3070 lines
92 KiB
Markdown
3070 lines
92 KiB
Markdown
---
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- mteb
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model-index:
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- name: bge-large-en-v1.5
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results:
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_counterfactual
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name: MTEB AmazonCounterfactualClassification (en)
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config: en
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split: test
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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metrics:
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- type: accuracy
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value: 75.8507462686567
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- type: ap
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value: 38.566457320228245
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- type: f1
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value: 69.69386648043475
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_polarity
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name: MTEB AmazonPolarityClassification
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config: default
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split: test
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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metrics:
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- type: accuracy
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value: 92.416675
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- type: ap
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value: 89.1928861155922
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- type: f1
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value: 92.39477019574215
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_reviews_multi
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name: MTEB AmazonReviewsClassification (en)
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config: en
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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value: 48.175999999999995
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- type: f1
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value: 47.80712792870253
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- task:
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type: Retrieval
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dataset:
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type: arguana
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name: MTEB ArguAna
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 40.184999999999995
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- type: map_at_10
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value: 55.654
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- type: map_at_100
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value: 56.25
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- type: map_at_1000
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value: 56.255
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- type: map_at_3
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value: 51.742999999999995
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- type: map_at_5
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value: 54.129000000000005
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- type: mrr_at_1
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value: 40.967
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- type: mrr_at_10
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value: 55.96
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- type: mrr_at_100
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value: 56.54900000000001
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- type: mrr_at_1000
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value: 56.554
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- type: mrr_at_3
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value: 51.980000000000004
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- type: mrr_at_5
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value: 54.44
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- type: ndcg_at_1
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value: 40.184999999999995
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- type: ndcg_at_10
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value: 63.542
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- type: ndcg_at_100
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value: 65.96499999999999
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- type: ndcg_at_1000
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value: 66.08699999999999
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- type: ndcg_at_3
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value: 55.582
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- type: ndcg_at_5
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value: 59.855000000000004
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- type: precision_at_1
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value: 40.184999999999995
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- type: precision_at_10
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value: 8.841000000000001
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- type: precision_at_100
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value: 0.987
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- type: precision_at_1000
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value: 0.1
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- type: precision_at_3
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value: 22.238
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- type: precision_at_5
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value: 15.405
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- type: recall_at_1
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value: 40.184999999999995
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- type: recall_at_10
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value: 88.407
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- type: recall_at_100
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value: 98.72
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- type: recall_at_1000
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value: 99.644
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- type: recall_at_3
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value: 66.714
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- type: recall_at_5
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value: 77.027
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-p2p
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name: MTEB ArxivClusteringP2P
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config: default
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split: test
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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metrics:
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- type: v_measure
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value: 48.567077926750066
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-s2s
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name: MTEB ArxivClusteringS2S
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config: default
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split: test
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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metrics:
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- type: v_measure
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value: 43.19453389182364
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- task:
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type: Reranking
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dataset:
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type: mteb/askubuntudupquestions-reranking
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name: MTEB AskUbuntuDupQuestions
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config: default
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split: test
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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metrics:
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- type: map
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value: 64.46555939623092
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- type: mrr
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value: 77.82361605768807
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- task:
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type: STS
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dataset:
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type: mteb/biosses-sts
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name: MTEB BIOSSES
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config: default
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split: test
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
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- type: cos_sim_pearson
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value: 84.9554128814735
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- type: cos_sim_spearman
