at_100 |
|
value: 1.203 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 17.199 |
|
- type: precision_at_5 |
|
value: 12.466000000000001 |
|
- type: recall_at_1 |
|
value: 24.795 |
|
- type: recall_at_10 |
|
value: 53.421 |
|
- type: recall_at_100 |
|
value: 77.189 |
|
- type: recall_at_1000 |
|
value: 93.407 |
|
- type: recall_at_3 |
|
value: 39.051 |
|
- type: recall_at_5 |
|
value: 45.462 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.853499999999997 |
|
- type: map_at_10 |
|
value: 36.20433333333333 |
|
- type: map_at_100 |
|
value: 37.40391666666667 |
|
- type: map_at_1000 |
|
value: 37.515 |
|
- type: map_at_3 |
|
value: 33.39975 |
|
- type: map_at_5 |
|
value: 34.9665 |
|
- type: mrr_at_1 |
|
value: 31.62666666666667 |
|
- type: mrr_at_10 |
|
value: 40.436749999999996 |
|
- type: mrr_at_100 |
|
value: 41.260333333333335 |
|
- type: mrr_at_1000 |
|
value: 41.31525 |
|
- type: mrr_at_3 |
|
value: 38.06733333333332 |
|
- type: mrr_at_5 |
|
value: 39.41541666666667 |
|
- type: ndcg_at_1 |
|
value: 31.62666666666667 |
|
- type: ndcg_at_10 |
|
value: 41.63341666666667 |
|
- type: ndcg_at_100 |
|
value: 46.704166666666666 |
|
- type: ndcg_at_1000 |
|
value: 48.88483333333335 |
|
- type: ndcg_at_3 |
|
value: 36.896 |
|
- type: ndcg_at_5 |
|
value: 39.11891666666667 |
|
- type: precision_at_1 |
|
value: 31.62666666666667 |
|
- type: precision_at_10 |
|
value: 7.241083333333333 |
|
- type: precision_at_100 |
|
value: 1.1488333333333334 |
|
- type: precision_at_1000 |
|
value: 0.15250000000000002 |
|
- type: precision_at_3 |
|
value: 16.908333333333335 |
|
- type: precision_at_5 |
|
value: 11.942833333333333 |
|
- type: recall_at_1 |
|
value: 26.853499999999997 |
|
- type: recall_at_10 |
|
value: 53.461333333333336 |
|
- type: recall_at_100 |
|
value: 75.63633333333333 |
|
- type: recall_at_1000 |
|
value: 90.67016666666666 |
|
- type: recall_at_3 |
|
value: 40.24241666666667 |
|
- type: recall_at_5 |
|
value: 45.98608333333333 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.241999999999997 |
|
- type: map_at_10 |
|
value: 31.863999999999997 |
|
- type: map_at_100 |
|
value: 32.835 |
|
- type: map_at_1000 |
|
value: 32.928000000000004 |
|
- type: map_at_3 |
|
value: 29.694 |
|
- type: map_at_5 |
|
value: 30.978 |
|
- type: mrr_at_1 |
|
value: 28.374 |
|
- type: mrr_at_10 |
|
value: 34.814 |
|
- type: mrr_at_100 |
|
value: 35.596 |
|
- type: mrr_at_1000 |
|
value: 35.666 |
|
- type: mrr_at_3 |
|
value: 32.745000000000005 |
|
- type: mrr_at_5 |
|
value: 34.049 |
|
- type: ndcg_at_1 |
|
value: 28.374 |
|
- type: ndcg_at_10 |
|
value: 35.969 |
|
- type: ndcg_at_100 |
|
value: 40.708 |
|
- type: ndcg_at_1000 |
|
value: 43.08 |
|
- type: ndcg_at_3 |
|
value: 31.968999999999998 |
|
- type: ndcg_at_5 |
|
value: 34.069 |
|
- type: precision_at_1 |
|
value: 28.374 |
|
- type: precision_at_10 |
|
value: 5.583 |
|
- type: precision_at_100 |
|
value: 0.8630000000000001 |
|
- type: precision_at_1000 |
|
value: 0.11299999999999999 |
|
- type: precision_at_3 |
|
value: 13.547999999999998 |
|
- type: precision_at_5 |
|
value: 9.447999999999999 |
|
- type: recall_at_1 |
|
value: 25.241999999999997 |
|
- type: recall_at_10 |
|
value: 45.711 |
|
- type: recall_at_100 |
|
value: 67.482 |
|
- type: recall_at_1000 |
|
value: 85.13300000000001 |
|
- type: recall_at_3 |
|
value: 34.622 |
|
- type: recall_at_5 |
|
value: 40.043 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 17.488999999999997 |
|
- type: map_at_10 |
|
value: 25.142999999999997 |
|
- type: map_at_100 |
|
value: 26.244 |
|
- type: map_at_1000 |
|
value: 26.363999999999997 |
|
- type: map_at_3 |
|
value: 22.654 |
|
- type: map_at_5 |
|
value: 24.017 |
|
- type: mrr_at_1 |
|
value: 21.198 |
|
- type: mrr_at_10 |
|
value: 28.903000000000002 |
|
- type: mrr_at_100 |
|
value: 29.860999999999997 |
|
- type: mrr_at_1000 |
|
value: 29.934 |
|
- type: mrr_at_3 |
|
value: 26.634999999999998 |
|
- type: mrr_at_5 |
|
value: 27.903 |
|
- type: ndcg_at_1 |
|
value: 21.198 |
|
- type: ndcg_at_10 |
|
value: 29.982999999999997 |
|
- type: ndcg_at_100 |
|
value: 35.275 |
|
- type: ndcg_at_1000 |
|
value: 38.074000000000005 |
|
- type: ndcg_at_3 |
|
value: 25.502999999999997 |
|
- type: ndcg_at_5 |
|
value: 27.557 |
|
- type: precision_at_1 |
|
value: 21.198 |
|
- type: precision_at_10 |
|
value: 5.502 |
|
- type: precision_at_100 |
|
value: 0.942 |
|
- type: precision_at_1000 |
|
value: 0.136 |
|
- type: precision_at_3 |
|
value: 12.044 |
|
- type: precision_at_5 |
|
value: 8.782 |
|
- type: recall_at_1 |
|
value: 17.488999999999997 |
|
- type: recall_at_10 |
|
value: 40.821000000000005 |
|
- type: recall_at_100 |
|
value: 64.567 |
|
- type: recall_at_1000 |
|
value: 84.452 |
|
- type: recall_at_3 |
|
value: 28.351 |
|
- type: recall_at_5 |
|
value: 33.645 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.066000000000003 |
|
- type: map_at_10 |
|
value: 36.134 |
|
- type: map_at_100 |
|
value: 37.285000000000004 |
|
- type: map_at_1000 |
|
value: 37.389 |
|
- type: map_at_3 |
|
value: 33.522999999999996 |
|
- type: map_at_5 |
|
value: 34.905 |
|
- type: mrr_at_1 |
|
value: 31.436999999999998 |
|
- type: mrr_at_10 |
|
value: 40.225 |
|
- type: mrr_at_100 |
|
value: 41.079 |
|
- type: mrr_at_1000 |
|
value: 41.138000000000005 |
|
- type: mrr_at_3 |
|
value: 38.074999999999996 |
|
- type: mrr_at_5 |
|
value: 39.190000000000005 |
|
- type: ndcg_at_1 |
|
value: 31.436999999999998 |
|
- type: ndcg_at_10 |
|
value: 41.494 |
|
- type: ndcg_at_100 |
|
value: 46.678999999999995 |
|
- type: ndcg_at_1000 |
|
value: 48.964 |
|
- type: ndcg_at_3 |
|
value: 36.828 |
|
- type: ndcg_at_5 |
|
value: 38.789 |
|
- type: precision_at_1 |
|
value: 31.436999999999998 |
|
- type: precision_at_10 |
|
value: 6.931 |
|
- type: precision_at_100 |
|
value: 1.072 |
|
- type: precision_at_1000 |
|
value: 0.13799999999999998 |
|
- type: precision_at_3 |
|
value: 16.729 |
|
- type: precision_at_5 |
|
value: 11.567 |
|
- type: recall_at_1 |
|
value: 27.066000000000003 |
|
- type: recall_at_10 |
|
value: 53.705000000000005 |
|
- type: recall_at_100 |
|
value: 75.968 |
|
- type: recall_at_1000 |
|
value: 91.937 |
|
- type: recall_at_3 |
|
value: 40.865 |
|
- type: recall_at_5 |
|
value: 45.739999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 24.979000000000003 |
|
- type: map_at_10 |
|
value: 32.799 |
|
- type: map_at_100 |
|
value: 34.508 |
|
