Hugging Face's logo

thtang
/
ALL_862873

Feature Extraction
Transformers
PyTorch
bert
mteb
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
ALL_862873 / README.md
thtang's picture
thtang
Update README.md
ee45003
preview code
|
raw
history blame contribute delete
108 kB
---
tags:
- mteb
model-index:
- name: ALL_862873
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 50.805970149253746
- type: ap
value: 21.350961103104364
- type: f1
value: 46.546166439875044
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 52.567125000000004
- type: ap
value: 51.37893936391345
- type: f1
value: 51.8411977908125
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.63
- type: f1
value: 21.964526516204575
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.991
- type: map_at_10
value: 4.095
- type: map_at_100
value: 4.763
- type: map_at_1000
value: 4.8759999999999994
- type: map_at_3
value: 3.3070000000000004
- type: map_at_5
value: 3.73
- type: mrr_at_1
value: 2.0629999999999997
- type: mrr_at_10
value: 4.119
- type: mrr_at_100
value: 4.787
- type: mrr_at_1000
value: 4.9
- type: mrr_at_3
value: 3.331
- type: mrr_at_5
value: 3.768
- type: ndcg_at_1
value: 1.991
- type: ndcg_at_10
value: 5.346
- type: ndcg_at_100
value: 9.181000000000001
- type: ndcg_at_1000
value: 13.004
- type: ndcg_at_3
value: 3.7199999999999998
- type: ndcg_at_5
value: 4.482
- type: precision_at_1
value: 1.991
- type: precision_at_10
value: 0.9390000000000001
- type: precision_at_100
value: 0.28700000000000003
- type: precision_at_1000
value: 0.061
- type: precision_at_3
value: 1.636
- type: precision_at_5
value: 1.351
- type: recall_at_1
value: 1.991
- type: recall_at_10
value: 9.388
- type: recall_at_100
value: 28.663
- type: recall_at_1000
value: 60.597
- type: recall_at_3
value: 4.9079999999999995
- type: recall_at_5
value: 6.757000000000001
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 14.790995349964428
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 12.248406292959412
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 44.88116875696166
- type: mrr
value: 56.07439651760981
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 19.26573437410263
- type: cos_sim_spearman
value: 21.34145013484056
- type: euclidean_pearson
value: 22.39226418475093
- type: euclidean_spearman
value: 23.511981519581447
- type: manhattan_pearson
value: 22.14346931904813
- type: manhattan_spearman
value: 23.39390654000631
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 36.42857142857143
- type: f1
value: 34.81640976406094
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 13.94296328377691
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 9.790764523161606
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.968
- type: map_at_10
value: 2.106
- type: map_at_100
value: 2.411
- type: map_at_1000
value: 2.4899999999999998
- type: map_at_3
value: 1.797
- type: map_at_5
value: 1.9959999999999998
- type: mrr_at_1
value: 1.717
- type: mrr_at_10
value: 3.0349999999999997
- type: mrr_at_100
value: 3.4029999999999996
- type: mrr_at_1000
value: 3.486
- type: mrr_at_3
value: 2.6470000000000002
- type: mrr_at_5
value: 2.876
- type: ndcg_at_1
value: 1.717
- type: ndcg_at_10
value: 2.9059999999999997
- type: ndcg_at_100
value: 4.715
- type: ndcg_at_1000
value: 7.318
- type: ndcg_at_3
value: 2.415
- type: ndcg_at_5
value: 2.682
- type: precision_at_1
value: 1.717
- type: precision_at_10
value: 0.658
- type: precision_at_100
value: 0.197
- type: precision_at_1000
value: 0.054
- type: precision_at_3
value: 1.431
- type: precision_at_5
value: 1.059
- type: recall_at_1
value: 0.968
- type: recall_at_10
value: 4.531000000000001
- type: recall_at_100
value: 13.081000000000001
- type: recall_at_1000
value: 32.443
- type: recall_at_3
value: 2.8850000000000002
- type: recall_at_5
value: 3.768
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.9390000000000001
- type: map_at_10
value: 1.516
- type: map_at_100
value: 1.6680000000000001
- type: map_at_1000
value: 1.701
- type: map_at_3
value: 1.314
- type: map_at_5
value: 1.388
- type: mrr_at_1
value: 1.146
- type: mrr_at_10
value: 1.96
- type: mrr_at_100
value: 2.166
- type: mrr_at_1000
value: 2.207
- type: mrr_at_3
value: 1.72
- type: mrr_at_5
value: 1.796
- type: ndcg_at_1
value: 1.146
- type: ndcg_at_10
value: 1.9769999999999999
- type: ndcg_at_100
value: 2.8400000000000003
- type: ndcg_at_1000
value: 4.035
- type: ndcg_at_3
value: 1.5859999999999999
- type: ndcg_at_5
value: 1.6709999999999998
- type: precision_at_1
value: 1.146
- type: precision_at_10
value: 0.43299999999999994
- type: precision_at_100
value: 0.11100000000000002
- type: precision_at_1000
value: 0.027999999999999997
- type: precision_at_3
value: 0.8699999999999999
- type: precision_at_5
value: 0.611
- type: recall_at_1
value: 0.9390000000000001
- type: recall_at_10
value: 2.949
- type: recall_at_100
value: 6.737
- type: recall_at_1000
value: 15.604999999999999
- type: recall_at_3
value: 1.846
- type: recall_at_5
value: 2.08
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.28
- type: map_at_10
value: 2.157
- type: map_at_100
value: 2.401
- type: map_at_1000
value: 2.4570000000000003
- type: map_at_3
value: 1.865
- type: map_at_5
value: 1.928
- type: mrr_at_1
value: 1.505
- type: mrr_at_10
value: 2.52
- type: mrr_at_100
value: 2.782
- type: mrr_at_1000
value: 2.8400000000000003
- type: mrr_at_3
value: 2.1839999999999997
- type: mrr_at_5
value: 2.2689999999999997
- type: ndcg_at_1
value: 1.505
- type: ndcg_at_10
value: 2.798
- type: ndcg_at_100
value: 4.2090000000000005
- type: ndcg_at_1000
value: 6.105
- type: ndcg_at_3
value: 2.157
- type: ndcg_at_5
value: 2.258
- type: precision_at_1
value: 1.505
- type: precision_at_10
value: 0.5519999999999999
- type: precision_at_100
value: 0.146
- type: precision_at_1000
value: 0.034999999999999996
- type: precision_at_3
value: 1.024
- type: precision_at_5
value: 0.7020000000000001
- type: recall_at_1
value: 1.28
- type: recall_at_10
value: 4.455
- type: recall_at_100
value: 11.169
- type: recall_at_1000
value: 26.046000000000003
- type: recall_at_3
value: 2.6270000000000002
- type: recall_at_5
value: 2.899
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.264
- type: map_at_10
value: 0.615
- type: map_at_100
value: 0.76
- type: map_at_1000
value: 0.803
- type: map_at_3
value: 0.40499999999999997
- type: map_at_5
value: 0.512
- type: mrr_at_1
value: 0.33899999999999997
- type: mrr_at_10
value: 0.718
- type: mrr_at_100
value: 0.8880000000000001
- type: mrr_at_1000
value: 0.935
- type: mrr_at_3
value: 0.508
- type: mrr_at_5
value: 0.616
- type: ndcg_at_1
value: 0.33899999999999997
- type: ndcg_at_10
value: 0.9079999999999999
- type: ndcg_at_100
value: 1.9029999999999998
- type: ndcg_at_1000
value: 3.4939999999999998
- type: ndcg_at_3
value: 0.46499999999999997
- type: ndcg_at_5
value: 0.655