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value: 84.65373612172036
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- type: euclidean_pearson
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value: 83.2905059954138
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- type: euclidean_spearman
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value: 84.52240782811128
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- type: manhattan_pearson
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value: 82.99533802997436
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- type: manhattan_spearman
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value: 84.20673798475734
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- task:
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type: Classification
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dataset:
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type: mteb/banking77
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name: MTEB Banking77Classification
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config: default
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split: test
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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metrics:
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- type: accuracy
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value: 87.78896103896103
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- type: f1
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value: 87.77189310964883
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-p2p
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name: MTEB BiorxivClusteringP2P
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config: default
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split: test
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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metrics:
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- type: v_measure
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value: 39.714538337650495
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-s2s
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name: MTEB BiorxivClusteringS2S
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config: default
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split: test
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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metrics:
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- type: v_measure
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value: 36.90108349284447
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackAndroidRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 32.795
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- type: map_at_10
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value: 43.669000000000004
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- type: map_at_100
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value: 45.151
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- type: map_at_1000
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value: 45.278
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- type: map_at_3
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value: 40.006
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- type: map_at_5
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value: 42.059999999999995
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- type: mrr_at_1
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value: 39.771
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- type: mrr_at_10
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value: 49.826
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- type: mrr_at_100
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value: 50.504000000000005
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- type: mrr_at_1000
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value: 50.549
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- type: mrr_at_3
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value: 47.115
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- type: mrr_at_5
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value: 48.832
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- type: ndcg_at_1
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value: 39.771
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- type: ndcg_at_10
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value: 50.217999999999996
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- type: ndcg_at_100
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value: 55.454
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- type: ndcg_at_1000
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value: 57.37
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- type: ndcg_at_3
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value: 44.885000000000005
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- type: ndcg_at_5
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value: 47.419
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- type: precision_at_1
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value: 39.771
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- type: precision_at_10
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value: 9.642000000000001
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- type: precision_at_100
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value: 1.538
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- type: precision_at_1000
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value: 0.198
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- type: precision_at_3
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value: 21.268
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- type: precision_at_5
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value: 15.536
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- type: recall_at_1
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value: 32.795
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- type: recall_at_10
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value: 62.580999999999996
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- type: recall_at_100
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value: 84.438
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- type: recall_at_1000
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value: 96.492
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- type: recall_at_3
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value: 47.071000000000005
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- type: recall_at_5
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value: 54.079
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackEnglishRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 32.671
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- type: map_at_10
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value: 43.334
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- type: map_at_100
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value: 44.566
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- type: map_at_1000
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value: 44.702999999999996
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- type: map_at_3
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value: 40.343
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- type: map_at_5
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value: 41.983
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- type: mrr_at_1
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value: 40.764
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- type: mrr_at_10
|
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value: 49.382
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- type: mrr_at_100
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value: 49.988
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- type: mrr_at_1000
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value: 50.03300000000001
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- type: mrr_at_3
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value: 47.293
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- type: mrr_at_5
|
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value: 48.51
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- type: ndcg_at_1
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value: 40.764
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- type: ndcg_at_10
|
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value: 49.039
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- type: ndcg_at_100
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value: 53.259
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- type: ndcg_at_1000
|
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value: 55.253
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- type: ndcg_at_3
|
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value: 45.091
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- type: ndcg_at_5
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value: 46.839999999999996
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- type: precision_at_1
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value: 40.764
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- type: precision_at_10
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value: 9.191
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- type: precision_at_100
