Add new SentenceTransformer model

35a5526 verified 9 months ago

94.6 kB

	---
	tags:
	- sentence-transformers
	- feature-extraction
	- sentence-similarity
	- transformers
	- mteb
	model-index:
	- name: bge-large-en-v1.5
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 75.8507462686567
	- type: ap
	value: 38.566457320228245
	- type: f1
	value: 69.69386648043475
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 92.416675
	- type: ap
	value: 89.1928861155922
	- type: f1
	value: 92.39477019574215
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 48.175999999999995
	- type: f1
	value: 47.80712792870253
	- task:
	type: Retrieval
	dataset:
	type: arguana
	name: MTEB ArguAna
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 40.184999999999995
	- type: map_at_10
	value: 55.654
	- type: map_at_100
	value: 56.25
	- type: map_at_1000
	value: 56.255
	- type: map_at_3
	value: 51.742999999999995
	- type: map_at_5
	value: 54.129000000000005
	- type: mrr_at_1
	value: 40.967
	- type: mrr_at_10
	value: 55.96
	- type: mrr_at_100
	value: 56.54900000000001
	- type: mrr_at_1000
	value: 56.554
	- type: mrr_at_3
	value: 51.980000000000004
	- type: mrr_at_5
	value: 54.44
	- type: ndcg_at_1
	value: 40.184999999999995
	- type: ndcg_at_10
	value: 63.542
	- type: ndcg_at_100
	value: 65.96499999999999
	- type: ndcg_at_1000
	value: 66.08699999999999
	- type: ndcg_at_3
	value: 55.582
	- type: ndcg_at_5
	value: 59.855000000000004
	- type: precision_at_1
	value: 40.184999999999995
	- type: precision_at_10
	value: 8.841000000000001
	- type: precision_at_100
	value: 0.987
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 22.238
	- type: precision_at_5
	value: 15.405
	- type: recall_at_1
	value: 40.184999999999995
	- type: recall_at_10
	value: 88.407
	- type: recall_at_100
	value: 98.72
	- type: recall_at_1000
	value: 99.644
	- type: recall_at_3
	value: 66.714
	- type: recall_at_5
	value: 77.027
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 48.567077926750066
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 43.19453389182364
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 64.46555939623092
	- type: mrr
	value: 77.82361605768807
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 84.9554128814735
	- type: cos_sim_spearman
	value: 84.65373612172036
	- type: euclidean_pearson
	value: 83.2905059954138
	- type: euclidean_spearman
	value: 84.52240782811128
	- type: manhattan_pearson
	value: 82.99533802997436
	- type: manhattan_spearman
	value: 84.20673798475734
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 87.78896103896103
	- type: f1
	value: 87.77189310964883
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 39.714538337650495
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 36.90108349284447
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 32.795
	- type: map_at_10
	value: 43.669000000000004
	- type: map_at_100
	value: 45.151
	- type: map_at_1000
	value: 45.278
	- type: map_at_3
	value: 40.006
	- type: map_at_5
	value: 42.059999999999995
	- type: mrr_at_1
	value: 39.771
	- type: mrr_at_10
	value: 49.826
	- type: mrr_at_100
	value: 50.504000000000005
	- type: mrr_at_1000
	value: 50.549
	- type: mrr_at_3
	value: 47.115
	- type: mrr_at_5
	value: 48.832
	- type: ndcg_at_1
	value: 39.771
	- type: ndcg_at_10
	value: 50.217999999999996
	- type: ndcg_at_100
	value: 55.454
	- type: ndcg_at_1000
	value: 57.37
	- type: ndcg_at_3
	value: 44.885000000000005
	- type: ndcg_at_5
	value: 47.419
	- type: precision_at_1
	value: 39.771
	- type: precision_at_10
	value: 9.642000000000001
	- type: precision_at_100
	value: 1.538
	- type: precision_at_1000
	value: 0.198
	- type: precision_at_3
	value: 21.268
	- type: precision_at_5
	value: 15.536
	- type: recall_at_1
	value: 32.795
	- type: recall_at_10
	value: 62.580999999999996
	- type: recall_at_100
	value: 84.438
	- type: recall_at_1000
	value: 96.492
	- type: recall_at_3
	value: 47.071000000000005
	- type: recall_at_5
	value: 54.079
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 32.671
	- type: map_at_10
	value: 43.334
	- type: map_at_100
	value: 44.566
	- type: map_at_1000
	value: 44.702999999999996
	- type: map_at_3
	value: 40.343
	- type: map_at_5
	value: 41.983
	- type: mrr_at_1
	value: 40.764
	- type: mrr_at_10
	value: 49.382
	- type: mrr_at_100
	value: 49.988
	- type: mrr_at_1000
	value: 50.03300000000001
	- type: mrr_at_3
	value: 47.293
	- type: mrr_at_5
	value: 48.51
	- type: ndcg_at_1
	value: 40.764
	- type: ndcg_at_10
	value: 49.039
	- type: ndcg_at_100
	value: 53.259
	- type: ndcg_at_1000
	value: 55.253
	- type: ndcg_at_3
	value: 45.091
	- type: ndcg_at_5
	value: 46.839999999999996
	- type: precision_at_1
	value: 40.764
	- type: precision_at_10
	value: 9.191
	- type: precision_at_100
	value: 1.476
	- type: precision_at_1000
	value: 0.19499999999999998
	- type: precision_at_3
	value: 21.72
	- type: precision_at_5
	value: 15.299
	- type: recall_at_1
	value: 32.671
	- type: recall_at_10
	value: 58.816
	- type: recall_at_100
	value: 76.654
	- type: recall_at_1000
	value: 89.05999999999999
	- type: recall_at_3
	value: 46.743
	- type: recall_at_5
	value: 51.783
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 40.328
	- type: map_at_10
	value: 53.32599999999999
	- type: map_at_100
	value: 54.37499999999999
	- type: map_at_1000
	value: 54.429
	- type: map_at_3
	value: 49.902
	- type: map_at_5
	value: 52.002
	- type: mrr_at_1
	value: 46.332
	- type: mrr_at_10
	value: 56.858
	- type: mrr_at_100
	value: 57.522
	- type: mrr_at_1000
	value: 57.54899999999999
	- type: mrr_at_3
	value: 54.472
	- type: mrr_at_5
	value: 55.996
	- type: ndcg_at_1
	value: 46.332
	- type: ndcg_at_10
	value: 59.313
	- type: ndcg_at_100
	value: 63.266999999999996
	- type: ndcg_at_1000
	value: 64.36
	- type: ndcg_at_3
	value: 53.815000000000005
	- type: ndcg_at_5
	value: 56.814
	- type: precision_at_1
	value: 46.332
	- type: precision_at_10
	value: 9.53
	- type: precision_at_100
	value: 1.238
	- type: precision_at_1000
	value: 0.13699999999999998
	- type: precision_at_3
	value: 24.054000000000002
	- type: precision_at_5
	value: 16.589000000000002
	- type: recall_at_1
	value: 40.328
	- type: recall_at_10
	value: 73.421
	- type: recall_at_100
	value: 90.059
	- type: recall_at_1000
	value: 97.81
	- type: recall_at_3
	value: 59.009
	- type: recall_at_5
	value: 66.352
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 27.424
	- type: map_at_10
	value: 36.332
	- type: map_at_100
	value: 37.347
	- type: map_at_1000
	value: 37.422
	- type: map_at_3
	value: 33.743
	- type: map_at_5
	value: 35.176
	- type: mrr_at_1
	value: 29.153000000000002
	- type: mrr_at_10
	value: 38.233
	- type: mrr_at_100
	value: 39.109
	- type: mrr_at_1000
	value: 39.164
	- type: mrr_at_3
	value: 35.876000000000005
	- type: mrr_at_5
	value: 37.169000000000004
	- type: ndcg_at_1
	value: 29.153000000000002
	- type: ndcg_at_10
	value: 41.439
	- type: ndcg_at_100
	value: 46.42
	- type: ndcg_at_1000
	value: 48.242000000000004
	- type: ndcg_at_3
	value: 36.362
	- type: ndcg_at_5
	value: 38.743
	- type: precision_at_1
	value: 29.153000000000002
	- type: precision_at_10
	value: 6.315999999999999
	- type: precision_at_100
	value: 0.927
	- type: precision_at_1000
	value: 0.11199999999999999
	- type: precision_at_3