- type: precision_at_1
value: 0.33899999999999997
- type: precision_at_10
value: 0.192
- type: precision_at_100
value: 0.079
- type: precision_at_1000
value: 0.023
- type: precision_at_3
value: 0.22599999999999998
- type: precision_at_5
value: 0.22599999999999998
- type: recall_at_1
value: 0.264
- type: recall_at_10
value: 1.789
- type: recall_at_100
value: 6.927
- type: recall_at_1000
value: 19.922
- type: recall_at_3
value: 0.5459999999999999
- type: recall_at_5
value: 0.9979999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.5599999999999999
- type: map_at_10
value: 0.9129999999999999
- type: map_at_100
value: 1.027
- type: map_at_1000
value: 1.072
- type: map_at_3
value: 0.715
- type: map_at_5
value: 0.826
- type: mrr_at_1
value: 0.8710000000000001
- type: mrr_at_10
value: 1.331
- type: mrr_at_100
value: 1.494
- type: mrr_at_1000
value: 1.547
- type: mrr_at_3
value: 1.119
- type: mrr_at_5
value: 1.269
- type: ndcg_at_1
value: 0.8710000000000001
- type: ndcg_at_10
value: 1.2590000000000001
- type: ndcg_at_100
value: 2.023
- type: ndcg_at_1000
value: 3.737
- type: ndcg_at_3
value: 0.8750000000000001
- type: ndcg_at_5
value: 1.079
- type: precision_at_1
value: 0.8710000000000001
- type: precision_at_10
value: 0.28600000000000003
- type: precision_at_100
value: 0.086
- type: precision_at_1000
value: 0.027999999999999997
- type: precision_at_3
value: 0.498
- type: precision_at_5
value: 0.42300000000000004
- type: recall_at_1
value: 0.5599999999999999
- type: recall_at_10
value: 1.907
- type: recall_at_100
value: 5.492
- type: recall_at_1000
value: 18.974
- type: recall_at_3
value: 0.943
- type: recall_at_5
value: 1.41
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.9720000000000002
- type: map_at_10
value: 2.968
- type: map_at_100
value: 3.2009999999999996
- type: map_at_1000
value: 3.2680000000000002
- type: map_at_3
value: 2.683
- type: map_at_5
value: 2.8369999999999997
- type: mrr_at_1
value: 2.406
- type: mrr_at_10
value: 3.567
- type: mrr_at_100
value: 3.884
- type: mrr_at_1000
value: 3.948
- type: mrr_at_3
value: 3.2239999999999998
- type: mrr_at_5
value: 3.383
- type: ndcg_at_1
value: 2.406
- type: ndcg_at_10
value: 3.63
- type: ndcg_at_100
value: 5.155
- type: ndcg_at_1000
value: 7.381
- type: ndcg_at_3
value: 3.078
- type: ndcg_at_5
value: 3.3070000000000004
- type: precision_at_1
value: 2.406
- type: precision_at_10
value: 0.635
- type: precision_at_100
value: 0.184
- type: precision_at_1000
value: 0.048
- type: precision_at_3
value: 1.4120000000000001
- type: precision_at_5
value: 1.001
- type: recall_at_1
value: 1.9720000000000002
- type: recall_at_10
value: 5.152
- type: recall_at_100
value: 12.173
- type: recall_at_1000
value: 28.811999999999998
- type: recall_at_3
value: 3.556
- type: recall_at_5
value: 4.181
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.346
- type: map_at_10
value: 0.619
- type: map_at_100
value: 0.743
- type: map_at_1000
value: 0.788
- type: map_at_3
value: 0.5369999999999999
- type: map_at_5
value: 0.551
- type: mrr_at_1
value: 0.571
- type: mrr_at_10
value: 1.0619999999999998
- type: mrr_at_100
value: 1.2109999999999999
- type: mrr_at_1000
value: 1.265
- type: mrr_at_3
value: 0.818
- type: mrr_at_5
value: 0.927
- type: ndcg_at_1
value: 0.571
- type: ndcg_at_10
value: 0.919
- type: ndcg_at_100
value: 1.688
- type: ndcg_at_1000
value: 3.3649999999999998
- type: ndcg_at_3
value: 0.6779999999999999
- type: ndcg_at_5
value: 0.7230000000000001
- type: precision_at_1
value: 0.571
- type: precision_at_10
value: 0.27399999999999997
- type: precision_at_100
value: 0.084
- type: precision_at_1000
value: 0.029
- type: precision_at_3
value: 0.381
- type: precision_at_5
value: 0.32
- type: recall_at_1
value: 0.346
- type: recall_at_10
value: 1.397
- type: recall_at_100
value: 5.079000000000001
- type: recall_at_1000
value: 18.060000000000002
- type: recall_at_3
value: 0.774
- type: recall_at_5
value: 0.8340000000000001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.69
- type: map_at_10
value: 0.897
- type: map_at_100
value: 1.0030000000000001
- type: map_at_1000
value: 1.034
- type: map_at_3
value: 0.818
- type: map_at_5
value: 0.864
- type: mrr_at_1
value: 0.767
- type: mrr_at_10
value: 1.008
- type: mrr_at_100
value: 1.145
- type: mrr_at_1000
value: 1.183
- type: mrr_at_3
value: 0.895
- type: mrr_at_5
value: 0.9560000000000001
- type: ndcg_at_1
value: 0.767
- type: ndcg_at_10
value: 1.0739999999999998
- type: ndcg_at_100
value: 1.757
- type: ndcg_at_1000
value: 2.9090000000000003
- type: ndcg_at_3
value: 0.881
- type: ndcg_at_5
value: 0.9769999999999999
- type: precision_at_1
value: 0.767
- type: precision_at_10
value: 0.184
- type: precision_at_100
value: 0.06
- type: precision_at_1000
value: 0.018000000000000002
- type: precision_at_3
value: 0.358
- type: precision_at_5
value: 0.27599999999999997
- type: recall_at_1
value: 0.69
- type: recall_at_10
value: 1.508
- type: recall_at_100
value: 4.858
- type: recall_at_1000
value: 14.007
- type: recall_at_3
value: 0.997
- type: recall_at_5
value: 1.2269999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.338
- type: map_at_10
value: 0.661
- type: map_at_100
value: 0.7969999999999999
- type: map_at_1000
value: 0.8290000000000001
- type: map_at_3
value: 0.5559999999999999
- type: map_at_5
value: 0.5910000000000001
- type: mrr_at_1
value: 0.482
- type: mrr_at_10
value: 0.88
- type: mrr_at_100
value: 1.036
- type: mrr_at_1000
value: 1.075
- type: mrr_at_3
value: 0.74
- type: mrr_at_5
value: 0.779
- type: ndcg_at_1
value: 0.482
- type: ndcg_at_10
value: 0.924
- type: ndcg_at_100
value: 1.736
- type: ndcg_at_1000
value: 2.926
- type: ndcg_at_3
value: 0.677
- type: ndcg_at_5
value: 0.732
- type: precision_at_1
value: 0.482
- type: precision_at_10
value: 0.20600000000000002
- type: precision_at_100
value: 0.078
- type: precision_at_1000
value: 0.023
- type: precision_at_3
value: 0.367
- type: precision_at_5
value: 0.255
- type: recall_at_1
value: 0.338
- type: recall_at_10
value: 1.545
- type: recall_at_100
value: 5.38
- type: recall_at_1000
value: 14.609
- type: recall_at_3
value: 0.826
- type: recall_at_5
value: 0.975
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.8240000000000001
- type: map_at_10
value: 1.254
- type: map_at_100
value: 1.389
- type: map_at_1000
value: 1.419
- type: map_at_3
value: 1.158
- type: map_at_5
value: 1.189
- type: mrr_at_1
value: 0.9329999999999999
- type: mrr_at_10
value: 1.4200000000000002
- type: mrr_at_100
value: 1.59
- type: mrr_at_1000
value: 1.629
- type: mrr_at_3
value: 1.29
- type: mrr_at_5
value: 1.332
- type: ndcg_at_1
value: 0.9329999999999999
- type: ndcg_at_10
value: 1.53
- type: ndcg_at_100
value: 2.418
- type: ndcg_at_1000
value: 3.7310000000000003
- type: ndcg_at_3
value: 1.302
- type: ndcg_at_5
value: 1.363
- type: precision_at_1
value: 0.9329999999999999