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value: 1.476
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- type: precision_at_1000
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value: 0.19499999999999998
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- type: precision_at_3
|
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value: 21.72
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- type: precision_at_5
|
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value: 15.299
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- type: recall_at_1
|
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value: 32.671
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- type: recall_at_10
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value: 58.816
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- type: recall_at_100
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value: 76.654
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- type: recall_at_1000
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value: 89.05999999999999
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- type: recall_at_3
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value: 46.743
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- type: recall_at_5
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value: 51.783
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGamingRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 40.328
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- type: map_at_10
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value: 53.32599999999999
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- type: map_at_100
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value: 54.37499999999999
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- type: map_at_1000
|
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value: 54.429
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- type: map_at_3
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value: 49.902
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- type: map_at_5
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value: 52.002
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- type: mrr_at_1
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value: 46.332
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- type: mrr_at_10
|
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value: 56.858
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- type: mrr_at_100
|
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value: 57.522
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- type: mrr_at_1000
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value: 57.54899999999999
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- type: mrr_at_3
|
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value: 54.472
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- type: mrr_at_5
|
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value: 55.996
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- type: ndcg_at_1
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value: 46.332
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- type: ndcg_at_10
|
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value: 59.313
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- type: ndcg_at_100
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value: 63.266999999999996
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- type: ndcg_at_1000
|
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value: 64.36
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- type: ndcg_at_3
|
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value: 53.815000000000005
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- type: ndcg_at_5
|
|
value: 56.814
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- type: precision_at_1
|
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value: 46.332
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- type: precision_at_10
|
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value: 9.53
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- type: precision_at_100
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value: 1.238
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- type: precision_at_1000
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value: 0.13699999999999998
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- type: precision_at_3
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value: 24.054000000000002
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- type: precision_at_5
|
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value: 16.589000000000002
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- type: recall_at_1
|
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value: 40.328
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- type: recall_at_10
|
|
value: 73.421
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- type: recall_at_100
|
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value: 90.059
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- type: recall_at_1000
|
|
value: 97.81
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- type: recall_at_3
|
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value: 59.009
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- type: recall_at_5
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value: 66.352
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- task:
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type: Retrieval
|
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGisRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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|
value: 27.424
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- type: map_at_10
|
|
value: 36.332
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- type: map_at_100
|
|
value: 37.347
|
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- type: map_at_1000
|
|
value: 37.422
|
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- type: map_at_3
|
|
value: 33.743
|
|
- type: map_at_5
|
|
value: 35.176
|
|
- type: mrr_at_1
|
|
value: 29.153000000000002
|
|
- type: mrr_at_10
|
|
value: 38.233
|
|
- type: mrr_at_100
|
|
value: 39.109
|
|
- type: mrr_at_1000
|
|
value: 39.164
|
|
- type: mrr_at_3
|
|
value: 35.876000000000005
|
|
- type: mrr_at_5
|
|
value: 37.169000000000004
|
|
- type: ndcg_at_1
|
|
value: 29.153000000000002
|
|
- type: ndcg_at_10
|
|
value: 41.439
|
|
- type: ndcg_at_100
|
|
value: 46.42
|
|
- type: ndcg_at_1000
|
|
value: 48.242000000000004
|
|
- type: ndcg_at_3
|
|
value: 36.362
|
|
- type: ndcg_at_5
|
|
value: 38.743
|
|
- type: precision_at_1
|
|
value: 29.153000000000002
|
|
- type: precision_at_10
|
|
value: 6.315999999999999
|
|
- type: precision_at_100
|
|
value: 0.927
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 15.443000000000001
|
|
- type: precision_at_5
|
|
value: 10.644
|
|
- type: recall_at_1
|
|
value: 27.424
|
|
- type: recall_at_10
|
|
value: 55.364000000000004
|
|
- type: recall_at_100
|
|
value: 78.211
|
|
- type: recall_at_1000
|
|
value: 91.74600000000001
|
|
- type: recall_at_3
|
|
value: 41.379
|
|
- type: recall_at_5
|
|
value: 47.14
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|
- task:
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type: Retrieval
|
|
dataset:
|
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type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
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|
config: default
|
|
split: test
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|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 19.601
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|
- type: map_at_10
|
|
value: 27.826
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|
- type: map_at_100
|
|
value: 29.017
|
|
- type: map_at_1000
|
|
value: 29.137
|
|
- type: map_at_3
|
|
value: 25.125999999999998
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|
- type: map_at_5
|
|
value: 26.765
|
|
- type: mrr_at_1
|
|
value: 24.005000000000003
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|
- type: mrr_at_10
|
|
value: 32.716
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|
- type: mrr_at_100
|
|
value: 33.631
|
|
- type: mrr_at_1000
|
|
value: 33.694
|
|
- type: mrr_at_3
|
|
value: 29.934
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|
- type: mrr_at_5
|
|
value: 31.630999999999997
|
|
- type: ndcg_at_1
|
|
value: 24.005000000000003
|
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- type: ndcg_at_10
|
|
value: 33.158
|
|
- type: ndcg_at_100
|
|
value: 38.739000000000004
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|
- type: ndcg_at_1000
|
|
value: 41.495
|
|
- type: ndcg_at_3
|
|
value: 28.185
|
|
- type: ndcg_at_5
|
|
value: 30.796
|
|
- type: precision_at_1
|
|
value: 24.005000000000003
|
|
- type: precision_at_10
|
|
value: 5.908
|
|
- type: precision_at_100
|
|
value: 1.005
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 13.391
|
|
- type: precision_at_5
|
|
value: 9.876
|
|
- type: recall_at_1
|
|
value: 19.601
|
|
- type: recall_at_10
|
|
value: 44.746
|
|
- type: recall_at_100
|
|
value: 68.82300000000001
|
|
- type: recall_at_1000
|
|
value: 88.215
|
|
- type: recall_at_3
|
|
value: 31.239
|
|
- type: recall_at_5
|
|
value: 37.695
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 30.130000000000003
|
|
- type: map_at_10
|
|
value: 40.96
|
|
- type: map_at_100
|
|
value: 42.282
|
|
- type: map_at_1000
|
|
value: 42.392
|
|
- type: map_at_3
|
|
value: 37.889
|
|
- type: map_at_5
|
|
value: 39.661
|
|
- type: mrr_at_1
|
|
value: 36.958999999999996
|
|
- type: mrr_at_10
|
|