	value: 15.443000000000001
	- type: precision_at_5
	value: 10.644
	- type: recall_at_1
	value: 27.424
	- type: recall_at_10
	value: 55.364000000000004
	- type: recall_at_100
	value: 78.211
	- type: recall_at_1000
	value: 91.74600000000001
	- type: recall_at_3
	value: 41.379
	- type: recall_at_5
	value: 47.14
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 19.601
	- type: map_at_10
	value: 27.826
	- type: map_at_100
	value: 29.017
	- type: map_at_1000
	value: 29.137
	- type: map_at_3
	value: 25.125999999999998
	- type: map_at_5
	value: 26.765
	- type: mrr_at_1
	value: 24.005000000000003
	- type: mrr_at_10
	value: 32.716
	- type: mrr_at_100
	value: 33.631
	- type: mrr_at_1000
	value: 33.694
	- type: mrr_at_3
	value: 29.934
	- type: mrr_at_5
	value: 31.630999999999997
	- type: ndcg_at_1
	value: 24.005000000000003
	- type: ndcg_at_10
	value: 33.158
	- type: ndcg_at_100
	value: 38.739000000000004
	- type: ndcg_at_1000
	value: 41.495
	- type: ndcg_at_3
	value: 28.185
	- type: ndcg_at_5
	value: 30.796
	- type: precision_at_1
	value: 24.005000000000003
	- type: precision_at_10
	value: 5.908
	- type: precision_at_100
	value: 1.005
	- type: precision_at_1000
	value: 0.13899999999999998
	- type: precision_at_3
	value: 13.391
	- type: precision_at_5
	value: 9.876
	- type: recall_at_1
	value: 19.601
	- type: recall_at_10
	value: 44.746
	- type: recall_at_100
	value: 68.82300000000001
	- type: recall_at_1000
	value: 88.215
	- type: recall_at_3
	value: 31.239
	- type: recall_at_5
	value: 37.695
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 30.130000000000003
	- type: map_at_10
	value: 40.96
	- type: map_at_100
	value: 42.282
	- type: map_at_1000
	value: 42.392
	- type: map_at_3
	value: 37.889
	- type: map_at_5
	value: 39.661
	- type: mrr_at_1
	value: 36.958999999999996
	- type: mrr_at_10
	value: 46.835
	- type: mrr_at_100
	value: 47.644
	- type: mrr_at_1000
	value: 47.688
	- type: mrr_at_3
	value: 44.562000000000005
	- type: mrr_at_5
	value: 45.938
	- type: ndcg_at_1
	value: 36.958999999999996
	- type: ndcg_at_10
	value: 47.06
	- type: ndcg_at_100
	value: 52.345
	- type: ndcg_at_1000
	value: 54.35
	- type: ndcg_at_3
	value: 42.301
	- type: ndcg_at_5
	value: 44.635999999999996
	- type: precision_at_1
	value: 36.958999999999996
	- type: precision_at_10
	value: 8.479000000000001
	- type: precision_at_100
	value: 1.284
	- type: precision_at_1000
	value: 0.163
	- type: precision_at_3
	value: 20.244
	- type: precision_at_5
	value: 14.224999999999998
	- type: recall_at_1
	value: 30.130000000000003
	- type: recall_at_10
	value: 59.27
	- type: recall_at_100
	value: 81.195
	- type: recall_at_1000
	value: 94.21199999999999
	- type: recall_at_3
	value: 45.885
	- type: recall_at_5
	value: 52.016
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 26.169999999999998
	- type: map_at_10
	value: 36.451
	- type: map_at_100
	value: 37.791000000000004
	- type: map_at_1000
	value: 37.897
	- type: map_at_3
	value: 33.109
	- type: map_at_5
	value: 34.937000000000005
	- type: mrr_at_1
	value: 32.877
	- type: mrr_at_10
	value: 42.368
	- type: mrr_at_100
	value: 43.201
	- type: mrr_at_1000
	value: 43.259
	- type: mrr_at_3
	value: 39.763999999999996
	- type: mrr_at_5
	value: 41.260000000000005
	- type: ndcg_at_1
	value: 32.877
	- type: ndcg_at_10
	value: 42.659000000000006
	- type: ndcg_at_100
	value: 48.161
	- type: ndcg_at_1000
	value: 50.345
	- type: ndcg_at_3
	value: 37.302
	- type: ndcg_at_5
	value: 39.722
	- type: precision_at_1
	value: 32.877
	- type: precision_at_10
	value: 7.9
	- type: precision_at_100
	value: 1.236
	- type: precision_at_1000
	value: 0.158
	- type: precision_at_3
	value: 17.846
	- type: precision_at_5
	value: 12.9
	- type: recall_at_1
	value: 26.169999999999998
	- type: recall_at_10
	value: 55.35
	- type: recall_at_100
	value: 78.755
	- type: recall_at_1000
	value: 93.518
	- type: recall_at_3
	value: 40.176
	- type: recall_at_5
	value: 46.589000000000006
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 27.15516666666667
	- type: map_at_10
	value: 36.65741666666667
	- type: map_at_100
	value: 37.84991666666666
	- type: map_at_1000
	value: 37.96316666666667
	- type: map_at_3
	value: 33.74974999999999
	- type: map_at_5
	value: 35.3765
	- type: mrr_at_1
	value: 32.08233333333334
	- type: mrr_at_10
	value: 41.033833333333334
	- type: mrr_at_100
	value: 41.84524999999999
	- type: mrr_at_1000
	value: 41.89983333333333
	- type: mrr_at_3
	value: 38.62008333333333
	- type: mrr_at_5
	value: 40.03441666666666
	- type: ndcg_at_1
	value: 32.08233333333334
	- type: ndcg_at_10
	value: 42.229
	- type: ndcg_at_100
	value: 47.26716666666667
	- type: ndcg_at_1000
	value: 49.43466666666667
	- type: ndcg_at_3
	value: 37.36408333333333
	- type: ndcg_at_5
	value: 39.6715
	- type: precision_at_1
	value: 32.08233333333334
	- type: precision_at_10
	value: 7.382583333333334
	- type: precision_at_100
	value: 1.16625
	- type: precision_at_1000
	value: 0.15408333333333332
	- type: precision_at_3
	value: 17.218
	- type: precision_at_5
	value: 12.21875
	- type: recall_at_1
	value: 27.15516666666667
	- type: recall_at_10
	value: 54.36683333333333
	- type: recall_at_100
	value: 76.37183333333333
	- type: recall_at_1000
	value: 91.26183333333333
	- type: recall_at_3
	value: 40.769916666666674
	- type: recall_at_5
	value: 46.702333333333335
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 25.749
	- type: map_at_10
	value: 33.001999999999995
	- type: map_at_100
	value: 33.891
	- type: map_at_1000
	value: 33.993
	- type: map_at_3
	value: 30.703999999999997
	- type: map_at_5
	value: 31.959
	- type: mrr_at_1
	value: 28.834
	- type: mrr_at_10
	value: 35.955
	- type: mrr_at_100
	value: 36.709
	- type: mrr_at_1000
	value: 36.779
	- type: mrr_at_3
	value: 33.947
	- type: mrr_at_5
	value: 35.089
	- type: ndcg_at_1
	value: 28.834
	- type: ndcg_at_10
	value: 37.329
	- type: ndcg_at_100
	value: 41.79
	- type: ndcg_at_1000
	value: 44.169000000000004
	- type: ndcg_at_3
	value: 33.184999999999995
	- type: ndcg_at_5
	value: 35.107
	- type: precision_at_1
	value: 28.834
	- type: precision_at_10
	value: 5.7669999999999995
	- type: precision_at_100
	value: 0.876
	- type: precision_at_1000
	value: 0.11399999999999999
	- type: precision_at_3
	value: 14.213000000000001
	- type: precision_at_5
	value: 9.754999999999999
	- type: recall_at_1
	value: 25.749
	- type: recall_at_10
	value: 47.791
	- type: recall_at_100
	value: 68.255
	- type: recall_at_1000
	value: 85.749
	- type: recall_at_3
	value: 36.199
	- type: recall_at_5
	value: 41.071999999999996
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 17.777
	- type: map_at_10
	value: 25.201
	- type: map_at_100
	value: 26.423999999999996
	- type: map_at_1000
	value: 26.544
	- type: map_at_3
	value: 22.869
	- type: map_at_5
	value: 24.023
	- type: mrr_at_1
	value: 21.473
	- type: mrr_at_10
	value: 29.12
	- type: mrr_at_100
	value: 30.144
	- type: mrr_at_1000
	value: 30.215999999999998
	- type: mrr_at_3
	value: 26.933
	- type: mrr_at_5
	value: 28.051
	- type: ndcg_at_1
	value: 21.473
	- type: ndcg_at_10
	value: 30.003
	- type: ndcg_at_100
	value: 35.766
	- type: ndcg_at_1000
	value: 38.501000000000005
	- type: ndcg_at_3
	value: 25.773000000000003
	- type: ndcg_at_5
	value: 27.462999999999997
	- type: precision_at_1