- type: precision_at_10
value: 0.271
- type: precision_at_100
value: 0.083
- type: precision_at_1000
value: 0.024
- type: precision_at_3
value: 0.622
- type: precision_at_5
value: 0.41000000000000003
- type: recall_at_1
value: 0.8240000000000001
- type: recall_at_10
value: 2.1999999999999997
- type: recall_at_100
value: 6.584
- type: recall_at_1000
value: 17.068
- type: recall_at_3
value: 1.5859999999999999
- type: recall_at_5
value: 1.7260000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.404
- type: map_at_10
value: 0.788
- type: map_at_100
value: 0.9860000000000001
- type: map_at_1000
value: 1.04
- type: map_at_3
value: 0.676
- type: map_at_5
value: 0.733
- type: mrr_at_1
value: 0.5930000000000001
- type: mrr_at_10
value: 1.278
- type: mrr_at_100
value: 1.545
- type: mrr_at_1000
value: 1.599
- type: mrr_at_3
value: 1.054
- type: mrr_at_5
value: 1.192
- type: ndcg_at_1
value: 0.5930000000000001
- type: ndcg_at_10
value: 1.1280000000000001
- type: ndcg_at_100
value: 2.2689999999999997
- type: ndcg_at_1000
value: 4.274
- type: ndcg_at_3
value: 0.919
- type: ndcg_at_5
value: 1.038
- type: precision_at_1
value: 0.5930000000000001
- type: precision_at_10
value: 0.296
- type: precision_at_100
value: 0.152
- type: precision_at_1000
value: 0.05
- type: precision_at_3
value: 0.527
- type: precision_at_5
value: 0.47400000000000003
- type: recall_at_1
value: 0.404
- type: recall_at_10
value: 1.601
- type: recall_at_100
value: 6.885
- type: recall_at_1000
value: 22.356
- type: recall_at_3
value: 0.9490000000000001
- type: recall_at_5
value: 1.206
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.185
- type: map_at_10
value: 0.192
- type: map_at_100
value: 0.271
- type: map_at_1000
value: 0.307
- type: map_at_3
value: 0.185
- type: map_at_5
value: 0.185
- type: mrr_at_1
value: 0.185
- type: mrr_at_10
value: 0.20500000000000002
- type: mrr_at_100
value: 0.292
- type: mrr_at_1000
value: 0.331
- type: mrr_at_3
value: 0.185
- type: mrr_at_5
value: 0.185
- type: ndcg_at_1
value: 0.185
- type: ndcg_at_10
value: 0.211
- type: ndcg_at_100
value: 0.757
- type: ndcg_at_1000
value: 1.928
- type: ndcg_at_3
value: 0.185
- type: ndcg_at_5
value: 0.185
- type: precision_at_1
value: 0.185
- type: precision_at_10
value: 0.037
- type: precision_at_100
value: 0.039
- type: precision_at_1000
value: 0.015
- type: precision_at_3
value: 0.062
- type: precision_at_5
value: 0.037
- type: recall_at_1
value: 0.185
- type: recall_at_10
value: 0.246
- type: recall_at_100
value: 3.05
- type: recall_at_1000
value: 12.5
- type: recall_at_3
value: 0.185
- type: recall_at_5
value: 0.185
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.241
- type: map_at_10
value: 0.372
- type: map_at_100
value: 0.45999999999999996
- type: map_at_1000
value: 0.47600000000000003
- type: map_at_3
value: 0.33999999999999997
- type: map_at_5
value: 0.359
- type: mrr_at_1
value: 0.651
- type: mrr_at_10
value: 1.03
- type: mrr_at_100
value: 1.2489999999999999
- type: mrr_at_1000
value: 1.282
- type: mrr_at_3
value: 0.9450000000000001
- type: mrr_at_5
value: 1.0030000000000001
- type: ndcg_at_1
value: 0.651
- type: ndcg_at_10
value: 0.588
- type: ndcg_at_100
value: 1.2550000000000001
- type: ndcg_at_1000
value: 1.9040000000000001
- type: ndcg_at_3
value: 0.547
- type: ndcg_at_5
value: 0.549
- type: precision_at_1
value: 0.651
- type: precision_at_10
value: 0.182
- type: precision_at_100
value: 0.086
- type: precision_at_1000
value: 0.02
- type: precision_at_3
value: 0.434
- type: precision_at_5
value: 0.313
- type: recall_at_1
value: 0.241
- type: recall_at_10
value: 0.63
- type: recall_at_100
value: 3.1759999999999997
- type: recall_at_1000
value: 7.175
- type: recall_at_3
value: 0.46299999999999997
- type: recall_at_5
value: 0.543
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.04
- type: map_at_10
value: 0.089
- type: map_at_100
value: 0.133
- type: map_at_1000
value: 0.165
- type: map_at_3
value: 0.054
- type: map_at_5
value: 0.056999999999999995
- type: mrr_at_1
value: 0.75
- type: mrr_at_10
value: 1.4749999999999999
- type: mrr_at_100
value: 1.8010000000000002
- type: mrr_at_1000
value: 1.847
- type: mrr_at_3
value: 1.208
- type: mrr_at_5
value: 1.333
- type: ndcg_at_1
value: 0.625
- type: ndcg_at_10
value: 0.428
- type: ndcg_at_100
value: 0.705
- type: ndcg_at_1000
value: 1.564
- type: ndcg_at_3
value: 0.5369999999999999
- type: ndcg_at_5
value: 0.468
- type: precision_at_1
value: 0.75
- type: precision_at_10
value: 0.375
- type: precision_at_100
value: 0.27499999999999997
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 0.583
- type: precision_at_5
value: 0.5
- type: recall_at_1
value: 0.04
- type: recall_at_10
value: 0.385
- type: recall_at_100
value: 1.2670000000000001
- type: recall_at_1000
value: 4.522
- type: recall_at_3
value: 0.07100000000000001
- type: recall_at_5
value: 0.08099999999999999
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 22.749999999999996
- type: f1
value: 19.335020165482693
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.257
- type: map_at_10
value: 0.416
- type: map_at_100
value: 0.451
- type: map_at_1000
value: 0.46499999999999997
- type: map_at_3
value: 0.37
- type: map_at_5
value: 0.386
- type: mrr_at_1
value: 0.27
- type: mrr_at_10
value: 0.44200000000000006
- type: mrr_at_100
value: 0.48
- type: mrr_at_1000
value: 0.49500000000000005
- type: mrr_at_3
value: 0.38999999999999996
- type: mrr_at_5
value: 0.411
- type: ndcg_at_1
value: 0.27
- type: ndcg_at_10
value: 0.51
- type: ndcg_at_100
value: 0.738
- type: ndcg_at_1000
value: 1.2630000000000001
- type: ndcg_at_3
value: 0.41000000000000003
- type: ndcg_at_5
value: 0.439
- type: precision_at_1
value: 0.27
- type: precision_at_10
value: 0.084
- type: precision_at_100
value: 0.021
- type: precision_at_1000
value: 0.006999999999999999
- type: precision_at_3
value: 0.17500000000000002
- type: precision_at_5
value: 0.123
- type: recall_at_1
value: 0.257
- type: recall_at_10
value: 0.786
- type: recall_at_100
value: 1.959
- type: recall_at_1000
value: 6.334
- type: recall_at_3
value: 0.49699999999999994
- type: recall_at_5
value: 0.5680000000000001
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.28900000000000003
- type: map_at_10
value: 0.475
- type: map_at_100
value: 0.559
- type: map_at_1000
value: 0.5930000000000001
- type: map_at_3
value: 0.38999999999999996
- type: map_at_5
value: 0.41700000000000004
- type: mrr_at_1
value: 0.772
- type: mrr_at_10
value: 1.107
- type: mrr_at_100
value: 1.269
- type: mrr_at_1000
value: 1.323
- type: mrr_at_3
value: 0.9520000000000001
- type: mrr_at_5
value: 1.0290000000000001
- type: ndcg_at_1
value: 0.772
- type: ndcg_at_10
value: 0.755
- type: ndcg_at_100
value: 1.256
- type: ndcg_at_1000
value: 2.55
- type: ndcg_at_3
value: 0.633
- type: ndcg_at_5
value: 0.639
- type: precision_at_1
value: 0.772
- type: precision_at_10