value: 46.835
|
|
- type: mrr_at_100
|
|
value: 47.644
|
|
- type: mrr_at_1000
|
|
value: 47.688
|
|
- type: mrr_at_3
|
|
value: 44.562000000000005
|
|
- type: mrr_at_5
|
|
value: 45.938
|
|
- type: ndcg_at_1
|
|
value: 36.958999999999996
|
|
- type: ndcg_at_10
|
|
value: 47.06
|
|
- type: ndcg_at_100
|
|
value: 52.345
|
|
- type: ndcg_at_1000
|
|
value: 54.35
|
|
- type: ndcg_at_3
|
|
value: 42.301
|
|
- type: ndcg_at_5
|
|
value: 44.635999999999996
|
|
- type: precision_at_1
|
|
value: 36.958999999999996
|
|
- type: precision_at_10
|
|
value: 8.479000000000001
|
|
- type: precision_at_100
|
|
value: 1.284
|
|
- type: precision_at_1000
|
|
value: 0.163
|
|
- type: precision_at_3
|
|
value: 20.244
|
|
- type: precision_at_5
|
|
value: 14.224999999999998
|
|
- type: recall_at_1
|
|
value: 30.130000000000003
|
|
- type: recall_at_10
|
|
value: 59.27
|
|
- type: recall_at_100
|
|
value: 81.195
|
|
- type: recall_at_1000
|
|
value: 94.21199999999999
|
|
- type: recall_at_3
|
|
value: 45.885
|
|
- type: recall_at_5
|
|
value: 52.016
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.169999999999998
|
|
- type: map_at_10
|
|
value: 36.451
|
|
- type: map_at_100
|
|
value: 37.791000000000004
|
|
- type: map_at_1000
|
|
value: 37.897
|
|
- type: map_at_3
|
|
value: 33.109
|
|
- type: map_at_5
|
|
value: 34.937000000000005
|
|
- type: mrr_at_1
|
|
value: 32.877
|
|
- type: mrr_at_10
|
|
value: 42.368
|
|
- type: mrr_at_100
|
|
value: 43.201
|
|
- type: mrr_at_1000
|
|
value: 43.259
|
|
- type: mrr_at_3
|
|
value: 39.763999999999996
|
|
- type: mrr_at_5
|
|
value: 41.260000000000005
|
|
- type: ndcg_at_1
|
|
value: 32.877
|
|
- type: ndcg_at_10
|
|
value: 42.659000000000006
|
|
- type: ndcg_at_100
|
|
value: 48.161
|
|
- type: ndcg_at_1000
|
|
value: 50.345
|
|
- type: ndcg_at_3
|
|
value: 37.302
|
|
- type: ndcg_at_5
|
|
value: 39.722
|
|
- type: precision_at_1
|
|
value: 32.877
|
|
- type: precision_at_10
|
|
value: 7.9
|
|
- type: precision_at_100
|
|
value: 1.236
|
|
- type: precision_at_1000
|
|
value: 0.158
|
|
- type: precision_at_3
|
|
value: 17.846
|
|
- type: precision_at_5
|
|
value: 12.9
|
|
- type: recall_at_1
|
|
value: 26.169999999999998
|
|
- type: recall_at_10
|
|
value: 55.35
|
|
- type: recall_at_100
|
|
value: 78.755
|
|
- type: recall_at_1000
|
|
value: 93.518
|
|
- type: recall_at_3
|
|
value: 40.176
|
|
- type: recall_at_5
|
|
value: 46.589000000000006
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.15516666666667
|
|
- type: map_at_10
|
|
value: 36.65741666666667
|
|
- type: map_at_100
|
|
value: 37.84991666666666
|
|
- type: map_at_1000
|
|
value: 37.96316666666667
|
|
- type: map_at_3
|
|
value: 33.74974999999999
|
|
- type: map_at_5
|
|
value: 35.3765
|
|
- type: mrr_at_1
|
|
value: 32.08233333333334
|
|
- type: mrr_at_10
|
|
value: 41.033833333333334
|
|
- type: mrr_at_100
|
|
value: 41.84524999999999
|
|
- type: mrr_at_1000
|
|
value: 41.89983333333333
|
|
- type: mrr_at_3
|
|
value: 38.62008333333333
|
|
- type: mrr_at_5
|
|
value: 40.03441666666666
|
|
- type: ndcg_at_1
|
|
value: 32.08233333333334
|
|
- type: ndcg_at_10
|
|
value: 42.229
|
|
- type: ndcg_at_100
|
|
value: 47.26716666666667
|
|
- type: ndcg_at_1000
|
|
value: 49.43466666666667
|
|
- type: ndcg_at_3
|
|
value: 37.36408333333333
|
|
- type: ndcg_at_5
|
|
value: 39.6715
|
|
- type: precision_at_1
|
|
value: 32.08233333333334
|
|
- type: precision_at_10
|
|
value: 7.382583333333334
|
|
- type: precision_at_100
|
|
value: 1.16625
|
|
- type: precision_at_1000
|
|
value: 0.15408333333333332
|
|
- type: precision_at_3
|
|
value: 17.218
|
|
- type: precision_at_5
|
|
value: 12.21875
|
|
- type: recall_at_1
|
|
value: 27.15516666666667
|
|
- type: recall_at_10
|
|
value: 54.36683333333333
|
|
- type: recall_at_100
|
|
value: 76.37183333333333
|
|
- type: recall_at_1000
|
|
value: 91.26183333333333
|
|
- type: recall_at_3
|
|
value: 40.769916666666674
|
|
- type: recall_at_5
|
|
value: 46.702333333333335
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.749
|
|
- type: map_at_10
|
|
value: 33.001999999999995
|
|
- type: map_at_100
|
|
value: 33.891
|
|
- type: map_at_1000
|
|
value: 33.993
|
|
- type: map_at_3
|
|
value: 30.703999999999997
|
|
- type: map_at_5
|
|
value: 31.959
|
|
- type: mrr_at_1
|
|
value: 28.834
|
|
- type: mrr_at_10
|
|
value: 35.955
|
|
- type: mrr_at_100
|
|
value: 36.709
|
|
- type: mrr_at_1000
|
|
value: 36.779
|
|
- type: mrr_at_3
|
|
value: 33.947
|
|
- type: mrr_at_5
|
|
value: 35.089
|
|
- type: ndcg_at_1
|
|
value: 28.834
|
|
- type: ndcg_at_10
|
|
value: 37.329
|
|
- type: ndcg_at_100
|
|
value: 41.79
|
|
- type: ndcg_at_1000
|
|
value: 44.169000000000004
|
|
- type: ndcg_at_3
|
|
value: 33.184999999999995
|
|
- type: ndcg_at_5
|
|
value: 35.107
|
|
- type: precision_at_1
|
|
value: 28.834
|
|
- type: precision_at_10
|
|
value: 5.7669999999999995
|
|
- type: precision_at_100
|
|
value: 0.876
|
|
- type: precision_at_1000
|
|
value: 0.11399999999999999
|
|
- type: precision_at_3
|
|
value: 14.213000000000001
|
|
- type: precision_at_5
|
|
value: 9.754999999999999
|
|
- type: recall_at_1
|
|
value: 25.749
|
|
- type: recall_at_10
|
|
value: 47.791
|
|
- type: recall_at_100
|
|
value: 68.255
|
|
- type: recall_at_1000
|
|
value: 85.749
|
|
- type: recall_at_3
|
|
value: 36.199
|
|
- type: recall_at_5
|
|
value: 41.071999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.777
|
|
- type: map_at_10
|
|
value: 25.201
|
|
- type: map_at_100
|
|
value: 26.423999999999996
|
|
- type: map_at_1000
|
|
value: 26.544
|
|
- type: map_at_3
|
|
value: 22.869
|
|
- type: map_at_5
|
|
value: 24.023
|
|
- type: mrr_at_1
|
|
value: 21.473
|
|
- type: mrr_at_10
|
|
value: 29.12
|
|
- type: mrr_at_100
|
|
value: 30.144
|
|
- type: mrr_at_1000
|
|
value: 30.215999999999998
|
|
- type: mrr_at_3
|
|
value: 26.933
|
|
- type: mrr_at_5
|
|
value: 28.051
|
|
- type: ndcg_at_1
|
|
value: 21.473
|
|
- type: ndcg_at_10
|
|
value: 30.003
|
|
- type: ndcg_at_100
|
|
value: 35.766
|
|
- type: ndcg_at_1000
|
|
value: 38.501000000000005
|
|
- type: ndcg_at_3
|
|
value: 25.773000000000003
|
|
- type: ndcg_at_5
|
|
value: 27.462999999999997
|
|
- type: precision_at_1
|
|
value: 21.473
|
|
- type: precision_at_10
|
|
value: 5.482
|
|
- type: precision_at_100
|
|
value: 0.975
|
|
- type: precision_at_1000
|
|
value: 0.13799999999999998
|
|
- type: precision_at_3
|
|
value: 12.205
|
|
- type: precision_at_5
|
|
value: 8.692
|
|
- type: recall_at_1
|
|
value: 17.777
|
|
- type: recall_at_10
|
|
value: 40.582
|
|
- type: recall_at_100
|
|
value: 66.305
|
|
- type: recall_at_1000
|
|
value: 85.636
|
|
- type: recall_at_3
|
|
value: 28.687
|
|
- type: recall_at_5
|
|
value: 33.089
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.677
|
|
- type: map_at_10
|
|
value: 36.309000000000005
|
|
- type: map_at_100
|
|
value: 37.403999999999996
|
|
- type: map_at_1000
|
|
value: 37.496
|
|
- type: map_at_3
|
|
value: 33.382
|
|
- type: map_at_5
|
|
value: 34.98
|
|
- type: mrr_at_1
|
|
value: 31.343
|
|
- type: mrr_at_10
|
|
value: 40.549
|
|
- type: mrr_at_100
|
|
value: 41.342
|
|
- type: mrr_at_1000
|
|
value: 41.397
|
|
- type: mrr_at_3
|
|
value: 38.029
|
|
- type: mrr_at_5
|
|
value: 39.451
|
|
- type: ndcg_at_1
|
|
value: 31.343
|
|
- type: ndcg_at_10
|
|
value: 42.1
|
|
- type: ndcg_at_100
|
|
value: 47.089999999999996
|
|
- type: ndcg_at_1000
|
|
value: 49.222
|
|
- type: ndcg_at_3
|
|
value: 36.836999999999996
|
|
- type: ndcg_at_5
|
|
value: 39.21
|
|
- type: precision_at_1
|
|
value: 31.343
|
|
- type: precision_at_10
|
|
value: 7.164
|
|
- type: precision_at_100
|
|
value: 1.0959999999999999
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 16.915
|
|
- type: precision_at_5
|
|
value: 11.940000000000001
|
|
- type: recall_at_1
|
|
value: 26.677
|
|
- type: recall_at_10
|
|
value: 55.54599999999999
|
|
- type: recall_at_100
|
|
value: 77.094
|
|
- type: recall_at_1000
|
|
value: 92.01
|
|
- type: recall_at_3
|
|
value: 41.191
|
|
- type: recall_at_5
|
|
value: 47.006
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.501
|
|
- type: map_at_10
|
|
value: 33.102
|
|
- type: map_at_100
|
|
value: 34.676
|
|
- type: map_at_1000
|
|
value: 34.888000000000005
|
|
- type: map_at_3
|
|
value: 29.944
|
|
- type: map_at_5
|
|
value: 31.613999999999997
|
|
- type: mrr_at_1
|
|
value: 29.447000000000003
|
|
- type: mrr_at_10
|
|
value: 37.996
|
|
- type: mrr_at_100
|
|
value: 38.946
|
|
- type: mrr_at_1000
|
|
value: 38.995000000000005
|
|
- type: mrr_at_3
|
|
value: 35.079
|
|
- type: mrr_at_5
|
|
value: 36.69
|
|
- type: ndcg_at_1
|
|
value: 29.447000000000003
|
|
- type: ndcg_at_10
|
|
value: 39.232
|
|
- type: ndcg_at_100
|
|
value: 45.247
|
|
- type: ndcg_at_1000
|
|
value: 47.613
|
|
- type: ndcg_at_3
|
|
value: 33.922999999999995
|
|
- type: ndcg_at_5
|
|
value: 36.284
|
|
- type: precision_at_1
|
|
value: 29.447000000000003
|
|
- type: precision_at_10
|
|
value: 7.648000000000001
|
|
- type: precision_at_100
|
|
value: 1.516
|
|
- type: precision_at_1000
|
|
value: 0.23900000000000002
|
|
- type: precision_at_3
|
|
value: 16.008
|
|
- type: precision_at_5
|
|
value: 11.779
|
|
- type: recall_at_1
|
|
value: 24.501
|
|
- type: recall_at_10
|
|
value: 51.18899999999999
|
|
- type: recall_at_100
|
|
value: 78.437
|
|
- type: recall_at_1000
|
|
value: 92.842
|
|
- type: recall_at_3
|
|
value: 35.808
|
|
- type: recall_at_5
|
|
value: 42.197
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.039
|
|
- type: map_at_10
|
|
value: 30.377
|
|
- type: map_at_100
|
|
value: 31.275
|
|
- type: map_at_1000
|
|
value: 31.379
|
|
- type: map_at_3
|
|
value: 27.98
|
|
- type: map_at_5
|
|
value: 29.358
|
|
- type: mrr_at_1
|
|
value: 24.03
|
|
- type: mrr_at_10
|
|
value: 32.568000000000005
|
|
- type: mrr_at_100
|
|
value: 33.403
|
|
- type: mrr_at_1000
|
|
value: 33.475
|
|
