Hugging Face's logo

BAAI
/
bge-base-en

Feature Extraction
Transformers
PyTorch
ONNX
Safetensors
English
bert
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
6
bge-base-en / README.md
ldwang's picture
ldwang
update
6747e44
preview code
|
raw
history blame
88.8 kB
---
tags:
- mteb
model-index:
- name: bge-base-en
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.73134328358209
- type: ap
value: 38.97277232632892
- type: f1
value: 69.81740361139785
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.56522500000001
- type: ap
value: 88.88821771869553
- type: f1
value: 92.54817512659696
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.91
- type: f1
value: 46.28536394320311
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.834
- type: map_at_10
value: 53.564
- type: map_at_100
value: 54.230000000000004
- type: map_at_1000
value: 54.235
- type: map_at_3
value: 49.49
- type: map_at_5
value: 51.784
- type: mrr_at_1
value: 39.26
- type: mrr_at_10
value: 53.744
- type: mrr_at_100
value: 54.410000000000004
- type: mrr_at_1000
value: 54.415
- type: mrr_at_3
value: 49.656
- type: mrr_at_5
value: 52.018
- type: ndcg_at_1
value: 38.834
- type: ndcg_at_10
value: 61.487
- type: ndcg_at_100
value: 64.303
- type: ndcg_at_1000
value: 64.408
- type: ndcg_at_3
value: 53.116
- type: ndcg_at_5
value: 57.248
- type: precision_at_1
value: 38.834
- type: precision_at_10
value: 8.663
- type: precision_at_100
value: 0.989
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.218999999999998
- type: precision_at_5
value: 14.737
- type: recall_at_1
value: 38.834
- type: recall_at_10
value: 86.629
- type: recall_at_100
value: 98.86200000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 63.656
- type: recall_at_5
value: 73.68400000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.88475477433035
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.85053138403176
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.23221013208242
- type: mrr
value: 74.64857318735436
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.4403443247284
- type: cos_sim_spearman
value: 85.5326718115169
- type: euclidean_pearson
value: 86.0114007449595
- type: euclidean_spearman
value: 86.05979225604875
- type: manhattan_pearson
value: 86.05423806568598
- type: manhattan_spearman
value: 86.02485170086835
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 86.44480519480518
- type: f1
value: 86.41301900941988
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 40.17547250880036
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 37.74514172687293
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.096000000000004
- type: map_at_10
value: 43.345
- type: map_at_100
value: 44.73
- type: map_at_1000
value: 44.85
- type: map_at_3
value: 39.956
- type: map_at_5
value: 41.727
- type: mrr_at_1
value: 38.769999999999996
- type: mrr_at_10
value: 48.742000000000004
- type: mrr_at_100
value: 49.474000000000004
- type: mrr_at_1000
value: 49.513
- type: mrr_at_3
value: 46.161
- type: mrr_at_5
value: 47.721000000000004
- type: ndcg_at_1
value: 38.769999999999996
- type: ndcg_at_10
value: 49.464999999999996
- type: ndcg_at_100
value: 54.632000000000005
- type: ndcg_at_1000
value: 56.52
- type: ndcg_at_3
value: 44.687
- type: ndcg_at_5
value: 46.814
- type: precision_at_1
value: 38.769999999999996
- type: precision_at_10
value: 9.471
- type: precision_at_100
value: 1.4909999999999999
- type: precision_at_1000
value: 0.194
- type: precision_at_3
value: 21.268
- type: precision_at_5
value: 15.079
- type: recall_at_1
value: 32.096000000000004
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 83.075
- type: recall_at_1000
value: 95.178
- type: recall_at_3
value: 47.009
- type: recall_at_5
value: 53.348
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.588
- type: map_at_10
value: 42.251
- type: map_at_100
value: 43.478
- type: map_at_1000
value: 43.617
- type: map_at_3
value: 39.381
- type: map_at_5
value: 41.141
- type: mrr_at_1
value: 41.21
- type: mrr_at_10
value: 48.765
- type: mrr_at_100
value: 49.403000000000006
- type: mrr_at_1000
value: 49.451
- type: mrr_at_3
value: 46.73
- type: mrr_at_5
value: 47.965999999999994
- type: ndcg_at_1
value: 41.21
- type: ndcg_at_10
value: 47.704
- type: ndcg_at_100
value: 51.916
- type: ndcg_at_1000
value: 54.013999999999996
- type: ndcg_at_3
value: 44.007000000000005
- type: ndcg_at_5
value: 45.936
- type: precision_at_1
value: 41.21
- type: precision_at_10
value: 8.885
- type: precision_at_100
value: 1.409
- type: precision_at_1000
value: 0.189
- type: precision_at_3
value: 21.274
- type: precision_at_5
value: 15.045
- type: recall_at_1
value: 32.588
- type: recall_at_10
value: 56.333
- type: recall_at_100
value: 74.251
- type: recall_at_1000
value: 87.518
- type: recall_at_3
value: 44.962
- type: recall_at_5
value: 50.609
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.308
- type: map_at_10
value: 53.12
- type: map_at_100
value: 54.123
- type: map_at_1000
value: 54.173
- type: map_at_3
value: 50.017999999999994
- type: map_at_5
value: 51.902
- type: mrr_at_1
value: 46.394999999999996