value: 0.262
- type: precision_at_100
value: 0.082
- type: precision_at_1000
value: 0.03
- type: precision_at_3
value: 0.46299999999999997
- type: precision_at_5
value: 0.33999999999999997
- type: recall_at_1
value: 0.28900000000000003
- type: recall_at_10
value: 0.976
- type: recall_at_100
value: 2.802
- type: recall_at_1000
value: 11.466
- type: recall_at_3
value: 0.54
- type: recall_at_5
value: 0.6479999999999999
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.257
- type: map_at_10
value: 0.395
- type: map_at_100
value: 0.436
- type: map_at_1000
value: 0.447
- type: map_at_3
value: 0.347
- type: map_at_5
value: 0.369
- type: mrr_at_1
value: 0.513
- type: mrr_at_10
value: 0.787
- type: mrr_at_100
value: 0.865
- type: mrr_at_1000
value: 0.8840000000000001
- type: mrr_at_3
value: 0.6930000000000001
- type: mrr_at_5
value: 0.738
- type: ndcg_at_1
value: 0.513
- type: ndcg_at_10
value: 0.587
- type: ndcg_at_100
value: 0.881
- type: ndcg_at_1000
value: 1.336
- type: ndcg_at_3
value: 0.46299999999999997
- type: ndcg_at_5
value: 0.511
- type: precision_at_1
value: 0.513
- type: precision_at_10
value: 0.151
- type: precision_at_100
value: 0.04
- type: precision_at_1000
value: 0.01
- type: precision_at_3
value: 0.311
- type: precision_at_5
value: 0.22399999999999998
- type: recall_at_1
value: 0.257
- type: recall_at_10
value: 0.756
- type: recall_at_100
value: 1.9849999999999999
- type: recall_at_1000
value: 5.111000000000001
- type: recall_at_3
value: 0.466
- type: recall_at_5
value: 0.5599999999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 50.76400000000001
- type: ap
value: 50.41569411130455
- type: f1
value: 50.14266303576945
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 0.14300000000000002
- type: map_at_10
value: 0.23700000000000002
- type: map_at_100
value: 0.27799999999999997
- type: map_at_1000
value: 0.291
- type: map_at_3
value: 0.197
- type: map_at_5
value: 0.215
- type: mrr_at_1
value: 0.14300000000000002
- type: mrr_at_10
value: 0.247
- type: mrr_at_100
value: 0.29
- type: mrr_at_1000
value: 0.303
- type: mrr_at_3
value: 0.201
- type: mrr_at_5
value: 0.219
- type: ndcg_at_1
value: 0.14300000000000002
- type: ndcg_at_10
value: 0.307
- type: ndcg_at_100
value: 0.5720000000000001
- type: ndcg_at_1000
value: 1.053
- type: ndcg_at_3
value: 0.215
- type: ndcg_at_5
value: 0.248
- type: precision_at_1
value: 0.14300000000000002
- type: precision_at_10
value: 0.056999999999999995
- type: precision_at_100
value: 0.02
- type: precision_at_1000
value: 0.006
- type: precision_at_3
value: 0.091
- type: precision_at_5
value: 0.07200000000000001
- type: recall_at_1
value: 0.14300000000000002
- type: recall_at_10
value: 0.522
- type: recall_at_100
value: 1.9009999999999998
- type: recall_at_1000
value: 5.893000000000001
- type: recall_at_3
value: 0.263
- type: recall_at_5
value: 0.34099999999999997
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 61.03283173734611
- type: f1
value: 61.24012492746259
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 29.68308253533972
- type: f1
value: 16.243459114946905
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 34.330867518493605
- type: f1
value: 33.176158044175935
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 44.13248150638871
- type: f1
value: 44.24904249078732
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 15.698400177259078
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 14.888797785310235
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 25.652445385382126
- type: mrr
value: 25.891573325600227
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.322
- type: map_at_10
value: 0.7230000000000001
- type: map_at_100
value: 1.248
- type: map_at_1000
value: 1.873
- type: map_at_3
value: 0.479
- type: map_at_5
value: 0.5700000000000001
- type: mrr_at_1
value: 6.502
- type: mrr_at_10
value: 10.735
- type: mrr_at_100
value: 11.848
- type: mrr_at_1000
value: 11.995000000000001
- type: mrr_at_3
value: 9.391
- type: mrr_at_5
value: 9.732000000000001
- type: ndcg_at_1
value: 6.037
- type: ndcg_at_10
value: 4.873
- type: ndcg_at_100
value: 5.959
- type: ndcg_at_1000
value: 14.424000000000001
- type: ndcg_at_3
value: 5.4559999999999995
- type: ndcg_at_5
value: 5.074
- type: precision_at_1
value: 6.192
- type: precision_at_10
value: 4.458
- type: precision_at_100
value: 2.5700000000000003
- type: precision_at_1000
value: 1.3679999999999999
- type: precision_at_3
value: 5.676
- type: precision_at_5
value: 4.954
- type: recall_at_1
value: 0.322
- type: recall_at_10
value: 1.545
- type: recall_at_100
value: 8.301
- type: recall_at_1000
value: 37.294
- type: recall_at_3
value: 0.623
- type: recall_at_5
value: 0.865
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.188
- type: map_at_10
value: 0.27
- type: map_at_100
value: 0.322
- type: map_at_1000
value: 0.335
- type: map_at_3
value: 0.246
- type: map_at_5
value: 0.246
- type: mrr_at_1
value: 0.203
- type: mrr_at_10
value: 0.28300000000000003
- type: mrr_at_100
value: 0.344
- type: mrr_at_1000
value: 0.357
- type: mrr_at_3
value: 0.261
- type: mrr_at_5
value: 0.261
- type: ndcg_at_1
value: 0.203
- type: ndcg_at_10
value: 0.329
- type: ndcg_at_100
value: 0.628
- type: ndcg_at_1000
value: 1.0959999999999999
- type: ndcg_at_3
value: 0.272
- type: ndcg_at_5
value: 0.272
- type: precision_at_1
value: 0.203
- type: precision_at_10
value: 0.055
- type: precision_at_100
value: 0.024
- type: precision_at_1000
value: 0.006999999999999999
- type: precision_at_3
value: 0.116
- type: precision_at_5
value: 0.06999999999999999
- type: recall_at_1
value: 0.188
- type: recall_at_10
value: 0.507
- type: recall_at_100
value: 1.883
- type: recall_at_1000
value: 5.609999999999999
- type: recall_at_3
value: 0.333
- type: recall_at_5
value: 0.333
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.016000000000002
- type: map_at_10
value: 28.977999999999998
- type: map_at_100
value: 29.579
- type: map_at_1000
value: 29.648999999999997
- type: map_at_3
value: 27.673
- type: map_at_5
value: 28.427000000000003
- type: mrr_at_1
value: 27.93
- type: mrr_at_10
value: 32.462999999999994
- type: mrr_at_100
value: 32.993
- type: mrr_at_1000
value: 33.044000000000004
- type: mrr_at_3
value: 31.252000000000002
- type: mrr_at_5
value: 31.968999999999998
- type: ndcg_at_1
value: 27.96
- type: ndcg_at_10
value: 31.954
- type: ndcg_at_100
value: 34.882000000000005
- type: ndcg_at_1000
value: 36.751
- type: ndcg_at_3
value: 29.767
- type: ndcg_at_5
value: 30.816
- type: precision_at_1
value: 27.96
- type: precision_at_10
value: 4.826
- type: precision_at_100
value: 0.697
- type: precision_at_1000
value: 0.093
- type: precision_at_3
value: 12.837000000000002
- type: precision_at_5
value: 8.559999999999999
- type: recall_at_1
value: 24.016000000000002
- type: recall_at_10
value: 37.574999999999996