- type: mrr_at_3
|
|
value: 30.436999999999998
|
|
- type: mrr_at_5
|
|
value: 31.796000000000003
|
|
- type: ndcg_at_1
|
|
value: 24.03
|
|
- type: ndcg_at_10
|
|
value: 35.198
|
|
- type: ndcg_at_100
|
|
value: 39.668
|
|
- type: ndcg_at_1000
|
|
value: 42.296
|
|
- type: ndcg_at_3
|
|
value: 30.709999999999997
|
|
- type: ndcg_at_5
|
|
value: 33.024
|
|
- type: precision_at_1
|
|
value: 24.03
|
|
- type: precision_at_10
|
|
value: 5.564
|
|
- type: precision_at_100
|
|
value: 0.828
|
|
- type: precision_at_1000
|
|
value: 0.117
|
|
- type: precision_at_3
|
|
value: 13.309000000000001
|
|
- type: precision_at_5
|
|
value: 9.39
|
|
- type: recall_at_1
|
|
value: 22.039
|
|
- type: recall_at_10
|
|
value: 47.746
|
|
- type: recall_at_100
|
|
value: 68.23599999999999
|
|
- type: recall_at_1000
|
|
value: 87.852
|
|
- type: recall_at_3
|
|
value: 35.852000000000004
|
|
- type: recall_at_5
|
|
value: 41.410000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 15.692999999999998
|
|
- type: map_at_10
|
|
value: 26.903
|
|
- type: map_at_100
|
|
value: 28.987000000000002
|
|
- type: map_at_1000
|
|
value: 29.176999999999996
|
|
- type: map_at_3
|
|
value: 22.137
|
|
- type: map_at_5
|
|
value: 24.758
|
|
- type: mrr_at_1
|
|
value: 35.57
|
|
- type: mrr_at_10
|
|
value: 47.821999999999996
|
|
- type: mrr_at_100
|
|
value: 48.608000000000004
|
|
- type: mrr_at_1000
|
|
value: 48.638999999999996
|
|
- type: mrr_at_3
|
|
value: 44.452000000000005
|
|
- type: mrr_at_5
|
|
value: 46.546
|
|
- type: ndcg_at_1
|
|
value: 35.57
|
|
- type: ndcg_at_10
|
|
value: 36.567
|
|
- type: ndcg_at_100
|
|
value: 44.085
|
|
- type: ndcg_at_1000
|
|
value: 47.24
|
|
- type: ndcg_at_3
|
|
value: 29.964000000000002
|
|
- type: ndcg_at_5
|
|
value: 32.511
|
|
- type: precision_at_1
|
|
value: 35.57
|
|
- type: precision_at_10
|
|
value: 11.485
|
|
- type: precision_at_100
|
|
value: 1.9619999999999997
|
|
- type: precision_at_1000
|
|
value: 0.256
|
|
- type: precision_at_3
|
|
value: 22.237000000000002
|
|
- type: precision_at_5
|
|
value: 17.471999999999998
|
|
- type: recall_at_1
|
|
value: 15.692999999999998
|
|
- type: recall_at_10
|
|
value: 43.056
|
|
- type: recall_at_100
|
|
value: 68.628
|
|
- type: recall_at_1000
|
|
value: 86.075
|
|
- type: recall_at_3
|
|
value: 26.918999999999997
|
|
- type: recall_at_5
|
|
value: 34.14
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.53
|
|
- type: map_at_10
|
|
value: 20.951
|
|
- type: map_at_100
|
|
value: 30.136000000000003
|
|
- type: map_at_1000
|
|
value: 31.801000000000002
|
|
- type: map_at_3
|
|
value: 15.021
|
|
- type: map_at_5
|
|
value: 17.471999999999998
|
|
- type: mrr_at_1
|
|
value: 71.0
|
|
- type: mrr_at_10
|
|
value: 79.176
|
|
- type: mrr_at_100
|
|
value: 79.418
|
|
- type: mrr_at_1000
|
|
value: 79.426
|
|
- type: mrr_at_3
|
|
value: 78.125
|
|
- type: mrr_at_5
|
|
value: 78.61200000000001
|
|
- type: ndcg_at_1
|
|
value: 58.5
|
|
- type: ndcg_at_10
|
|
value: 44.106
|
|
- type: ndcg_at_100
|
|
value: 49.268
|
|
- type: ndcg_at_1000
|
|
value: 56.711999999999996
|
|
- type: ndcg_at_3
|
|
value: 48.934
|
|
- type: ndcg_at_5
|
|
value: 45.826
|
|
- type: precision_at_1
|
|
value: 71.0
|
|
- type: precision_at_10
|
|
value: 35.0
|
|
- type: precision_at_100
|
|
value: 11.360000000000001
|
|
- type: precision_at_1000
|
|
value: 2.046
|
|
- type: precision_at_3
|
|
value: 52.833
|
|
- type: precision_at_5
|
|
value: 44.15
|
|
- type: recall_at_1
|
|
value: 9.53
|
|
- type: recall_at_10
|
|
value: 26.811
|
|
- type: recall_at_100
|
|
value: 55.916999999999994
|
|
- type: recall_at_1000
|
|
value: 79.973
|
|
- type: recall_at_3
|
|
value: 16.413
|
|
- type: recall_at_5
|
|
value: 19.980999999999998
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 51.519999999999996
|
|
- type: f1
|
|
value: 46.36601294761231
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 74.413
|
|
- type: map_at_10
|
|
value: 83.414
|
|
- type: map_at_100
|
|
value: 83.621
|
|
- type: map_at_1000
|
|
value: 83.635
|
|
- type: map_at_3
|
|
value: 82.337
|
|
- type: map_at_5
|
|
value: 83.039
|
|
- type: mrr_at_1
|
|
value: 80.19800000000001
|
|
- type: mrr_at_10
|
|
value: 87.715
|
|
- type: mrr_at_100
|
|
value: 87.778
|
|
- type: mrr_at_1000
|
|
value: 87.779
|
|
- type: mrr_at_3
|
|
value: 87.106
|
|
- type: mrr_at_5
|
|
value: 87.555
|
|
- type: ndcg_at_1
|
|
value: 80.19800000000001
|
|
- type: ndcg_at_10
|
|
value: 87.182
|
|
- type: ndcg_at_100
|
|
value: 87.90299999999999
|
|
- type: ndcg_at_1000
|
|
value: 88.143
|
|
- type: ndcg_at_3
|
|
value: 85.60600000000001
|
|
- type: ndcg_at_5
|
|
value: 86.541
|
|
- type: precision_at_1
|
|
value: 80.19800000000001
|
|
- type: precision_at_10
|
|
value: 10.531
|
|
- type: precision_at_100
|
|
value: 1.113
|
|
- type: precision_at_1000
|
|
value: 0.11499999999999999
|
|
- type: precision_at_3
|
|
value: 32.933
|
|
- type: precision_at_5
|
|
value: 20.429
|
|
- type: recall_at_1
|
|
value: 74.413
|
|
- type: recall_at_10
|
|
value: 94.363
|
|
- type: recall_at_100
|
|
value: 97.165
|
|
- type: recall_at_1000
|
|
value: 98.668
|
|
- type: recall_at_3
|
|
value: 90.108
|
|
- type: recall_at_5
|
|
value: 92.52
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.701
|
|
- type: map_at_10
|
|
value: 37.122
|
|
- type: map_at_100
|
|
value: 39.178000000000004
|
|
- type: map_at_1000
|
|
value: 39.326
|
|
- type: map_at_3
|
|
value: 32.971000000000004
|
|
- type: map_at_5
|
|
value: 35.332
|
|
- type: mrr_at_1
|
|
value: 44.753
|
|
- type: mrr_at_10
|
|
value: 53.452
|
|
- type: mrr_at_100
|
|
value: 54.198
|
|
- type: mrr_at_1000
|
|
value: 54.225
|
|
- type: mrr_at_3
|
|
value: 50.952
|
|
- type: mrr_at_5
|
|
value: 52.464
|
|
- type: ndcg_at_1
|
|
value: 44.753
|
|
- type: ndcg_at_10
|
|
value: 45.021
|
|
- type: ndcg_at_100
|
|
value: 52.028
|
|
- type: ndcg_at_1000
|
|
value: 54.596000000000004
|
|
- type: ndcg_at_3
|
|
value: 41.622
|
|
- type: ndcg_at_5
|
|
value: 42.736000000000004
|
|
- type: precision_at_1
|
|
value: 44.753
|
|
- type: precision_at_10
|
|
value: 12.284
|
|
- type: precision_at_100
|
|
value: 1.955
|
|
- type: precision_at_1000
|
|
value: 0.243
|
|
- type: precision_at_3
|
|
value: 27.828999999999997
|
|
- type: precision_at_5
|
|
value: 20.061999999999998
|
|
- type: recall_at_1
|
|
value: 22.701
|
|
- type: recall_at_10
|
|
value: 51.432
|
|
- type: recall_at_100
|
|
value: 77.009
|
|
- type: recall_at_1000
|
|
value: 92.511
|
|
- type: recall_at_3
|
|
value: 37.919000000000004
|
|
- type: recall_at_5
|
|
value: 44.131
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 40.189
|
|
- type: map_at_10
|
|
value: 66.24600000000001
|
|
- type: map_at_100
|
|
value: 67.098
|
|
- type: map_at_1000
|
|
value: 67.149
|
|
- type: map_at_3
|
|
value: 62.684
|
|
- type: map_at_5
|
|
value: 64.974
|
|
- type: mrr_at_1
|
|
value: 80.378
|
|
- type: mrr_at_10
|
|
value: 86.127
|
|
- type: mrr_at_100
|
|
value: 86.29299999999999
|
|
- type: mrr_at_1000
|
|
value: 86.297
|
|
- type: mrr_at_3
|
|
value: 85.31400000000001
|
|
- type: mrr_at_5
|
|
value: 85.858
|
|
- type: ndcg_at_1
|
|
value: 80.378
|
|
- type: ndcg_at_10
|
|
value: 74.101
|
|
- type: ndcg_at_100
|
|
value: 76.993
|
|
- type: ndcg_at_1000
|
|
value: 77.948
|
|
- type: ndcg_at_3
|
|
value: 69.232
|
|
- type: ndcg_at_5
|
|
value: 72.04599999999999
|
|
- type: precision_at_1
|
|
value: 80.378
|
|
- type: precision_at_10
|
|
value: 15.595999999999998
|
|
- type: precision_at_100
|
|
value: 1.7840000000000003
|
|
- type: precision_at_1000
|
|
value: 0.191
|
|
- type: precision_at_3
|
|
value: 44.884
|
|
- type: precision_at_5
|
|
value: 29.145
|
|
- type: recall_at_1
|
|
value: 40.189
|
|
- type: recall_at_10
|
|
value: 77.981
|
|
- type: recall_at_100
|
|
value: 89.21
|
|
- type: recall_at_1000
|
|
value: 95.48299999999999
|
|
- type: recall_at_3
|
|
value: 67.326
|
|
- type: recall_at_5
|
|
value: 72.863
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.84599999999999
|
|
- type: ap
|
|
value: 89.4710787567357
|
|
- type: f1
|
|
value: 92.83752676932258
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.132
|
|
- type: map_at_10
|
|
value: 35.543
|
|
- type: map_at_100
|
|
value: 36.702
|
|
- type: map_at_1000
|
|
value: 36.748999999999995
|
|
- type: map_at_3
|
|
value: 31.737
|
|
- type: map_at_5
|
|
value: 33.927
|
|
- type: mrr_at_1
|
|
value: 23.782
|
|
- type: mrr_at_10
|
|
value: 36.204
|
|
- type: mrr_at_100
|
|
value: 37.29
|
|
- type: mrr_at_1000
|
|
value: 37.330999999999996
|
|
- type: mrr_at_3
|
|
value: 32.458999999999996
|
|
- type: mrr_at_5
|
|
value: 34.631
|
|
- type: ndcg_at_1
|
|
value: 23.782
|
|
- type: ndcg_at_10
|
|
value: 42.492999999999995
|
|
- type: ndcg_at_100
|
|
value: 47.985
|
|
- type: ndcg_at_1000
|
|
value: 49.141
|
|
- type: ndcg_at_3
|
|
value: 34.748000000000005
|
|
- type: ndcg_at_5
|
|
value: 38.651
|
|
- type: precision_at_1
|
|
value: 23.782
|
|
- type: precision_at_10
|
|
value: 6.665
|
|
- type: precision_at_100
|
|
value: 0.941
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.776
|
|
- type: precision_at_5
|
|
value: 10.84
|
|
- type: recall_at_1
|
|
value: 23.132
|
|
- type: recall_at_10
|
|
value: 63.794
|
|
- type: recall_at_100
|
|
value: 89.027
|
|
- type: recall_at_1000
|
|
value: 97.807
|
|
- type: recall_at_3
|
|
value: 42.765
|
|
- type: recall_at_5
|
|
value: 52.11
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.59188326493388
|
|
- type: f1
|
|
value: 94.3842594786827
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.49384404924761
|
|
- type: f1
|
|
value: 59.7580539534629
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.56220578345663
|
|
- type: f1
|
|
value: 75.27228165561478
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 80.53463349024884
|
|
- type: f1
|
|
value: 80.4893958236536
