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---
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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-base-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: 76.14925373134328
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- type: ap
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value: 39.32336517995478
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- type: f1
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value: 70.16902252611425
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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: 93.386825
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- type: ap
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value: 90.21276917991995
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- type: f1
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value: 93.37741030006174
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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: 48.846000000000004
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- type: f1
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value: 48.14646269778261
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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: 40.754000000000005
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- type: map_at_10
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value: 55.761
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- type: map_at_100
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value: 56.330999999999996
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- type: map_at_1000
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value: 56.333999999999996
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- type: map_at_3
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value: 51.92
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- type: map_at_5
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value: 54.010999999999996
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- type: mrr_at_1
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value: 41.181
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- type: mrr_at_10
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value: 55.967999999999996
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- type: mrr_at_100
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value: 56.538
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- type: mrr_at_1000
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value: 56.542
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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: 54.208999999999996
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- type: ndcg_at_1
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value: 40.754000000000005
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- type: ndcg_at_10
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value: 63.605000000000004
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- type: ndcg_at_100
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value: 66.05199999999999
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- type: ndcg_at_1000
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value: 66.12
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- type: ndcg_at_3
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value: 55.708
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- type: ndcg_at_5
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value: 59.452000000000005
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- type: precision_at_1
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value: 40.754000000000005
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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: 0.991
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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: 15.149000000000001
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- type: recall_at_1
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value: 40.754000000000005
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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: 99.14699999999999
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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: 75.747
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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: 48.74884539679369
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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: 42.8075893810716
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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: 62.128470519187736
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- type: mrr
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value: 74.28065778481289
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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: 89.24629081484655
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- type: cos_sim_spearman
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value: 86.93752309911496
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- type: euclidean_pearson
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value: 87.58589628573816
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- type: euclidean_spearman
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value: 88.05622328825284
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- type: manhattan_pearson
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value: 87.5594959805773
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- type: manhattan_spearman
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value: 88.19658793233961
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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: 86.9512987012987
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- type: f1
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value: 86.92515357973708
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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: 39.10263762928872
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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: 36.69711517426737
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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: 32.327
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- type: map_at_10
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value: 44.099
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- type: map_at_100
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value: 45.525
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- type: map_at_1000
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value: 45.641999999999996
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- type: map_at_3
|
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value: 40.47
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- type: map_at_5
|