- type: recall_at_100
value: 50.843
- type: recall_at_1000
value: 64.654
- type: recall_at_3
value: 31.182
- type: recall_at_5
value: 34.055
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 18.38048892083281
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 27.103011764141478
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.18
- type: map_at_10
value: 0.457
- type: map_at_100
value: 0.634
- type: map_at_1000
value: 0.7000000000000001
- type: map_at_3
value: 0.333
- type: map_at_5
value: 0.387
- type: mrr_at_1
value: 0.8999999999999999
- type: mrr_at_10
value: 1.967
- type: mrr_at_100
value: 2.396
- type: mrr_at_1000
value: 2.495
- type: mrr_at_3
value: 1.567
- type: mrr_at_5
value: 1.7670000000000001
- type: ndcg_at_1
value: 0.8999999999999999
- type: ndcg_at_10
value: 1.022
- type: ndcg_at_100
value: 2.366
- type: ndcg_at_1000
value: 4.689
- type: ndcg_at_3
value: 0.882
- type: ndcg_at_5
value: 0.7929999999999999
- type: precision_at_1
value: 0.8999999999999999
- type: precision_at_10
value: 0.58
- type: precision_at_100
value: 0.263
- type: precision_at_1000
value: 0.084
- type: precision_at_3
value: 0.8999999999999999
- type: precision_at_5
value: 0.74
- type: recall_at_1
value: 0.18
- type: recall_at_10
value: 1.208
- type: recall_at_100
value: 5.373
- type: recall_at_1000
value: 17.112
- type: recall_at_3
value: 0.5579999999999999
- type: recall_at_5
value: 0.7779999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 55.229896309578905
- type: cos_sim_spearman
value: 48.54616726085393
- type: euclidean_pearson
value: 53.828130644322
- type: euclidean_spearman
value: 48.2907441223958
- type: manhattan_pearson
value: 53.72684612327582
- type: manhattan_spearman
value: 48.228319721712744
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 57.73555535277214
- type: cos_sim_spearman
value: 55.58790083939622
- type: euclidean_pearson
value: 61.009463373795384
- type: euclidean_spearman
value: 56.696846101196044
- type: manhattan_pearson
value: 60.875111392597894
- type: manhattan_spearman
value: 56.63100766160946
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 19.47269635955134
- type: cos_sim_spearman
value: 18.35951746300603
- type: euclidean_pearson
value: 23.130707248318714
- type: euclidean_spearman
value: 22.92241668287248
- type: manhattan_pearson
value: 22.99371642148021
- type: manhattan_spearman
value: 22.770233678121897
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 31.78346805351368
- type: cos_sim_spearman
value: 28.84281669682782
- type: euclidean_pearson
value: 34.508176962091156
- type: euclidean_spearman
value: 32.269242265609975
- type: manhattan_pearson
value: 34.41366600914297
- type: manhattan_spearman
value: 32.15352239729175
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 29.550332218260465
- type: cos_sim_spearman
value: 29.188654452524528
- type: euclidean_pearson
value: 33.80339596511417
- type: euclidean_spearman
value: 33.49607278843874
- type: manhattan_pearson
value: 33.589427741967334
- type: manhattan_spearman
value: 33.288312003652884
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 27.163752699585885
- type: cos_sim_spearman
value: 39.0544187582685
- type: euclidean_pearson
value: 38.93841642732113
- type: euclidean_spearman
value: 42.861814968921294
- type: manhattan_pearson
value: 38.78821319739337
- type: manhattan_spearman
value: 42.757121435678954
- 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: 57.15429605615292
- type: cos_sim_spearman
value: 61.21576579300284
- type: euclidean_pearson
value: 59.2835939062064
- type: euclidean_spearman
value: 60.902713241808236
- type: manhattan_pearson
value: 59.510770285546364
- type: manhattan_spearman
value: 61.02979474159327
- 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: 41.81726547830133
- type: cos_sim_spearman
value: 44.45123398124273
- type: euclidean_pearson
value: 46.44144033159064
- type: euclidean_spearman
value: 46.61348337508052
- type: manhattan_pearson
value: 46.48092744041165
- type: manhattan_spearman
value: 46.78049599791891
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 46.085942179295465
- type: cos_sim_spearman
value: 44.394736992467365
- type: euclidean_pearson
value: 47.06981069147408
- type: euclidean_spearman
value: 45.40499474054004
- type: manhattan_pearson
value: 46.96497631950794
- type: manhattan_spearman
value: 45.31936619298336
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 43.89526517578129
- type: mrr
value: 64.30753070458954
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.417
- type: map_at_10
value: 2.189
- type: map_at_100
value: 2.5669999999999997
- type: map_at_1000
value: 2.662
- type: map_at_3
value: 1.694
- type: map_at_5
value: 1.928
- type: mrr_at_1
value: 1.667
- type: mrr_at_10
value: 2.4899999999999998
- type: mrr_at_100
value: 2.8400000000000003
- type: mrr_at_1000
value: 2.928
- type: mrr_at_3
value: 1.944
- type: mrr_at_5
value: 2.178
- type: ndcg_at_1
value: 1.667
- type: ndcg_at_10
value: 2.913
- type: ndcg_at_100
value: 5.482
- type: ndcg_at_1000
value: 8.731
- type: ndcg_at_3
value: 1.867
- type: ndcg_at_5
value: 2.257
- type: precision_at_1
value: 1.667
- type: precision_at_10
value: 0.567
- type: precision_at_100
value: 0.213
- type: precision_at_1000
value: 0.053
- type: precision_at_3
value: 0.7779999999999999
- type: precision_at_5
value: 0.6669999999999999
- type: recall_at_1
value: 1.417
- type: recall_at_10
value: 5.028
- type: recall_at_100
value: 18.5
- type: recall_at_1000
value: 45.072
- type: recall_at_3
value: 2.083
- type: recall_at_5
value: 3.083
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.02871287128713
- type: cos_sim_ap
value: 17.404404071912694
- type: cos_sim_f1
value: 25.89285714285714
- type: cos_sim_precision
value: 29.292929292929294
- type: cos_sim_recall
value: 23.200000000000003
- type: dot_accuracy
value: 99.0118811881188
- type: dot_ap
value: 5.4739000785007335
- type: dot_f1
value: 12.178702570379436
- type: dot_precision
value: 8.774250440917108
- type: dot_recall
value: 19.900000000000002
- type: euclidean_accuracy
value: 99.03663366336633
- type: euclidean_ap
value: 19.20851069839796
- type: euclidean_f1
value: 27.16555612506407
- type: euclidean_precision
value: 27.865404837013667
- type: euclidean_recall
value: 26.5
- type: manhattan_accuracy
value: 99.03663366336633
- type: manhattan_ap
value: 19.12862913626528
- type: manhattan_f1
value: 26.96629213483146
- type: manhattan_precision
value: 28.99884925201381
- type: manhattan_recall
value: 25.2
- type: max_accuracy
value: 99.03663366336633
- type: max_ap
value: 19.20851069839796
- type: max_f1
value: 27.16555612506407
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 23.657118721775905
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 27.343558395037043
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 23.346327148080043