- type: map_at_1000 |
|
value: 34.719 |
|
- type: map_at_3 |
|
value: 29.947000000000003 |
|
- type: map_at_5 |
|
value: 31.584 |
|
- type: mrr_at_1 |
|
value: 30.237000000000002 |
|
- type: mrr_at_10 |
|
value: 37.651 |
|
- type: mrr_at_100 |
|
value: 38.805 |
|
- type: mrr_at_1000 |
|
value: 38.851 |
|
- type: mrr_at_3 |
|
value: 35.046 |
|
- type: mrr_at_5 |
|
value: 36.548 |
|
- type: ndcg_at_1 |
|
value: 30.237000000000002 |
|
- type: ndcg_at_10 |
|
value: 38.356 |
|
- type: ndcg_at_100 |
|
value: 44.906 |
|
- type: ndcg_at_1000 |
|
value: 47.299 |
|
- type: ndcg_at_3 |
|
value: 33.717999999999996 |
|
- type: ndcg_at_5 |
|
value: 35.946 |
|
- type: precision_at_1 |
|
value: 30.237000000000002 |
|
- type: precision_at_10 |
|
value: 7.292 |
|
- type: precision_at_100 |
|
value: 1.496 |
|
- type: precision_at_1000 |
|
value: 0.23600000000000002 |
|
- type: precision_at_3 |
|
value: 15.547 |
|
- type: precision_at_5 |
|
value: 11.344 |
|
- type: recall_at_1 |
|
value: 24.979000000000003 |
|
- type: recall_at_10 |
|
value: 48.624 |
|
- type: recall_at_100 |
|
value: 77.932 |
|
- type: recall_at_1000 |
|
value: 92.66499999999999 |
|
- type: recall_at_3 |
|
value: 35.217 |
|
- type: recall_at_5 |
|
value: 41.394 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.566 |
|
- type: map_at_10 |
|
value: 30.945 |
|
- type: map_at_100 |
|
value: 31.759999999999998 |
|
- type: map_at_1000 |
|
value: 31.855 |
|
- type: map_at_3 |
|
value: 28.64 |
|
- type: map_at_5 |
|
value: 29.787000000000003 |
|
- type: mrr_at_1 |
|
value: 24.954 |
|
- type: mrr_at_10 |
|
value: 33.311 |
|
- type: mrr_at_100 |
|
value: 34.050000000000004 |
|
- type: mrr_at_1000 |
|
value: 34.117999999999995 |
|
- type: mrr_at_3 |
|
value: 31.238 |
|
- type: mrr_at_5 |
|
value: 32.329 |
|
- type: ndcg_at_1 |
|
value: 24.954 |
|
- type: ndcg_at_10 |
|
value: 35.676 |
|
- type: ndcg_at_100 |
|
value: 39.931 |
|
- type: ndcg_at_1000 |
|
value: 42.43 |
|
- type: ndcg_at_3 |
|
value: 31.365 |
|
- type: ndcg_at_5 |
|
value: 33.184999999999995 |
|
- type: precision_at_1 |
|
value: 24.954 |
|
- type: precision_at_10 |
|
value: 5.564 |
|
- type: precision_at_100 |
|
value: 0.826 |
|
- type: precision_at_1000 |
|
value: 0.116 |
|
- type: precision_at_3 |
|
value: 13.555 |
|
- type: precision_at_5 |
|
value: 9.168 |
|
- type: recall_at_1 |
|
value: 22.566 |
|
- type: recall_at_10 |
|
value: 47.922 |
|
- type: recall_at_100 |
|
value: 67.931 |
|
- type: recall_at_1000 |
|
value: 86.653 |
|
- type: recall_at_3 |
|
value: 36.103 |
|
- type: recall_at_5 |
|
value: 40.699000000000005 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.950000000000003 |
|
- type: map_at_10 |
|
value: 28.612 |
|
- type: map_at_100 |
|
value: 30.476999999999997 |
|
- type: map_at_1000 |
|
value: 30.674 |
|
- type: map_at_3 |
|
value: 24.262 |
|
- type: map_at_5 |
|
value: 26.554 |
|
- type: mrr_at_1 |
|
value: 38.241 |
|
- type: mrr_at_10 |
|
value: 50.43 |
|
- type: mrr_at_100 |
|
value: 51.059 |
|
- type: mrr_at_1000 |
|
value: 51.090999999999994 |
|
- type: mrr_at_3 |
|
value: 47.514 |
|
- type: mrr_at_5 |
|
value: 49.246 |
|
- type: ndcg_at_1 |
|
value: 38.241 |
|
- type: ndcg_at_10 |
|
value: 38.218 |
|
- type: ndcg_at_100 |
|
value: 45.003 |
|
- type: ndcg_at_1000 |
|
value: 48.269 |
|
- type: ndcg_at_3 |
|
value: 32.568000000000005 |
|
- type: ndcg_at_5 |
|
value: 34.400999999999996 |
|
- type: precision_at_1 |
|
value: 38.241 |
|
- type: precision_at_10 |
|
value: 11.674 |
|
- type: precision_at_100 |
|
value: 1.913 |
|
- type: precision_at_1000 |
|
value: 0.252 |
|
- type: precision_at_3 |
|
value: 24.387 |
|
- type: precision_at_5 |
|
value: 18.163 |
|
- type: recall_at_1 |
|
value: 16.950000000000003 |
|
- type: recall_at_10 |
|
value: 43.769000000000005 |
|
- type: recall_at_100 |
|
value: 66.875 |
|
- type: recall_at_1000 |
|
value: 84.92699999999999 |
|
- type: recall_at_3 |
|
value: 29.353 |
|
- type: recall_at_5 |
|
value: 35.467 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 9.276 |
|
- type: map_at_10 |
|
value: 20.848 |
|
- type: map_at_100 |
|
value: 29.804000000000002 |
|
- type: map_at_1000 |
|
value: 31.398 |
|
- type: map_at_3 |
|
value: 14.886 |
|
- type: map_at_5 |
|
value: 17.516000000000002 |
|
- type: mrr_at_1 |
|
value: 71 |
|
- type: mrr_at_10 |
|
value: 78.724 |
|
- type: mrr_at_100 |
|
value: 78.976 |
|
- type: mrr_at_1000 |
|
value: 78.986 |
|
- type: mrr_at_3 |
|
value: 77.333 |
|
- type: mrr_at_5 |
|
value: 78.021 |
|
- type: ndcg_at_1 |
|
value: 57.875 |
|
- type: ndcg_at_10 |
|
value: 43.855 |
|
- type: ndcg_at_100 |
|
value: 48.99 |
|
- type: ndcg_at_1000 |
|
value: 56.141 |
|
- type: ndcg_at_3 |
|
value: 48.914 |
|
- type: ndcg_at_5 |
|
value: 45.961 |
|
- type: precision_at_1 |
|
value: 71 |
|
- type: precision_at_10 |
|
value: 34.575 |
|
- type: precision_at_100 |
|
value: 11.182 |
|
- type: precision_at_1000 |
|
value: 2.044 |
|
- type: precision_at_3 |
|
value: 52.5 |
|
- type: precision_at_5 |
|
value: 44.2 |
|
- type: recall_at_1 |
|
value: 9.276 |
|
- type: recall_at_10 |
|
value: 26.501 |
|
- type: recall_at_100 |
|
value: 55.72899999999999 |
|
- type: recall_at_1000 |
|
value: 78.532 |
|
- type: recall_at_3 |
|
value: 16.365 |
|
- type: recall_at_5 |
|
value: 20.154 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/emotion |
|
name: MTEB EmotionClassification |
|
config: default |
|
split: test |
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
|
metrics: |
|
- type: accuracy |
|
value: 52.71 |
|
- type: f1 |
|
value: 47.74801556489574 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 73.405 |
|
- type: map_at_10 |
|
value: 82.822 |
|
- type: map_at_100 |
|
value: 83.042 |
|
- type: map_at_1000 |
|
value: 83.055 |
|
- type: map_at_3 |
|
value: 81.65299999999999 |
|
- type: map_at_5 |
|
value: 82.431 |
|
- type: mrr_at_1 |
|
value: 79.178 |
|
- type: mrr_at_10 |
|
value: 87.02 |
|
- type: mrr_at_100 |
|
value: 87.095 |
|
- type: mrr_at_1000 |
|
value: 87.09700000000001 |
|
- type: mrr_at_3 |
|
value: 86.309 |
|
- type: mrr_at_5 |
|
value: 86.824 |
|
- type: ndcg_at_1 |
|
value: 79.178 |
|
- type: ndcg_at_10 |
|
value: 86.72 |
|
- type: ndcg_at_100 |
|
value: 87.457 |
|
- type: ndcg_at_1000 |
|
value: 87.691 |
|
- type: ndcg_at_3 |
|
value: 84.974 |
|
- type: ndcg_at_5 |
|
value: 86.032 |
|
- type: precision_at_1 |
|
value: 79.178 |
|
- type: precision_at_10 |
|
value: 10.548 |
|