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value: 42.36
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- type: mrr_at_1
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value: 39.199
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- type: mrr_at_10
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value: 49.651
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- type: mrr_at_100
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value: 50.29
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- type: mrr_at_1000
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value: 50.329
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- type: mrr_at_3
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value: 46.924
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- type: mrr_at_5
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value: 48.548
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- type: ndcg_at_1
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value: 39.199
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- type: ndcg_at_10
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value: 50.773
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- type: ndcg_at_100
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value: 55.67999999999999
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- type: ndcg_at_1000
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value: 57.495
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- type: ndcg_at_3
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value: 45.513999999999996
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- type: ndcg_at_5
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value: 47.703
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- type: precision_at_1
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value: 39.199
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- type: precision_at_10
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value: 9.914000000000001
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- type: precision_at_100
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value: 1.5310000000000001
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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: 21.984
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- type: precision_at_5
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value: 15.737000000000002
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- type: recall_at_1
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value: 32.327
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- type: recall_at_10
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value: 63.743
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- type: recall_at_100
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value: 84.538
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- type: recall_at_1000
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value: 96.089
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- type: recall_at_3
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value: 48.065000000000005
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- type: recall_at_5
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value: 54.519
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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: 42.954
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- type: map_at_100
|
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value: 44.151
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- type: map_at_1000
|
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value: 44.287
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- type: map_at_3
|
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value: 39.912
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- type: map_at_5
|
|
value: 41.798
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- type: mrr_at_1
|
|
value: 41.465
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- type: mrr_at_10
|
|
value: 49.351
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- type: mrr_at_100
|
|
value: 49.980000000000004
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- type: mrr_at_1000
|
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value: 50.016000000000005
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- type: mrr_at_3
|
|
value: 47.144000000000005
|
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- type: mrr_at_5
|
|
value: 48.592999999999996
|
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- type: ndcg_at_1
|
|
value: 41.465
|
|
- type: ndcg_at_10
|
|
value: 48.565999999999995
|
|
- type: ndcg_at_100
|
|
value: 52.76499999999999
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- type: ndcg_at_1000
|
|
value: 54.749
|
|
- type: ndcg_at_3
|
|
value: 44.57
|
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- type: ndcg_at_5
|
|
value: 46.759
|
|
- type: precision_at_1
|
|
value: 41.465
|
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- type: precision_at_10
|
|
value: 9.107999999999999
|
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- type: precision_at_100
|
|
value: 1.433
|
|
- type: precision_at_1000
|
|
value: 0.191
|
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- type: precision_at_3
|
|
value: 21.423000000000002
|
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- type: precision_at_5
|
|
value: 15.414
|
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- type: recall_at_1
|
|
value: 32.671
|
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- type: recall_at_10
|
|
value: 57.738
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- type: recall_at_100
|
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value: 75.86500000000001
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- type: recall_at_1000
|
|
value: 88.36
|
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- type: recall_at_3
|
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value: 45.626
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- type: recall_at_5
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value: 51.812000000000005
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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: 41.185
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- type: map_at_10
|
|
value: 53.929
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- type: map_at_100
|
|
value: 54.92
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- type: map_at_1000
|
|
value: 54.967999999999996
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- type: map_at_3
|
|
value: 50.70400000000001
|
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- type: map_at_5
|
|
value: 52.673
|
|
- type: mrr_at_1
|
|
value: 47.398
|
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- type: mrr_at_10
|
|
value: 57.303000000000004
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- type: mrr_at_100
|
|
value: 57.959
|
|
- type: mrr_at_1000
|
|
value: 57.985
|
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- type: mrr_at_3