	value: 21.473
	- type: precision_at_10
	value: 5.482
	- type: precision_at_100
	value: 0.975
	- type: precision_at_1000
	value: 0.13799999999999998
	- type: precision_at_3
	value: 12.205
	- type: precision_at_5
	value: 8.692
	- type: recall_at_1
	value: 17.777
	- type: recall_at_10
	value: 40.582
	- type: recall_at_100
	value: 66.305
	- type: recall_at_1000
	value: 85.636
	- type: recall_at_3
	value: 28.687
	- type: recall_at_5
	value: 33.089
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 26.677
	- type: map_at_10
	value: 36.309000000000005
	- type: map_at_100
	value: 37.403999999999996
	- type: map_at_1000
	value: 37.496
	- type: map_at_3
	value: 33.382
	- type: map_at_5
	value: 34.98
	- type: mrr_at_1
	value: 31.343
	- type: mrr_at_10
	value: 40.549
	- type: mrr_at_100
	value: 41.342
	- type: mrr_at_1000
	value: 41.397
	- type: mrr_at_3
	value: 38.029
	- type: mrr_at_5
	value: 39.451
	- type: ndcg_at_1
	value: 31.343
	- type: ndcg_at_10
	value: 42.1
	- type: ndcg_at_100
	value: 47.089999999999996
	- type: ndcg_at_1000
	value: 49.222
	- type: ndcg_at_3
	value: 36.836999999999996
	- type: ndcg_at_5
	value: 39.21
	- type: precision_at_1
	value: 31.343
	- type: precision_at_10
	value: 7.164
	- type: precision_at_100
	value: 1.0959999999999999
	- type: precision_at_1000
	value: 0.13899999999999998
	- type: precision_at_3
	value: 16.915
	- type: precision_at_5
	value: 11.940000000000001
	- type: recall_at_1
	value: 26.677
	- type: recall_at_10
	value: 55.54599999999999
	- type: recall_at_100
	value: 77.094
	- type: recall_at_1000
	value: 92.01
	- type: recall_at_3
	value: 41.191
	- type: recall_at_5
	value: 47.006
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 24.501
	- type: map_at_10
	value: 33.102
	- type: map_at_100
	value: 34.676
	- type: map_at_1000
	value: 34.888000000000005
	- type: map_at_3
	value: 29.944
	- type: map_at_5
	value: 31.613999999999997
	- type: mrr_at_1
	value: 29.447000000000003
	- type: mrr_at_10
	value: 37.996
	- type: mrr_at_100
	value: 38.946
	- type: mrr_at_1000
	value: 38.995000000000005
	- type: mrr_at_3
	value: 35.079
	- type: mrr_at_5
	value: 36.69
	- type: ndcg_at_1
	value: 29.447000000000003
	- type: ndcg_at_10
	value: 39.232
	- type: ndcg_at_100
	value: 45.247
	- type: ndcg_at_1000
	value: 47.613
	- type: ndcg_at_3
	value: 33.922999999999995
	- type: ndcg_at_5
	value: 36.284
	- type: precision_at_1
	value: 29.447000000000003
	- type: precision_at_10
	value: 7.648000000000001
	- type: precision_at_100
	value: 1.516
	- type: precision_at_1000
	value: 0.23900000000000002
	- type: precision_at_3
	value: 16.008
	- type: precision_at_5
	value: 11.779
	- type: recall_at_1
	value: 24.501
	- type: recall_at_10
	value: 51.18899999999999
	- type: recall_at_100
	value: 78.437
	- type: recall_at_1000
	value: 92.842
	- type: recall_at_3
	value: 35.808
	- type: recall_at_5
	value: 42.197
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 22.039
	- type: map_at_10
	value: 30.377
	- type: map_at_100
	value: 31.275
	- type: map_at_1000
	value: 31.379
	- type: map_at_3
	value: 27.98
	- type: map_at_5
	value: 29.358
	- type: mrr_at_1
	value: 24.03
	- type: mrr_at_10
	value: 32.568000000000005
	- type: mrr_at_100
	value: 33.403
	- type: mrr_at_1000
	value: 33.475
	- type: mrr_at_3
	value: 30.436999999999998
	- type: mrr_at_5
	value: 31.796000000000003
	- type: ndcg_at_1
	value: 24.03
	- type: ndcg_at_10
	value: 35.198
	- type: ndcg_at_100
	value: 39.668
	- type: ndcg_at_1000
	value: 42.296
	- type: ndcg_at_3
	value: 30.709999999999997
	- type: ndcg_at_5
	value: 33.024
	- type: precision_at_1
	value: 24.03
	- type: precision_at_10
	value: 5.564
	- type: precision_at_100
	value: 0.828
	- type: precision_at_1000
	value: 0.117
	- type: precision_at_3
	value: 13.309000000000001
	- type: precision_at_5
	value: 9.39
	- type: recall_at_1
	value: 22.039
	- type: recall_at_10
	value: 47.746
	- type: recall_at_100
	value: 68.23599999999999
	- type: recall_at_1000
	value: 87.852
	- type: recall_at_3
	value: 35.852000000000004
	- type: recall_at_5
	value: 41.410000000000004
	- task:
	type: Retrieval
	dataset:
	type: climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 15.692999999999998
	- type: map_at_10
	value: 26.903
	- type: map_at_100
	value: 28.987000000000002
	- type: map_at_1000
	value: 29.176999999999996
	- type: map_at_3
	value: 22.137
	- type: map_at_5
	value: 24.758
	- type: mrr_at_1
	value: 35.57
	- type: mrr_at_10
	value: 47.821999999999996
	- type: mrr_at_100
	value: 48.608000000000004
	- type: mrr_at_1000
	value: 48.638999999999996
	- type: mrr_at_3
	value: 44.452000000000005
	- type: mrr_at_5
	value: 46.546
	- type: ndcg_at_1
	value: 35.57
	- type: ndcg_at_10
	value: 36.567
	- type: ndcg_at_100
	value: 44.085
	- type: ndcg_at_1000
	value: 47.24
	- type: ndcg_at_3
	value: 29.964000000000002
	- type: ndcg_at_5
	value: 32.511
	- type: precision_at_1
	value: 35.57
	- type: precision_at_10
	value: 11.485
	- type: precision_at_100
	value: 1.9619999999999997
	- type: precision_at_1000
	value: 0.256
	- type: precision_at_3
	value: 22.237000000000002
	- type: precision_at_5
	value: 17.471999999999998
	- type: recall_at_1
	value: 15.692999999999998
	- type: recall_at_10
	value: 43.056
	- type: recall_at_100
	value: 68.628
	- type: recall_at_1000
	value: 86.075
	- type: recall_at_3
	value: 26.918999999999997
	- type: recall_at_5
	value: 34.14
	- task:
	type: Retrieval
	dataset:
	type: dbpedia-entity
	name: MTEB DBPedia
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 9.53
	- type: map_at_10
	value: 20.951
	- type: map_at_100
	value: 30.136000000000003
	- type: map_at_1000
	value: 31.801000000000002
	- type: map_at_3
	value: 15.021
	- type: map_at_5
	value: 17.471999999999998
	- type: mrr_at_1
	value: 71.0
	- type: mrr_at_10
	value: 79.176
	- type: mrr_at_100
	value: 79.418
	- type: mrr_at_1000
	value: 79.426
	- type: mrr_at_3
	value: 78.125
	- type: mrr_at_5
	value: 78.61200000000001
	- type: ndcg_at_1
	value: 58.5
	- type: ndcg_at_10
	value: 44.106
	- type: ndcg_at_100
	value: 49.268
	- type: ndcg_at_1000
	value: 56.711999999999996
	- type: ndcg_at_3
	value: 48.934
	- type: ndcg_at_5
	value: 45.826
	- type: precision_at_1
	value: 71.0
	- type: precision_at_10
	value: 35.0
	- type: precision_at_100
	value: 11.360000000000001
	- type: precision_at_1000
	value: 2.046
	- type: precision_at_3
	value: 52.833
	- type: precision_at_5
	value: 44.15
	- type: recall_at_1
	value: 9.53
	- type: recall_at_10
	value: 26.811
	- type: recall_at_100
	value: 55.916999999999994
	- type: recall_at_1000
	value: 79.973
	- type: recall_at_3
	value: 16.413
	- type: recall_at_5
	value: 19.980999999999998
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 51.519999999999996
	- type: f1
	value: 46.36601294761231
	- task:
	type: Retrieval
	dataset:
	type: fever
	name: MTEB FEVER
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 74.413
	- type: map_at_10
	value: 83.414
	- type: map_at_100
	value: 83.621
	- type: map_at_1000
	value: 83.635
	- type: map_at_3
	value: 82.337
	- type: map_at_5
	value: 83.039
	- type: mrr_at_1
	value: 80.19800000000001
	- type: mrr_at_10
	value: 87.715
	- type: mrr_at_100
	value: 87.778
	- type: mrr_at_1000
	value: 87.779
	- type: mrr_at_3
	value: 87.106
	- type: mrr_at_5