- type: mrr
value: 21.99097063067651
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.032
- type: map_at_10
value: 0.157
- type: map_at_100
value: 0.583
- type: map_at_1000
value: 1.48
- type: map_at_3
value: 0.066
- type: map_at_5
value: 0.105
- type: mrr_at_1
value: 10
- type: mrr_at_10
value: 16.99
- type: mrr_at_100
value: 18.284
- type: mrr_at_1000
value: 18.394
- type: mrr_at_3
value: 14.000000000000002
- type: mrr_at_5
value: 15.8
- type: ndcg_at_1
value: 8
- type: ndcg_at_10
value: 7.504
- type: ndcg_at_100
value: 5.339
- type: ndcg_at_1000
value: 6.046
- type: ndcg_at_3
value: 8.358
- type: ndcg_at_5
value: 8.142000000000001
- type: precision_at_1
value: 10
- type: precision_at_10
value: 8.6
- type: precision_at_100
value: 5.9799999999999995
- type: precision_at_1000
value: 2.976
- type: precision_at_3
value: 9.333
- type: precision_at_5
value: 9.2
- type: recall_at_1
value: 0.032
- type: recall_at_10
value: 0.252
- type: recall_at_100
value: 1.529
- type: recall_at_1000
value: 6.364
- type: recall_at_3
value: 0.08499999999999999
- type: recall_at_5
value: 0.154
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.44200000000000006
- type: map_at_10
value: 0.996
- type: map_at_100
value: 1.317
- type: map_at_1000
value: 1.624
- type: map_at_3
value: 0.736
- type: map_at_5
value: 0.951
- type: mrr_at_1
value: 4.082
- type: mrr_at_10
value: 10.102
- type: mrr_at_100
value: 10.978
- type: mrr_at_1000
value: 11.1
- type: mrr_at_3
value: 7.8229999999999995
- type: mrr_at_5
value: 9.252
- type: ndcg_at_1
value: 4.082
- type: ndcg_at_10
value: 3.821
- type: ndcg_at_100
value: 5.682
- type: ndcg_at_1000
value: 10.96
- type: ndcg_at_3
value: 4.813
- type: ndcg_at_5
value: 4.757
- type: precision_at_1
value: 4.082
- type: precision_at_10
value: 3.061
- type: precision_at_100
value: 1.367
- type: precision_at_1000
value: 0.46299999999999997
- type: precision_at_3
value: 4.7620000000000005
- type: precision_at_5
value: 4.898000000000001
- type: recall_at_1
value: 0.44200000000000006
- type: recall_at_10
value: 2.059
- type: recall_at_100
value: 7.439
- type: recall_at_1000
value: 25.191000000000003
- type: recall_at_3
value: 1.095
- type: recall_at_5
value: 1.725
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 54.925999999999995
- type: ap
value: 9.658236434063275
- type: f1
value: 43.469829154993064
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 40.7498585172609
- type: f1
value: 40.720120106546574
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 20.165152514024733
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 77.59432556476128
- type: cos_sim_ap
value: 30.37846072188074
- type: cos_sim_f1
value: 37.9231242656521
- type: cos_sim_precision
value: 24.064474898814172
- type: cos_sim_recall
value: 89.41952506596306
- type: dot_accuracy
value: 77.42146986946415
- type: dot_ap
value: 24.073476661930034
- type: dot_f1
value: 37.710580857735025
- type: dot_precision
value: 23.61083383243495
- type: dot_recall
value: 93.61477572559367
- type: euclidean_accuracy
value: 77.64797043571556
- type: euclidean_ap
value: 31.892152386237594
- type: euclidean_f1
value: 38.21154759481647
- type: euclidean_precision
value: 25.719243766554023
- type: euclidean_recall
value: 74.30079155672823
- type: manhattan_accuracy
value: 77.6539309769327
- type: manhattan_ap
value: 31.89545356309865
- type: manhattan_f1
value: 38.16428166172855
- type: manhattan_precision
value: 25.07247577238466
- type: manhattan_recall
value: 79.86807387862797
- type: max_accuracy
value: 77.6539309769327
- type: max_ap
value: 31.89545356309865
- type: max_f1
value: 38.21154759481647
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 76.56886715566422
- type: cos_sim_ap
value: 44.04480929059786
- type: cos_sim_f1
value: 43.73100054674686
- type: cos_sim_precision
value: 30.540367168647098
- type: cos_sim_recall
value: 76.97874961502926
- type: dot_accuracy
value: 74.80110218496526
- type: dot_ap
value: 26.487746384962758
- type: dot_f1
value: 40.91940608182585
- type: dot_precision
value: 25.9157358738502
- type: dot_recall
value: 97.18201416692331
- type: euclidean_accuracy
value: 76.97054371870998
- type: euclidean_ap
value: 47.079120397438416
- type: euclidean_f1
value: 45.866182572614115
- type: euclidean_precision
value: 34.580791490692945
- type: euclidean_recall
value: 68.0859254696643
- type: manhattan_accuracy
value: 76.96084138626927
- type: manhattan_ap
value: 47.168701873575976
- type: manhattan_f1
value: 45.985439966237614
- type: manhattan_precision
value: 34.974321938693635
- type: manhattan_recall
value: 67.11579919926086
- type: max_accuracy
value: 76.97054371870998
- type: max_ap
value: 47.168701873575976
- type: max_f1
value: 45.985439966237614
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 3.322530620021471
- type: cos_sim_spearman
value: 3.7583567993545195
- type: euclidean_pearson
value: 3.743782192206081
- type: euclidean_spearman
value: 3.758336694921531
- type: manhattan_pearson
value: 3.845233721819267
- type: manhattan_spearman
value: 3.8542743797718026
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 8.552640773272078
- type: cos_sim_spearman
value: 10.086360519713061
- type: euclidean_pearson
value: 9.902099049347935
- type: euclidean_spearman
value: 10.086351512635042
- type: manhattan_pearson
value: 9.898006826713932
- type: manhattan_spearman
value: 10.076531690161783
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 21.955999999999996
- type: f1
value: 20.596128116112816
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 17.6945509937099
- type: cos_sim_spearman
value: 19.312286927022825
- type: euclidean_pearson
value: 19.259393744977515
- type: euclidean_spearman
value: 19.312290390892713
- type: manhattan_pearson
value: 19.223527109645772
- type: manhattan_spearman
value: 19.32655209742963
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 18.657841790313405
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 16.82483158478091
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 19.71658789133091
- type: mrr
value: 23.480595238095237
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 22.475972401039495
- type: mrr
value: 25.993650793650797
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 1.026
- type: map_at_10
value: 1.6389999999999998
- type: map_at_100
value: 1.875
- type: map_at_1000
value: 1.9529999999999998
- type: map_at_3
value: 1.417
- type: map_at_5
value: 1.5110000000000001
- type: mrr_at_1
value: 1.525
- type: mrr_at_10
value: 2.478
- type: mrr_at_100
value: 2.779
- type: mrr_at_1000
value: 2.861
- type: mrr_at_3
value: 2.105
- type: mrr_at_5
value: 2.283
- type: ndcg_at_1
value: 1.525
- type: ndcg_at_10
value: 2.222
- type: ndcg_at_100
value: 3.81
- type: ndcg_at_1000
value: 6.465999999999999
- type: ndcg_at_3