- type: precision_at_100 |
|
value: 1.113 |
|
- type: precision_at_1000 |
|
value: 0.11499999999999999 |
|
- type: precision_at_3 |
|
value: 32.848 |
|
- type: precision_at_5 |
|
value: 20.45 |
|
- type: recall_at_1 |
|
value: 73.405 |
|
- type: recall_at_10 |
|
value: 94.39699999999999 |
|
- type: recall_at_100 |
|
value: 97.219 |
|
- type: recall_at_1000 |
|
value: 98.675 |
|
- type: recall_at_3 |
|
value: 89.679 |
|
- type: recall_at_5 |
|
value: 92.392 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.651 |
|
- type: map_at_10 |
|
value: 36.886 |
|
- type: map_at_100 |
|
value: 38.811 |
|
- type: map_at_1000 |
|
value: 38.981 |
|
- type: map_at_3 |
|
value: 32.538 |
|
- type: map_at_5 |
|
value: 34.763 |
|
- type: mrr_at_1 |
|
value: 44.444 |
|
- type: mrr_at_10 |
|
value: 53.168000000000006 |
|
- type: mrr_at_100 |
|
value: 53.839000000000006 |
|
- type: mrr_at_1000 |
|
value: 53.869 |
|
- type: mrr_at_3 |
|
value: 50.54 |
|
- type: mrr_at_5 |
|
value: 52.068000000000005 |
|
- type: ndcg_at_1 |
|
value: 44.444 |
|
- type: ndcg_at_10 |
|
value: 44.994 |
|
- type: ndcg_at_100 |
|
value: 51.599 |
|
- type: ndcg_at_1000 |
|
value: 54.339999999999996 |
|
- type: ndcg_at_3 |
|
value: 41.372 |
|
- type: ndcg_at_5 |
|
value: 42.149 |
|
- type: precision_at_1 |
|
value: 44.444 |
|
- type: precision_at_10 |
|
value: 12.407 |
|
- type: precision_at_100 |
|
value: 1.9269999999999998 |
|
- type: precision_at_1000 |
|
value: 0.242 |
|
- type: precision_at_3 |
|
value: 27.726 |
|
- type: precision_at_5 |
|
value: 19.814999999999998 |
|
- type: recall_at_1 |
|
value: 22.651 |
|
- type: recall_at_10 |
|
value: 52.075 |
|
- type: recall_at_100 |
|
value: 76.51400000000001 |
|
- type: recall_at_1000 |
|
value: 92.852 |
|
- type: recall_at_3 |
|
value: 37.236000000000004 |
|
- type: recall_at_5 |
|
value: 43.175999999999995 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.777 |
|
- type: map_at_10 |
|
value: 66.79899999999999 |
|
- type: map_at_100 |
|
value: 67.65299999999999 |
|
- type: map_at_1000 |
|
value: 67.706 |
|
- type: map_at_3 |
|
value: 63.352 |
|
- type: map_at_5 |
|
value: 65.52900000000001 |
|
- type: mrr_at_1 |
|
value: 81.553 |
|
- type: mrr_at_10 |
|
value: 86.983 |
|
- type: mrr_at_100 |
|
value: 87.132 |
|
- type: mrr_at_1000 |
|
value: 87.136 |
|
- type: mrr_at_3 |
|
value: 86.156 |
|
- type: mrr_at_5 |
|
value: 86.726 |
|
- type: ndcg_at_1 |
|
value: 81.553 |
|
- type: ndcg_at_10 |
|
value: 74.64 |
|
- type: ndcg_at_100 |
|
value: 77.459 |
|
- type: ndcg_at_1000 |
|
value: 78.43 |
|
- type: ndcg_at_3 |
|
value: 69.878 |
|
- type: ndcg_at_5 |
|
value: 72.59400000000001 |
|
- type: precision_at_1 |
|
value: 81.553 |
|
- type: precision_at_10 |
|
value: 15.654000000000002 |
|
- type: precision_at_100 |
|
value: 1.783 |
|
- type: precision_at_1000 |
|
value: 0.191 |
|
- type: precision_at_3 |
|
value: 45.199 |
|
- type: precision_at_5 |
|
value: 29.267 |
|
- type: recall_at_1 |
|
value: 40.777 |
|
- type: recall_at_10 |
|
value: 78.271 |
|
- type: recall_at_100 |
|
value: 89.129 |
|
- type: recall_at_1000 |
|
value: 95.49 |
|
- type: recall_at_3 |
|
value: 67.79899999999999 |
|
- type: recall_at_5 |
|
value: 73.167 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/imdb |
|
name: MTEB ImdbClassification |
|
config: default |
|
split: test |
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
|
metrics: |
|
- type: accuracy |
|
value: 93.5064 |
|
- type: ap |
|
value: 90.25495114444111 |
|
- type: f1 |
|
value: 93.5012434973381 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.301 |
|
- type: map_at_10 |
|
value: 35.657 |
|
- type: map_at_100 |
|
value: 36.797000000000004 |
|
- type: map_at_1000 |
|
value: 36.844 |
|
- type: map_at_3 |
|
value: 31.743 |
|
- type: map_at_5 |
|
value: 34.003 |
|
- type: mrr_at_1 |
|
value: 23.854 |
|
- type: mrr_at_10 |
|
value: 36.242999999999995 |
|
- type: mrr_at_100 |
|
value: 37.32 |
|
- type: mrr_at_1000 |
|
value: 37.361 |
|
- type: mrr_at_3 |
|
value: 32.4 |
|
- type: mrr_at_5 |
|
value: 34.634 |
|
- type: ndcg_at_1 |
|
value: 23.868000000000002 |
|
- type: ndcg_at_10 |
|
value: 42.589 |
|
- type: ndcg_at_100 |
|
value: 48.031 |
|
- type: ndcg_at_1000 |
|
value: 49.189 |
|
- type: ndcg_at_3 |
|
value: 34.649 |
|
- type: ndcg_at_5 |
|
value: 38.676 |
|
- type: precision_at_1 |
|
value: 23.868000000000002 |
|
- type: precision_at_10 |
|
value: 6.6850000000000005 |
|
- type: precision_at_100 |
|
value: 0.9400000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 14.651 |
|
- type: precision_at_5 |
|
value: 10.834000000000001 |
|
- type: recall_at_1 |
|
value: 23.301 |
|
- type: recall_at_10 |
|
value: 63.88700000000001 |
|
- type: recall_at_100 |
|
value: 88.947 |
|
- type: recall_at_1000 |
|
value: 97.783 |
|
- type: recall_at_3 |
|
value: 42.393 |
|
- type: recall_at_5 |
|
value: 52.036 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_domain |
|
name: MTEB MTOPDomainClassification (en) |
|
config: en |
|
split: test |
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
|
metrics: |
|
- type: accuracy |
|
value: 94.64888280893753 |
|
- type: f1 |
|
value: 94.41310774203512 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/mtop_intent |
|
name: MTEB MTOPIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
|
metrics: |
|
- type: accuracy |
|
value: 79.72184222526221 |
|
- type: f1 |
|
value: 61.522034067350106 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (en) |
|
config: en |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 79.60659045057163 |
|
- type: f1 |
|
value: 77.268649687049 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (en) |
|
config: en |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 81.83254875588432 |
|
- type: f1 |
|
value: 81.61520635919082 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-p2p |
|
name: MTEB MedrxivClusteringP2P |
|
config: default |
|
split: test |
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
|
metrics: |
|
- type: v_measure |
|
value: 36.31529875009507 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/medrxiv-clustering-s2s |
|
name: MTEB MedrxivClusteringS2S |
|
config: default |
|
split: test |
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
|
metrics: |
|
- type: v_measure |
|
value: 31.734233714415073 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/mind_small |
|
name: MTEB MindSmallReranking |
|
config: default |
|