- type: mrr_at_10
value: 56.531
- type: mrr_at_100
value: 57.19800000000001
- type: mrr_at_1000
value: 57.225
- type: mrr_at_3
value: 54.368
- type: mrr_at_5
value: 55.713
- type: ndcg_at_1
value: 46.394999999999996
- type: ndcg_at_10
value: 58.811
- type: ndcg_at_100
value: 62.834
- type: ndcg_at_1000
value: 63.849999999999994
- type: ndcg_at_3
value: 53.88699999999999
- type: ndcg_at_5
value: 56.477999999999994
- type: precision_at_1
value: 46.394999999999996
- type: precision_at_10
value: 9.398
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 24.221999999999998
- type: precision_at_5
value: 16.539
- type: recall_at_1
value: 40.308
- type: recall_at_10
value: 72.146
- type: recall_at_100
value: 89.60900000000001
- type: recall_at_1000
value: 96.733
- type: recall_at_3
value: 58.91499999999999
- type: recall_at_5
value: 65.34299999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.383000000000003
- type: map_at_10
value: 35.802
- type: map_at_100
value: 36.756
- type: map_at_1000
value: 36.826
- type: map_at_3
value: 32.923
- type: map_at_5
value: 34.577999999999996
- type: mrr_at_1
value: 29.604999999999997
- type: mrr_at_10
value: 37.918
- type: mrr_at_100
value: 38.732
- type: mrr_at_1000
value: 38.786
- type: mrr_at_3
value: 35.198
- type: mrr_at_5
value: 36.808
- type: ndcg_at_1
value: 29.604999999999997
- type: ndcg_at_10
value: 40.836
- type: ndcg_at_100
value: 45.622
- type: ndcg_at_1000
value: 47.427
- type: ndcg_at_3
value: 35.208
- type: ndcg_at_5
value: 38.066
- type: precision_at_1
value: 29.604999999999997
- type: precision_at_10
value: 6.226
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 14.463000000000001
- type: precision_at_5
value: 10.35
- type: recall_at_1
value: 27.383000000000003
- type: recall_at_10
value: 54.434000000000005
- type: recall_at_100
value: 76.632
- type: recall_at_1000
value: 90.25
- type: recall_at_3
value: 39.275
- type: recall_at_5
value: 46.225
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.885
- type: map_at_10
value: 25.724000000000004
- type: map_at_100
value: 26.992
- type: map_at_1000
value: 27.107999999999997
- type: map_at_3
value: 23.04
- type: map_at_5
value: 24.529
- type: mrr_at_1
value: 22.264
- type: mrr_at_10
value: 30.548
- type: mrr_at_100
value: 31.593
- type: mrr_at_1000
value: 31.657999999999998
- type: mrr_at_3
value: 27.756999999999998
- type: mrr_at_5
value: 29.398999999999997
- type: ndcg_at_1
value: 22.264
- type: ndcg_at_10
value: 30.902
- type: ndcg_at_100
value: 36.918
- type: ndcg_at_1000
value: 39.735
- type: ndcg_at_3
value: 25.915
- type: ndcg_at_5
value: 28.255999999999997
- type: precision_at_1
value: 22.264
- type: precision_at_10
value: 5.634
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.055
- type: recall_at_1
value: 17.885
- type: recall_at_10
value: 42.237
- type: recall_at_100
value: 68.489
- type: recall_at_1000
value: 88.721
- type: recall_at_3
value: 28.283
- type: recall_at_5
value: 34.300000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.737000000000002
- type: map_at_10
value: 39.757
- type: map_at_100
value: 40.992
- type: map_at_1000
value: 41.102
- type: map_at_3
value: 36.612
- type: map_at_5
value: 38.413000000000004
- type: mrr_at_1
value: 35.804
- type: mrr_at_10
value: 45.178000000000004
- type: mrr_at_100
value: 45.975
- type: mrr_at_1000
value: 46.021
- type: mrr_at_3
value: 42.541000000000004
- type: mrr_at_5
value: 44.167
- type: ndcg_at_1
value: 35.804
- type: ndcg_at_10
value: 45.608
- type: ndcg_at_100
value: 50.746
- type: ndcg_at_1000
value: 52.839999999999996
- type: ndcg_at_3
value: 40.52
- type: ndcg_at_5
value: 43.051
- type: precision_at_1
value: 35.804
- type: precision_at_10
value: 8.104
- type: precision_at_100
value: 1.256
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 19.121
- type: precision_at_5
value: 13.532
- type: recall_at_1
value: 29.737000000000002
- type: recall_at_10
value: 57.66
- type: recall_at_100
value: 79.121
- type: recall_at_1000
value: 93.023
- type: recall_at_3
value: 43.13
- type: recall_at_5
value: 49.836000000000006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.299
- type: map_at_10
value: 35.617
- type: map_at_100
value: 36.972
- type: map_at_1000
value: 37.096000000000004
- type: map_at_3
value: 32.653999999999996
- type: map_at_5
value: 34.363
- type: mrr_at_1
value: 32.877
- type: mrr_at_10
value: 41.423
- type: mrr_at_100
value: 42.333999999999996
- type: mrr_at_1000
value: 42.398
- type: mrr_at_3
value: 39.193
- type: mrr_at_5
value: 40.426
- type: ndcg_at_1
value: 32.877
- type: ndcg_at_10
value: 41.271
- type: ndcg_at_100
value: 46.843
- type: ndcg_at_1000
value: 49.366
- type: ndcg_at_3
value: 36.735
- type: ndcg_at_5
value: 38.775999999999996
- type: precision_at_1
value: 32.877
- type: precision_at_10
value: 7.580000000000001
- type: precision_at_100
value: 1.192
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 17.541999999999998
- type: precision_at_5
value: 12.443
- type: recall_at_1
value: 26.299
- type: recall_at_10
value: 52.256
- type: recall_at_100
value: 75.919
- type: recall_at_1000
value: 93.185
- type: recall_at_3