value: 1.7489999999999999
- type: ndcg_at_5
value: 1.8980000000000001
- type: precision_at_1
value: 1.525
- type: precision_at_10
value: 0.543
- type: precision_at_100
value: 0.187
- type: precision_at_1000
value: 0.055
- type: precision_at_3
value: 0.992
- type: precision_at_5
value: 0.76
- type: recall_at_1
value: 1.026
- type: recall_at_10
value: 3.1780000000000004
- type: recall_at_100
value: 10.481
- type: recall_at_1000
value: 29.735
- type: recall_at_3
value: 1.8849999999999998
- type: recall_at_5
value: 2.2560000000000002
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 54.99699338544799
- type: cos_sim_ap
value: 57.78007274332544
- type: cos_sim_f1
value: 67.95391338895512
- type: cos_sim_precision
value: 51.46846413095811
- type: cos_sim_recall
value: 99.9766191255553
- type: dot_accuracy
value: 54.99699338544799
- type: dot_ap
value: 57.7791056074979
- type: dot_f1
value: 67.95391338895512
- type: dot_precision
value: 51.46846413095811
- type: dot_recall
value: 99.9766191255553
- type: euclidean_accuracy
value: 54.99699338544799
- type: euclidean_ap
value: 57.7800760462191
- type: euclidean_f1
value: 67.95391338895512
- type: euclidean_precision
value: 51.46846413095811
- type: euclidean_recall
value: 99.9766191255553
- type: manhattan_accuracy
value: 55.05712567648827
- type: manhattan_ap
value: 57.8146828916844
- type: manhattan_f1
value: 67.95900532295227
- type: manhattan_precision
value: 51.46811070998797
- type: manhattan_recall
value: 100
- type: max_accuracy
value: 55.05712567648827
- type: max_ap
value: 57.8146828916844
- type: max_f1
value: 67.95900532295227
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 0.632
- type: map_at_10
value: 1.7510000000000001
- type: map_at_100
value: 2.004
- type: map_at_1000
value: 2.0660000000000003
- type: map_at_3
value: 1.493
- type: map_at_5
value: 1.635
- type: mrr_at_1
value: 0.632
- type: mrr_at_10
value: 1.7670000000000001
- type: mrr_at_100
value: 2.02
- type: mrr_at_1000
value: 2.081
- type: mrr_at_3
value: 1.528
- type: mrr_at_5
value: 1.649
- type: ndcg_at_1
value: 0.632
- type: ndcg_at_10
value: 2.32
- type: ndcg_at_100
value: 3.758
- type: ndcg_at_1000
value: 5.894
- type: ndcg_at_3
value: 1.7850000000000001
- type: ndcg_at_5
value: 2.044
- type: precision_at_1
value: 0.632
- type: precision_at_10
value: 0.411
- type: precision_at_100
value: 0.11399999999999999
- type: precision_at_1000
value: 0.03
- type: precision_at_3
value: 0.878
- type: precision_at_5
value: 0.653
- type: recall_at_1
value: 0.632
- type: recall_at_10
value: 4.109999999999999
- type: recall_at_100
value: 11.222
- type: recall_at_1000
value: 29.083
- type: recall_at_3
value: 2.634
- type: recall_at_5
value: 3.267
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 1.436
- type: map_at_10
value: 3.4099999999999997
- type: map_at_100
value: 4.128
- type: map_at_1000
value: 4.282
- type: map_at_3
value: 2.423
- type: map_at_5
value: 2.927
- type: mrr_at_1
value: 6
- type: mrr_at_10
value: 9.701
- type: mrr_at_100
value: 10.347000000000001
- type: mrr_at_1000
value: 10.427999999999999
- type: mrr_at_3
value: 8.267
- type: mrr_at_5
value: 9.004
- type: ndcg_at_1
value: 6
- type: ndcg_at_10
value: 5.856
- type: ndcg_at_100
value: 9.063
- type: ndcg_at_1000
value: 12.475999999999999
- type: ndcg_at_3
value: 5.253
- type: ndcg_at_5
value: 5.223
- type: precision_at_1
value: 6
- type: precision_at_10
value: 3.125
- type: precision_at_100
value: 0.812
- type: precision_at_1000
value: 0.169
- type: precision_at_3
value: 4.7669999999999995
- type: precision_at_5
value: 4.15
- type: recall_at_1
value: 1.436
- type: recall_at_10
value: 6.544999999999999
- type: recall_at_100
value: 16.634999999999998
- type: recall_at_1000
value: 33.987
- type: recall_at_3
value: 3.144
- type: recall_at_5
value: 4.519
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 4.1000000000000005
- type: map_at_10
value: 7.911
- type: map_at_100
value: 8.92
- type: map_at_1000
value: 9.033
- type: map_at_3
value: 6.4
- type: map_at_5
value: 7.23
- type: mrr_at_1
value: 4.1000000000000005
- type: mrr_at_10
value: 7.911
- type: mrr_at_100
value: 8.92
- type: mrr_at_1000
value: 9.033
- type: mrr_at_3
value: 6.4
- type: mrr_at_5
value: 7.23
- type: ndcg_at_1
value: 4.1000000000000005
- type: ndcg_at_10
value: 10.374
- type: ndcg_at_100
value: 15.879999999999999
- type: ndcg_at_1000
value: 19.246
- type: ndcg_at_3
value: 7.217
- type: ndcg_at_5
value: 8.706
- type: precision_at_1
value: 4.1000000000000005
- type: precision_at_10
value: 1.8399999999999999
- type: precision_at_100
value: 0.45599999999999996
- type: precision_at_1000
value: 0.073
- type: precision_at_3
value: 3.2
- type: precision_at_5
value: 2.64
- type: recall_at_1
value: 4.1000000000000005
- type: recall_at_10
value: 18.4
- type: recall_at_100
value: 45.6
- type: recall_at_1000
value: 72.89999999999999
- type: recall_at_3
value: 9.6
- type: recall_at_5
value: 13.200000000000001
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 20.353982300884958
- type: f1
value: 12.69588085868714
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 55.497185741088174
- type: ap
value: 20.43046737602198
- type: f1
value: 48.93980371558734
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 32.588967426128654
- type: cos_sim_spearman
value: 42.14900040682406
- type: euclidean_pearson
value: 39.568373451615685
- type: euclidean_spearman
value: 42.14899152396297
- type: manhattan_pearson
value: 39.5220710244444
- type: manhattan_spearman
value: 42.14787636056146
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 1.1655156335725807
- type: mrr
value: 0.2361111111111111
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 1.9029999999999998
- type: map_at_10
value: 2.9139999999999997
- type: map_at_100
value: 3.2259999999999995
- type: map_at_1000
value: 3.2870000000000004
- type: map_at_3
value: 2.483
- type: map_at_5
value: 2.71
- type: mrr_at_1
value: 2.02
- type: mrr_at_10
value: 3.064
- type: mrr_at_100
value: 3.382
- type: mrr_at_1000
value: 3.4419999999999997
- type: mrr_at_3
value: 2.622
- type: mrr_at_5
value: 2.855
- type: ndcg_at_1
value: 2.02
- type: ndcg_at_10
value: 3.639
- type: ndcg_at_100
value: 5.431
- type: ndcg_at_1000
value: 7.404
- type: ndcg_at_3
value: 2.723
- type: ndcg_at_5
value: 3.1350000000000002
- type: precision_at_1
value: 2.02
- type: precision_at_10
value: 0.626
- type: precision_at_100
value: 0.159
- type: precision_at_1000
value: 0.033
- type: precision_at_3
value: 1.17
- type: precision_at_5
value: 0.9199999999999999
- type: recall_at_1
value: 1.9029999999999998
- type: recall_at_10
value: 5.831
- type: recall_at_100
value: 14.737
- type: recall_at_1000
value: 30.84
- type: recall_at_3
value: 3.2870000000000004
- type: recall_at_5
value: 4.282
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 25.3866845998655