|
|
value: 54.932
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- type: mrr_at_5
|
|
value: 56.464999999999996
|
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- type: ndcg_at_1
|
|
value: 47.398
|
|
- type: ndcg_at_10
|
|
value: 59.653
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- type: ndcg_at_100
|
|
value: 63.627
|
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- type: ndcg_at_1000
|
|
value: 64.596
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- type: ndcg_at_3
|
|
value: 54.455
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- type: ndcg_at_5
|
|
value: 57.245000000000005
|
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- type: precision_at_1
|
|
value: 47.398
|
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- type: precision_at_10
|
|
value: 9.524000000000001
|
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- type: precision_at_100
|
|
value: 1.243
|
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- type: precision_at_1000
|
|
value: 0.13699999999999998
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- type: precision_at_3
|
|
value: 24.389
|
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- type: precision_at_5
|
|
value: 16.752
|
|
- type: recall_at_1
|
|
value: 41.185
|
|
- type: recall_at_10
|
|
value: 73.193
|
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- type: recall_at_100
|
|
value: 90.357
|
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- type: recall_at_1000
|
|
value: 97.253
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- type: recall_at_3
|
|
value: 59.199999999999996
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- type: recall_at_5
|
|
value: 66.118
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- task:
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type: Retrieval
|
|
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
|
|
value: 27.27
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- type: map_at_10
|
|
value: 36.223
|
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- type: map_at_100
|
|
value: 37.218
|
|
- type: map_at_1000
|
|
value: 37.293
|
|
- type: map_at_3
|
|
value: 33.503
|
|
- type: map_at_5
|
|
value: 35.097
|
|
- type: mrr_at_1
|
|
value: 29.492
|
|
- type: mrr_at_10
|
|
value: 38.352000000000004
|
|
- type: mrr_at_100
|
|
value: 39.188
|
|
- type: mrr_at_1000
|
|
value: 39.247
|
|
- type: mrr_at_3
|
|
value: 35.876000000000005
|
|
- type: mrr_at_5
|
|
value: 37.401
|
|
- type: ndcg_at_1
|
|
value: 29.492
|
|
- type: ndcg_at_10
|
|
value: 41.239
|
|
- type: ndcg_at_100
|
|
value: 46.066
|
|
- type: ndcg_at_1000
|
|
value: 47.992000000000004
|
|
- type: ndcg_at_3
|
|
value: 36.11
|
|
- type: ndcg_at_5
|
|
value: 38.772
|
|
- type: precision_at_1
|
|
value: 29.492
|
|
- type: precision_at_10
|
|
value: 6.260000000000001
|
|
- type: precision_at_100
|
|
value: 0.914
|
|
- type: precision_at_1000
|
|
value: 0.11100000000000002
|
|
- type: precision_at_3
|
|
value: 15.104000000000001
|
|
- type: precision_at_5
|
|
value: 10.644
|
|
- type: recall_at_1
|
|
value: 27.27
|
|
- type: recall_at_10
|
|
value: 54.589
|
|
- type: recall_at_100
|
|
value: 76.70700000000001
|
|
- type: recall_at_1000
|
|
value: 91.158
|
|
- type: recall_at_3
|
|
value: 40.974
|
|
- type: recall_at_5
|
|
value: 47.327000000000005
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.848
|
|
- type: map_at_10
|
|
value: 26.207
|
|
- type: map_at_100
|
|
value: 27.478
|
|
- type: map_at_1000
|
|
value: 27.602
|
|
- type: map_at_3
|
|
value: 23.405
|
|
- type: map_at_5
|
|
value: 24.98
|
|
- type: mrr_at_1
|
|
value: 21.891
|
|
- type: mrr_at_10
|
|
value: 31.041999999999998
|
|
- type: mrr_at_100
|
|
value: 32.092
|
|
- type: mrr_at_1000
|
|
value: 32.151999999999994
|
|
- type: mrr_at_3
|
|
value: 28.358
|
|
- type: mrr_at_5
|
|
value: 29.969
|
|
- type: ndcg_at_1
|
|
value: 21.891
|
|
- type: ndcg_at_10
|
|
value: 31.585
|
|
- type: ndcg_at_100
|
|
value: 37.531
|
|
- type: ndcg_at_1000
|
|
value: 40.256
|
|
- type: ndcg_at_3
|
|
value: 26.508
|
|
- type: ndcg_at_5
|
|
value: 28.894
|
|
- type: precision_at_1
|
|
value: 21.891
|
|
- type: precision_at_10
|
|
value: 5.795999999999999
|
|
- type: precision_at_100
|
|
value: 0.9990000000000001
|
|
- type: precision_at_1000
|
|
value: 0.13799999999999998
|
|
- type: precision_at_3
|
|
value: 12.769
|
|
- type: precision_at_5
|
|
value: 9.279
|
|
- type: recall_at_1
|
|
value: 17.848
|
|
- type: recall_at_10
|
|
value: 43.452
|
|
- type: recall_at_100
|
|
value: 69.216
|
|
- type: recall_at_1000
|
|
value: 88.102
|
|
- type: recall_at_3
|
|
value: 29.18
|
|
- type: recall_at_5
|
|
value: 35.347
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 30.94
|
|
- type: map_at_10
|
|
value: 41.248000000000005
|
|
- type: map_at_100
|
|
value: 42.495
|
|
- type: map_at_1000
|
|
value: 42.602000000000004
|
|
- type: map_at_3
|
|
value: 37.939
|
|
- type: map_at_5
|
|
value: 39.924
|
|
- type: mrr_at_1
|
|
value: 37.824999999999996
|
|
- type: mrr_at_10
|
|
value: 47.041
|
|
- type: mrr_at_100
|
|
value: 47.83
|
|
- type: mrr_at_1000
|
|
value: 47.878
|
|
- type: mrr_at_3
|
|
value: 44.466
|
|
- type: mrr_at_5
|
|
value: 46.111999999999995
|
|
- type: ndcg_at_1
|
|
value: 37.824999999999996
|
|
- type: ndcg_at_10
|
|
value: 47.223
|
|
- type: ndcg_at_100
|
|
value: 52.394
|
|
- type: ndcg_at_1000
|
|
value: 54.432
|
|
- type: ndcg_at_3
|
|
value: 42.032000000000004
|
|
- type: ndcg_at_5
|
|
value: 44.772
|
|
- type: precision_at_1
|
|
value: 37.824999999999996
|
|
- type: precision_at_10
|
|
value: 8.393
|
|
- type: precision_at_100
|
|
value: 1.2890000000000001
|
|
- type: precision_at_1000
|
|
value: 0.164
|
|
- type: precision_at_3
|
|
value: 19.698
|
|
- type: precision_at_5
|
|
value: 14.013
|
|
- type: recall_at_1
|
|
value: 30.94
|
|
- type: recall_at_10
|
|
value: 59.316
|
|
- type: recall_at_100
|
|
value: 80.783
|
|
- type: recall_at_1000
|
|
value: 94.15400000000001
|
|
- type: recall_at_3
|
|
value: 44.712
|
|
- type: recall_at_5
|
|
value: 51.932
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.104
|
|
- type: map_at_10
|
|
value: 36.675999999999995
|
|
- type: map_at_100
|
|
value: 38.076
|
|
- type: map_at_1000
|
|
value: 38.189
|
|
- type: map_at_3
|
|
value: 33.733999999999995
|
|
- type: map_at_5
|
|
value: 35.287
|
|
- type: mrr_at_1
|
|
value: 33.904
|
|
- type: mrr_at_10
|
|
value: 42.55
|
|
- type: mrr_at_100
|
|
value: 43.434
|
|
- type: mrr_at_1000
|
|
value: 43.494
|
|
- type: mrr_at_3
|
|
value: 40.126
|
|
- type: mrr_at_5
|
|
value: 41.473
|
|
- type: ndcg_at_1
|
|
value: 33.904
|
|
- type: ndcg_at_10
|
|
value: 42.414
|
|
- type: ndcg_at_100
|
|
value: 48.203
|
|
- type: ndcg_at_1000
|
|
value: 50.437
|
|
- type: ndcg_at_3
|
|
value: 37.633
|
|
- type: ndcg_at_5
|
|
value: 39.67
|
|
- type: precision_at_1
|
|
value: 33.904
|
|
- type: precision_at_10
|
|
value: 7.82
|
|
- type: precision_at_100
|
|
value: 1.2409999999999999
|
|
- type: precision_at_1000
|
|
value: 0.159
|
|
- type: precision_at_3
|
|
value: 17.884
|
|
- type: precision_at_5
|
|
value: 12.648000000000001
|
|