split: test |
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
|
metrics: |
|
- type: map |
|
value: 30.994501713009452 |
|
- type: mrr |
|
value: 32.13512850703073 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 6.603000000000001 |
|
- type: map_at_10 |
|
value: 13.767999999999999 |
|
- type: map_at_100 |
|
value: 17.197000000000003 |
|
- type: map_at_1000 |
|
value: 18.615000000000002 |
|
- type: map_at_3 |
|
value: 10.567 |
|
- type: map_at_5 |
|
value: 12.078999999999999 |
|
- type: mrr_at_1 |
|
value: 44.891999999999996 |
|
- type: mrr_at_10 |
|
value: 53.75299999999999 |
|
- type: mrr_at_100 |
|
value: 54.35 |
|
- type: mrr_at_1000 |
|
value: 54.388000000000005 |
|
- type: mrr_at_3 |
|
value: 51.495999999999995 |
|
- type: mrr_at_5 |
|
value: 52.688 |
|
- type: ndcg_at_1 |
|
value: 43.189 |
|
- type: ndcg_at_10 |
|
value: 34.567 |
|
- type: ndcg_at_100 |
|
value: 32.273 |
|
- type: ndcg_at_1000 |
|
value: 41.321999999999996 |
|
- type: ndcg_at_3 |
|
value: 40.171 |
|
- type: ndcg_at_5 |
|
value: 37.502 |
|
- type: precision_at_1 |
|
value: 44.582 |
|
- type: precision_at_10 |
|
value: 25.139 |
|
- type: precision_at_100 |
|
value: 7.739999999999999 |
|
- type: precision_at_1000 |
|
value: 2.054 |
|
- type: precision_at_3 |
|
value: 37.152 |
|
- type: precision_at_5 |
|
value: 31.826999999999998 |
|
- type: recall_at_1 |
|
value: 6.603000000000001 |
|
- type: recall_at_10 |
|
value: 17.023 |
|
- type: recall_at_100 |
|
value: 32.914 |
|
- type: recall_at_1000 |
|
value: 64.44800000000001 |
|
- type: recall_at_3 |
|
value: 11.457 |
|
- type: recall_at_5 |
|
value: 13.816 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 30.026000000000003 |
|
- type: map_at_10 |
|
value: 45.429 |
|
- type: map_at_100 |
|
value: 46.45 |
|
- type: map_at_1000 |
|
value: 46.478 |
|
- type: map_at_3 |
|
value: 41.147 |
|
- type: map_at_5 |
|
value: 43.627 |
|
- type: mrr_at_1 |
|
value: 33.951 |
|
- type: mrr_at_10 |
|
value: 47.953 |
|
- type: mrr_at_100 |
|
value: 48.731 |
|
- type: mrr_at_1000 |
|
value: 48.751 |
|
- type: mrr_at_3 |
|
value: 44.39 |
|
- type: mrr_at_5 |
|
value: 46.533 |
|
- type: ndcg_at_1 |
|
value: 33.951 |
|
- type: ndcg_at_10 |
|
value: 53.24100000000001 |
|
- type: ndcg_at_100 |
|
value: 57.599999999999994 |
|
- type: ndcg_at_1000 |
|
value: 58.270999999999994 |
|
- type: ndcg_at_3 |
|
value: 45.190999999999995 |
|
- type: ndcg_at_5 |
|
value: 49.339 |
|
- type: precision_at_1 |
|
value: 33.951 |
|
- type: precision_at_10 |
|
value: 8.856 |
|
- type: precision_at_100 |
|
value: 1.133 |
|
- type: precision_at_1000 |
|
value: 0.12 |
|
- type: precision_at_3 |
|
value: 20.713 |
|
- type: precision_at_5 |
|
value: 14.838000000000001 |
|
- type: recall_at_1 |
|
value: 30.026000000000003 |
|
- type: recall_at_10 |
|
value: 74.512 |
|
- type: recall_at_100 |
|
value: 93.395 |
|
- type: recall_at_1000 |
|
value: 98.402 |
|
- type: recall_at_3 |
|
value: 53.677 |
|
- type: recall_at_5 |
|
value: 63.198 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 71.41300000000001 |
|
- type: map_at_10 |
|
value: 85.387 |
|
- type: map_at_100 |
|
value: 86.027 |
|
- type: map_at_1000 |
|
value: 86.041 |
|
- type: map_at_3 |
|
value: 82.543 |
|
- type: map_at_5 |
|
value: 84.304 |
|
- type: mrr_at_1 |
|
value: 82.35 |
|
- type: mrr_at_10 |
|
value: 88.248 |
|
- type: mrr_at_100 |
|
value: 88.348 |
|
- type: mrr_at_1000 |
|
value: 88.349 |
|
- type: mrr_at_3 |
|
value: 87.348 |
|
- type: mrr_at_5 |
|
value: 87.96300000000001 |
|
- type: ndcg_at_1 |
|
value: 82.37 |
|
- type: ndcg_at_10 |
|
value: 88.98 |
|
- type: ndcg_at_100 |
|
value: 90.16499999999999 |
|
- type: ndcg_at_1000 |
|
value: 90.239 |
|
- type: ndcg_at_3 |
|
value: 86.34100000000001 |
|
- type: ndcg_at_5 |
|
value: 87.761 |
|
- type: precision_at_1 |
|
value: 82.37 |
|
- type: precision_at_10 |
|
value: 13.471 |
|
- type: precision_at_100 |
|
value: 1.534 |
|
- type: precision_at_1000 |
|
value: 0.157 |
|
- type: precision_at_3 |
|
value: 37.827 |
|
- type: precision_at_5 |
|
value: 24.773999999999997 |
|
- type: recall_at_1 |
|
value: 71.41300000000001 |
|
- type: recall_at_10 |
|
value: 95.748 |
|
- type: recall_at_100 |
|
value: 99.69200000000001 |
|
- type: recall_at_1000 |
|
value: 99.98 |
|
- type: recall_at_3 |
|
value: 87.996 |
|
- type: recall_at_5 |
|
value: 92.142 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering |
|
name: MTEB RedditClustering |
|
config: default |
|
split: test |
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
|
metrics: |
|
- type: v_measure |
|
value: 56.96878497780007 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/reddit-clustering-p2p |
|
name: MTEB RedditClusteringP2P |
|
config: default |
|
split: test |
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
|
metrics: |
|
- type: v_measure |
|
value: 65.31371347128074 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 5.287 |
|
- type: map_at_10 |
|
value: 13.530000000000001 |
|
- type: map_at_100 |
|
value: 15.891 |
|
- type: map_at_1000 |
|
value: 16.245 |
|
- type: map_at_3 |
|
value: 9.612 |
|
- type: map_at_5 |
|
value: 11.672 |
|
- type: mrr_at_1 |
|
value: 26 |
|
- type: mrr_at_10 |
|
value: 37.335 |
|
- type: mrr_at_100 |
|
value: 38.443 |
|
- type: mrr_at_1000 |
|
value: 38.486 |
|
- type: mrr_at_3 |
|
value: 33.783 |
|
- type: mrr_at_5 |
|
value: 36.028 |
|
- type: ndcg_at_1 |
|
value: 26 |
|
- type: ndcg_at_10 |
|
value: 22.215 |
|
- type: ndcg_at_100 |
|
value: 31.101 |
|
- type: ndcg_at_1000 |
|
value: 36.809 |
|
- type: ndcg_at_3 |
|
value: 21.104 |
|
- type: ndcg_at_5 |
|
value: 18.759999999999998 |
|
- type: precision_at_1 |
|
value: 26 |
|
- type: precision_at_10 |
|
value: 11.43 |
|
- type: precision_at_100 |
|
value: 2.424 |
|
- type: precision_at_1000 |
|
value: 0.379 |
|
- type: precision_at_3 |
|
value: 19.7 |
|
- type: precision_at_5 |
|
value: 16.619999999999997 |
|
- type: recall_at_1 |
|
value: 5.287 |
|
- type: recall_at_10 |
|
value: 23.18 |
|
- type: recall_at_100 |
|
value: 49.208 |
|
- type: recall_at_1000 |
|
value: 76.85300000000001 |
|
- type: recall_at_3 |
|
value: 11.991999999999999 |
|
- type: recall_at_5 |
|
value: 16.85 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sickr-sts |
|
name: MTEB SICK-R |
|
config: default |
|
split: test |
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.87834913790886 |
|
- type: cos_sim_spearman |
|