value: 39.271
- type: recall_at_5
value: 44.901
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.05741666666667
- type: map_at_10
value: 36.086416666666665
- type: map_at_100
value: 37.26916666666667
- type: map_at_1000
value: 37.38191666666666
- type: map_at_3
value: 33.34225
- type: map_at_5
value: 34.86425
- type: mrr_at_1
value: 32.06008333333333
- type: mrr_at_10
value: 40.36658333333333
- type: mrr_at_100
value: 41.206500000000005
- type: mrr_at_1000
value: 41.261083333333325
- type: mrr_at_3
value: 38.01208333333334
- type: mrr_at_5
value: 39.36858333333333
- type: ndcg_at_1
value: 32.06008333333333
- type: ndcg_at_10
value: 41.3535
- type: ndcg_at_100
value: 46.42066666666666
- type: ndcg_at_1000
value: 48.655166666666666
- type: ndcg_at_3
value: 36.78041666666667
- type: ndcg_at_5
value: 38.91783333333334
- type: precision_at_1
value: 32.06008333333333
- type: precision_at_10
value: 7.169833333333332
- type: precision_at_100
value: 1.1395
- type: precision_at_1000
value: 0.15158333333333332
- type: precision_at_3
value: 16.852
- type: precision_at_5
value: 11.8645
- type: recall_at_1
value: 27.05741666666667
- type: recall_at_10
value: 52.64491666666666
- type: recall_at_100
value: 74.99791666666667
- type: recall_at_1000
value: 90.50524999999999
- type: recall_at_3
value: 39.684000000000005
- type: recall_at_5
value: 45.37225
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.607999999999997
- type: map_at_10
value: 32.28
- type: map_at_100
value: 33.261
- type: map_at_1000
value: 33.346
- type: map_at_3
value: 30.514999999999997
- type: map_at_5
value: 31.415
- type: mrr_at_1
value: 28.988000000000003
- type: mrr_at_10
value: 35.384
- type: mrr_at_100
value: 36.24
- type: mrr_at_1000
value: 36.299
- type: mrr_at_3
value: 33.717000000000006
- type: mrr_at_5
value: 34.507
- type: ndcg_at_1
value: 28.988000000000003
- type: ndcg_at_10
value: 36.248000000000005
- type: ndcg_at_100
value: 41.034
- type: ndcg_at_1000
value: 43.35
- type: ndcg_at_3
value: 32.987
- type: ndcg_at_5
value: 34.333999999999996
- type: precision_at_1
value: 28.988000000000003
- type: precision_at_10
value: 5.506
- type: precision_at_100
value: 0.853
- type: precision_at_1000
value: 0.11199999999999999
- type: precision_at_3
value: 14.11
- type: precision_at_5
value: 9.417
- type: recall_at_1
value: 25.607999999999997
- type: recall_at_10
value: 45.344
- type: recall_at_100
value: 67.132
- type: recall_at_1000
value: 84.676
- type: recall_at_3
value: 36.02
- type: recall_at_5
value: 39.613
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.44
- type: map_at_10
value: 25.651000000000003
- type: map_at_100
value: 26.735
- type: map_at_1000
value: 26.86
- type: map_at_3
value: 23.409
- type: map_at_5
value: 24.604
- type: mrr_at_1
value: 22.195
- type: mrr_at_10
value: 29.482000000000003
- type: mrr_at_100
value: 30.395
- type: mrr_at_1000
value: 30.471999999999998
- type: mrr_at_3
value: 27.409
- type: mrr_at_5
value: 28.553
- type: ndcg_at_1
value: 22.195
- type: ndcg_at_10
value: 30.242
- type: ndcg_at_100
value: 35.397
- type: ndcg_at_1000
value: 38.287
- type: ndcg_at_3
value: 26.201
- type: ndcg_at_5
value: 28.008
- type: precision_at_1
value: 22.195
- type: precision_at_10
value: 5.372
- type: precision_at_100
value: 0.9259999999999999
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 12.228
- type: precision_at_5
value: 8.727
- type: recall_at_1
value: 18.44
- type: recall_at_10
value: 40.325
- type: recall_at_100
value: 63.504000000000005
- type: recall_at_1000
value: 83.909
- type: recall_at_3
value: 28.925
- type: recall_at_5
value: 33.641
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.535999999999998
- type: map_at_10
value: 35.358000000000004
- type: map_at_100
value: 36.498999999999995
- type: map_at_1000
value: 36.597
- type: map_at_3
value: 32.598
- type: map_at_5
value: 34.185
- type: mrr_at_1
value: 31.25
- type: mrr_at_10
value: 39.593
- type: mrr_at_100
value: 40.443
- type: mrr_at_1000
value: 40.498
- type: mrr_at_3
value: 37.018
- type: mrr_at_5
value: 38.492
- type: ndcg_at_1
value: 31.25
- type: ndcg_at_10
value: 40.71
- type: ndcg_at_100
value: 46.079
- type: ndcg_at_1000
value: 48.287
- type: ndcg_at_3
value: 35.667
- type: ndcg_at_5
value: 38.080000000000005
- type: precision_at_1
value: 31.25
- type: precision_at_10
value: 6.847
- type: precision_at_100
value: 1.079
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 16.262
- type: precision_at_5
value: 11.455
- type: recall_at_1
value: 26.535999999999998
- type: recall_at_10
value: 52.92099999999999
- type: recall_at_100
value: 76.669
- type: recall_at_1000
value: 92.096
- type: recall_at_3
value: 38.956
- type: recall_at_5
value: 45.239000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.691
- type: map_at_10
value: 33.417
- type: map_at_100
value: 35.036
- type: map_at_1000
value: 35.251
- type: map_at_3
value: 30.646
- type: map_at_5
value: 32.177
- type: mrr_at_1
value: 30.04
- type: mrr_at_10
value: 37.905
- type: mrr_at_100
value: 38.929
- type: mrr_at_1000
value: 38.983000000000004
- type: mrr_at_3
value: 35.276999999999994