- type: recall_at_1
|
|
value: 27.104
|
|
- type: recall_at_10
|
|
value: 53.563
|
|
- type: recall_at_100
|
|
value: 78.557
|
|
- type: recall_at_1000
|
|
value: 93.533
|
|
- type: recall_at_3
|
|
value: 39.92
|
|
- type: recall_at_5
|
|
value: 45.457
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.707749999999997
|
|
- type: map_at_10
|
|
value: 36.961
|
|
- type: map_at_100
|
|
value: 38.158833333333334
|
|
- type: map_at_1000
|
|
value: 38.270333333333326
|
|
- type: map_at_3
|
|
value: 34.07183333333334
|
|
- type: map_at_5
|
|
value: 35.69533333333334
|
|
- type: mrr_at_1
|
|
value: 32.81875
|
|
- type: mrr_at_10
|
|
value: 41.293
|
|
- type: mrr_at_100
|
|
value: 42.116499999999995
|
|
- type: mrr_at_1000
|
|
value: 42.170249999999996
|
|
- type: mrr_at_3
|
|
value: 38.83983333333333
|
|
- type: mrr_at_5
|
|
value: 40.29775
|
|
- type: ndcg_at_1
|
|
value: 32.81875
|
|
- type: ndcg_at_10
|
|
value: 42.355
|
|
- type: ndcg_at_100
|
|
value: 47.41374999999999
|
|
- type: ndcg_at_1000
|
|
value: 49.5805
|
|
- type: ndcg_at_3
|
|
value: 37.52825
|
|
- type: ndcg_at_5
|
|
value: 39.83266666666667
|
|
- type: precision_at_1
|
|
value: 32.81875
|
|
- type: precision_at_10
|
|
value: 7.382416666666666
|
|
- type: precision_at_100
|
|
value: 1.1640833333333334
|
|
- type: precision_at_1000
|
|
value: 0.15383333333333335
|
|
- type: precision_at_3
|
|
value: 17.134166666666665
|
|
- type: precision_at_5
|
|
value: 12.174833333333336
|
|
- type: recall_at_1
|
|
value: 27.707749999999997
|
|
- type: recall_at_10
|
|
value: 53.945
|
|
- type: recall_at_100
|
|
value: 76.191
|
|
- type: recall_at_1000
|
|
value: 91.101
|
|
- type: recall_at_3
|
|
value: 40.39083333333334
|
|
- type: recall_at_5
|
|
value: 46.40083333333333
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.482
|
|
- type: map_at_10
|
|
value: 33.201
|
|
- type: map_at_100
|
|
value: 34.107
|
|
- type: map_at_1000
|
|
value: 34.197
|
|
- type: map_at_3
|
|
value: 31.174000000000003
|
|
- type: map_at_5
|
|
value: 32.279
|
|
- type: mrr_at_1
|
|
value: 29.908
|
|
- type: mrr_at_10
|
|
value: 36.235
|
|
- type: mrr_at_100
|
|
value: 37.04
|
|
- type: mrr_at_1000
|
|
value: 37.105
|
|
- type: mrr_at_3
|
|
value: 34.355999999999995
|
|
- type: mrr_at_5
|
|
value: 35.382999999999996
|
|
- type: ndcg_at_1
|
|
value: 29.908
|
|
- type: ndcg_at_10
|
|
value: 37.325
|
|
- type: ndcg_at_100
|
|
value: 41.795
|
|
- type: ndcg_at_1000
|
|
value: 44.105
|
|
- type: ndcg_at_3
|
|
value: 33.555
|
|
- type: ndcg_at_5
|
|
value: 35.266999999999996
|
|
- type: precision_at_1
|
|
value: 29.908
|
|
- type: precision_at_10
|
|
value: 5.721
|
|
- type: precision_at_100
|
|
value: 0.8630000000000001
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 14.008000000000001
|
|
- type: precision_at_5
|
|
value: 9.754999999999999
|
|
- type: recall_at_1
|
|
value: 26.482
|
|
- type: recall_at_10
|
|
value: 47.072
|
|
- type: recall_at_100
|
|
value: 67.27
|
|
- type: recall_at_1000
|
|
value: 84.371
|
|
- type: recall_at_3
|
|
value: 36.65
|
|
- type: recall_at_5
|
|
value: 40.774
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 18.815
|
|
- type: map_at_10
|
|
value: 26.369999999999997
|
|
- type: map_at_100
|
|
value: 27.458
|
|
- type: map_at_1000
|
|
value: 27.588
|
|
- type: map_at_3
|
|
value: 23.990000000000002
|
|
- type: map_at_5
|
|
value: 25.345000000000002
|
|
- type: mrr_at_1
|
|
value: 22.953000000000003
|
|
- type: mrr_at_10
|
|
value: 30.342999999999996
|
|
- type: mrr_at_100
|
|
value: 31.241000000000003
|
|
- type: mrr_at_1000
|
|
value: 31.319000000000003
|
|
- type: mrr_at_3
|
|
value: 28.16
|
|
- type: mrr_at_5
|
|
value: 29.406
|
|
- type: ndcg_at_1
|
|
value: 22.953000000000003
|
|
- type: ndcg_at_10
|
|
value: 31.151
|
|
- type: ndcg_at_100
|
|
value: 36.309000000000005
|
|
- type: ndcg_at_1000
|
|
value: 39.227000000000004
|
|
- type: ndcg_at_3
|
|
value: 26.921
|
|
- type: ndcg_at_5
|
|
value: 28.938000000000002
|
|
- type: precision_at_1
|
|
value: 22.953000000000003
|
|
- type: precision_at_10
|
|
value: 5.602
|
|
- type: precision_at_100
|
|
value: 0.9530000000000001
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 12.606
|
|
- type: precision_at_5
|
|
value: 9.119
|
|
- type: recall_at_1
|
|
value: 18.815
|
|
- type: recall_at_10
|
|
value: 41.574
|
|
- type: recall_at_100
|
|
value: 64.84400000000001
|
|
- type: recall_at_1000
|
|
value: 85.406
|
|
- type: recall_at_3
|
|
value: 29.694
|
|
- type: recall_at_5
|
|
value: 34.935
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.840999999999998
|
|
- type: map_at_10
|
|
value: 36.797999999999995
|
|
- type: map_at_100
|
|
value: 37.993
|
|
- type: map_at_1000
|
|
value: 38.086999999999996
|
|
- type: map_at_3
|
|
value: 34.050999999999995
|
|
- type: map_at_5
|
|
value: 35.379
|
|
- type: mrr_at_1
|
|
value: 32.649
|
|
- type: mrr_at_10
|
|
value: 41.025
|
|
- type: mrr_at_100
|
|
value: 41.878
|
|
- type: mrr_at_1000
|
|
value: 41.929
|
|
- type: mrr_at_3
|
|
value: 38.573
|
|
- type: mrr_at_5
|
|
value: 39.715
|
|
- type: ndcg_at_1
|
|
value: 32.649
|
|
- type: ndcg_at_10
|
|
value: 42.142
|
|
- type: ndcg_at_100
|
|
value: 47.558
|
|
- type: ndcg_at_1000
|
|
value: 49.643
|
|
- type: ndcg_at_3
|
|
value: 37.12
|
|
- type: ndcg_at_5
|
|
value: 38.983000000000004
|
|
- type: precision_at_1
|
|
value: 32.649
|
|
- type: precision_at_10
|
|
value: 7.08
|
|
- type: precision_at_100
|
|
value: 1.1039999999999999
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 16.698
|
|
- type: precision_at_5
|
|
value: 11.511000000000001
|
|
- type: recall_at_1
|
|
value: 27.840999999999998
|
|
- type: recall_at_10
|
|
value: 54.245
|
|
- type: recall_at_100
|
|
value: 77.947
|
|
- type: recall_at_1000
|
|
value: 92.36999999999999
|
|
- type: recall_at_3
|
|
value: 40.146
|
|
- type: recall_at_5
|
|
value: 44.951
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.529000000000003
|
|
- type: map_at_10
|
|
value: 35.010000000000005
|
|
- type: map_at_100
|
|
value: 36.647
|
|
- type: map_at_1000
|
|
value: 36.857
|
|
- type: map_at_3
|
|
value: 31.968000000000004
|
|
- type: map_at_5
|
|
value: 33.554
|
|
- type: mrr_at_1
|
|
value: 31.818
|
|
- type: mrr_at_10
|
|
value: 39.550999999999995
|
|
- type: mrr_at_100
|
|
value: 40.54
|
|
- type: mrr_at_1000
|
|
value: 40.596
|
|
- type: mrr_at_3
|
|
value: 36.726
|
|
- type: mrr_at_5
|
|
value: 38.416
|
|
- type: ndcg_at_1
|
|
value: 31.818
|
|
- type: ndcg_at_10
|
|
value: 40.675
|
|
- type: ndcg_at_100
|
|
value: 46.548
|
|
- type: ndcg_at_1000
|
|
value: 49.126
|
|
- type: ndcg_at_3
|
|
value: 35.829
|
|
- type: ndcg_at_5
|
|
value: 38.0
|
|
- type: precision_at_1
|
|
value: 31.818
|
|
- type: precision_at_10
|
|
value: 7.826
|
|
- type: precision_at_100
|
|
value: 1.538
|
|
- type: precision_at_1000
|
|
value: 0.24
|
|
- type: precision_at_3
|
|
value: 16.601
|
|
- type: precision_at_5
|
|
value: 12.095
|
|
- type: recall_at_1
|
|
value: 26.529000000000003
|
|
- type: recall_at_10
|
|
value: 51.03
|
|
- type: recall_at_100
|
|
value: 77.556
|
|
- type: recall_at_1000
|
|
value: 93.804
|
|
- type: recall_at_3
|
|
value: 36.986000000000004