value: 81.04583513112122 |
|
- type: euclidean_pearson |
|
value: 81.20484174558065 |
|
- type: euclidean_spearman |
|
value: 80.76430832561769 |
|
- type: manhattan_pearson |
|
value: 81.21416730978615 |
|
- type: manhattan_spearman |
|
value: 80.7797637394211 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts12-sts |
|
name: MTEB STS12 |
|
config: default |
|
split: test |
|
revision: a0d554a64d88156834ff5ae9920b964011b16384 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 86.56143998865157 |
|
- type: cos_sim_spearman |
|
value: 79.75387012744471 |
|
- type: euclidean_pearson |
|
value: 83.7877519997019 |
|
- type: euclidean_spearman |
|
value: 79.90489748003296 |
|
- type: manhattan_pearson |
|
value: 83.7540590666095 |
|
- type: manhattan_spearman |
|
value: 79.86434577931573 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts13-sts |
|
name: MTEB STS13 |
|
config: default |
|
split: test |
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.92102564177941 |
|
- type: cos_sim_spearman |
|
value: 84.98234585939103 |
|
- type: euclidean_pearson |
|
value: 84.47729567593696 |
|
- type: euclidean_spearman |
|
value: 85.09490696194469 |
|
- type: manhattan_pearson |
|
value: 84.38622951588229 |
|
- type: manhattan_spearman |
|
value: 85.02507171545574 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts14-sts |
|
name: MTEB STS14 |
|
config: default |
|
split: test |
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.1891164763377 |
|
- type: cos_sim_spearman |
|
value: 80.7997969966883 |
|
- type: euclidean_pearson |
|
value: 80.48572256162396 |
|
- type: euclidean_spearman |
|
value: 80.57851903536378 |
|
- type: manhattan_pearson |
|
value: 80.4324819433651 |
|
- type: manhattan_spearman |
|
value: 80.5074526239062 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts15-sts |
|
name: MTEB STS15 |
|
config: default |
|
split: test |
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.64319975116025 |
|
- type: cos_sim_spearman |
|
value: 84.88671197763652 |
|
- type: euclidean_pearson |
|
value: 84.74692193293231 |
|
- type: euclidean_spearman |
|
value: 85.27151722073653 |
|
- type: manhattan_pearson |
|
value: 84.72460516785438 |
|
- type: manhattan_spearman |
|
value: 85.26518899786687 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts16-sts |
|
name: MTEB STS16 |
|
config: default |
|
split: test |
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 83.24687565822381 |
|
- type: cos_sim_spearman |
|
value: 85.60418454111263 |
|
- type: euclidean_pearson |
|
value: 84.85829740169851 |
|
- type: euclidean_spearman |
|
value: 85.66378014138306 |
|
- type: manhattan_pearson |
|
value: 84.84672408808835 |
|
- type: manhattan_spearman |
|
value: 85.63331924364891 |
|
- 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: 84.87758895415485 |
|
- type: cos_sim_spearman |
|
value: 85.8193745617297 |
|
- type: euclidean_pearson |
|
value: 85.78719118848134 |
|
- type: euclidean_spearman |
|
value: 84.35797575385688 |
|
- type: manhattan_pearson |
|
value: 85.97919844815692 |
|
- type: manhattan_spearman |
|
value: 84.58334745175151 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (en) |
|
config: en |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 67.27076035963599 |
|
- type: cos_sim_spearman |
|
value: 67.21433656439973 |
|
- type: euclidean_pearson |
|
value: 68.07434078679324 |
|
- type: euclidean_spearman |
|
value: 66.0249731719049 |
|
- type: manhattan_pearson |
|
value: 67.95495198947476 |
|
- type: manhattan_spearman |
|
value: 65.99893908331886 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/stsbenchmark-sts |
|
name: MTEB STSBenchmark |
|
config: default |
|
split: test |
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 82.22437747056817 |
|
- type: cos_sim_spearman |
|
value: 85.0995685206174 |
|
- type: euclidean_pearson |
|
value: 84.08616925603394 |
|
- type: euclidean_spearman |
|
value: 84.89633925691658 |
|
- type: manhattan_pearson |
|
value: 84.08332675923133 |
|
- type: manhattan_spearman |
|
value: 84.8858228112915 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/scidocs-reranking |
|
name: MTEB SciDocsRR |
|
config: default |
|
split: test |
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
|
metrics: |
|
- type: map |
|
value: 87.6909022589666 |
|
- type: mrr |
|
value: 96.43341952165481 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.660999999999994 |
|
- type: map_at_10 |
|
value: 67.625 |
|
- type: map_at_100 |
|
value: 68.07600000000001 |
|
- type: map_at_1000 |
|
value: 68.10199999999999 |
|
- type: map_at_3 |
|
value: 64.50399999999999 |
|
- type: map_at_5 |
|
value: 66.281 |
|
- type: mrr_at_1 |
|
value: 61 |
|
- type: mrr_at_10 |
|
value: 68.953 |
|
- type: mrr_at_100 |
|
value: 69.327 |
|
- type: mrr_at_1000 |
|
value: 69.352 |
|
- type: mrr_at_3 |
|
value: 66.833 |
|
- type: mrr_at_5 |
|
value: 68.05 |
|
- type: ndcg_at_1 |
|
value: 61 |
|
- type: ndcg_at_10 |
|
value: 72.369 |
|
- type: ndcg_at_100 |
|
value: 74.237 |
|
- type: ndcg_at_1000 |
|
value: 74.939 |
|
- type: ndcg_at_3 |
|
value: 67.284 |
|
- type: ndcg_at_5 |
|
value: 69.72500000000001 |
|
- type: precision_at_1 |
|
value: 61 |
|
- type: precision_at_10 |
|
value: 9.733 |
|
- type: precision_at_100 |
|
value: 1.0670000000000002 |
|
- type: precision_at_1000 |
|
value: 0.11199999999999999 |
|
- type: precision_at_3 |
|
value: 26.222 |
|
- type: precision_at_5 |
|
value: 17.4 |
|
- type: recall_at_1 |
|
value: 57.660999999999994 |
|
- type: recall_at_10 |
|
value: 85.656 |
|
- type: recall_at_100 |
|
value: 93.833 |
|
- type: recall_at_1000 |
|
value: 99.333 |
|
- type: recall_at_3 |
|
value: 71.961 |
|
- type: recall_at_5 |
|
value: 78.094 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/sprintduplicatequestions-pairclassification |
|
name: MTEB SprintDuplicateQuestions |
|
config: default |
|
split: test |
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 99.86930693069307 |
|
- type: cos_sim_ap |
|
value: 96.76685487950894 |
|
- type: cos_sim_f1 |
|
value: 93.44587884806354 |
|
- type: cos_sim_precision |
|
value: 92.80078895463511 |
|
- type: cos_sim_recall |
|
value: 94.1 |
|
- type: dot_accuracy |
|
value: 99.54356435643564 |
|
- type: dot_ap |
|
value: 81.18659960405607 |
|
- type: dot_f1 |
|
value: 75.78008915304605 |
|
- type: dot_precision |
|
value: 75.07360157016683 |
|
- type: dot_recall |
|
value: 76.5 |