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 32.56100273484962
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.470380028839607
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 32.06102792457849
|
|
- type: mrr
|
|
value: 33.30709199672238
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 6.776999999999999
|
|
- type: map_at_10
|
|
value: 14.924000000000001
|
|
- type: map_at_100
|
|
value: 18.955
|
|
- type: map_at_1000
|
|
value: 20.538999999999998
|
|
- type: map_at_3
|
|
value: 10.982
|
|
- type: map_at_5
|
|
value: 12.679000000000002
|
|
- type: mrr_at_1
|
|
value: 47.988
|
|
- type: mrr_at_10
|
|
value: 57.232000000000006
|
|
- type: mrr_at_100
|
|
value: 57.818999999999996
|
|
- type: mrr_at_1000
|
|
value: 57.847
|
|
- type: mrr_at_3
|
|
value: 54.901999999999994
|
|
- type: mrr_at_5
|
|
value: 56.481
|
|
- type: ndcg_at_1
|
|
value: 46.594
|
|
- type: ndcg_at_10
|
|
value: 38.129000000000005
|
|
- type: ndcg_at_100
|
|
value: 35.54
|
|
- type: ndcg_at_1000
|
|
value: 44.172
|
|
- type: ndcg_at_3
|
|
value: 43.025999999999996
|
|
- type: ndcg_at_5
|
|
value: 41.052
|
|
- type: precision_at_1
|
|
value: 47.988
|
|
- type: precision_at_10
|
|
value: 28.111000000000004
|
|
- type: precision_at_100
|
|
value: 8.929
|
|
- type: precision_at_1000
|
|
value: 2.185
|
|
- type: precision_at_3
|
|
value: 40.144000000000005
|
|
- type: precision_at_5
|
|
value: 35.232
|
|
- type: recall_at_1
|
|
value: 6.776999999999999
|
|
- type: recall_at_10
|
|
value: 19.289
|
|
- type: recall_at_100
|
|
value: 36.359
|
|
- type: recall_at_1000
|
|
value: 67.54
|
|
- type: recall_at_3
|
|
value: 11.869
|
|
- type: recall_at_5
|
|
value: 14.999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.108000000000004
|
|
- type: map_at_10
|
|
value: 47.126000000000005
|
|
- type: map_at_100
|
|
value: 48.171
|
|
- type: map_at_1000
|
|
value: 48.199
|
|
- type: map_at_3
|
|
value: 42.734
|
|
- type: map_at_5
|
|
value: 45.362
|
|
- type: mrr_at_1
|
|
value: 34.936
|
|
- type: mrr_at_10
|
|
value: 49.571
|
|
- type: mrr_at_100
|
|
value: 50.345
|
|
- type: mrr_at_1000
|
|
value: 50.363
|
|
- type: mrr_at_3
|
|
value: 45.959
|
|
- type: mrr_at_5
|
|
value: 48.165
|
|
- type: ndcg_at_1
|
|
value: 34.936
|
|
- type: ndcg_at_10
|
|
value: 55.028999999999996
|
|
- type: ndcg_at_100
|
|
value: 59.244
|
|
- type: ndcg_at_1000
|
|
value: 59.861
|
|
- type: ndcg_at_3
|
|
value: 46.872
|
|
- type: ndcg_at_5
|
|
value: 51.217999999999996
|
|
- type: precision_at_1
|
|
value: 34.936
|
|
- type: precision_at_10
|
|
value: 9.099
|
|
- type: precision_at_100
|
|
value: 1.145
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 21.456
|
|
- type: precision_at_5
|
|
value: 15.411
|
|
- type: recall_at_1
|
|
value: 31.108000000000004
|
|
- type: recall_at_10
|
|
value: 76.53999999999999
|
|
- type: recall_at_100
|
|
value: 94.39
|
|
- type: recall_at_1000
|
|
value: 98.947
|
|
- type: recall_at_3
|
|
value: 55.572
|
|
- type: recall_at_5
|
|
value: 65.525
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.56400000000001
|
|
- type: map_at_10
|
|
value: 85.482
|
|
- type: map_at_100
|
|
value: 86.114
|
|
- type: map_at_1000
|
|
value: 86.13
|
|
- type: map_at_3
|
|
value: 82.607
|
|
- type: map_at_5
|
|
value: 84.405
|
|
- type: mrr_at_1
|
|
value: 82.42
|
|
- type: mrr_at_10
|
|
value: 88.304
|
|
- type: mrr_at_100
|
|
value: 88.399
|
|
- type: mrr_at_1000
|
|
value: 88.399
|
|
- type: mrr_at_3
|
|
value: 87.37
|
|
- type: mrr_at_5
|
|
value: 88.024
|
|
- type: ndcg_at_1
|
|
value: 82.45
|
|
- type: ndcg_at_10
|
|
value: 89.06500000000001
|
|
- type: ndcg_at_100
|
|
value: 90.232
|
|
- type: ndcg_at_1000
|
|
value: 90.305
|
|
- type: ndcg_at_3
|
|
value: 86.375
|
|
- type: ndcg_at_5
|
|
value: 87.85300000000001
|
|
- type: precision_at_1
|
|
value: 82.45
|
|
- type: precision_at_10
|
|
value: 13.486999999999998
|
|
- type: precision_at_100
|
|
value: 1.534
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.813
|
|
- type: precision_at_5
|
|
value: 24.773999999999997
|
|
- type: recall_at_1
|
|
value: 71.56400000000001
|
|
- type: recall_at_10
|
|
value: 95.812
|
|
- type: recall_at_100
|
|
value: 99.7
|
|
- type: recall_at_1000
|
|
value: 99.979
|
|
- type: recall_at_3
|
|
value: 87.966
|
|
- type: recall_at_5
|
|
value: 92.268
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 57.241876648614145
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 64.66212576446223
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.308
|
|
- type: map_at_10
|
|
value: 13.803
|
|
- type: map_at_100
|
|
value: 16.176
|
|
- type: map_at_1000
|
|
value: 16.561
|
|
- type: map_at_3
|
|
value: 9.761000000000001
|
|
- type: map_at_5
|
|
value: 11.802
|
|
- type: mrr_at_1
|
|
value: 26.200000000000003
|
|
- type: mrr_at_10
|
|
value: 37.621
|
|
- type: mrr_at_100
|
|
value: 38.767
|
|
- type: mrr_at_1000
|
|
value: 38.815
|
|
- type: mrr_at_3
|
|
value: 34.117
|
|
- type: mrr_at_5
|
|
value: 36.107
|
|
- type: ndcg_at_1
|
|
value: 26.200000000000003
|
|
- type: ndcg_at_10
|
|
value: 22.64
|
|
- type: ndcg_at_100
|
|
value: 31.567
|
|
- type: ndcg_at_1000
|
|
value: 37.623
|
|
- type: ndcg_at_3
|
|
value: 21.435000000000002
|
|
- type: ndcg_at_5
|
|
value: 18.87
|
|
- type: precision_at_1
|
|
value: 26.200000000000003
|
|
- type: precision_at_10
|
|
value: 11.74
|
|
- type: precision_at_100
|
|
value: 2.465
|
|
- type: precision_at_1000
|
|
value: 0.391
|
|
- type: precision_at_3
|
|
value: 20.033
|
|
- type: precision_at_5
|
|
value: 16.64
|
|
- type: recall_at_1
|
|
value: 5.308
|
|
- type: recall_at_10
|
|
value: 23.794999999999998
|
|
- type: recall_at_100
|
|
value: 50.015
|
|
- type: recall_at_1000
|
|
value: 79.283
|
|
- type: recall_at_3
|
|
value: 12.178
|
|
- type: recall_at_5
|
|
value: 16.882
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.93231134675553
|
|
- type: cos_sim_spearman
|
|
value: 81.68319292603205
|
|
- type: euclidean_pearson
|
|
value: 81.8396814380367
|
|
- type: euclidean_spearman
|
|
value: 81.24641903349945
|
|
- type: manhattan_pearson
|
|
value: 81.84698799204274
|
|
- type: manhattan_spearman
|
|
value: 81.24269997904105
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.73241671587446
|
|
- type: cos_sim_spearman
|
|
value: 79.05091082971826
|
|
- type: euclidean_pearson
|
|
value: 83.91146869578044
|
|
- type: euclidean_spearman
|
|
value: 79.87978465370936
|
|
- type: manhattan_pearson
|
|
value: 83.90888338917678
|
|
- type: manhattan_spearman
|
|
value: 79.87482848584241
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.14970731146177
|
|
- type: cos_sim_spearman
|
|
value: 86.37363490084627
|
|
- type: euclidean_pearson
|
|
value: 83.02154218530433
|
|
- type: euclidean_spearman
|
|
value: 83.80258761957367
|
|
- type: manhattan_pearson
|
|
value: 83.01664495119347
|
|
- type: manhattan_spearman
|
|
value: 83.77567458007952
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.40474139886784
|
|
- type: cos_sim_spearman
|
|
value: 82.77768789165984
|
|
- type: euclidean_pearson
|
|
value: 80.7065877443695
|
|
- type: euclidean_spearman
|
|
value: 81.375940662505
|
|
- type: manhattan_pearson
|
|
value: 80.6507552270278
|
|
- type: manhattan_spearman
|
|
value: 81.32782179098741
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.08585968722274
|
|
- type: cos_sim_spearman
|
|
value: 88.03110031451399
|
|
- type: euclidean_pearson
|
|
value: 85.74012019602384
|
|
- type: euclidean_spearman
|
|
value: 86.13592849438209
|
|
- type: manhattan_pearson
|
|
value: 85.74404842369206
|
|
- type: manhattan_spearman
|
|
value: 86.14492318960154
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.95069052788875
|
|
- type: cos_sim_spearman
|
|
value: 86.4867991595147
|
|
- type: euclidean_pearson
|
|
value: 84.31013325754635
|
|
- type: euclidean_spearman
|
|
value: 85.01529258006482
|
|
- type: manhattan_pearson
|
|
value: 84.26995570085374
|
|
- type: manhattan_spearman
|
|
value: 84.96982104986162
|
|
- 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: 87.54617647971897
|
|
- type: cos_sim_spearman
|
|
value: 87.49834181751034
|
|
- type: euclidean_pearson
|
|
value: 86.01015322577122
|
|
- type: euclidean_spearman
|
|
value: 84.63362652063199
|
|
- type: manhattan_pearson
|
|
value: 86.13807574475706
|
|
- type: manhattan_spearman
|
|
value: 84.7772370721132
|
|
- 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: 67.20047755786615
|
|
- type: cos_sim_spearman
|
|
value: 67.05324077987636
|
|
- type: euclidean_pearson
|
|
value: 66.91930642976601
|
|
- type: euclidean_spearman
|
|
value: 65.21491856099105
|
|
- type: manhattan_pearson
|
|
value: 66.78756851976624
|
|
- type: manhattan_spearman
|
|
value: 65.12356257740728
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.19852871539686
|
|
- type: cos_sim_spearman
|
|
value: 87.5161895296395
|
|
- type: euclidean_pearson
|
|
value: 84.59848645207485
|
|
- type: euclidean_spearman
|
|
value: 85.26427328757919
|
|
- type: manhattan_pearson
|
|
value: 84.59747366996524
|
|
- type: manhattan_spearman
|
|
value: 85.24045855146915
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 87.63320317811032
|
|
- type: mrr
|
|
value: 96.26242947321379
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 60.928000000000004
|
|
- type: map_at_10
|
|
value: 70.112
|
|
- type: map_at_100
|
|
value: 70.59299999999999
|
|
- type: map_at_1000
|
|
value: 70.623
|
|
- type: map_at_3
|
|
value: 66.846
|
|
- type: map_at_5
|
|
value: 68.447
|
|
- type: mrr_at_1
|
|
value: 64.0
|
|
- type: mrr_at_10
|
|
value: 71.212
|
|
- type: mrr_at_100
|
|
value: 71.616
|
|
- type: mrr_at_1000
|
|
value: 71.64500000000001
|
|
- type: mrr_at_3
|
|
value: 68.77799999999999
|
|
- type: mrr_at_5
|
|
value: 70.094
|
|
- type: ndcg_at_1
|
|
value: 64.0
|
|
- type: ndcg_at_10
|
|
value: 74.607
|
|
- type: ndcg_at_100
|
|
value: 76.416
|
|