|
|
- type: recall_at_5
|
|
value: 43.096000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.480999999999998
|
|
- type: map_at_10
|
|
value: 30.817
|
|
- type: map_at_100
|
|
value: 31.838
|
|
- type: map_at_1000
|
|
value: 31.932
|
|
- type: map_at_3
|
|
value: 28.011999999999997
|
|
- type: map_at_5
|
|
value: 29.668
|
|
- type: mrr_at_1
|
|
value: 25.323
|
|
- type: mrr_at_10
|
|
value: 33.072
|
|
- type: mrr_at_100
|
|
value: 33.926
|
|
- type: mrr_at_1000
|
|
value: 33.993
|
|
- type: mrr_at_3
|
|
value: 30.436999999999998
|
|
- type: mrr_at_5
|
|
value: 32.092
|
|
- type: ndcg_at_1
|
|
value: 25.323
|
|
- type: ndcg_at_10
|
|
value: 35.514
|
|
- type: ndcg_at_100
|
|
value: 40.489000000000004
|
|
- type: ndcg_at_1000
|
|
value: 42.908
|
|
- type: ndcg_at_3
|
|
value: 30.092000000000002
|
|
- type: ndcg_at_5
|
|
value: 32.989000000000004
|
|
- type: precision_at_1
|
|
value: 25.323
|
|
- type: precision_at_10
|
|
value: 5.545
|
|
- type: precision_at_100
|
|
value: 0.861
|
|
- type: precision_at_1000
|
|
value: 0.117
|
|
- type: precision_at_3
|
|
value: 12.446
|
|
- type: precision_at_5
|
|
value: 9.131
|
|
- type: recall_at_1
|
|
value: 23.480999999999998
|
|
- type: recall_at_10
|
|
value: 47.825
|
|
- type: recall_at_100
|
|
value: 70.652
|
|
- type: recall_at_1000
|
|
value: 88.612
|
|
- type: recall_at_3
|
|
value: 33.537
|
|
- type: recall_at_5
|
|
value: 40.542
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 13.333999999999998
|
|
- type: map_at_10
|
|
value: 22.524
|
|
- type: map_at_100
|
|
value: 24.506
|
|
- type: map_at_1000
|
|
value: 24.715
|
|
- type: map_at_3
|
|
value: 19.022
|
|
- type: map_at_5
|
|
value: 20.693
|
|
- type: mrr_at_1
|
|
value: 29.186
|
|
- type: mrr_at_10
|
|
value: 41.22
|
|
- type: mrr_at_100
|
|
value: 42.16
|
|
- type: mrr_at_1000
|
|
value: 42.192
|
|
- type: mrr_at_3
|
|
value: 38.013000000000005
|
|
- type: mrr_at_5
|
|
value: 39.704
|
|
- type: ndcg_at_1
|
|
value: 29.186
|
|
- type: ndcg_at_10
|
|
value: 31.167
|
|
- type: ndcg_at_100
|
|
value: 38.879000000000005
|
|
- type: ndcg_at_1000
|
|
value: 42.376000000000005
|
|
- type: ndcg_at_3
|
|
value: 25.817
|
|
- type: ndcg_at_5
|
|
value: 27.377000000000002
|
|
- type: precision_at_1
|
|
value: 29.186
|
|
- type: precision_at_10
|
|
value: 9.693999999999999
|
|
- type: precision_at_100
|
|
value: 1.8030000000000002
|
|
- type: precision_at_1000
|
|
value: 0.246
|
|
- type: precision_at_3
|
|
value: 19.11
|
|
- type: precision_at_5
|
|
value: 14.344999999999999
|
|
- type: recall_at_1
|
|
value: 13.333999999999998
|
|
- type: recall_at_10
|
|
value: 37.092000000000006
|
|
- type: recall_at_100
|
|
value: 63.651
|
|
- type: recall_at_1000
|
|
value: 83.05
|
|
- type: recall_at_3
|
|
value: 23.74
|
|
- type: recall_at_5
|
|
value: 28.655
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.151
|
|
- type: map_at_10
|
|
value: 19.653000000000002
|
|
- type: map_at_100
|
|
value: 28.053
|
|
- type: map_at_1000
|
|
value: 29.709000000000003
|
|
- type: map_at_3
|
|
value: 14.191
|
|
- type: map_at_5
|
|
value: 16.456
|
|
- type: mrr_at_1
|
|
value: 66.25
|
|
- type: mrr_at_10
|
|
value: 74.4
|
|
- type: mrr_at_100
|
|
value: 74.715
|
|
- type: mrr_at_1000
|
|
value: 74.726
|
|
- type: mrr_at_3
|
|
value: 72.417
|
|
- type: mrr_at_5
|
|
value: 73.667
|
|
- type: ndcg_at_1
|
|
value: 54.25
|
|
- type: ndcg_at_10
|
|
value: 40.77
|
|
- type: ndcg_at_100
|
|
value: 46.359
|
|
- type: ndcg_at_1000
|
|
value: 54.193000000000005
|
|
- type: ndcg_at_3
|
|
value: 44.832
|
|
- type: ndcg_at_5
|
|
value: 42.63
|
|
- type: precision_at_1
|
|
value: 66.25
|
|
- type: precision_at_10
|
|
value: 32.175
|
|
- type: precision_at_100
|
|
value: 10.668
|
|
- type: precision_at_1000
|
|
value: 2.067
|
|
- type: precision_at_3
|
|
value: 47.667
|
|
- type: precision_at_5
|
|
value: 41.3
|
|
- type: recall_at_1
|
|
value: 9.151
|
|
- type: recall_at_10
|
|
value: 25.003999999999998
|
|
- type: recall_at_100
|
|
value: 52.976
|
|
- type: recall_at_1000
|
|
value: 78.315
|
|
- type: recall_at_3
|
|
value: 15.487
|
|
- type: recall_at_5
|
|
value: 18.999
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 51.89999999999999
|
|
- type: f1
|
|
value: 46.47777925067403
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 73.706
|
|
- type: map_at_10
|
|
value: 82.423
|
|
- type: map_at_100
|
|
value: 82.67999999999999
|
|
- type: map_at_1000
|
|
value: 82.694
|
|
- type: map_at_3
|
|
value: 81.328
|
|
- type: map_at_5
|
|
value: 82.001
|
|
- type: mrr_at_1
|
|
value: 79.613
|
|
- type: mrr_at_10
|
|
value: 87.07000000000001
|
|
- type: mrr_at_100
|
|
value: 87.169
|
|
- type: mrr_at_1000
|
|
value: 87.17
|
|
- type: mrr_at_3
|
|
value: 86.404
|
|
- type: mrr_at_5
|
|
value: 86.856
|
|
- type: ndcg_at_1
|
|
value: 79.613
|
|
- type: ndcg_at_10
|
|
value: 86.289
|
|
- type: ndcg_at_100
|
|
value: 87.201
|
|
- type: ndcg_at_1000
|
|
value: 87.428
|
|
- type: ndcg_at_3
|
|
value: 84.625
|
|
- type: ndcg_at_5
|
|
value: 85.53699999999999
|
|
- type: precision_at_1
|
|
value: 79.613
|
|
- type: precision_at_10
|
|
value: 10.399
|
|
- type: precision_at_100
|
|
value: 1.1079999999999999
|
|
- type: precision_at_1000
|
|
value: 0.11499999999999999
|
|
- type: precision_at_3
|
|
value: 32.473
|
|
- type: precision_at_5
|
|
value: 20.132
|
|
- type: recall_at_1
|
|
value: 73.706
|
|
- type: recall_at_10
|
|
value: 93.559
|
|
- type: recall_at_100
|
|
value: 97.188
|
|
- type: recall_at_1000
|
|
value: 98.555
|
|
- type: recall_at_3
|
|
value: 88.98700000000001
|
|
- type: recall_at_5
|
|
value: 91.373
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 19.841
|
|
- type: map_at_10
|
|
value: 32.643
|
|
- type: map_at_100
|
|
value: 34.575
|
|
- type: map_at_1000
|
|
value: 34.736
|
|
- type: map_at_3
|
|
value: 28.317999999999998
|
|
- type: map_at_5
|
|
value: 30.964000000000002
|
|
- type: mrr_at_1
|
|
value: 39.660000000000004
|
|
- type: mrr_at_10
|
|
value: 48.620000000000005
|
|
- type: mrr_at_100
|
|
value: 49.384
|
|
- type: mrr_at_1000
|
|
value: 49.415
|
|
- type: mrr_at_3
|
|
value: 45.988
|
|
- type: mrr_at_5
|
|
value: 47.361
|
|
- type: ndcg_at_1
|
|
value: 39.660000000000004
|
|
- type: ndcg_at_10
|
|
value: 40.646
|
|
- type: ndcg_at_100
|
|
value: 47.657
|
|
- type: ndcg_at_1000
|
|
value: 50.428
|
|
- type: ndcg_at_3
|
|
value: 36.689
|
|
- type: ndcg_at_5
|
|
value: 38.211
|
|
- type: precision_at_1
|
|
value: 39.660000000000004
|
|
- type: precision_at_10
|
|
value: 11.235000000000001
|
|
- type: precision_at_100
|
|
value: 1.8530000000000002
|
|
- type: precision_at_1000
|
|
value: 0.23600000000000002
|
|
- type: precision_at_3
|
|
value: 24.587999999999997
|
|
- type: precision_at_5
|
|
value: 18.395
|
|
- type: recall_at_1
|
|
value: 19.841
|
|
- type: recall_at_10
|
|
value: 48.135
|
|
- type: recall_at_100
|
|
value: 74.224
|
|
- type: recall_at_1000
|
|
value: 90.826
|
|
- type: recall_at_3
|
|
value: 33.536
|
|
- type: recall_at_5
|
|
value: 40.311
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 40.358
|
|
- type: map_at_10
|
|
value: 64.497
|
|
- type: map_at_100
|
|
value: 65.362
|
|