	value: 87.555
	- type: ndcg_at_1
	value: 80.19800000000001
	- type: ndcg_at_10
	value: 87.182
	- type: ndcg_at_100
	value: 87.90299999999999
	- type: ndcg_at_1000
	value: 88.143
	- type: ndcg_at_3
	value: 85.60600000000001
	- type: ndcg_at_5
	value: 86.541
	- type: precision_at_1
	value: 80.19800000000001
	- type: precision_at_10
	value: 10.531
	- type: precision_at_100
	value: 1.113
	- type: precision_at_1000
	value: 0.11499999999999999
	- type: precision_at_3
	value: 32.933
	- type: precision_at_5
	value: 20.429
	- type: recall_at_1
	value: 74.413
	- type: recall_at_10
	value: 94.363
	- type: recall_at_100
	value: 97.165
	- type: recall_at_1000
	value: 98.668
	- type: recall_at_3
	value: 90.108
	- type: recall_at_5
	value: 92.52
	- task:
	type: Retrieval
	dataset:
	type: fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 22.701
	- type: map_at_10
	value: 37.122
	- type: map_at_100
	value: 39.178000000000004
	- type: map_at_1000
	value: 39.326
	- type: map_at_3
	value: 32.971000000000004
	- type: map_at_5
	value: 35.332
	- type: mrr_at_1
	value: 44.753
	- type: mrr_at_10
	value: 53.452
	- type: mrr_at_100
	value: 54.198
	- type: mrr_at_1000
	value: 54.225
	- type: mrr_at_3
	value: 50.952
	- type: mrr_at_5
	value: 52.464
	- type: ndcg_at_1
	value: 44.753
	- type: ndcg_at_10
	value: 45.021
	- type: ndcg_at_100
	value: 52.028
	- type: ndcg_at_1000
	value: 54.596000000000004
	- type: ndcg_at_3
	value: 41.622
	- type: ndcg_at_5
	value: 42.736000000000004
	- type: precision_at_1
	value: 44.753
	- type: precision_at_10
	value: 12.284
	- type: precision_at_100
	value: 1.955
	- type: precision_at_1000
	value: 0.243
	- type: precision_at_3
	value: 27.828999999999997
	- type: precision_at_5
	value: 20.061999999999998
	- type: recall_at_1
	value: 22.701
	- type: recall_at_10
	value: 51.432
	- type: recall_at_100
	value: 77.009
	- type: recall_at_1000
	value: 92.511
	- type: recall_at_3
	value: 37.919000000000004
	- type: recall_at_5
	value: 44.131
	- task:
	type: Retrieval
	dataset:
	type: hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 40.189
	- type: map_at_10
	value: 66.24600000000001
	- type: map_at_100
	value: 67.098
	- type: map_at_1000
	value: 67.149
	- type: map_at_3
	value: 62.684
	- type: map_at_5
	value: 64.974
	- type: mrr_at_1
	value: 80.378
	- type: mrr_at_10
	value: 86.127
	- type: mrr_at_100
	value: 86.29299999999999
	- type: mrr_at_1000
	value: 86.297
	- type: mrr_at_3
	value: 85.31400000000001
	- type: mrr_at_5
	value: 85.858
	- type: ndcg_at_1
	value: 80.378
	- type: ndcg_at_10
	value: 74.101
	- type: ndcg_at_100
	value: 76.993
	- type: ndcg_at_1000
	value: 77.948
	- type: ndcg_at_3
	value: 69.232
	- type: ndcg_at_5
	value: 72.04599999999999
	- type: precision_at_1
	value: 80.378
	- type: precision_at_10
	value: 15.595999999999998
	- type: precision_at_100
	value: 1.7840000000000003
	- type: precision_at_1000
	value: 0.191
	- type: precision_at_3
	value: 44.884
	- type: precision_at_5
	value: 29.145
	- type: recall_at_1
	value: 40.189
	- type: recall_at_10
	value: 77.981
	- type: recall_at_100
	value: 89.21
	- type: recall_at_1000
	value: 95.48299999999999
	- type: recall_at_3
	value: 67.326
	- type: recall_at_5
	value: 72.863
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 92.84599999999999
	- type: ap
	value: 89.4710787567357
	- type: f1
	value: 92.83752676932258
	- task:
	type: Retrieval
	dataset:
	type: msmarco
	name: MTEB MSMARCO
	config: default
	split: dev
	revision: None
	metrics:
	- type: map_at_1
	value: 23.132
	- type: map_at_10
	value: 35.543
	- type: map_at_100
	value: 36.702
	- type: map_at_1000
	value: 36.748999999999995
	- type: map_at_3
	value: 31.737
	- type: map_at_5
	value: 33.927
	- type: mrr_at_1
	value: 23.782
	- type: mrr_at_10
	value: 36.204
	- type: mrr_at_100
	value: 37.29
	- type: mrr_at_1000
	value: 37.330999999999996
	- type: mrr_at_3
	value: 32.458999999999996
	- type: mrr_at_5
	value: 34.631
	- type: ndcg_at_1
	value: 23.782
	- type: ndcg_at_10
	value: 42.492999999999995
	- type: ndcg_at_100
	value: 47.985
	- type: ndcg_at_1000
	value: 49.141
	- type: ndcg_at_3
	value: 34.748000000000005
	- type: ndcg_at_5
	value: 38.651
	- type: precision_at_1
	value: 23.782
	- type: precision_at_10
	value: 6.665
	- type: precision_at_100
	value: 0.941
	- type: precision_at_1000
	value: 0.104
	- type: precision_at_3
	value: 14.776
	- type: precision_at_5
	value: 10.84
	- type: recall_at_1
	value: 23.132
	- type: recall_at_10
	value: 63.794
	- type: recall_at_100
	value: 89.027
	- type: recall_at_1000
	value: 97.807
	- type: recall_at_3
	value: 42.765
	- type: recall_at_5
	value: 52.11
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 94.59188326493388
	- type: f1
	value: 94.3842594786827
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 79.49384404924761
	- type: f1
	value: 59.7580539534629
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 77.56220578345663
	- type: f1
	value: 75.27228165561478
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (en)
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 80.53463349024884
	- type: f1
	value: 80.4893958236536
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 32.56100273484962
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 31.470380028839607
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 32.06102792457849
	- type: mrr
	value: 33.30709199672238
	- task:
	type: Retrieval
	dataset:
	type: nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 6.776999999999999
	- type: map_at_10
	value: 14.924000000000001
	- type: map_at_100
	value: 18.955
	- type: map_at_1000
	value: 20.538999999999998
	- type: map_at_3
	value: 10.982
	- type: map_at_5
	value: 12.679000000000002
	- type: mrr_at_1
	value: 47.988
	- type: mrr_at_10
	value: 57.232000000000006
	- type: mrr_at_100
	value: 57.818999999999996
	- type: mrr_at_1000
	value: 57.847
	- type: mrr_at_3
	value: 54.901999999999994
	- type: mrr_at_5
	value: 56.481
	- type: ndcg_at_1
	value: 46.594
	- type: ndcg_at_10
	value: 38.129000000000005
	- type: ndcg_at_100
	value: 35.54
	- type: ndcg_at_1000
	value: 44.172
	- type: ndcg_at_3
	value: 43.025999999999996
	- type: ndcg_at_5
	value: 41.052
	- type: precision_at_1
	value: 47.988
	- type: precision_at_10
	value: 28.111000000000004
	- type: precision_at_100
	value: 8.929
	- type: precision_at_1000
	value: 2.185
	- type: precision_at_3
	value: 40.144000000000005
	- type: precision_at_5
	value: 35.232
	- type: recall_at_1
	value: 6.776999999999999
	- type: recall_at_10
	value: 19.289
	- type: recall_at_100
	value: 36.359
	- type: recall_at_1000
	value: 67.54
	- type: recall_at_3
	value: 11.869
	- type: recall_at_5
	value: 14.999
	- task:
	type: Retrieval
	dataset:
	type: nq
	name: MTEB NQ
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 31.108000000000004
	- type: map_at_10
	value: 47.126000000000005
	- type: map_at_100
	value: 48.171
	- type: map_at_1000
	value: 48.199
	- type: map_at_3
	value: 42.734
	- type: map_at_5
	value: 45.362
	- type: mrr_at_1
	value: 34.936
	- type: mrr_at_10
	value: 49.571
	- type: mrr_at_100
	value: 50.345
	- type: mrr_at_1000
	value: 50.363
	- type: mrr_at_3
	value: 45.959
	- type: mrr_at_5
	value: 48.165
	- type: ndcg_at_1
	value: 34.936
	- type: ndcg_at_10