- type: ndcg_at_1000
|
|
value: 77.102
|
|
- type: ndcg_at_3
|
|
value: 69.126
|
|
- type: ndcg_at_5
|
|
value: 71.41300000000001
|
|
- type: precision_at_1
|
|
value: 64.0
|
|
- type: precision_at_10
|
|
value: 9.933
|
|
- type: precision_at_100
|
|
value: 1.077
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 26.556
|
|
- type: precision_at_5
|
|
value: 17.467
|
|
- type: recall_at_1
|
|
value: 60.928000000000004
|
|
- type: recall_at_10
|
|
value: 87.322
|
|
- type: recall_at_100
|
|
value: 94.833
|
|
- type: recall_at_1000
|
|
value: 100.0
|
|
- type: recall_at_3
|
|
value: 72.628
|
|
- type: recall_at_5
|
|
value: 78.428
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.86237623762376
|
|
- type: cos_sim_ap
|
|
value: 96.72586477206649
|
|
- type: cos_sim_f1
|
|
value: 93.01858362631845
|
|
- type: cos_sim_precision
|
|
value: 93.4409687184662
|
|
- type: cos_sim_recall
|
|
value: 92.60000000000001
|
|
- type: dot_accuracy
|
|
value: 99.78019801980199
|
|
- type: dot_ap
|
|
value: 93.72748205246228
|
|
- type: dot_f1
|
|
value: 89.04109589041096
|
|
- type: dot_precision
|
|
value: 87.16475095785441
|
|
- type: dot_recall
|
|
value: 91.0
|
|
- type: euclidean_accuracy
|
|
value: 99.85445544554456
|
|
- type: euclidean_ap
|
|
value: 96.6661459876145
|
|
- type: euclidean_f1
|
|
value: 92.58337481333997
|
|
- type: euclidean_precision
|
|
value: 92.17046580773042
|
|
- type: euclidean_recall
|
|
value: 93.0
|
|
- type: manhattan_accuracy
|
|
value: 99.85445544554456
|
|
- type: manhattan_ap
|
|
value: 96.6883549244056
|
|
- type: manhattan_f1
|
|
value: 92.57598405580468
|
|
- type: manhattan_precision
|
|
value: 92.25422045680239
|
|
- type: manhattan_recall
|
|
value: 92.9
|
|
- type: max_accuracy
|
|
value: 99.86237623762376
|
|
- type: max_ap
|
|
value: 96.72586477206649
|
|
- type: max_f1
|
|
value: 93.01858362631845
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 66.39930057069995
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 34.96398659903402
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 55.946944700355395
|
|
- type: mrr
|
|
value: 56.97151398438164
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 31.541657650692905
|
|
- type: cos_sim_spearman
|
|
value: 31.605804192286303
|
|
- type: dot_pearson
|
|
value: 28.26905996736398
|
|
- type: dot_spearman
|
|
value: 27.864801765851187
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.22599999999999998
|
|
- type: map_at_10
|
|
value: 1.8870000000000002
|
|
- type: map_at_100
|
|
value: 9.78
|
|
- type: map_at_1000
|
|
value: 22.514
|
|
- type: map_at_3
|
|
value: 0.6669999999999999
|
|
- type: map_at_5
|
|
value: 1.077
|
|
- type: mrr_at_1
|
|
value: 82.0
|
|
- type: mrr_at_10
|
|
value: 89.86699999999999
|
|
- type: mrr_at_100
|
|
value: 89.86699999999999
|
|
- type: mrr_at_1000
|
|
value: 89.86699999999999
|
|
- type: mrr_at_3
|
|
value: 89.667
|
|
- type: mrr_at_5
|
|
value: 89.667
|
|
- type: ndcg_at_1
|
|
value: 79.0
|
|
- type: ndcg_at_10
|
|
value: 74.818
|
|
- type: ndcg_at_100
|
|
value: 53.715999999999994
|
|
- type: ndcg_at_1000
|
|
value: 47.082
|
|
- type: ndcg_at_3
|
|
value: 82.134
|
|
- type: ndcg_at_5
|
|
value: 79.81899999999999
|
|
- type: precision_at_1
|
|
value: 82.0
|
|
- type: precision_at_10
|
|
value: 78.0
|
|
- type: precision_at_100
|
|
value: 54.48
|
|
- type: precision_at_1000
|
|
value: 20.518
|
|
- type: precision_at_3
|
|
value: 87.333
|
|
- type: precision_at_5
|
|
value: 85.2
|
|
- type: recall_at_1
|
|
value: 0.22599999999999998
|
|
- type: recall_at_10
|
|
value: 2.072
|
|
- type: recall_at_100
|
|
value: 13.013
|
|
- type: recall_at_1000
|
|
value: 43.462
|
|
- type: recall_at_3
|
|
value: 0.695
|
|
- type: recall_at_5
|
|
value: 1.139
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.328
|
|
- type: map_at_10
|
|
value: 9.795
|
|
- type: map_at_100
|
|
value: 15.801000000000002
|
|
- type: map_at_1000
|
|
value: 17.23
|
|
- type: map_at_3
|
|
value: 4.734
|
|
- type: map_at_5
|
|
value: 6.644
|
|
- type: mrr_at_1
|
|
value: 30.612000000000002
|
|
- type: mrr_at_10
|
|
value: 46.902
|
|
- type: mrr_at_100
|
|
value: 47.495
|
|
- type: mrr_at_1000
|
|
value: 47.495
|
|
- type: mrr_at_3
|
|
value: 41.156
|
|
- type: mrr_at_5
|
|
value: 44.218
|
|
- type: ndcg_at_1
|
|
value: 28.571
|
|
- type: ndcg_at_10
|
|
value: 24.806
|
|
- type: ndcg_at_100
|
|
value: 36.419000000000004
|
|
- type: ndcg_at_1000
|
|
value: 47.272999999999996
|
|
- type: ndcg_at_3
|
|
value: 25.666
|
|
- type: ndcg_at_5
|
|
value: 25.448999999999998
|
|
- type: precision_at_1
|
|
value: 30.612000000000002
|
|
- type: precision_at_10
|
|
value: 23.061
|
|
- type: precision_at_100
|
|
value: 7.714
|
|
- type: precision_at_1000
|
|
value: 1.484
|
|
- type: precision_at_3
|
|
value: 26.531
|
|
- type: precision_at_5
|
|
value: 26.122
|
|
- type: recall_at_1
|
|
value: 2.328
|
|
- type: recall_at_10
|
|
value: 16.524
|
|
- type: recall_at_100
|
|
value: 47.179
|
|
- type: recall_at_1000
|
|
value: 81.22200000000001
|
|
- type: recall_at_3
|
|
value: 5.745
|
|
- type: recall_at_5
|
|
value: 9.339
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 70.9142
|
|
- type: ap
|
|
value: 14.335574772555415
|
|
- type: f1
|
|
value: 54.62839595194111
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.94340690435768
|
|
- type: f1
|
|
value: 60.286487936731916
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 51.26597708987974
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 87.48882398521786
|
|
- type: cos_sim_ap
|
|
value: 79.04326607602204
|
|
- type: cos_sim_f1
|
|
value: 71.64566826860633
|
|
- type: cos_sim_precision
|
|
value: 70.55512918905092
|
|
- type: cos_sim_recall
|
|
value: 72.77044854881267
|
|
- type: dot_accuracy
|
|
value: 84.19264469213805
|
|
- type: dot_ap
|
|
value: 67.96360043562528
|
|
- type: dot_f1
|
|
value: 64.06418393006827
|
|
- type: dot_precision
|
|
value: 58.64941898706424
|
|
- type: dot_recall
|
|
value: 70.58047493403694
|
|
- type: euclidean_accuracy
|
|
value: 87.45902127913214
|
|
- type: euclidean_ap
|
|
value: 78.9742237648272
|
|
- type: euclidean_f1
|
|
value: 71.5553235908142
|
|
- type: euclidean_precision
|
|
value: 70.77955601445535
|
|
- type: euclidean_recall
|
|
value: 72.34828496042216
|
|
- type: manhattan_accuracy
|
|
value: 87.41729749061214
|
|
- type: manhattan_ap
|
|
value: 78.90073137580596
|
|
- type: manhattan_f1
|
|
value: 71.3942611553533
|
|
- type: manhattan_precision
|
|
value: 68.52705653967483
|
|
- type: manhattan_recall
|
|
value: 74.51187335092348
|
|
- type: max_accuracy
|
|
value: 87.48882398521786
|
|
- type: max_ap
|
|
value: 79.04326607602204
|
|
- type: max_f1
|
|
value: 71.64566826860633
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.68125897465751
|
|
- type: cos_sim_ap
|
|
value: 85.6003454431979
|
|
- type: cos_sim_f1
|
|
value: 77.6957163958641
|
|
- type: cos_sim_precision
|
|
value: 73.0110366307807
|
|
- type: cos_sim_recall
|
|
value: 83.02279026793964
|
|
- type: dot_accuracy
|
|
value: 87.7672992587418
|
|
- type: dot_ap
|
|
value: 82.4971301112899
|
|
- type: dot_f1
|
|
value: 75.90528233151184
|
|
- type: dot_precision
|
|
value: 72.0370626469368
|
|
- type: dot_recall
|
|
value: 80.21250384970742
|
|
- type: euclidean_accuracy
|
|
value: 88.4503434625684
|
|
- type: euclidean_ap
|
|
value: 84.91949884748384
|
|
- type: euclidean_f1
|
|
value: 76.92365018444684
|
|
- type: euclidean_precision
|
|
value: 74.53245721712759
|
|
- type: euclidean_recall
|
|
value: 79.47336002463813
|
|
- type: manhattan_accuracy
|
|
value: 88.47556952691427
|
|
- type: manhattan_ap
|
|
value: 84.8963689101517
|
|
- type: manhattan_f1
|
|
value: 76.85901249256395
|
|
- type: manhattan_precision
|
|
value: 74.31693989071039
|
|
- type: manhattan_recall
|
|
value: 79.58115183246073
|
|
- type: max_accuracy
|
|
value: 88.68125897465751
|
|
- type: max_ap
|
|
value: 85.6003454431979
|
|
- type: max_f1
|
|
value: 77.6957163958641
|
|
license: mit
|
|
language:
|
|
- en
|
|
---
|
|
|
|
|
|
<h1 align="center">FlagEmbedding</h1>
|
|
|
|
|
|
<h4 align="center">
|
|
<p>
|
|
<a href=#model-list>Model List</a> |
|
|
<a href=#frequently-asked-questions>FAQ</a> |
|
|
<a href=#usage>Usage</a> |
|
|
<a href="#evaluation">Evaluation</a> |
|
|
<a href="#train">Train</a> |
|
|
<a href="#contact">Contact</a> |
|
|
<a href="#citation">Citation</a> |
|
|
<a href="#license">License</a>
|
|
<p>
|
|
</h4>
|
|
|
|
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
|
|
|
|
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
|
|
|
|
|
|
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
|
|
|
|
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
|
|
|
|
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
|
|
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
|
|
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
|
|
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
|
|
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
|
|
|
|
## News
|
|
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
|
|
It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
|
|
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
|
|
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
|
|
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
|
|
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
|
|
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
|
|
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
|
|
- 09/12/2023: New models:
|
|
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
|
|
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
|
|
|
|
|
|
<details>
|
|
<summary>More</summary>
|
|
<!-- ### More -->
|
|
|
|
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
|
|
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
|
|
- 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.