- type: map_at_1000
|
|
value: 65.41900000000001
|
|
- type: map_at_3
|
|
value: 61.06700000000001
|
|
- type: map_at_5
|
|
value: 63.317
|
|
- type: mrr_at_1
|
|
value: 80.716
|
|
- type: mrr_at_10
|
|
value: 86.10799999999999
|
|
- type: mrr_at_100
|
|
value: 86.265
|
|
- type: mrr_at_1000
|
|
value: 86.27
|
|
- type: mrr_at_3
|
|
value: 85.271
|
|
- type: mrr_at_5
|
|
value: 85.82499999999999
|
|
- type: ndcg_at_1
|
|
value: 80.716
|
|
- type: ndcg_at_10
|
|
value: 72.597
|
|
- type: ndcg_at_100
|
|
value: 75.549
|
|
- type: ndcg_at_1000
|
|
value: 76.61
|
|
- type: ndcg_at_3
|
|
value: 67.874
|
|
- type: ndcg_at_5
|
|
value: 70.655
|
|
- type: precision_at_1
|
|
value: 80.716
|
|
- type: precision_at_10
|
|
value: 15.148
|
|
- type: precision_at_100
|
|
value: 1.745
|
|
- type: precision_at_1000
|
|
value: 0.188
|
|
- type: precision_at_3
|
|
value: 43.597
|
|
- type: precision_at_5
|
|
value: 28.351
|
|
- type: recall_at_1
|
|
value: 40.358
|
|
- type: recall_at_10
|
|
value: 75.739
|
|
- type: recall_at_100
|
|
value: 87.259
|
|
- type: recall_at_1000
|
|
value: 94.234
|
|
- type: recall_at_3
|
|
value: 65.39500000000001
|
|
- type: recall_at_5
|
|
value: 70.878
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.80799999999998
|
|
- type: ap
|
|
value: 86.81350378180757
|
|
- type: f1
|
|
value: 90.79901248314215
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.096
|
|
- type: map_at_10
|
|
value: 34.384
|
|
- type: map_at_100
|
|
value: 35.541
|
|
- type: map_at_1000
|
|
value: 35.589999999999996
|
|
- type: map_at_3
|
|
value: 30.496000000000002
|
|
- type: map_at_5
|
|
value: 32.718
|
|
- type: mrr_at_1
|
|
value: 22.750999999999998
|
|
- type: mrr_at_10
|
|
value: 35.024
|
|
- type: mrr_at_100
|
|
value: 36.125
|
|
- type: mrr_at_1000
|
|
value: 36.168
|
|
- type: mrr_at_3
|
|
value: 31.225
|
|
- type: mrr_at_5
|
|
value: 33.416000000000004
|
|
- type: ndcg_at_1
|
|
value: 22.750999999999998
|
|
- type: ndcg_at_10
|
|
value: 41.351
|
|
- type: ndcg_at_100
|
|
value: 46.92
|
|
- type: ndcg_at_1000
|
|
value: 48.111
|
|
- type: ndcg_at_3
|
|
value: 33.439
|
|
- type: ndcg_at_5
|
|
value: 37.407000000000004
|
|
- type: precision_at_1
|
|
value: 22.750999999999998
|
|
- type: precision_at_10
|
|
value: 6.564
|
|
- type: precision_at_100
|
|
value: 0.935
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.288
|
|
- type: precision_at_5
|
|
value: 10.581999999999999
|
|
- type: recall_at_1
|
|
value: 22.096
|
|
- type: recall_at_10
|
|
value: 62.771
|
|
- type: recall_at_100
|
|
value: 88.529
|
|
- type: recall_at_1000
|
|
value: 97.55
|
|
- type: recall_at_3
|
|
value: 41.245
|
|
- type: recall_at_5
|
|
value: 50.788
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.16780665754673
|
|
- type: f1
|
|
value: 93.96331194859894
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.90606475148198
|
|
- type: f1
|
|
value: 58.58344986604187
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.14660390047075
|
|
- type: f1
|
|
value: 74.31533923533614
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 80.16139878950908
|
|
- type: f1
|
|
value: 80.18532656824924
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 32.949880906135085
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.56300351524862
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 31.196521894371315
|
|
- type: mrr
|
|
value: 32.22644231694389
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 6.783
|
|
- type: map_at_10
|
|
value: 14.549000000000001
|
|
- type: map_at_100
|
|
value: 18.433
|
|
- type: map_at_1000
|
|
value: 19.949
|
|
- type: map_at_3
|
|
value: 10.936
|
|
- type: map_at_5
|
|
value: 12.514
|
|
- type: mrr_at_1
|
|
value: 47.368
|
|
- type: mrr_at_10
|
|
value: 56.42
|
|
- type: mrr_at_100
|
|
value: 56.908
|
|
- type: mrr_at_1000
|
|
value: 56.95
|
|
- type: mrr_at_3
|
|
value: 54.283
|
|
- type: mrr_at_5
|
|
value: 55.568
|
|
- type: ndcg_at_1
|
|
value: 45.666000000000004
|
|
- type: ndcg_at_10
|
|
value: 37.389
|
|
- type: ndcg_at_100
|
|
value: 34.253
|
|
- type: ndcg_at_1000
|
|
value: 43.059999999999995
|
|
- type: ndcg_at_3
|
|
value: 42.725
|
|
- type: ndcg_at_5
|
|
value: 40.193
|
|
- type: precision_at_1
|
|
value: 47.368
|
|
- type: precision_at_10
|
|
value: 27.988000000000003
|
|
- type: precision_at_100
|
|
value: 8.672
|
|
- type: precision_at_1000
|
|
value: 2.164
|
|
- type: precision_at_3
|
|
value: 40.248
|
|
- type: precision_at_5
|
|
value: 34.737
|
|
- type: recall_at_1
|
|
value: 6.783
|
|
- type: recall_at_10
|
|
value: 17.838
|
|
- type: recall_at_100
|
|
value: 33.672000000000004
|
|
- type: recall_at_1000
|
|
value: 66.166
|
|
- type: recall_at_3
|
|
value: 11.849
|
|
- type: recall_at_5
|
|
value: 14.205000000000002
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.698999999999998
|
|
- type: map_at_10
|
|
value: 46.556
|
|
- type: map_at_100
|
|
value: 47.652
|
|
- type: map_at_1000
|
|
value: 47.68
|
|
- type: map_at_3
|
|
value: 42.492000000000004
|
|
- type: map_at_5
|
|
value: 44.763999999999996
|
|
- type: mrr_at_1
|
|
value: 35.747
|
|
- type: mrr_at_10
|
|
value: 49.242999999999995
|
|
- type: mrr_at_100
|
|
value: 50.052
|
|
- type: mrr_at_1000
|
|
value: 50.068
|
|
- type: mrr_at_3
|
|
value: 45.867000000000004
|
|
- type: mrr_at_5
|
|
value: 47.778999999999996
|
|
- type: ndcg_at_1
|
|
value: 35.717999999999996
|
|
- type: ndcg_at_10
|
|
value: 54.14600000000001
|
|
- type: ndcg_at_100
|
|
value: 58.672999999999995
|
|
- type: ndcg_at_1000
|
|
value: 59.279
|
|
- type: ndcg_at_3
|
|
value: 46.407
|
|
- type: ndcg_at_5
|
|
value: 50.181
|
|
- type: precision_at_1
|
|
value: 35.717999999999996
|
|
- type: precision_at_10
|
|
value: 8.844000000000001
|
|
- type: precision_at_100
|
|
value: 1.139
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 20.993000000000002
|
|
- type: precision_at_5
|
|
value: 14.791000000000002
|
|
- type: recall_at_1
|
|
value: 31.698999999999998
|
|
- type: recall_at_10
|
|
value: 74.693
|
|
- type: recall_at_100
|
|
value: 94.15299999999999
|
|
- type: recall_at_1000
|
|
value: 98.585
|
|
- type: recall_at_3
|
|
value: 54.388999999999996
|
|
- type: recall_at_5
|
|
value: 63.08200000000001
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.283
|
|
- type: map_at_10
|
|
value: 85.24000000000001
|
|
- type: map_at_100
|
|
value: 85.882
|
|
- type: map_at_1000
|
|
value: 85.897
|
|
- type: map_at_3
|
|
value: 82.326
|
|
- type: map_at_5
|
|
value: 84.177
|
|
- type: mrr_at_1
|
|
value: 82.21000000000001
|
|
- type: mrr_at_10
|
|
value: 88.228
|
|
- type: mrr_at_100
|
|
value: 88.32
|
|
- type: mrr_at_1000
|
|
value: 88.32
|
|
- type: mrr_at_3
|
|
value: 87.323
|
|
- type: mrr_at_5
|
|
value: 87.94800000000001
|
|
- type: ndcg_at_1
|
|
value: 82.17999999999999
|
|
- type: ndcg_at_10
|
|
value: 88.9
|
|
- type: ndcg_at_100
|
|
value: 90.079
|
|
- type: ndcg_at_1000
|
|
value: 90.158
|
|
- type: ndcg_at_3
|
|
value: 86.18299999999999
|
|
- type: ndcg_at_5
|
|
value: 87.71799999999999
|
|
- type: precision_at_1
|
|
value: 82.17999999999999
|
|
- type: precision_at_10
|
|
value: 13.464
|
|
- type: precision_at_100
|
|
value: 1.533