	value: 55.028999999999996
	- type: ndcg_at_100
	value: 59.244
	- type: ndcg_at_1000
	value: 59.861
	- type: ndcg_at_3
	value: 46.872
	- type: ndcg_at_5
	value: 51.217999999999996
	- type: precision_at_1
	value: 34.936
	- type: precision_at_10
	value: 9.099
	- type: precision_at_100
	value: 1.145
	- type: precision_at_1000
	value: 0.12
	- type: precision_at_3
	value: 21.456
	- type: precision_at_5
	value: 15.411
	- type: recall_at_1
	value: 31.108000000000004
	- type: recall_at_10
	value: 76.53999999999999
	- type: recall_at_100
	value: 94.39
	- type: recall_at_1000
	value: 98.947
	- type: recall_at_3
	value: 55.572
	- type: recall_at_5
	value: 65.525
	- task:
	type: Retrieval
	dataset:
	type: quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 71.56400000000001
	- type: map_at_10
	value: 85.482
	- type: map_at_100
	value: 86.114
	- type: map_at_1000
	value: 86.13
	- type: map_at_3
	value: 82.607
	- type: map_at_5
	value: 84.405
	- type: mrr_at_1
	value: 82.42
	- type: mrr_at_10
	value: 88.304
	- type: mrr_at_100
	value: 88.399
	- type: mrr_at_1000
	value: 88.399
	- type: mrr_at_3
	value: 87.37
	- type: mrr_at_5
	value: 88.024
	- type: ndcg_at_1
	value: 82.45
	- type: ndcg_at_10
	value: 89.06500000000001
	- type: ndcg_at_100
	value: 90.232
	- type: ndcg_at_1000
	value: 90.305
	- type: ndcg_at_3
	value: 86.375
	- type: ndcg_at_5
	value: 87.85300000000001
	- type: precision_at_1
	value: 82.45
	- type: precision_at_10
	value: 13.486999999999998
	- type: precision_at_100
	value: 1.534
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 37.813
	- type: precision_at_5
	value: 24.773999999999997
	- type: recall_at_1
	value: 71.56400000000001
	- type: recall_at_10
	value: 95.812
	- type: recall_at_100
	value: 99.7
	- type: recall_at_1000
	value: 99.979
	- type: recall_at_3
	value: 87.966
	- type: recall_at_5
	value: 92.268
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 57.241876648614145
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 64.66212576446223
	- task:
	type: Retrieval
	dataset:
	type: scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 5.308
	- type: map_at_10
	value: 13.803
	- type: map_at_100
	value: 16.176
	- type: map_at_1000
	value: 16.561
	- type: map_at_3
	value: 9.761000000000001
	- type: map_at_5
	value: 11.802
	- type: mrr_at_1
	value: 26.200000000000003
	- type: mrr_at_10
	value: 37.621
	- type: mrr_at_100
	value: 38.767
	- type: mrr_at_1000
	value: 38.815
	- type: mrr_at_3
	value: 34.117
	- type: mrr_at_5
	value: 36.107
	- type: ndcg_at_1
	value: 26.200000000000003
	- type: ndcg_at_10
	value: 22.64
	- type: ndcg_at_100
	value: 31.567
	- type: ndcg_at_1000
	value: 37.623
	- type: ndcg_at_3
	value: 21.435000000000002
	- type: ndcg_at_5
	value: 18.87
	- type: precision_at_1
	value: 26.200000000000003
	- type: precision_at_10
	value: 11.74
	- type: precision_at_100
	value: 2.465
	- type: precision_at_1000
	value: 0.391
	- type: precision_at_3
	value: 20.033
	- type: precision_at_5
	value: 16.64
	- type: recall_at_1
	value: 5.308
	- type: recall_at_10
	value: 23.794999999999998
	- type: recall_at_100
	value: 50.015
	- type: recall_at_1000
	value: 79.283
	- type: recall_at_3
	value: 12.178
	- type: recall_at_5
	value: 16.882
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 84.93231134675553
	- type: cos_sim_spearman
	value: 81.68319292603205
	- type: euclidean_pearson
	value: 81.8396814380367
	- type: euclidean_spearman
	value: 81.24641903349945
	- type: manhattan_pearson
	value: 81.84698799204274
	- type: manhattan_spearman
	value: 81.24269997904105
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 86.73241671587446
	- type: cos_sim_spearman
	value: 79.05091082971826
	- type: euclidean_pearson
	value: 83.91146869578044
	- type: euclidean_spearman
	value: 79.87978465370936
	- type: manhattan_pearson
	value: 83.90888338917678
	- type: manhattan_spearman
	value: 79.87482848584241
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 85.14970731146177
	- type: cos_sim_spearman
	value: 86.37363490084627
	- type: euclidean_pearson
	value: 83.02154218530433
	- type: euclidean_spearman
	value: 83.80258761957367
	- type: manhattan_pearson
	value: 83.01664495119347
	- type: manhattan_spearman
	value: 83.77567458007952
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 83.40474139886784
	- type: cos_sim_spearman
	value: 82.77768789165984
	- type: euclidean_pearson
	value: 80.7065877443695
	- type: euclidean_spearman
	value: 81.375940662505
	- type: manhattan_pearson
	value: 80.6507552270278
	- type: manhattan_spearman
	value: 81.32782179098741
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 87.08585968722274
	- type: cos_sim_spearman
	value: 88.03110031451399
	- type: euclidean_pearson
	value: 85.74012019602384
	- type: euclidean_spearman
	value: 86.13592849438209
	- type: manhattan_pearson
	value: 85.74404842369206
	- type: manhattan_spearman
	value: 86.14492318960154
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 84.95069052788875
	- type: cos_sim_spearman
	value: 86.4867991595147
	- type: euclidean_pearson
	value: 84.31013325754635
	- type: euclidean_spearman
	value: 85.01529258006482
	- type: manhattan_pearson
	value: 84.26995570085374
	- type: manhattan_spearman
	value: 84.96982104986162
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-en)
	config: en-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 87.54617647971897
	- type: cos_sim_spearman
	value: 87.49834181751034
	- type: euclidean_pearson
	value: 86.01015322577122
	- type: euclidean_spearman
	value: 84.63362652063199
	- type: manhattan_pearson
	value: 86.13807574475706
	- type: manhattan_spearman
	value: 84.7772370721132
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (en)
	config: en
	split: test
	revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
	metrics:
	- type: cos_sim_pearson
	value: 67.20047755786615
	- type: cos_sim_spearman
	value: 67.05324077987636
	- type: euclidean_pearson
	value: 66.91930642976601
	- type: euclidean_spearman
	value: 65.21491856099105
	- type: manhattan_pearson
	value: 66.78756851976624
	- type: manhattan_spearman
	value: 65.12356257740728
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 86.19852871539686
	- type: cos_sim_spearman
	value: 87.5161895296395
	- type: euclidean_pearson
	value: 84.59848645207485
	- type: euclidean_spearman
	value: 85.26427328757919
	- type: manhattan_pearson
	value: 84.59747366996524
	- type: manhattan_spearman
	value: 85.24045855146915
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 87.63320317811032
	- type: mrr
	value: 96.26242947321379
	- task:
	type: Retrieval
	dataset:
	type: scifact
	name: MTEB SciFact
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 60.928000000000004
	- type: map_at_10
	value: 70.112
	- type: map_at_100
	value: 70.59299999999999
	- type: map_at_1000
	value: 70.623
	- type: map_at_3
	value: 66.846
	- type: map_at_5
	value: 68.447
	- type: mrr_at_1
	value: 64.0
	- type: mrr_at_10
	value: 71.212
	- type: mrr_at_100
	value: 71.616
	- type: mrr_at_1000
	value: 71.64500000000001
	- type: mrr_at_3
	value: 68.77799999999999
	- type: mrr_at_5
	value: 70.094
	- type: ndcg_at_1
	value: 64.0
	- type: ndcg_at_10
	value: 74.607
	- type: ndcg_at_100
	value: 76.416
	- type: ndcg_at_1000
	value: 77.102