- type: mrr_at_5
value: 36.897000000000006
- type: ndcg_at_1
value: 30.04
- type: ndcg_at_10
value: 39.037
- type: ndcg_at_100
value: 44.944
- type: ndcg_at_1000
value: 47.644
- type: ndcg_at_3
value: 34.833999999999996
- type: ndcg_at_5
value: 36.83
- type: precision_at_1
value: 30.04
- type: precision_at_10
value: 7.4510000000000005
- type: precision_at_100
value: 1.492
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 16.337
- type: precision_at_5
value: 11.897
- type: recall_at_1
value: 24.691
- type: recall_at_10
value: 49.303999999999995
- type: recall_at_100
value: 76.20400000000001
- type: recall_at_1000
value: 93.30000000000001
- type: recall_at_3
value: 36.594
- type: recall_at_5
value: 42.41
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.118
- type: map_at_10
value: 30.714999999999996
- type: map_at_100
value: 31.656000000000002
- type: map_at_1000
value: 31.757
- type: map_at_3
value: 28.355000000000004
- type: map_at_5
value: 29.337000000000003
- type: mrr_at_1
value: 25.323
- type: mrr_at_10
value: 32.93
- type: mrr_at_100
value: 33.762
- type: mrr_at_1000
value: 33.829
- type: mrr_at_3
value: 30.775999999999996
- type: mrr_at_5
value: 31.774
- type: ndcg_at_1
value: 25.323
- type: ndcg_at_10
value: 35.408
- type: ndcg_at_100
value: 40.083
- type: ndcg_at_1000
value: 42.542
- type: ndcg_at_3
value: 30.717
- type: ndcg_at_5
value: 32.385000000000005
- type: precision_at_1
value: 25.323
- type: precision_at_10
value: 5.564
- type: precision_at_100
value: 0.843
- type: precision_at_1000
value: 0.116
- type: precision_at_3
value: 13.001
- type: precision_at_5
value: 8.834999999999999
- type: recall_at_1
value: 23.118
- type: recall_at_10
value: 47.788000000000004
- type: recall_at_100
value: 69.37
- type: recall_at_1000
value: 87.47399999999999
- type: recall_at_3
value: 34.868
- type: recall_at_5
value: 39.001999999999995
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 14.288
- type: map_at_10
value: 23.256
- type: map_at_100
value: 25.115
- type: map_at_1000
value: 25.319000000000003
- type: map_at_3
value: 20.005
- type: map_at_5
value: 21.529999999999998
- type: mrr_at_1
value: 31.401
- type: mrr_at_10
value: 42.251
- type: mrr_at_100
value: 43.236999999999995
- type: mrr_at_1000
value: 43.272
- type: mrr_at_3
value: 39.164
- type: mrr_at_5
value: 40.881
- type: ndcg_at_1
value: 31.401
- type: ndcg_at_10
value: 31.615
- type: ndcg_at_100
value: 38.982
- type: ndcg_at_1000
value: 42.496
- type: ndcg_at_3
value: 26.608999999999998
- type: ndcg_at_5
value: 28.048000000000002
- type: precision_at_1
value: 31.401
- type: precision_at_10
value: 9.536999999999999
- type: precision_at_100
value: 1.763
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 19.153000000000002
- type: precision_at_5
value: 14.228
- type: recall_at_1
value: 14.288
- type: recall_at_10
value: 36.717
- type: recall_at_100
value: 61.9
- type: recall_at_1000
value: 81.676
- type: recall_at_3
value: 24.203
- type: recall_at_5
value: 28.793999999999997
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.019
- type: map_at_10
value: 19.963
- type: map_at_100
value: 28.834
- type: map_at_1000
value: 30.537999999999997
- type: map_at_3
value: 14.45
- type: map_at_5
value: 16.817999999999998
- type: mrr_at_1
value: 65.75
- type: mrr_at_10
value: 74.646
- type: mrr_at_100
value: 74.946
- type: mrr_at_1000
value: 74.95100000000001
- type: mrr_at_3
value: 72.625
- type: mrr_at_5
value: 74.012
- type: ndcg_at_1
value: 54
- type: ndcg_at_10
value: 42.014
- type: ndcg_at_100
value: 47.527
- type: ndcg_at_1000
value: 54.911
- type: ndcg_at_3
value: 46.586
- type: ndcg_at_5
value: 43.836999999999996
- type: precision_at_1
value: 65.75
- type: precision_at_10
value: 33.475
- type: precision_at_100
value: 11.16
- type: precision_at_1000
value: 2.145
- type: precision_at_3
value: 50.083
- type: precision_at_5
value: 42.55
- type: recall_at_1
value: 9.019
- type: recall_at_10
value: 25.558999999999997
- type: recall_at_100
value: 53.937999999999995
- type: recall_at_1000
value: 77.67399999999999
- type: recall_at_3
value: 15.456
- type: recall_at_5
value: 19.259
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.635
- type: f1
value: 47.692783881403926
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 76.893
- type: map_at_10
value: 84.897
- type: map_at_100
value: 85.122
- type: map_at_1000
value: 85.135
- type: map_at_3
value: 83.88
- type: map_at_5
value: 84.565
- type: mrr_at_1
value: 83.003
- type: mrr_at_10
value: 89.506
- type: mrr_at_100
value: 89.574
- type: mrr_at_1000
value: 89.575
- type: mrr_at_3
value: 88.991
- type: mrr_at_5
value: 89.349
- type: ndcg_at_1
value: 83.003
- type: ndcg_at_10
value: 88.351
- type: ndcg_at_100
value: 89.128
- type: ndcg_at_1000
value: 89.34100000000001
- type: ndcg_at_3
value: 86.92
- type: ndcg_at_5
value: 87.78200000000001
- type: precision_at_1
value: 83.003
- type: precision_at_10
value: 10.517999999999999
- type: precision_at_100
value: 1.115
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.062999999999995
- type: precision_at_5
value: 20.498
- type: recall_at_1