|
- type: euclidean_accuracy |
|
value: 99.87326732673267 |
|
- type: euclidean_ap |
|
value: 96.8102411908941 |
|
- type: euclidean_f1 |
|
value: 93.6127744510978 |
|
- type: euclidean_precision |
|
value: 93.42629482071713 |
|
- type: euclidean_recall |
|
value: 93.8 |
|
- type: manhattan_accuracy |
|
value: 99.87425742574257 |
|
- type: manhattan_ap |
|
value: 96.82857341435529 |
|
- type: manhattan_f1 |
|
value: 93.62129583124059 |
|
- type: manhattan_precision |
|
value: 94.04641775983855 |
|
- type: manhattan_recall |
|
value: 93.2 |
|
- type: max_accuracy |
|
value: 99.87425742574257 |
|
- type: max_ap |
|
value: 96.82857341435529 |
|
- type: max_f1 |
|
value: 93.62129583124059 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering |
|
name: MTEB StackExchangeClustering |
|
config: default |
|
split: test |
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
|
metrics: |
|
- type: v_measure |
|
value: 65.92560972698926 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/stackexchange-clustering-p2p |
|
name: MTEB StackExchangeClusteringP2P |
|
config: default |
|
split: test |
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
|
metrics: |
|
- type: v_measure |
|
value: 34.92797240259008 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: mteb/stackoverflowdupquestions-reranking |
|
name: MTEB StackOverflowDupQuestions |
|
config: default |
|
split: test |
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
|
metrics: |
|
- type: map |
|
value: 55.244624045597654 |
|
- type: mrr |
|
value: 56.185303666921314 |
|
- task: |
|
type: Summarization |
|
dataset: |
|
type: mteb/summeval |
|
name: MTEB SummEval |
|
config: default |
|
split: test |
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 31.02491987312937 |
|
- type: cos_sim_spearman |
|
value: 32.055592206679734 |
|
- type: dot_pearson |
|
value: 24.731627575422557 |
|
- type: dot_spearman |
|
value: 24.308029077069733 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.231 |
|
- type: map_at_10 |
|
value: 1.899 |
|
- type: map_at_100 |
|
value: 9.498 |
|
- type: map_at_1000 |
|
value: 20.979999999999997 |
|
- type: map_at_3 |
|
value: 0.652 |
|
- type: map_at_5 |
|
value: 1.069 |
|
- 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: 86 |
|
- type: ndcg_at_10 |
|
value: 75.375 |
|
- type: ndcg_at_100 |
|
value: 52.891999999999996 |
|
- type: ndcg_at_1000 |
|
value: 44.952999999999996 |
|
- type: ndcg_at_3 |
|
value: 81.05 |
|
- type: ndcg_at_5 |
|
value: 80.175 |
|
- type: precision_at_1 |
|
value: 88 |
|
- type: precision_at_10 |
|
value: 79 |
|
- type: precision_at_100 |
|
value: 53.16 |
|
- type: precision_at_1000 |
|
value: 19.408 |
|
- type: precision_at_3 |
|
value: 85.333 |
|
- type: precision_at_5 |
|
value: 84 |
|
- type: recall_at_1 |
|
value: 0.231 |
|
- type: recall_at_10 |
|
value: 2.078 |
|
- type: recall_at_100 |
|
value: 12.601 |
|
- type: recall_at_1000 |
|
value: 41.296 |
|
- type: recall_at_3 |
|
value: 0.6779999999999999 |
|
- type: recall_at_5 |
|
value: 1.1360000000000001 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 2.782 |
|
- type: map_at_10 |
|
value: 10.204 |
|
- type: map_at_100 |
|
value: 16.176 |
|
- type: map_at_1000 |
|
value: 17.456 |
|
- type: map_at_3 |
|
value: 5.354 |
|
- type: map_at_5 |
|
value: 7.503 |
|
- type: mrr_at_1 |
|
value: 40.816 |
|
- type: mrr_at_10 |
|
value: 54.010000000000005 |
|
- type: mrr_at_100 |
|
value: 54.49 |
|
- type: mrr_at_1000 |
|
value: 54.49 |
|
- type: mrr_at_3 |
|
value: 48.980000000000004 |
|
- type: mrr_at_5 |
|
value: 51.735 |
|
- type: ndcg_at_1 |
|
value: 36.735 |
|
- type: ndcg_at_10 |
|
value: 26.61 |
|
- type: ndcg_at_100 |
|
value: 36.967 |
|
- type: ndcg_at_1000 |
|
value: 47.274 |
|
- type: ndcg_at_3 |
|
value: 30.363 |
|
- type: ndcg_at_5 |
|
value: 29.448999999999998 |
|
- type: precision_at_1 |
|
value: 40.816 |
|
- type: precision_at_10 |
|
value: 23.878 |
|
- type: precision_at_100 |
|
value: 7.693999999999999 |
|
- type: precision_at_1000 |
|
value: 1.4489999999999998 |
|
- type: precision_at_3 |
|
value: 31.293 |
|
- type: precision_at_5 |
|
value: 29.796 |
|
- type: recall_at_1 |
|
value: 2.782 |
|
- type: recall_at_10 |
|
value: 16.485 |
|
- type: recall_at_100 |
|
value: 46.924 |
|
- type: recall_at_1000 |
|
value: 79.365 |
|
- type: recall_at_3 |
|
value: 6.52 |
|
- type: recall_at_5 |
|
value: 10.48 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/toxic_conversations_50k |
|
name: MTEB ToxicConversationsClassification |
|
config: default |
|
split: test |
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
|
metrics: |
|
- type: accuracy |
|
value: 70.08300000000001 |
|
- type: ap |
|
value: 13.91559884590195 |
|
- type: f1 |
|
value: 53.956838444291364 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/tweet_sentiment_extraction |
|
name: MTEB TweetSentimentExtractionClassification |
|
config: default |
|
split: test |
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
|
metrics: |
|
- type: accuracy |
|
value: 59.34069043576683 |
|
- type: f1 |
|
value: 59.662041994618406 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: mteb/twentynewsgroups-clustering |
|
name: MTEB TwentyNewsgroupsClustering |
|
config: default |
|
split: test |
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
|
metrics: |
|
- type: v_measure |
|
value: 53.70780611078653 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twittersemeval2015-pairclassification |
|
name: MTEB TwitterSemEval2015 |
|
config: default |
|
split: test |
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.10734934732073 |
|
- type: cos_sim_ap |
|
value: 77.58349999516054 |
|
- type: cos_sim_f1 |
|
value: 70.25391395868965 |
|
- type: cos_sim_precision |
|
value: 70.06035161374967 |
|
- type: cos_sim_recall |
|
value: 70.44854881266491 |
|
- type: dot_accuracy |
|
value: 80.60439887941826 |
|
- type: dot_ap |
|
value: 54.52935200483575 |
|
- type: dot_f1 |
|
value: 54.170444242973716 |
|
- type: dot_precision |
|
value: 47.47715534366309 |
|
- type: dot_recall |
|
value: 63.06068601583114 |
|
- type: euclidean_accuracy |
|
value: 87.26828396018358 |
|
- type: euclidean_ap |
|
value: 78.00158454104036 |
|
- type: euclidean_f1 |
|
value: 70.70292457670601 |
|
- type: euclidean_precision |
|
value: 68.79680479281079 |
|
- type: euclidean_recall |
|
value: 72.71767810026385 |
|
- type: manhattan_accuracy |
|