	- type: ndcg_at_3
	value: 69.126
	- type: ndcg_at_5
	value: 71.41300000000001
	- type: precision_at_1
	value: 64.0
	- type: precision_at_10
	value: 9.933
	- type: precision_at_100
	value: 1.077
	- type: precision_at_1000
	value: 0.11299999999999999
	- type: precision_at_3
	value: 26.556
	- type: precision_at_5
	value: 17.467
	- type: recall_at_1
	value: 60.928000000000004
	- type: recall_at_10
	value: 87.322
	- type: recall_at_100
	value: 94.833
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 72.628
	- type: recall_at_5
	value: 78.428
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.86237623762376
	- type: cos_sim_ap
	value: 96.72586477206649
	- type: cos_sim_f1
	value: 93.01858362631845
	- type: cos_sim_precision
	value: 93.4409687184662
	- type: cos_sim_recall
	value: 92.60000000000001
	- type: dot_accuracy
	value: 99.78019801980199
	- type: dot_ap
	value: 93.72748205246228
	- type: dot_f1
	value: 89.04109589041096
	- type: dot_precision
	value: 87.16475095785441
	- type: dot_recall
	value: 91.0
	- type: euclidean_accuracy
	value: 99.85445544554456
	- type: euclidean_ap
	value: 96.6661459876145
	- type: euclidean_f1
	value: 92.58337481333997
	- type: euclidean_precision
	value: 92.17046580773042
	- type: euclidean_recall
	value: 93.0
	- type: manhattan_accuracy
	value: 99.85445544554456
	- type: manhattan_ap
	value: 96.6883549244056
	- type: manhattan_f1
	value: 92.57598405580468
	- type: manhattan_precision
	value: 92.25422045680239
	- type: manhattan_recall
	value: 92.9
	- type: max_accuracy
	value: 99.86237623762376
	- type: max_ap
	value: 96.72586477206649
	- type: max_f1
	value: 93.01858362631845
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 66.39930057069995
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 34.96398659903402
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 55.946944700355395
	- type: mrr
	value: 56.97151398438164
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 31.541657650692905
	- type: cos_sim_spearman
	value: 31.605804192286303
	- type: dot_pearson
	value: 28.26905996736398
	- type: dot_spearman
	value: 27.864801765851187
	- task:
	type: Retrieval
	dataset:
	type: trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 0.22599999999999998
	- type: map_at_10
	value: 1.8870000000000002
	- type: map_at_100
	value: 9.78
	- type: map_at_1000
	value: 22.514
	- type: map_at_3
	value: 0.6669999999999999
	- type: map_at_5
	value: 1.077
	- type: mrr_at_1
	value: 82.0
	- type: mrr_at_10
	value: 89.86699999999999
	- type: mrr_at_100
	value: 89.86699999999999
	- type: mrr_at_1000
	value: 89.86699999999999
	- type: mrr_at_3
	value: 89.667
	- type: mrr_at_5
	value: 89.667
	- type: ndcg_at_1
	value: 79.0
	- type: ndcg_at_10
	value: 74.818
	- type: ndcg_at_100
	value: 53.715999999999994
	- type: ndcg_at_1000
	value: 47.082
	- type: ndcg_at_3
	value: 82.134
	- type: ndcg_at_5
	value: 79.81899999999999
	- type: precision_at_1
	value: 82.0
	- type: precision_at_10
	value: 78.0
	- type: precision_at_100
	value: 54.48
	- type: precision_at_1000
	value: 20.518
	- type: precision_at_3
	value: 87.333
	- type: precision_at_5
	value: 85.2
	- type: recall_at_1
	value: 0.22599999999999998
	- type: recall_at_10
	value: 2.072
	- type: recall_at_100
	value: 13.013
	- type: recall_at_1000
	value: 43.462
	- type: recall_at_3
	value: 0.695
	- type: recall_at_5
	value: 1.139
	- task:
	type: Retrieval
	dataset:
	type: webis-touche2020
	name: MTEB Touche2020
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 2.328
	- type: map_at_10
	value: 9.795
	- type: map_at_100
	value: 15.801000000000002
	- type: map_at_1000
	value: 17.23
	- type: map_at_3
	value: 4.734
	- type: map_at_5
	value: 6.644
	- type: mrr_at_1
	value: 30.612000000000002
	- type: mrr_at_10
	value: 46.902
	- type: mrr_at_100
	value: 47.495
	- type: mrr_at_1000
	value: 47.495
	- type: mrr_at_3
	value: 41.156
	- type: mrr_at_5
	value: 44.218
	- type: ndcg_at_1
	value: 28.571
	- type: ndcg_at_10
	value: 24.806
	- type: ndcg_at_100
	value: 36.419000000000004
	- type: ndcg_at_1000
	value: 47.272999999999996
	- type: ndcg_at_3
	value: 25.666
	- type: ndcg_at_5
	value: 25.448999999999998
	- type: precision_at_1
	value: 30.612000000000002
	- type: precision_at_10
	value: 23.061
	- type: precision_at_100
	value: 7.714
	- type: precision_at_1000
	value: 1.484
	- type: precision_at_3
	value: 26.531
	- type: precision_at_5
	value: 26.122
	- type: recall_at_1
	value: 2.328
	- type: recall_at_10
	value: 16.524
	- type: recall_at_100
	value: 47.179
	- type: recall_at_1000
	value: 81.22200000000001
	- type: recall_at_3
	value: 5.745
	- type: recall_at_5
	value: 9.339
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 70.9142
	- type: ap
	value: 14.335574772555415
	- type: f1
	value: 54.62839595194111
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 59.94340690435768
	- type: f1
	value: 60.286487936731916
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 51.26597708987974
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 87.48882398521786
	- type: cos_sim_ap
	value: 79.04326607602204
	- type: cos_sim_f1
	value: 71.64566826860633
	- type: cos_sim_precision
	value: 70.55512918905092
	- type: cos_sim_recall
	value: 72.77044854881267
	- type: dot_accuracy
	value: 84.19264469213805
	- type: dot_ap
	value: 67.96360043562528
	- type: dot_f1
	value: 64.06418393006827
	- type: dot_precision
	value: 58.64941898706424
	- type: dot_recall
	value: 70.58047493403694
	- type: euclidean_accuracy
	value: 87.45902127913214
	- type: euclidean_ap
	value: 78.9742237648272
	- type: euclidean_f1
	value: 71.5553235908142
	- type: euclidean_precision
	value: 70.77955601445535
	- type: euclidean_recall
	value: 72.34828496042216
	- type: manhattan_accuracy
	value: 87.41729749061214
	- type: manhattan_ap
	value: 78.90073137580596
	- type: manhattan_f1
	value: 71.3942611553533
	- type: manhattan_precision
	value: 68.52705653967483
	- type: manhattan_recall
	value: 74.51187335092348
	- type: max_accuracy
	value: 87.48882398521786
	- type: max_ap
	value: 79.04326607602204
	- type: max_f1
	value: 71.64566826860633
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 88.68125897465751
	- type: cos_sim_ap
	value: 85.6003454431979
	- type: cos_sim_f1
	value: 77.6957163958641
	- type: cos_sim_precision
	value: 73.0110366307807
	- type: cos_sim_recall
	value: 83.02279026793964
	- type: dot_accuracy
	value: 87.7672992587418
	- type: dot_ap
	value: 82.4971301112899
	- type: dot_f1
	value: 75.90528233151184
	- type: dot_precision
	value: 72.0370626469368
	- type: dot_recall
	value: 80.21250384970742
	- type: euclidean_accuracy
	value: 88.4503434625684
	- type: euclidean_ap
	value: 84.91949884748384
	- type: euclidean_f1
	value: 76.92365018444684
	- type: euclidean_precision
	value: 74.53245721712759
	- type: euclidean_recall
	value: 79.47336002463813
	- type: manhattan_accuracy
	value: 88.47556952691427
	- type: manhattan_ap
	value: 84.8963689101517
	- type: manhattan_f1
	value: 76.85901249256395
	- type: manhattan_precision
	value: 74.31693989071039
	- type: manhattan_recall
	value: 79.58115183246073
	- type: max_accuracy
	value: 88.68125897465751
	- type: max_ap
	value: 85.6003454431979
	- type: max_f1
	value: 77.6957163958641
	license: mit
	language:
	- en
	---