- type: f1
value: 23.404809615998495
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 40.34969737726966
- type: f1
value: 37.88244646590394
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 1.5
- type: map_at_10
value: 2.0740000000000003
- type: map_at_100
value: 2.2079999999999997
- type: map_at_1000
value: 2.241
- type: map_at_3
value: 1.933
- type: map_at_5
value: 2.023
- type: mrr_at_1
value: 1.5
- type: mrr_at_10
value: 2.0740000000000003
- type: mrr_at_100
value: 2.2079999999999997
- type: mrr_at_1000
value: 2.241
- type: mrr_at_3
value: 1.933
- type: mrr_at_5
value: 2.023
- type: ndcg_at_1
value: 1.5
- type: ndcg_at_10
value: 2.368
- type: ndcg_at_100
value: 3.309
- type: ndcg_at_1000
value: 4.593
- type: ndcg_at_3
value: 2.0789999999999997
- type: ndcg_at_5
value: 2.242
- type: precision_at_1
value: 1.5
- type: precision_at_10
value: 0.33
- type: precision_at_100
value: 0.084
- type: precision_at_1000
value: 0.019
- type: precision_at_3
value: 0.8330000000000001
- type: precision_at_5
value: 0.58
- type: recall_at_1
value: 1.5
- type: recall_at_10
value: 3.3000000000000003
- type: recall_at_100
value: 8.4
- type: recall_at_1000
value: 19.400000000000002
- type: recall_at_3
value: 2.5
- type: recall_at_5
value: 2.9000000000000004
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 38.94
- type: f1
value: 38.4171730136538
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 54.141851651326476
- type: cos_sim_ap
value: 55.63298007661861
- type: cos_sim_f1
value: 67.85195936139333
- type: cos_sim_precision
value: 51.68601437258153
- type: cos_sim_recall
value: 98.73284054910243
- type: dot_accuracy
value: 54.141851651326476
- type: dot_ap
value: 55.63298007661861
- type: dot_f1
value: 67.85195936139333
- type: dot_precision
value: 51.68601437258153
- type: dot_recall
value: 98.73284054910243
- type: euclidean_accuracy
value: 54.141851651326476
- type: euclidean_ap
value: 55.63298007661861
- type: euclidean_f1
value: 67.85195936139333
- type: euclidean_precision
value: 51.68601437258153
- type: euclidean_recall
value: 98.73284054910243
- type: manhattan_accuracy
value: 54.03356794802382
- type: manhattan_ap
value: 55.650247173847944
- type: manhattan_f1
value: 67.83667621776503
- type: manhattan_precision
value: 51.32791327913279
- type: manhattan_recall
value: 100
- type: max_accuracy
value: 54.141851651326476
- type: max_ap
value: 55.650247173847944
- type: max_f1
value: 67.85195936139333
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 56.88999999999999
- type: ap
value: 56.075855594697835
- type: f1
value: 56.31094564241924
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 10.023575042969506
- type: cos_sim_spearman
value: 6.135169971774927
- type: euclidean_pearson
value: 9.219072035876794
- type: euclidean_spearman
value: 6.147945631319713
- type: manhattan_pearson
value: 9.208267921398097
- type: manhattan_spearman
value: 6.156480815791583
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 5.7230819885069435
- type: cos_sim_spearman
value: 6.116111130034651
- type: euclidean_pearson
value: 5.9142712292657205
- type: euclidean_spearman
value: 6.115732664912588
- type: manhattan_pearson
value: 5.892970378623552
- type: manhattan_spearman
value: 6.100463075081302
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 18.353401358720397
- type: cos_sim_spearman
value: 33.700002511275095
- type: euclidean_pearson
value: 27.654605278731136
- type: euclidean_spearman
value: 33.700002511275095
- type: manhattan_pearson
value: 29.174977260571083
- type: manhattan_spearman
value: 33.901862553268366
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 44.66287398363386
- type: cos_sim_spearman
value: 45.60317964713117
- type: euclidean_pearson
value: 47.434263079423
- type: euclidean_spearman
value: 45.603111040461606
- type: manhattan_pearson
value: 47.3272049502668
- type: manhattan_spearman
value: 45.506449459872805
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 60.05480951659048
- type: mrr
value: 69.58201013422746
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 1.159
- type: map_at_10
value: 2.624
- type: map_at_100
value: 3.259
- type: map_at_1000
value: 3.4090000000000003
- type: map_at_3
value: 1.9109999999999998
- type: map_at_5
value: 2.254
- type: mrr_at_1
value: 5.87
- type: mrr_at_10
value: 8.530999999999999
- type: mrr_at_100
value: 9.142999999999999
- type: mrr_at_1000
value: 9.229
- type: mrr_at_3
value: 7.498
- type: mrr_at_5
value: 8.056000000000001
- type: ndcg_at_1
value: 5.87
- type: ndcg_at_10
value: 4.641
- type: ndcg_at_100
value: 7.507999999999999
- type: ndcg_at_1000
value: 10.823
- type: ndcg_at_3
value: 4.775
- type: ndcg_at_5
value: 4.515000000000001
- type: precision_at_1
value: 5.87
- type: precision_at_10
value: 2.632
- type: precision_at_100
value: 0.762
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 4.2299999999999995
- type: precision_at_5
value: 3.5450000000000004
- type: recall_at_1
value: 1.159
- type: recall_at_10
value: 4.816
- type: recall_at_100
value: 13.841999999999999
- type: recall_at_1000
value: 30.469
- type: recall_at_3
value: 2.413
- type: recall_at_5
value: 3.3300000000000005
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 26.786000000000005
- type: f1
value: 25.70512339530705
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 20.691386720429243
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 17.1882521768033
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 2.9000000000000004
- type: map_at_10
value: 4.051
- type: map_at_100
value: 4.277
- type: map_at_1000
value: 4.315
- type: map_at_3
value: 3.567
- type: map_at_5
value: 3.897
- type: mrr_at_1
value: 2.9000000000000004
- type: mrr_at_10
value: 4.051
- type: mrr_at_100
value: 4.277
- type: mrr_at_1000
value: 4.315
- type: mrr_at_3
value: 3.567
- type: mrr_at_5
value: 3.897
- type: ndcg_at_1
value: 2.9000000000000004
- type: ndcg_at_10
value: 4.772
- type: ndcg_at_100
value: 6.214
- type: ndcg_at_1000
value: 7.456
- type: ndcg_at_3
value: 3.805
- type: ndcg_at_5
value: 4.390000000000001
- type: precision_at_1
value: 2.9000000000000004
- type: precision_at_10
value: 0.7100000000000001
- type: precision_at_100
value: 0.146
- type: precision_at_1000
value: 0.025
- type: precision_at_3
value: 1.5
- type: precision_at_5
value: 1.18
- type: recall_at_1
value: 2.9000000000000004
- type: recall_at_10
value: 7.1
- type: recall_at_100
value: 14.6
- type: recall_at_1000
value: 24.9
- type: recall_at_3
value: 4.5
- type: recall_at_5
value: 5.8999999999999995
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 56.21999999999999
- type: ap
value: 36.53654363772411
- type: f1
value: 54.922396485449674
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1468721 with parameters:
```
{'batch_size': 160, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->