value: 76.893
- type: recall_at_10
value: 94.374
- type: recall_at_100
value: 97.409
- type: recall_at_1000
value: 98.687
- type: recall_at_3
value: 90.513
- type: recall_at_5
value: 92.709
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.829
- type: map_at_10
value: 32.86
- type: map_at_100
value: 34.838
- type: map_at_1000
value: 35.006
- type: map_at_3
value: 28.597
- type: map_at_5
value: 31.056
- type: mrr_at_1
value: 41.358
- type: mrr_at_10
value: 49.542
- type: mrr_at_100
value: 50.29900000000001
- type: mrr_at_1000
value: 50.334999999999994
- type: mrr_at_3
value: 46.579
- type: mrr_at_5
value: 48.408
- type: ndcg_at_1
value: 41.358
- type: ndcg_at_10
value: 40.758
- type: ndcg_at_100
value: 47.799
- type: ndcg_at_1000
value: 50.589
- type: ndcg_at_3
value: 36.695
- type: ndcg_at_5
value: 38.193
- type: precision_at_1
value: 41.358
- type: precision_at_10
value: 11.142000000000001
- type: precision_at_100
value: 1.8350000000000002
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 24.023
- type: precision_at_5
value: 17.963
- type: recall_at_1
value: 20.829
- type: recall_at_10
value: 47.467999999999996
- type: recall_at_100
value: 73.593
- type: recall_at_1000
value: 90.122
- type: recall_at_3
value: 32.74
- type: recall_at_5
value: 39.608
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 40.324
- type: map_at_10
value: 64.183
- type: map_at_100
value: 65.037
- type: map_at_1000
value: 65.094
- type: map_at_3
value: 60.663
- type: map_at_5
value: 62.951
- type: mrr_at_1
value: 80.648
- type: mrr_at_10
value: 86.005
- type: mrr_at_100
value: 86.157
- type: mrr_at_1000
value: 86.162
- type: mrr_at_3
value: 85.116
- type: mrr_at_5
value: 85.703
- type: ndcg_at_1
value: 80.648
- type: ndcg_at_10
value: 72.351
- type: ndcg_at_100
value: 75.279
- type: ndcg_at_1000
value: 76.357
- type: ndcg_at_3
value: 67.484
- type: ndcg_at_5
value: 70.31500000000001
- type: precision_at_1
value: 80.648
- type: precision_at_10
value: 15.103
- type: precision_at_100
value: 1.7399999999999998
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 43.232
- type: precision_at_5
value: 28.165000000000003
- type: recall_at_1
value: 40.324
- type: recall_at_10
value: 75.517
- type: recall_at_100
value: 86.982
- type: recall_at_1000
value: 94.072
- type: recall_at_3
value: 64.848
- type: recall_at_5
value: 70.41199999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.4
- type: ap
value: 87.4422032289312
- type: f1
value: 91.39249564302281
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 22.03
- type: map_at_10
value: 34.402
- type: map_at_100
value: 35.599
- type: map_at_1000
value: 35.648
- type: map_at_3
value: 30.603
- type: map_at_5
value: 32.889
- type: mrr_at_1
value: 22.679
- type: mrr_at_10
value: 35.021
- type: mrr_at_100
value: 36.162
- type: mrr_at_1000
value: 36.205
- type: mrr_at_3
value: 31.319999999999997
- type: mrr_at_5
value: 33.562
- type: ndcg_at_1
value: 22.692999999999998
- type: ndcg_at_10
value: 41.258
- type: ndcg_at_100
value: 46.967
- type: ndcg_at_1000
value: 48.175000000000004
- type: ndcg_at_3
value: 33.611000000000004
- type: ndcg_at_5
value: 37.675
- type: precision_at_1
value: 22.692999999999998
- type: precision_at_10
value: 6.5089999999999995
- type: precision_at_100
value: 0.936
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.413
- type: precision_at_5
value: 10.702
- type: recall_at_1
value: 22.03
- type: recall_at_10
value: 62.248000000000005
- type: recall_at_100
value: 88.524
- type: recall_at_1000
value: 97.714
- type: recall_at_3
value: 41.617
- type: recall_at_5
value: 51.359
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.36844505243957
- type: f1
value: 94.12408743818202
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.43410852713177
- type: f1
value: 58.501855709435624
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 76.04909213180902
- type: f1
value: 74.1800860395823
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.76126429051781
- type: f1
value: 79.85705217473232
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.70119520292863
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.33544316467486
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 30.75499243990726
- type: mrr
value: 31.70602251821063
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.451999999999999
- type: map_at_10
value: 13.918
- type: map_at_100
value: 17.316000000000003
- type: map_at_1000
value: 18.747
- type: map_at_3
value: 10.471
- type: map_at_5
value: 12.104
- type: mrr_at_1
value: 46.749
- type: mrr_at_10
value: 55.717000000000006
- type: mrr_at_100
value: 56.249
- type: mrr_at_1000
value: 56.288000000000004
- type: mrr_at_3
value: 53.818
- type: mrr_at_5
value: 55.103
- type: ndcg_at_1
value: 45.201
- type: ndcg_at_10
value: 35.539
- type: ndcg_at_100
value: 32.586
- type: ndcg_at_1000
value: 41.486000000000004
- type: ndcg_at_3
value: 41.174
- type: ndcg_at_5
value: 38.939
- type: precision_at_1
value: 46.749
- type: precision_at_10
value: 25.944
- type: precision_at_100
value: 8.084
- type: precision_at_1000
value: 2.076
- type: precision_at_3