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.693
|
|
- type: precision_at_5
|
|
value: 24.792
|
|
- type: recall_at_1
|
|
value: 71.283
|
|
- type: recall_at_10
|
|
value: 95.742
|
|
- type: recall_at_100
|
|
value: 99.67200000000001
|
|
- type: recall_at_1000
|
|
value: 99.981
|
|
- type: recall_at_3
|
|
value: 87.888
|
|
- type: recall_at_5
|
|
value: 92.24
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 56.24267063669042
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 62.88056988932578
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.903
|
|
- type: map_at_10
|
|
value: 13.202
|
|
- type: map_at_100
|
|
value: 15.5
|
|
- type: map_at_1000
|
|
value: 15.870999999999999
|
|
- type: map_at_3
|
|
value: 9.407
|
|
- type: map_at_5
|
|
value: 11.238
|
|
- type: mrr_at_1
|
|
value: 24.2
|
|
- type: mrr_at_10
|
|
value: 35.867
|
|
- type: mrr_at_100
|
|
value: 37.001
|
|
- type: mrr_at_1000
|
|
value: 37.043
|
|
- type: mrr_at_3
|
|
value: 32.5
|
|
- type: mrr_at_5
|
|
value: 34.35
|
|
- type: ndcg_at_1
|
|
value: 24.2
|
|
- type: ndcg_at_10
|
|
value: 21.731
|
|
- type: ndcg_at_100
|
|
value: 30.7
|
|
- type: ndcg_at_1000
|
|
value: 36.618
|
|
- type: ndcg_at_3
|
|
value: 20.72
|
|
- type: ndcg_at_5
|
|
value: 17.954
|
|
- type: precision_at_1
|
|
value: 24.2
|
|
- type: precision_at_10
|
|
value: 11.33
|
|
- type: precision_at_100
|
|
value: 2.4410000000000003
|
|
- type: precision_at_1000
|
|
value: 0.386
|
|
- type: precision_at_3
|
|
value: 19.667
|
|
- type: precision_at_5
|
|
value: 15.86
|
|
- type: recall_at_1
|
|
value: 4.903
|
|
- type: recall_at_10
|
|
value: 22.962
|
|
- type: recall_at_100
|
|
value: 49.563
|
|
- type: recall_at_1000
|
|
value: 78.238
|
|
- type: recall_at_3
|
|
value: 11.953
|
|
- type: recall_at_5
|
|
value: 16.067999999999998
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.12694254604078
|
|
- type: cos_sim_spearman
|
|
value: 80.30141815181918
|
|
- type: euclidean_pearson
|
|
value: 81.34015449877128
|
|
- type: euclidean_spearman
|
|
value: 80.13984197010849
|
|
- type: manhattan_pearson
|
|
value: 81.31767068124086
|
|
- type: manhattan_spearman
|
|
value: 80.11720513114103
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.13112984010417
|
|
- type: cos_sim_spearman
|
|
value: 78.03063573402875
|
|
- type: euclidean_pearson
|
|
value: 83.51928418844804
|
|
- type: euclidean_spearman
|
|
value: 78.4045235411144
|
|
- type: manhattan_pearson
|
|
value: 83.49981637388689
|
|
- type: manhattan_spearman
|
|
value: 78.4042575139372
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.50327987379504
|
|
- type: cos_sim_spearman
|
|
value: 84.18556767756205
|
|
- type: euclidean_pearson
|
|
value: 82.69684424327679
|
|
- type: euclidean_spearman
|
|
value: 83.5368106038335
|
|
- type: manhattan_pearson
|
|
value: 82.57967581007374
|
|
- type: manhattan_spearman
|
|
value: 83.43009053133697
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.50756863007814
|
|
- type: cos_sim_spearman
|
|
value: 82.27204331279108
|
|
- type: euclidean_pearson
|
|
value: 81.39535251429741
|
|
- type: euclidean_spearman
|
|
value: 81.84386626336239
|
|
- type: manhattan_pearson
|
|
value: 81.34281737280695
|
|
- type: manhattan_spearman
|
|
value: 81.81149375673166
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.8727714856726
|
|
- type: cos_sim_spearman
|
|
value: 87.95738287792312
|
|
- type: euclidean_pearson
|
|
value: 86.62920602795887
|
|
- type: euclidean_spearman
|
|
value: 87.05207355381243
|
|
- type: manhattan_pearson
|
|
value: 86.53587918472225
|
|
- type: manhattan_spearman
|
|
value: 86.95382961029586
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.52240359769479
|
|
- type: cos_sim_spearman
|
|
value: 85.47685776238286
|
|
- type: euclidean_pearson
|
|
value: 84.25815333483058
|
|
- type: euclidean_spearman
|
|
value: 85.27415639683198
|
|
- type: manhattan_pearson
|
|
value: 84.29127757025637
|
|
- type: manhattan_spearman
|
|
value: 85.30226224917351
|
|
- 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: 86.42501708915708
|
|
- type: cos_sim_spearman
|
|
value: 86.42276182795041
|
|
- type: euclidean_pearson
|
|
value: 86.5408207354761
|
|
- type: euclidean_spearman
|
|
value: 85.46096321750838
|
|
- type: manhattan_pearson
|
|
value: 86.54177303026881
|
|
- type: manhattan_spearman
|
|
value: 85.50313151916117
|
|
- 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: 64.86521089250766
|
|
- type: cos_sim_spearman
|
|
value: 65.94868540323003
|
|
- type: euclidean_pearson
|
|
value: 67.16569626533084
|
|
- type: euclidean_spearman
|
|
value: 66.37667004134917
|
|
- type: manhattan_pearson
|
|
value: 67.1482365102333
|
|
- type: manhattan_spearman
|
|
value: 66.53240122580029
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.64746265365318
|
|
- type: cos_sim_spearman
|
|
value: 86.41888825906786
|
|
- type: euclidean_pearson
|
|
value: 85.27453642725811
|
|
- type: euclidean_spearman
|
|
value: 85.94095796602544
|
|
- type: manhattan_pearson
|
|
value: 85.28643660505334
|
|
- type: manhattan_spearman
|
|
value: 85.95028003260744
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 87.48903153618527
|
|
- type: mrr
|
|
value: 96.41081503826601
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 58.594
|
|
- type: map_at_10
|
|
value: 69.296
|
|
- type: map_at_100
|
|
value: 69.782
|
|
- type: map_at_1000
|
|
value: 69.795
|
|
- type: map_at_3
|
|
value: 66.23
|
|
- type: map_at_5
|
|
value: 68.293
|
|
- type: mrr_at_1
|
|
value: 61.667
|
|
- type: mrr_at_10
|
|
value: 70.339
|
|
- type: mrr_at_100
|
|
value: 70.708
|
|
- type: mrr_at_1000
|
|
value: 70.722
|
|
- type: mrr_at_3
|
|
value: 68.0
|
|
- type: mrr_at_5
|
|
value: 69.56700000000001
|
|
- type: ndcg_at_1
|
|
value: 61.667
|
|
- type: ndcg_at_10
|
|
value: 74.039
|
|
- type: ndcg_at_100
|
|
value: 76.103
|
|
- type: ndcg_at_1000
|
|
value: 76.47800000000001
|
|
- type: ndcg_at_3
|
|
value: 68.967
|
|
- type: ndcg_at_5
|
|
value: 71.96900000000001
|
|
- type: precision_at_1
|
|
value: 61.667
|
|
- type: precision_at_10
|
|
value: 9.866999999999999
|
|
- type: precision_at_100
|
|
value: 1.097
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 27.111
|
|
- type: precision_at_5
|
|
value: 18.2
|
|
- type: recall_at_1
|
|
value: 58.594
|
|
- type: recall_at_10
|
|
value: 87.422
|
|
- type: recall_at_100
|
|
value: 96.667
|
|
- type: recall_at_1000
|
|
value: 99.667
|
|
- type: recall_at_3
|
|
value: 74.217
|
|
- type: recall_at_5
|
|
value: 81.539
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.85049504950496
|
|
- type: cos_sim_ap
|
|
value: 96.33111544137081
|
|
- type: cos_sim_f1
|
|
value: 92.35443037974684
|
|
- type: cos_sim_precision
|
|
value: 93.53846153846153
|
|
- type: cos_sim_recall
|
|
value: 91.2
|
|
- type: dot_accuracy
|
|
value: 99.82376237623762
|
|
- type: dot_ap
|
|
value: 95.38082527310888
|
|
- type: dot_f1
|
|
value: 90.90909090909092
|
|
- type: dot_precision
|
|
value: 92.90187891440502
|
|
- type: dot_recall
|
|
value: 89.0
|
|
- type: euclidean_accuracy
|
|
value: 99.84851485148515
|
|
- type: euclidean_ap
|
|