value: 87.11330988853788 |
|
- type: manhattan_ap |
|
value: 77.92527099601855 |
|
- type: manhattan_f1 |
|
value: 70.76488706365502 |
|
- type: manhattan_precision |
|
value: 68.89055472263868 |
|
- type: manhattan_recall |
|
value: 72.74406332453826 |
|
- type: max_accuracy |
|
value: 87.26828396018358 |
|
- type: max_ap |
|
value: 78.00158454104036 |
|
- type: max_f1 |
|
value: 70.76488706365502 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: mteb/twitterurlcorpus-pairclassification |
|
name: MTEB TwitterURLCorpus |
|
config: default |
|
split: test |
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 87.80804905499282 |
|
- type: cos_sim_ap |
|
value: 83.06187782630936 |
|
- type: cos_sim_f1 |
|
value: 74.99716435403985 |
|
- type: cos_sim_precision |
|
value: 73.67951860931579 |
|
- type: cos_sim_recall |
|
value: 76.36279642747151 |
|
- type: dot_accuracy |
|
value: 81.83141227151008 |
|
- type: dot_ap |
|
value: 67.18241090841795 |
|
- type: dot_f1 |
|
value: 62.216037571751606 |
|
- type: dot_precision |
|
value: 56.749381227391005 |
|
- type: dot_recall |
|
value: 68.84816753926701 |
|
- type: euclidean_accuracy |
|
value: 87.91671517832887 |
|
- type: euclidean_ap |
|
value: 83.56538942001427 |
|
- type: euclidean_f1 |
|
value: 75.7327253337256 |
|
- type: euclidean_precision |
|
value: 72.48856036606828 |
|
- type: euclidean_recall |
|
value: 79.28087465352634 |
|
- type: manhattan_accuracy |
|
value: 87.86626304963713 |
|
- type: manhattan_ap |
|
value: 83.52939841172832 |
|
- type: manhattan_f1 |
|
value: 75.73635656329888 |
|
- type: manhattan_precision |
|
value: 72.99150182103836 |
|
- type: manhattan_recall |
|
value: 78.69571912534647 |
|
- type: max_accuracy |
|
value: 87.91671517832887 |
|
- type: max_ap |
|
value: 83.56538942001427 |
|
- type: max_f1 |
|
value: 75.73635656329888 |
|
license: mit |
|
language: |
|
- en |
|
pipeline_tag: sentence-similarity |
|
--- |
|
|
|
<h1 align="center">FlagEmbedding</h1> |
|
|
|
|
|
<h4 align="center"> |
|
<p> |
|
<a href=#model-list>Model List</a> | |
|
<a href=#usage>Usage</a> | |
|
<a href="#evaluation">Evaluation</a> | |
|
<a href="#train">Train</a> | |
|
<a href="#license">License</a> |
|
<p> |
|
</h4> |
|
|
|
For more details please refer to our GitHub repo: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
|
|
|
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
|
|
|
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. |
|
And it also can be used in vector databases for LLMs. |
|
|
|
************* 🌟**Updates**🌟 ************* |
|
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
|
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: |
|
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. |
|
|
|
|
|
## Model List |
|
|
|
`bge` is short for `BAAI general embedding`. |
|
|
|
| Model | Language | Description | query instruction for retrieval | |
|
|:-------------------------------|:--------:| :--------:| :--------:| |
|
| [BAAI/bge-large-en](https://huggingface.co./BAAI/bge-large-en) | English | :trophy: rank **1st** in [MTEB](https://huggingface.co./spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-base-en](https://huggingface.co./BAAI/bge-base-en) | English | rank **2nd** in [MTEB](https://huggingface.co./spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-small-en](https://huggingface.co./BAAI/bge-small-en) | English | a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
|
| [BAAI/bge-large-zh](https://huggingface.co./BAAI/bge-large-zh) | Chinese | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | |
|
| [BAAI/bge-large-zh-noinstruct](https://huggingface.co./BAAI/bge-large-zh-noinstruct) | Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | | |
|
| [BAAI/bge-base-zh](https://huggingface.co./BAAI/bge-base-zh) | Chinese | a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | |
|
| [BAAI/bge-small-zh](https://huggingface.co./BAAI/bge-small-zh) | Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | |
|
|
|
|
|
|
|
## Usage |
|
|
|
* **Using FlagEmbedding** |
|
``` |
|
pip install -U FlagEmbedding |
|
``` |
|
See [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. |
|
|
|
```python |
|
from FlagEmbedding import FlagModel |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：") |
|
embeddings = model.encode(sentences) |
|
print(embeddings) |
|
# for retrieval task, please use encode_queries() which will automatically add the instruction to each query |
|
# corpus in retrieval task can still use encode() or encode_corpus() |
|
queries = ['query_1', 'query_2'] |
|
passages = ["样例段落-1", "样例段落-2"] |
|
q_embeddings = model.encode_queries(queries) |
|
p_embeddings = model.encode(passages) |
|
scores = q_embeddings @ p_embeddings.T |
|
``` |
|
The value of argument `query_instruction_for_retrieval` see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
|
|
|
FlagModel will use all available GPUs when encoding, please set `os.environ["CUDA_VISIBLE_DEVICES"]` to choose GPU. |
|
|
|
|
|
* **Using Sentence-Transformers** |
|
|
|
Using this model also is easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
model = SentenceTransformer('BAAI/bge-large-zh') |
|
embeddings = model.encode(sentences, normalize_embeddings=True) |
|
print(embeddings) |
|
``` |
|
For retrieval task, |
|
each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
queries = ["手机开不了机怎么办？"] |
|
passages = ["样例段落-1", "样例段落-2"] |
|
instruction = "为这个句子生成表示以用于检索相关文章：" |
|
model = SentenceTransformer('BAAI/bge-large-zh') |
|
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
|
p_embeddings = model.encode(passages, normalize_embeddings=True) |
|
scores = q_embeddings @ p_embeddings.T |
|
``` |
|
|
|
* **Using HuggingFace Transformers** |
|
|
|
With transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of first token (i.e., [CLS]) as the sentence embedding. |
|
|
|
```python |
|
from transformers import AutoTokenizer, AutoModel |
|
import torch |
|
# Sentences we want sentence embeddings for |
|
sentences = ["样例数据-1", "样例数据-2"] |
|
# Load model from HuggingFace Hub |
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh') |
|
model = AutoModel.from_pretrained('BAAI/bge-large-zh') |
|
# Tokenize sentences |
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
|
# for retrieval task, add an instruction to query |
|
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
|
# Compute token embeddings |
|