value: 38.7
- type: precision_at_5
value: 33.56
- type: recall_at_1
value: 6.451999999999999
- type: recall_at_10
value: 17.302
- type: recall_at_100
value: 32.14
- type: recall_at_1000
value: 64.12
- type: recall_at_3
value: 11.219
- type: recall_at_5
value: 13.993
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.037
- type: map_at_10
value: 46.565
- type: map_at_100
value: 47.606
- type: map_at_1000
value: 47.636
- type: map_at_3
value: 42.459
- type: map_at_5
value: 44.762
- type: mrr_at_1
value: 36.181999999999995
- type: mrr_at_10
value: 49.291000000000004
- type: mrr_at_100
value: 50.059
- type: mrr_at_1000
value: 50.078
- type: mrr_at_3
value: 45.829
- type: mrr_at_5
value: 47.797
- type: ndcg_at_1
value: 36.153
- type: ndcg_at_10
value: 53.983000000000004
- type: ndcg_at_100
value: 58.347
- type: ndcg_at_1000
value: 59.058
- type: ndcg_at_3
value: 46.198
- type: ndcg_at_5
value: 50.022
- type: precision_at_1
value: 36.153
- type: precision_at_10
value: 8.763
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 20.751
- type: precision_at_5
value: 14.646999999999998
- type: recall_at_1
value: 32.037
- type: recall_at_10
value: 74.008
- type: recall_at_100
value: 92.893
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 53.705999999999996
- type: recall_at_5
value: 62.495
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.152
- type: map_at_10
value: 85.104
- type: map_at_100
value: 85.745
- type: map_at_1000
value: 85.761
- type: map_at_3
value: 82.175
- type: map_at_5
value: 84.066
- type: mrr_at_1
value: 82.03
- type: mrr_at_10
value: 88.115
- type: mrr_at_100
value: 88.21
- type: mrr_at_1000
value: 88.211
- type: mrr_at_3
value: 87.19200000000001
- type: mrr_at_5
value: 87.85
- type: ndcg_at_1
value: 82.03
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.96300000000001
- type: ndcg_at_1000
value: 90.056
- type: ndcg_at_3
value: 86.051
- type: ndcg_at_5
value: 87.63499999999999
- type: precision_at_1
value: 82.03
- type: precision_at_10
value: 13.450000000000001
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.627
- type: precision_at_5
value: 24.784
- type: recall_at_1
value: 71.152
- type: recall_at_10
value: 95.649
- type: recall_at_100
value: 99.58200000000001
- type: recall_at_1000
value: 99.981
- type: recall_at_3
value: 87.767
- type: recall_at_5
value: 92.233
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 56.48713646277477
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 63.394940772438545
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.043
- type: map_at_10
value: 12.949
- type: map_at_100
value: 15.146
- type: map_at_1000
value: 15.495000000000001
- type: map_at_3
value: 9.333
- type: map_at_5
value: 11.312999999999999
- type: mrr_at_1
value: 24.9
- type: mrr_at_10
value: 35.958
- type: mrr_at_100
value: 37.152
- type: mrr_at_1000
value: 37.201
- type: mrr_at_3
value: 32.667
- type: mrr_at_5
value: 34.567
- type: ndcg_at_1
value: 24.9
- type: ndcg_at_10
value: 21.298000000000002
- type: ndcg_at_100
value: 29.849999999999998
- type: ndcg_at_1000
value: 35.506
- type: ndcg_at_3
value: 20.548
- type: ndcg_at_5
value: 18.064
- type: precision_at_1
value: 24.9
- type: precision_at_10
value: 10.9
- type: precision_at_100
value: 2.331
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 19.267
- type: precision_at_5
value: 15.939999999999998
- type: recall_at_1
value: 5.043
- type: recall_at_10
value: 22.092
- type: recall_at_100
value: 47.323
- type: recall_at_1000
value: 74.553
- type: recall_at_3
value: 11.728
- type: recall_at_5
value: 16.188
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.7007085938325
- type: cos_sim_spearman
value: 80.0171084446234
- type: euclidean_pearson
value: 81.28133218355893
- type: euclidean_spearman
value: 79.99291731740131
- type: manhattan_pearson
value: 81.22926922327846
- type: manhattan_spearman
value: 79.94444878127038
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.7411883252923
- type: cos_sim_spearman
value: 77.93462937801245
- type: euclidean_pearson
value: 83.00858563882404
- type: euclidean_spearman
value: 77.82717362433257
- type: manhattan_pearson
value: 82.92887645790769
- type: manhattan_spearman
value: 77.78807488222115
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.04222459361023
- type: cos_sim_spearman
value: 83.85931509330395
- type: euclidean_pearson
value: 83.26916063876055
- type: euclidean_spearman
value: 83.98621985648353
- type: manhattan_pearson
value: 83.14935679184327
- type: manhattan_spearman
value: 83.87938828586304
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 81.41136639535318
- type: cos_sim_spearman
value: 81.51200091040481
- type: euclidean_pearson
value: 81.45382456114775
- type: euclidean_spearman
value: 81.46201181707931
- type: manhattan_pearson
value: 81.37243088439584
- type: manhattan_spearman
value: 81.39828421893426
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 85.71942451732227
- type: cos_sim_spearman
value: 87.33044482064973
- type: euclidean_pearson
value: 86.58580899365178
- type: euclidean_spearman
value: 87.09206723832895
- type: manhattan_pearson