value: 96.32316003996347
|
|
- type: euclidean_f1
|
|
value: 92.2071392659628
|
|
- type: euclidean_precision
|
|
value: 92.71991911021233
|
|
- type: euclidean_recall
|
|
value: 91.7
|
|
- type: manhattan_accuracy
|
|
value: 99.84851485148515
|
|
- type: manhattan_ap
|
|
value: 96.3655668249217
|
|
- type: manhattan_f1
|
|
value: 92.18356026222895
|
|
- type: manhattan_precision
|
|
value: 92.98067141403867
|
|
- type: manhattan_recall
|
|
value: 91.4
|
|
- type: max_accuracy
|
|
value: 99.85049504950496
|
|
- type: max_ap
|
|
value: 96.3655668249217
|
|
- type: max_f1
|
|
value: 92.35443037974684
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 65.94861371629051
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.009430451385
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 54.61164066427969
|
|
- type: mrr
|
|
value: 55.49710603938544
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 30.622620124907662
|
|
- type: cos_sim_spearman
|
|
value: 31.0678351356163
|
|
- type: dot_pearson
|
|
value: 30.863727693306814
|
|
- type: dot_spearman
|
|
value: 31.230306567021255
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.22
|
|
- type: map_at_10
|
|
value: 2.011
|
|
- type: map_at_100
|
|
value: 10.974
|
|
- type: map_at_1000
|
|
value: 25.819
|
|
- type: map_at_3
|
|
value: 0.6649999999999999
|
|
- type: map_at_5
|
|
value: 1.076
|
|
- type: mrr_at_1
|
|
value: 86.0
|
|
- type: mrr_at_10
|
|
value: 91.8
|
|
- type: mrr_at_100
|
|
value: 91.8
|
|
- type: mrr_at_1000
|
|
value: 91.8
|
|
- type: mrr_at_3
|
|
value: 91.0
|
|
- type: mrr_at_5
|
|
value: 91.8
|
|
- type: ndcg_at_1
|
|
value: 82.0
|
|
- type: ndcg_at_10
|
|
value: 78.07300000000001
|
|
- type: ndcg_at_100
|
|
value: 58.231
|
|
- type: ndcg_at_1000
|
|
value: 51.153000000000006
|
|
- type: ndcg_at_3
|
|
value: 81.123
|
|
- type: ndcg_at_5
|
|
value: 81.059
|
|
- type: precision_at_1
|
|
value: 86.0
|
|
- type: precision_at_10
|
|
value: 83.0
|
|
- type: precision_at_100
|
|
value: 59.38
|
|
- type: precision_at_1000
|
|
value: 22.55
|
|
- type: precision_at_3
|
|
value: 87.333
|
|
- type: precision_at_5
|
|
value: 86.8
|
|
- type: recall_at_1
|
|
value: 0.22
|
|
- type: recall_at_10
|
|
value: 2.2079999999999997
|
|
- type: recall_at_100
|
|
value: 14.069
|
|
- type: recall_at_1000
|
|
value: 47.678
|
|
- type: recall_at_3
|
|
value: 0.7040000000000001
|
|
- type: recall_at_5
|
|
value: 1.161
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.809
|
|
- type: map_at_10
|
|
value: 10.394
|
|
- type: map_at_100
|
|
value: 16.598
|
|
- type: map_at_1000
|
|
value: 18.142
|
|
- type: map_at_3
|
|
value: 5.572
|
|
- type: map_at_5
|
|
value: 7.1370000000000005
|
|
- type: mrr_at_1
|
|
value: 32.653
|
|
- type: mrr_at_10
|
|
value: 46.564
|
|
- type: mrr_at_100
|
|
value: 47.469
|
|
- type: mrr_at_1000
|
|
value: 47.469
|
|
- type: mrr_at_3
|
|
value: 42.177
|
|
- type: mrr_at_5
|
|
value: 44.524
|
|
- type: ndcg_at_1
|
|
value: 30.612000000000002
|
|
- type: ndcg_at_10
|
|
value: 25.701
|
|
- type: ndcg_at_100
|
|
value: 37.532
|
|
- type: ndcg_at_1000
|
|
value: 48.757
|
|
- type: ndcg_at_3
|
|
value: 28.199999999999996
|
|
- type: ndcg_at_5
|
|
value: 25.987
|
|
- type: precision_at_1
|
|
value: 32.653
|
|
- type: precision_at_10
|
|
value: 23.469
|
|
- type: precision_at_100
|
|
value: 7.9799999999999995
|
|
- type: precision_at_1000
|
|
value: 1.5350000000000001
|
|
- type: precision_at_3
|
|
value: 29.932
|
|
- type: precision_at_5
|
|
value: 26.122
|
|
- type: recall_at_1
|
|
value: 2.809
|
|
- type: recall_at_10
|
|
value: 16.887
|
|
- type: recall_at_100
|
|
value: 48.67
|
|
- type: recall_at_1000
|
|
value: 82.89699999999999
|
|
- type: recall_at_3
|
|
value: 6.521000000000001
|
|
- type: recall_at_5
|
|
value: 9.609
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.57860000000001
|
|
- type: ap
|
|
value: 13.82629211536393
|
|
- type: f1
|
|
value: 54.59860966183956
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.38030560271647
|
|
- type: f1
|
|
value: 59.69685552567865
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 51.4736717043405
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 86.92853311080646
|
|
- type: cos_sim_ap
|
|
value: 77.67872502591382
|
|
- type: cos_sim_f1
|
|
value: 70.33941236068895
|
|
- type: cos_sim_precision
|
|
value: 67.63273258645884
|
|
- type: cos_sim_recall
|
|
value: 73.27176781002639
|
|
- type: dot_accuracy
|
|
value: 85.79603027954938
|
|
- type: dot_ap
|
|
value: 73.73786190233379
|
|
- type: dot_f1
|
|
value: 67.3437901774235
|
|
- type: dot_precision
|
|
value: 65.67201604814443
|
|
- type: dot_recall
|
|
value: 69.10290237467018
|
|
- type: euclidean_accuracy
|
|
value: 86.94045419324074
|
|
- type: euclidean_ap
|
|
value: 77.6687791535167
|
|
- type: euclidean_f1
|
|
value: 70.47209214023542
|
|
- type: euclidean_precision
|
|
value: 67.7207492094381
|
|
- type: euclidean_recall
|
|
value: 73.45646437994723
|
|
- type: manhattan_accuracy
|
|
value: 86.87488823985218
|
|
- type: manhattan_ap
|
|
value: 77.63373392430728
|
|
- type: manhattan_f1
|
|
value: 70.40920716112532
|
|
- type: manhattan_precision
|
|
value: 68.31265508684864
|
|
- type: manhattan_recall
|
|
value: 72.63852242744063
|
|
- type: max_accuracy
|
|
value: 86.94045419324074
|
|
- type: max_ap
|
|
value: 77.67872502591382
|
|
- type: max_f1
|
|
value: 70.47209214023542
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.67155664221679
|
|
- type: cos_sim_ap
|
|
value: 85.64591703003417
|
|
- type: cos_sim_f1
|
|
value: 77.59531005352656
|
|
- type: cos_sim_precision
|
|
value: 73.60967184801382
|
|
- type: cos_sim_recall
|
|
value: 82.03726516784724
|
|
- type: dot_accuracy
|
|
value: 88.41541506578181
|
|
- type: dot_ap
|
|
value: 84.6482788957769
|
|
- type: dot_f1
|
|
value: 77.04748541466657
|
|
- type: dot_precision
|
|
value: 74.02440754931176
|
|
- type: dot_recall
|
|
value: 80.3279950723745
|
|
- type: euclidean_accuracy
|
|
value: 88.63080684596576
|
|
- type: euclidean_ap
|
|
value: 85.44570045321562
|
|
- type: euclidean_f1
|
|
value: 77.28769403336106
|
|
- type: euclidean_precision
|
|
value: 72.90600040958427
|
|
- type: euclidean_recall
|
|
value: 82.22975053895904
|
|
- type: manhattan_accuracy
|
|
value: 88.59393798269105
|
|
- type: manhattan_ap
|
|
value: 85.40271361038187
|
|
- type: manhattan_f1
|
|
value: 77.17606419344392
|
|
- type: manhattan_precision
|
|
value: 72.4447747078295
|
|
- type: manhattan_recall
|
|
value: 82.5685247921158
|
|
- type: max_accuracy
|
|
value: 88.67155664221679
|
|
- type: max_ap
|
|
value: 85.64591703003417
|
|
- type: max_f1
|
|
value: 77.59531005352656
|
|
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 which 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
|
|
|
|
```
|
|
|
|
#### Usage via infinity
|
|
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([email protected]) and Zheng Liu([email protected]).
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## Citation
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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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