|
|
|
|
</details>
|
|
|
|
|
|
## Model List
|
|
|
|
`bge` is short for `BAAI general embedding`.
|
|
|
|
| Model | Language | | Description | query instruction for retrieval [1] |
|
|
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
|
|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
|
|
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
|
|
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
|
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
|
|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
|
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
|
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
|
|
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
|
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
|
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
|
|
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :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 | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
|
|
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |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 | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :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-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `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 | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：` |
|
|
|
|
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
|
|
|
|
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
|
|
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
|
|
|
|
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
|
|
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
|
|
|
|
|
|
## Frequently asked questions
|
|
|
|
<details>
|
|
<summary>1. How to fine-tune bge embedding model?</summary>
|
|
|
|
<!-- ### How to fine-tune bge embedding model? -->
|
|
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
|
|
Some suggestions:
|
|
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
|
|
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
|
|
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
|
|
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
|
|
|
|
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
|
|
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
|
|
|
|
Since we finetune the models by contrastive learning with a temperature of 0.01,
|
|
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
|
|
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
|
|
|
|
For downstream tasks, such as passage retrieval or semantic similarity,
|
|
**what matters is the relative order of the scores, not the absolute value.**
|
|
If you need to filter similar sentences based on a similarity threshold,
|
|
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
|
|
|
|
</details>
|
|
|
|
<details>
|
|
<summary>3. When does the query instruction need to be used</summary>
|
|
|
|
<!-- ### When does the query instruction need to be used -->
|
|
|
|
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
|
|
No instruction only has a slight degradation in retrieval performance compared with using instruction.
|
|
So you can generate embedding without instruction in all cases for convenience.
|
|
|
|
For a retrieval task that uses short queries to find long related documents,
|
|
it is recommended to add instructions for these short queries.
|
|
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
|
|
In all cases, the documents/passages do not need to add the instruction.
|
|
|
|
</details>
|
|
|
|
|
|
## Usage
|
|
|
|
### Usage for Embedding Model
|
|
|
|
Here are some examples for using `bge` models with
|
|
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
|
|
|
|
#### Using FlagEmbedding
|
|
```
|
|
pip install -U FlagEmbedding
|
|
```
|
|
If it doesn't work for you, you can 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_1 = ["样例数据-1", "样例数据-2"]
|
|
sentences_2 = ["样例数据-3", "样例数据-4"]
|
|
model = FlagModel('BAAI/bge-large-zh-v1.5',
|
|
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
|
|
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
|
embeddings_1 = model.encode(sentences_1)
|
|
embeddings_2 = model.encode(sentences_2)
|
|
similarity = embeddings_1 @ embeddings_2.T
|
|
print(similarity)
|
|
|
|
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
|
|
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
|
|
queries = ['query_1', 'query_2']
|
|
passages = ["样例文档-1", "样例文档-2"]
|
|
q_embeddings = model.encode_queries(queries)
|
|
p_embeddings = model.encode(passages)
|
|
scores = q_embeddings @ p_embeddings.T
|
|
```
|
|
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
|
|
|
|
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
|
|
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
|
|
|
|
|
|
#### Using Sentence-Transformers
|
|
|
|
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
|
|
|
|
```
|
|
pip install -U sentence-transformers
|
|
```
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
sentences_1 = ["样例数据-1", "样例数据-2"]
|
|
sentences_2 = ["样例数据-3", "样例数据-4"]
|
|
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
|
|
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
|
|
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
|
|
similarity = embeddings_1 @ embeddings_2.T
|
|
print(similarity)
|
|
```
|
|
For s2p(short query to long passage) retrieval task,
|
|
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
|
|
But the instruction is not needed for passages.
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
queries = ['query_1', 'query_2']
|
|
passages = ["样例文档-1", "样例文档-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-v1.5')
|
|
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 Langchain
|
|
|
|
You can use `bge` in langchain like this:
|
|
```python
|
|
from langchain.embeddings import HuggingFaceBgeEmbeddings
|
|
model_name = "BAAI/bge-large-en-v1.5"
|
|
model_kwargs = {'device': 'cuda'}
|
|
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
|
|
model = HuggingFaceBgeEmbeddings(
|
|
model_name=model_name,
|
|
model_kwargs=model_kwargs,
|
|
encode_kwargs=encode_kwargs,
|
|
query_instruction="为这个句子生成表示以用于检索相关文章："
|
|
)
|
|
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
|
|
```
|
|
|
|
|
|
#### Using HuggingFace Transformers
|
|
|
|
With the 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 the 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-v1.5')
|
|
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
|
|
model.eval()
|
|
|
|
# Tokenize sentences
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
|
|
# 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)
|
|
```
|
|
|
|
#### Usage of the ONNX files
|
|
|
|
```python
|
|
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
|
|
|
|
import torch
|
|
from transformers import AutoModel, AutoTokenizer
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
|
|
model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
|
|
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")
|
|
|
|
# Sentences we want sentence embeddings for
|
|
sentences = ["样例数据-1", "样例数据-2"]
|
|
|
|
# Tokenize sentences
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
|
|
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
|
|
|
|
model_output_ort = model_ort(**encoded_input)
|
|
# Compute token embeddings
|
|
with torch.no_grad():
|
|
model_output = model(**encoded_input)
|
|
|
|
# model_output and model_output_ort are identical
|
|
|
|
```
|
|
|
|
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
|
|
```python
|
|
import asyncio
|
|
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
|
|
|
|
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
|
|
engine = AsyncEmbeddingEngine.from_args(
|
|
EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
|
|
))
|
|
|
|
async def main():
|
|
async with engine:
|
|
embeddings, usage = await engine.embed(sentences=sentences)
|
|
asyncio.run(main())
|
|
```
|
|
|
|
### Usage for Reranker
|
|
|
|
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
|
|
You can get a relevance score by inputting query and passage to the reranker.
|
|
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
|
|
|
|
|
|
#### Using FlagEmbedding
|
|
```
|
|
pip install -U FlagEmbedding
|
|
```
|
|
|
|
Get relevance scores (higher scores indicate more relevance):
|
|
```python
|
|
from FlagEmbedding import FlagReranker
|
|
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
|
|
|
|
score = reranker.compute_score(['query', 'passage'])
|
|
print(score)
|
|
|
|
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
|
|
print(scores)
|
|
```
|
|
|
|
|
|
#### Using Huggingface transformers
|
|
|
|
```python
|
|
import torch
|
|
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
|
|
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
|
|
model.eval()
|
|
|
|
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
|
|
with torch.no_grad():
|
|
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
|
|
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
|
|
print(scores)
|
|
```
|
|
|
|
## Evaluation
|
|
|
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
|
|
For 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) |
|
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
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| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
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| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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- **C-MTEB**:
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We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
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Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
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| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
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|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
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| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
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| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
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| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
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| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
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| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
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| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
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| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
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| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
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| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
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| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
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| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
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| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
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| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
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| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
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| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
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- **Reranking**:
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See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
|
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| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
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|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
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| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
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| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
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| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
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| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
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| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
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| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
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| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
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| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
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| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
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| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
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\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
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## Train
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### BAAI Embedding
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We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
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**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
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We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
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Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
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More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
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### BGE Reranker
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Cross-encoder will perform full-attention over the input pair,
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which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
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Therefore, it can be used to re-rank the top-k documents returned by embedding model.
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We train the cross-encoder on a multilingual pair data,
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The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
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More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
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## Contact
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If you have any question or suggestion related to this project, feel free to open an issue or pull request.
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You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).
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## Citation
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|
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If you find this repository useful, please consider giving a star :star: and citation
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```
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@misc{bge_embedding,
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title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
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author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
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year={2023},
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eprint={2309.07597},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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## License
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FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
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