value: 86.47460784157013
- type: manhattan_spearman
value: 86.98367656583076
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.55868078863449
- type: cos_sim_spearman
value: 85.38299230074065
- type: euclidean_pearson
value: 84.64715256244595
- type: euclidean_spearman
value: 85.49112229604047
- type: manhattan_pearson
value: 84.60814346792462
- type: manhattan_spearman
value: 85.44886026766822
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 84.99292526370614
- type: cos_sim_spearman
value: 85.58139465695983
- type: euclidean_pearson
value: 86.51325066734084
- type: euclidean_spearman
value: 85.56736418284562
- type: manhattan_pearson
value: 86.48190836601357
- type: manhattan_spearman
value: 85.51616256224258
- 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: 64.54124715078807
- type: cos_sim_spearman
value: 65.32134275948374
- type: euclidean_pearson
value: 67.09791698300816
- type: euclidean_spearman
value: 65.79468982468465
- type: manhattan_pearson
value: 67.13304723693966
- type: manhattan_spearman
value: 65.68439995849283
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.4231099581624
- type: cos_sim_spearman
value: 85.95475815226862
- type: euclidean_pearson
value: 85.00339401999706
- type: euclidean_spearman
value: 85.74133081802971
- type: manhattan_pearson
value: 85.00407987181666
- type: manhattan_spearman
value: 85.77509596397363
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.25666719585716
- type: mrr
value: 96.32769917083642
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.828
- type: map_at_10
value: 68.369
- type: map_at_100
value: 68.83399999999999
- type: map_at_1000
value: 68.856
- type: map_at_3
value: 65.38000000000001
- type: map_at_5
value: 67.06299999999999
- type: mrr_at_1
value: 61
- type: mrr_at_10
value: 69.45400000000001
- type: mrr_at_100
value: 69.785
- type: mrr_at_1000
value: 69.807
- type: mrr_at_3
value: 67
- type: mrr_at_5
value: 68.43299999999999
- type: ndcg_at_1
value: 61
- type: ndcg_at_10
value: 73.258
- type: ndcg_at_100
value: 75.173
- type: ndcg_at_1000
value: 75.696
- type: ndcg_at_3
value: 68.162
- type: ndcg_at_5
value: 70.53399999999999
- type: precision_at_1
value: 61
- type: precision_at_10
value: 9.8
- type: precision_at_100
value: 1.087
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27
- type: precision_at_5
value: 17.666999999999998
- type: recall_at_1
value: 57.828
- type: recall_at_10
value: 87.122
- type: recall_at_100
value: 95.667
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 73.139
- type: recall_at_5
value: 79.361
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85247524752475
- type: cos_sim_ap
value: 96.25640197639723
- type: cos_sim_f1
value: 92.37851662404091
- type: cos_sim_precision
value: 94.55497382198953
- type: cos_sim_recall
value: 90.3
- type: dot_accuracy
value: 99.76138613861386
- type: dot_ap
value: 93.40295864389073
- type: dot_f1
value: 87.64267990074441
- type: dot_precision
value: 86.99507389162562
- type: dot_recall
value: 88.3
- type: euclidean_accuracy
value: 99.85049504950496
- type: euclidean_ap
value: 96.24254350525462
- type: euclidean_f1
value: 92.32323232323232
- type: euclidean_precision
value: 93.26530612244898
- type: euclidean_recall
value: 91.4
- type: manhattan_accuracy
value: 99.85346534653465
- type: manhattan_ap
value: 96.2635334753325
- type: manhattan_f1
value: 92.37899073120495
- type: manhattan_precision
value: 95.22292993630573
- type: manhattan_recall
value: 89.7
- type: max_accuracy
value: 99.85346534653465
- type: max_ap
value: 96.2635334753325
- type: max_f1
value: 92.37899073120495
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 65.83905786483794
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.031896152126436
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.551326709447146
- type: mrr
value: 55.43758222986165
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.305688567308874
- type: cos_sim_spearman
value: 29.27135743434515
- type: dot_pearson
value: 30.336741878796563
- type: dot_spearman
value: 30.513365725895937
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 1.92
- type: map_at_100
value: 10.519
- type: map_at_1000
value: 23.874000000000002
- type: map_at_3
value: 0.629
- type: map_at_5
value: 1.0290000000000001
- type: mrr_at_1
value: 88
- type: mrr_at_10
value: 93.5
- type: mrr_at_100
value: 93.5
- type: mrr_at_1000
value: 93.5
- type: mrr_at_3
value: 93
- type: mrr_at_5
value: 93.5
- type: ndcg_at_1
value: 84
- type: ndcg_at_10
value: 76.447
- type: ndcg_at_100
value: 56.516
- type: ndcg_at_1000
value: 48.583999999999996
- type: ndcg_at_3
value: 78.877
- type: ndcg_at_5
value: 79.174
- type: precision_at_1
value: 88
- type: precision_at_10
value: 80.60000000000001
- type: precision_at_100
value: 57.64
- type: precision_at_1000
value: 21.227999999999998
- type: precision_at_3
value: 82
- type: precision_at_5
value: 83.6
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.128
- type: recall_at_100
value: 13.767
- type: recall_at_1000
value: 44.958
- type: recall_at_3
value: 0.654
- type: recall_at_5
value: 1.111