	<h1 align="center">FlagEmbedding</h1>


	<h4 align="center">
	<p>
	<a href=#model-list>Model List</a> \|
	<a href=#frequently-asked-questions>FAQ</a> \|
	<a href=#usage>Usage</a> \|
	<a href="#evaluation">Evaluation</a> \|
	<a href="#train">Train</a> \|
	<a href="#contact">Contact</a> \|
	<a href="#citation">Citation</a> \|
	<a href="#license">License</a>
	<p>
	</h4>

	For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).

	If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).


	[English](README.md) \| [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)

	FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:

	- Long-Context LLM: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
	- Fine-tuning of LM : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
	- Dense Retrieval: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
	- Reranker Model: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
	- Benchmark: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)

	## News
	- 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
	It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
	[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
	- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
	- 12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
	- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
	- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
	- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
	- 09/12/2023: New models:
	- New reranker model: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
	- update embedding model: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.


	<details>
	<summary>More</summary>
	<!-- ### More -->

	- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
	- 08/09/2023: BGE Models are integrated into Langchain, you can use it like [this](#using-langchain); C-MTEB leaderboard is [available](https://huggingface.co/spaces/mteb/leaderboard).
	- 08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗
	- 08/02/2023: Release `bge-large-`(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark!* :tada: :tada:
	- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (C-MTEB), consisting of 31 test dataset.

	</details>


	## Model List

	`bge` is short for `BAAI general embedding`.

	\| Model \| Language \| \| Description \| query instruction for retrieval [1] \|
	\|:-------------------------------\|:--------:\| :--------:\| :--------:\|:--------:\|
	\| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) \| Multilingual \| [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) \| Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) \| \|
	\| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) \| English \| [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) \| a unified embedding model to support diverse retrieval augmentation needs for LLMs \| See [README](./FlagEmbedding/llm_embedder/README.md) \|
	\| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient [2] \| \|
	\| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) \| Chinese and English \| [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) \| a cross-encoder model which is more accurate but less efficient [2] \| \|
	\| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| version 1.5 with more reasonable similarity distribution \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-en` \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| English \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \|a small-scale model but with competitive performance \| `Represent this sentence for searching relevant passages: ` \|
	\| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| :trophy: rank 1st in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a base-scale model but with similar ability to `bge-large-zh` \| `为这个句子生成表示以用于检索相关文章：` \|
	\| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) \| Chinese \| [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) \| a small-scale model but with competitive performance \| `为这个句子生成表示以用于检索相关文章：` \|

	[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, no instruction needs to be added to passages.

	[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
	For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.

	All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
	If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .


	## Frequently asked questions

	<details>
	<summary>1. How to fine-tune bge embedding model?</summary>

	<!-- ### How to fine-tune bge embedding model? -->
	Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
	Some suggestions:
	- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
	- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
	- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.


	</details>

	<details>
	<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>

	<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
	Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.

	Since we finetune the models by contrastive learning with a temperature of 0.01,
	the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
	So a similarity score greater than 0.5 does not indicate that the two sentences are similar.

	For downstream tasks, such as passage retrieval or semantic similarity,
	what matters is the relative order of the scores, not the absolute value.
	If you need to filter similar sentences based on a similarity threshold,
	please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).

	</details>

	<details>
	<summary>3. When does the query instruction need to be used</summary>

	<!-- ### When does the query instruction need to be used -->

	For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
	No instruction only has a slight degradation in retrieval performance compared with using instruction.
	So you can generate embedding without instruction in all cases for convenience.

	For a retrieval task that uses short queries to find long related documents,
	it is recommended to add instructions for these short queries.
	The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.
	In all cases, the documents/passages do not need to add the instruction.

	</details>


	## Usage

	### Usage for Embedding Model

	Here are some examples for using `bge` models with
	[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).

	#### Using FlagEmbedding
	```
	pip install -U FlagEmbedding
	```
	If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.

	```python
	from FlagEmbedding import FlagModel
	sentences_1 = ["样例数据-1", "样例数据-2"]
	sentences_2 = ["样例数据-3", "样例数据-4"]
	model = FlagModel('BAAI/bge-large-zh-v1.5',
	query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
	use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
	embeddings_1 = model.encode(sentences_1)
	embeddings_2 = model.encode(sentences_2)
	similarity = embeddings_1 @ embeddings_2.T
	print(similarity)

	# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
	# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
	queries = ['query_1', 'query_2']
	passages = ["样例文档-1", "样例文档-2"]
	q_embeddings = model.encode_queries(queries)
	p_embeddings = model.encode(passages)
	scores = q_embeddings @ p_embeddings.T
	```
	For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).

	By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
	You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.


	#### Using Sentence-Transformers

	You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):

	```
	pip install -U sentence-transformers
	```
	```python
	from sentence_transformers import SentenceTransformer
	sentences_1 = ["样例数据-1", "样例数据-2"]
	sentences_2 = ["样例数据-3", "样例数据-4"]
	model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
	embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
	embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
	similarity = embeddings_1 @ embeddings_2.T
	print(similarity)
	```
	For s2p(short query to long passage) retrieval task,
	each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
	But the instruction is not needed for passages.
	```python
	from sentence_transformers import SentenceTransformer
	queries = ['query_1', 'query_2']
	passages = ["样例文档-1", "样例文档-2"]
	instruction = "为这个句子生成表示以用于检索相关文章："

	model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
	q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
	p_embeddings = model.encode(passages, normalize_embeddings=True)
	scores = q_embeddings @ p_embeddings.T
	```

	#### Using Langchain

	You can use `bge` in langchain like this:
	```python
	from langchain.embeddings import HuggingFaceBgeEmbeddings
	model_name = "BAAI/bge-large-en-v1.5"
	model_kwargs = {'device': 'cuda'}
	encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
	model = HuggingFaceBgeEmbeddings(
	model_name=model_name,
	model_kwargs=model_kwargs,
	encode_kwargs=encode_kwargs,
	query_instruction="为这个句子生成表示以用于检索相关文章："
	)
	model.query_instruction = "为这个句子生成表示以用于检索相关文章："
	```


	#### Using HuggingFace Transformers

	With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.

	```python
	from transformers import AutoTokenizer, AutoModel
	import torch
	# Sentences we want sentence embeddings for
	sentences = ["样例数据-1", "样例数据-2"]

	# Load model from HuggingFace Hub
	tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
	model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
	model.eval()

	# Tokenize sentences
	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
	# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
	# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)
	# Perform pooling. In this case, cls pooling.
	sentence_embeddings = model_output[0][:, 0]
	# normalize embeddings
	sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
	print("Sentence embeddings:", sentence_embeddings)
	```

	#### Usage of the ONNX files

	```python
	from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore

	import torch
	from transformers import AutoModel, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-en-v1.5')
	model = AutoModel.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13")
	model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-large-en-v1.5', revision="refs/pr/13",file_name="onnx/model.onnx")

	# Sentences we want sentence embeddings for
	sentences = ["样例数据-1", "样例数据-2"]

	# Tokenize sentences
	encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
	# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
	# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')

	model_output_ort = model_ort(**encoded_input)
	# Compute token embeddings
	with torch.no_grad():
	model_output = model(**encoded_input)

	# model_output and model_output_ort are identical

	```

	Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
	```python
	import asyncio
	from infinity_emb import AsyncEmbeddingEngine, EngineArgs

	sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
	engine = AsyncEmbeddingEngine.from_args(
	EngineArgs(model_name_or_path = "BAAI/bge-large-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
	))

	async def main():
	async with engine:
	embeddings, usage = await engine.embed(sentences=sentences)
	asyncio.run(main())
	```

	### Usage for Reranker

	Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
	You can get a relevance score by inputting query and passage to the reranker.
	The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.