with torch.no_grad(): |
|
model_output = model(**encoded_input) |
|
# Perform pooling. In this case, cls pooling. |
|
sentence_embeddings = model_output[0][:, 0] |
|
# normalize embeddings |
|
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
|
print("Sentence embeddings:", sentence_embeddings) |
|
``` |
|
|
|
|
|
## Evaluation |
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
|
More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
|
|
|
- **MTEB**: |
|
|
|
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [**bge-large-en**](https://huggingface.co./BAAI/bge-large-en) | 1024 | 512 | **63.98** | **53.9** | **46.98** | 85.8 | **59.48** | 81.56 | 32.06 | **76.21** | |
|
| [**bge-base-en**](https://huggingface.co./BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | |
|
| [gte-large](https://huggingface.co./thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | |
|
| [gte-base](https://huggingface.co./thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co./intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | |
|
| [**bge-small-en**](https://huggingface.co./BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | |
|
| [instructor-xl](https://huggingface.co./hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | |
|
| [e5-base-v2](https://huggingface.co./intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | |
|
| [gte-small](https://huggingface.co./thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co./intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co./sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co./sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co./bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | |
|
| [all-MiniLM-L12-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L12-v2) | 384 | 512 | 56.53 | 42.69 | 41.81 | 82.41 | 58.44 | 79.8 | 27.9 | 63.21 | |
|
| [all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 | 56.26 | 41.95 | 42.35 | 82.37 | 58.04 | 78.9 | 30.81 | 63.05 | |
|
| [contriever-base-msmarco](https://huggingface.co./nthakur/contriever-base-msmarco) | 768 | 512 | 56.00 | 41.88 | 41.1 | 82.54 | 53.14 | 76.51 | 30.36 | 66.68 | |
|
| [sentence-t5-base](https://huggingface.co./sentence-transformers/sentence-t5-base) | 768 | 512 | 55.27 | 33.63 | 40.21 | 85.18 | 53.09 | 81.14 | 31.39 | 69.81 | |
|
|
|
|
|
|
|
- **C-MTEB**: |
|
We create a benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. |
|
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. |
|
|
|
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| [**bge-large-zh**](https://huggingface.co./BAAI/bge-large-zh) | 1024 | **64.20** | **71.53** | **53.23** | **78.94** | 72.26 | **65.11** | 48.39 | |
|
| [**bge-large-zh-noinstruct**](https://huggingface.co./BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 50.98 | 76.77 | **72.49** | 64.91 | **50.01** | |
|
| [**BAAI/bge-base-zh**](https://huggingface.co./BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 52.05 | 77.5 | 70.98 | 64.91 | 47.63 | |
|
| [**BAAI/bge-small-zh**](https://huggingface.co./BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 46.87 | 70.35 | 67.78 | 61.48 | 45.09 | |
|
| [m3e-base](https://huggingface.co./moka-ai/m3e-base) | 768 | 57.10 |56.91 | 48.15 | 63.99 | 70.28 | 59.34 | 47.68 | |
|
| [m3e-large](https://huggingface.co./moka-ai/m3e-large) | 1024 | 57.05 |54.75 | 48.64 | 64.3 | 71.22 | 59.66 | 48.88 | |
|
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 40.61 | 69.56 | 67.38 | 54.28 | 45.68 | |
|
| [luotuo](https://huggingface.co./silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 39.41 | 66.62 | 65.29 | 49.25 | 44.39 | |
|
| [text2vec](https://huggingface.co./shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 41.71 | 67.41 | 65.18 | 49.45 | 37.66 | |
|
| [text2vec-large](https://huggingface.co./GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 41.98 | 70.86 | 63.42 | 49.16 | 30.02 | |
|
|
|
|
|
|
|
## Train |
|
This section will introduce the way we used to train the general embedding. |
|
The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), |
|
and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md). |
|
|
|
|
|
**1. RetroMAE Pre-train** |
|
We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), |
|
which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). |
|
The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. |
|
In retromae, the mask ratio of encoder and decoder are 0.3, and 0.5 respectively. |
|
We used the AdamW optimizer and the learning rate is 2e-5. |
|
|
|
**Pre-training data**: |
|
- English: |
|
- [Pile](https://pile.eleuther.ai/) |
|
- [wikipedia](https://huggingface.co./datasets/wikipedia) |
|
- [msmarco](https://huggingface.co./datasets/Tevatron/msmarco-passage-corpus) |
|
- Chinese: |
|
- Subset of [wudao](https://github.com/BAAI-WuDao/Data) |
|
- [baidu-baike](https://baike.baidu.com/) |
|
|
|
|
|
**2. Finetune** |
|
We fine-tune the model using a contrastive objective. |
|
The format of input data is a triple`(query, positive, negative)`. |
|
Besides the negative in the triple, we also adopt in-batch negatives strategy. |
|
We employ the cross-device negatives sharing method to share negatives among different GPUs, |
|
which can dramatically **increase the number of negatives**. |
|
|
|
We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). |
|
We used the AdamW optimizer and the learning rate is 1e-5. |
|
The temperature for contrastive loss is 0.01. |
|
|
|
For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. |
|
For english, the instruction is `Represent this sentence for searching relevant passages: `; |
|
For chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`. |
|
In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks. |
|
|
|
|
|
The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). |
|
You can easily finetune your model with it. |
|
|
|
**Training data**: |
|
|
|
- For English, we collect 230M text pairs from [wikipedia](https://huggingface.co./datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on. |
|
|
|
- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on. |
|
|
|
**The data collection is to be released in the future.** |
|
|
|
We will continually update the embedding models and training codes, |
|
hoping to promote the development of the embedding model community. |
|
|
|
## License |
|
FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
|
|