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.5170000000000003
- type: map_at_10
value: 10.915
- type: map_at_100
value: 17.535
- type: map_at_1000
value: 19.042
- type: map_at_3
value: 5.689
- type: map_at_5
value: 7.837
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 49.547999999999995
- type: mrr_at_100
value: 50.653000000000006
- type: mrr_at_1000
value: 50.653000000000006
- type: mrr_at_3
value: 44.558
- type: mrr_at_5
value: 48.333
- type: ndcg_at_1
value: 32.653
- type: ndcg_at_10
value: 26.543
- type: ndcg_at_100
value: 38.946
- type: ndcg_at_1000
value: 49.406
- type: ndcg_at_3
value: 29.903000000000002
- type: ndcg_at_5
value: 29.231
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.265
- type: precision_at_100
value: 8.102
- type: precision_at_1000
value: 1.5
- type: precision_at_3
value: 31.293
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.5170000000000003
- type: recall_at_10
value: 16.88
- type: recall_at_100
value: 49.381
- type: recall_at_1000
value: 81.23899999999999
- type: recall_at_3
value: 6.965000000000001
- type: recall_at_5
value: 10.847999999999999
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.5942
- type: ap
value: 13.92074156956546
- type: f1
value: 54.671999698839066
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.39728353140916
- type: f1
value: 59.68980496759517
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.11181870104935
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 86.46957143708649
- type: cos_sim_ap
value: 76.16120197845457
- type: cos_sim_f1
value: 69.69919295671315
- type: cos_sim_precision
value: 64.94986326344576
- type: cos_sim_recall
value: 75.19788918205805
- type: dot_accuracy
value: 83.0780234845324
- type: dot_ap
value: 64.21717343541934
- type: dot_f1
value: 59.48375497624245
- type: dot_precision
value: 57.94345759319489
- type: dot_recall
value: 61.108179419525065
- type: euclidean_accuracy
value: 86.6543482148179
- type: euclidean_ap
value: 76.4527555010203
- type: euclidean_f1
value: 70.10156056477584
- type: euclidean_precision
value: 66.05975723622782
- type: euclidean_recall
value: 74.67018469656992
- type: manhattan_accuracy
value: 86.66030875603504
- type: manhattan_ap
value: 76.40304567255436
- type: manhattan_f1
value: 70.05275426328058
- type: manhattan_precision
value: 65.4666360926393
- type: manhattan_recall
value: 75.32981530343008
- type: max_accuracy
value: 86.66030875603504
- type: max_ap
value: 76.4527555010203
- type: max_f1
value: 70.10156056477584
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.42123646524624
- type: cos_sim_ap
value: 85.15431437761646
- type: cos_sim_f1
value: 76.98069301530742
- type: cos_sim_precision
value: 72.9314502239063
- type: cos_sim_recall
value: 81.50600554357868
- type: dot_accuracy
value: 86.70974502270346
- type: dot_ap
value: 80.77621563599457
- type: dot_f1
value: 73.87058697285117
- type: dot_precision
value: 68.98256396552877
- type: dot_recall
value: 79.50415768401602
- type: euclidean_accuracy
value: 88.46392672798541
- type: euclidean_ap
value: 85.20370297495491
- type: euclidean_f1
value: 77.01372369624886
- type: euclidean_precision
value: 73.39052800446397
- type: euclidean_recall
value: 81.01324299353249
- type: manhattan_accuracy
value: 88.43481973066325
- type: manhattan_ap
value: 85.16318289864545
- type: manhattan_f1
value: 76.90884877182597
- type: manhattan_precision
value: 74.01737396753062
- type: manhattan_recall
value: 80.03541730828458
- type: max_accuracy
value: 88.46392672798541
- type: max_ap
value: 85.20370297495491
- type: max_f1
value: 77.01372369624886
license: mit
language:
- en
---
**Recommend switching to newest [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5), which has more reasonable similarity distribution and same method of usage.**
<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>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search.
And it also can be used in vector databases for LLMs.
************* 🌟**Updates**🌟 *************
- 09/15/2023: Release [paper](https://arxiv.org/pdf/2309.07597.pdf) and [dataset](https://data.baai.ac.cn/details/BAAI-MTP).
- 09/12/2023: New Release:
- **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.
- 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.
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval\* |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [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 \** | |
| [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 \** | |
| [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：` |
\*: 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.
\**: 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.
## 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 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 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 pelease 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([email protected]) and Zheng Liu([email protected]).
## Citation
If you find our work helpful, please cite us:
```
@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.