	#### Using FlagEmbedding
	```
	pip install -U FlagEmbedding
	```

	Get relevance scores (higher scores indicate more relevance):
	```python
	from FlagEmbedding import FlagReranker
	reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

	score = reranker.compute_score(['query', 'passage'])
	print(score)

	scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
	print(scores)
	```


	#### Using Huggingface transformers

	```python
	import torch
	from transformers import AutoModelForSequenceClassification, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
	model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
	model.eval()

	pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
	with torch.no_grad():
	inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
	scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
	print(scores)
	```

	## Evaluation

	`baai-general-embedding` models achieve state-of-the-art performance on both MTEB and C-MTEB leaderboard!
	For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).

	- MTEB:

	\| Model Name \| Dimension \| Sequence Length \| Average (56) \| Retrieval (15) \|Clustering (11) \| Pair Classification (3) \| Reranking (4) \| STS (10) \| Summarization (1) \| Classification (12) \|
	\|:----:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|:---:\|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) \| 1024 \| 512 \| 63.98 \| 53.9 \| 46.98 \| 85.8 \| 59.48 \| 81.56 \| 32.06 \| 76.21 \|
	\| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) \| 768 \| 512 \| 63.36 \| 53.0 \| 46.32 \| 85.86 \| 58.7 \| 81.84 \| 29.27 \| 75.27 \|
	\| [gte-large](https://huggingface.co/thenlper/gte-large) \| 1024 \| 512 \| 63.13 \| 52.22 \| 46.84 \| 85.00 \| 59.13 \| 83.35 \| 31.66 \| 73.33 \|
	\| [gte-base](https://huggingface.co/thenlper/gte-base) \| 768 \| 512 \| 62.39 \| 51.14 \| 46.2 \| 84.57 \| 58.61 \| 82.3 \| 31.17 \| 73.01 \|
	\| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) \| 1024\| 512 \| 62.25 \| 50.56 \| 44.49 \| 86.03 \| 56.61 \| 82.05 \| 30.19 \| 75.24 \|
	\| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) \| 384 \| 512 \| 62.11 \| 51.82 \| 44.31 \| 83.78 \| 57.97 \| 80.72 \| 30.53 \| 74.37 \|
	\| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) \| 768 \| 512 \| 61.79 \| 49.26 \| 44.74 \| 86.62 \| 57.29 \| 83.06 \| 32.32 \| 61.79 \|
	\| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) \| 768 \| 512 \| 61.5 \| 50.29 \| 43.80 \| 85.73 \| 55.91 \| 81.05 \| 30.28 \| 73.84 \|
	\| [gte-small](https://huggingface.co/thenlper/gte-small) \| 384 \| 512 \| 61.36 \| 49.46 \| 44.89 \| 83.54 \| 57.7 \| 82.07 \| 30.42 \| 72.31 \|
	\| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) \| 1536 \| 8192 \| 60.99 \| 49.25 \| 45.9 \| 84.89 \| 56.32 \| 80.97 \| 30.8 \| 70.93 \|
	\| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) \| 384 \| 512 \| 59.93 \| 49.04 \| 39.92 \| 84.67 \| 54.32 \| 80.39 \| 31.16 \| 72.94 \|
	\| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) \| 768 \| 512 \| 59.51 \| 42.24 \| 43.72 \| 85.06 \| 56.42 \| 82.63 \| 30.08 \| 73.42 \|
	\| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) \| 768 \| 514 \| 57.78 \| 43.81 \| 43.69 \| 83.04 \| 59.36 \| 80.28 \| 27.49 \| 65.07 \|
	\| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) \| 4096 \| 2048 \| 57.59 \| 48.22 \| 38.93 \| 81.9 \| 55.65 \| 77.74 \| 33.6 \| 66.19 \|



	- C-MTEB:
	We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
	Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.

	\| Model \| Embedding dimension \| Avg \| Retrieval \| STS \| PairClassification \| Classification \| Reranking \| Clustering \|
	\|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [m3e-base](https://huggingface.co/moka-ai/m3e-base) \| 768 \| 57.10 \| 56.91 \| 50.47 \| 63.99 \| 67.52 \| 59.34 \| 47.68 \|
	\| [m3e-large](https://huggingface.co/moka-ai/m3e-large) \| 1024 \| 57.05 \| 54.75 \| 50.42 \| 64.3 \| 68.2 \| 59.66 \| 48.88 \|
	\| [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 \|
	\| [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 \|
	\| [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 \|
	\| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) \| 1024 \| 49.37 \| 44.4 \| 42.78 \| 66.62 \| 61 \| 49.25 \| 44.39 \|
	\| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) \| 768 \| 47.63 \| 38.79 \| 43.41 \| 67.41 \| 62.19 \| 49.45 \| 37.66 \|
	\| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) \| 1024 \| 47.36 \| 41.94 \| 44.97 \| 70.86 \| 60.66 \| 49.16 \| 30.02 \|


	- Reranking:
	See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.

	\| Model \| T2Reranking \| T2RerankingZh2En\* \| T2RerankingEn2Zh\* \| MMarcoReranking \| CMedQAv1 \| CMedQAv2 \| Avg \|
	\|:-------------------------------\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|:--------:\|
	\| text2vec-base-multilingual \| 64.66 \| 62.94 \| 62.51 \| 14.37 \| 48.46 \| 48.6 \| 50.26 \|
	\| multilingual-e5-small \| 65.62 \| 60.94 \| 56.41 \| 29.91 \| 67.26 \| 66.54 \| 57.78 \|
	\| multilingual-e5-large \| 64.55 \| 61.61 \| 54.28 \| 28.6 \| 67.42 \| 67.92 \| 57.4 \|
	\| multilingual-e5-base \| 64.21 \| 62.13 \| 54.68 \| 29.5 \| 66.23 \| 66.98 \| 57.29 \|
	\| m3e-base \| 66.03 \| 62.74 \| 56.07 \| 17.51 \| 77.05 \| 76.76 \| 59.36 \|
	\| m3e-large \| 66.13 \| 62.72 \| 56.1 \| 16.46 \| 77.76 \| 78.27 \| 59.57 \|
	\| bge-base-zh-v1.5 \| 66.49 \| 63.25 \| 57.02 \| 29.74 \| 80.47 \| 84.88 \| 63.64 \|
	\| bge-large-zh-v1.5 \| 65.74 \| 63.39 \| 57.03 \| 28.74 \| 83.45 \| 85.44 \| 63.97 \|
	\| [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 \|
	\| [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 \|

	\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks

	## Train

	### BAAI Embedding

	We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
	You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).
	We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
	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.
	More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).



	### BGE Reranker

	Cross-encoder will perform full-attention over the input pair,
	which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
	Therefore, it can be used to re-rank the top-k documents returned by embedding model.
	We train the cross-encoder on a multilingual pair data,
	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).
	More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)


	## Contact
	If you have any question or suggestion related to this project, feel free to open an issue or pull request.
	You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn).


	## Citation

	If you find this repository useful, please consider giving a star :star: and citation

	```
	@misc{bge_embedding,
	title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
	author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
	year={2023},
	eprint={2309.07597},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	## License
	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.