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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: Dmeta-embedding
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 65.60825224706932
- type: cos_sim_spearman
value: 71.12862586297193
- type: euclidean_pearson
value: 70.18130275750404
- type: euclidean_spearman
value: 71.12862586297193
- type: manhattan_pearson
value: 70.14470398075396
- type: manhattan_spearman
value: 71.05226975911737
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 65.52386345655479
- type: cos_sim_spearman
value: 64.64245253181382
- type: euclidean_pearson
value: 73.20157662981914
- type: euclidean_spearman
value: 64.64245253178956
- type: manhattan_pearson
value: 73.22837571756348
- type: manhattan_spearman
value: 64.62632334391418
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 44.925999999999995
- type: f1
value: 42.82555191308971
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.35236446393156
- type: cos_sim_spearman
value: 72.29629643702184
- type: euclidean_pearson
value: 70.94570179874498
- type: euclidean_spearman
value: 72.29629297226953
- type: manhattan_pearson
value: 70.84463025501125
- type: manhattan_spearman
value: 72.24527021975821
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 40.24232916894152
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 39.167806226929706
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 88.48837920106357
- type: mrr
value: 90.36861111111111
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 89.17878171657071
- type: mrr
value: 91.35805555555555
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.751
- type: map_at_10
value: 38.946
- type: map_at_100
value: 40.855000000000004
- type: map_at_1000
value: 40.953
- type: map_at_3
value: 34.533
- type: map_at_5
value: 36.905
- type: mrr_at_1
value: 39.235
- type: mrr_at_10
value: 47.713
- type: mrr_at_100
value: 48.71
- type: mrr_at_1000
value: 48.747
- type: mrr_at_3
value: 45.086
- type: mrr_at_5
value: 46.498
- type: ndcg_at_1
value: 39.235
- type: ndcg_at_10
value: 45.831
- type: ndcg_at_100
value: 53.162
- type: ndcg_at_1000
value: 54.800000000000004
- type: ndcg_at_3
value: 40.188
- type: ndcg_at_5
value: 42.387
- type: precision_at_1
value: 39.235
- type: precision_at_10
value: 10.273
- type: precision_at_100
value: 1.627
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 22.772000000000002
- type: precision_at_5
value: 16.524
- type: recall_at_1
value: 25.751
- type: recall_at_10
value: 57.411
- type: recall_at_100
value: 87.44
- type: recall_at_1000
value: 98.386
- type: recall_at_3
value: 40.416000000000004
- type: recall_at_5
value: 47.238
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.59591100420926
- type: cos_sim_ap
value: 90.65538153970263
- type: cos_sim_f1
value: 84.76466651795673
- type: cos_sim_precision
value: 81.04073363190446
- type: cos_sim_recall
value: 88.84732288987608
- type: dot_accuracy
value: 83.59591100420926
- type: dot_ap
value: 90.64355541781003
- type: dot_f1
value: 84.76466651795673
- type: dot_precision
value: 81.04073363190446
- type: dot_recall
value: 88.84732288987608
- type: euclidean_accuracy
value: 83.59591100420926
- type: euclidean_ap
value: 90.6547878194287
- type: euclidean_f1
value: 84.76466651795673
- type: euclidean_precision
value: 81.04073363190446
- type: euclidean_recall
value: 88.84732288987608
- type: manhattan_accuracy
value: 83.51172579675286
- type: manhattan_ap
value: 90.59941589844144
- type: manhattan_f1
value: 84.51827242524917
- type: manhattan_precision
value: 80.28613507258574
- type: manhattan_recall
value: 89.22141688099134
- type: max_accuracy
value: 83.59591100420926
- type: max_ap
value: 90.65538153970263
- type: max_f1
value: 84.76466651795673
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 63.251000000000005
- type: map_at_10
value: 72.442
- type: map_at_100
value: 72.79299999999999
- type: map_at_1000
value: 72.80499999999999
- type: map_at_3
value: 70.293
- type: map_at_5
value: 71.571
- type: mrr_at_1
value: 63.541000000000004
- type: mrr_at_10
value: 72.502
- type: mrr_at_100
value: 72.846
- type: mrr_at_1000
value: 72.858
- type: mrr_at_3
value: 70.39
- type: mrr_at_5
value: 71.654
- type: ndcg_at_1
value: 63.541000000000004
- type: ndcg_at_10
value: 76.774
- type: ndcg_at_100
value: 78.389
- type: ndcg_at_1000
value: 78.678
- type: ndcg_at_3
value: 72.47
- type: ndcg_at_5
value: 74.748
- type: precision_at_1
value: 63.541000000000004
- type: precision_at_10
value: 9.115
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 26.379
- type: precision_at_5
value: 16.965
- type: recall_at_1
value: 63.251000000000005
- type: recall_at_10
value: 90.253
- type: recall_at_100
value: 97.576
- type: recall_at_1000
value: 99.789
- type: recall_at_3
value: 78.635
- type: recall_at_5
value: 84.141
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.597
- type: map_at_10
value: 72.411
- type: map_at_100
value: 75.58500000000001
- type: map_at_1000
value: 75.64800000000001
- type: map_at_3
value: 49.61
- type: map_at_5
value: 62.527
- type: mrr_at_1
value: 84.65
- type: mrr_at_10
value: 89.43900000000001
- type: mrr_at_100
value: 89.525
- type: mrr_at_1000
value: 89.529
- type: mrr_at_3
value: 89
- type: mrr_at_5
value: 89.297
- type: ndcg_at_1
value: 84.65
- type: ndcg_at_10
value: 81.47
- type: ndcg_at_100
value: 85.198
- type: ndcg_at_1000
value: 85.828
- type: ndcg_at_3
value: 79.809
- type: ndcg_at_5
value: 78.55
- type: precision_at_1
value: 84.65
- type: precision_at_10
value: 39.595
- type: precision_at_100
value: 4.707
- type: precision_at_1000
value: 0.485
- type: precision_at_3
value: 71.61699999999999
- type: precision_at_5
value: 60.45
- type: recall_at_1
value: 23.597
- type: recall_at_10
value: 83.34
- type: recall_at_100
value: 95.19800000000001
- type: recall_at_1000
value: 98.509
- type: recall_at_3
value: 52.744
- type: recall_at_5
value: 68.411
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 53.1
- type: map_at_10
value: 63.359
- type: map_at_100
value: 63.9
- type: map_at_1000
value: 63.909000000000006
- type: map_at_3
value: 60.95
- type: map_at_5
value: 62.305
- type: mrr_at_1
value: 53.1
- type: mrr_at_10
value: 63.359
- type: mrr_at_100
value: 63.9
- type: mrr_at_1000
value: 63.909000000000006
- type: mrr_at_3
value: 60.95
- type: mrr_at_5
value: 62.305
- type: ndcg_at_1
value: 53.1
- type: ndcg_at_10
value: 68.418
- type: ndcg_at_100
value: 70.88499999999999
- type: ndcg_at_1000
value: 71.135
- type: ndcg_at_3
value: 63.50599999999999
- type: ndcg_at_5
value: 65.92
- type: precision_at_1
value: 53.1
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.633000000000003
- type: precision_at_5
value: 15.340000000000002
- type: recall_at_1
value: 53.1
- type: recall_at_10
value: 84.3
- type: recall_at_100
value: 95.5
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 70.89999999999999
- type: recall_at_5
value: 76.7
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 48.303193535975375
- type: f1
value: 35.96559358693866
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 85.06566604127579
- type: ap
value: 52.0596483757231
- type: f1
value: 79.5196835127668
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 74.48499423626059
- type: cos_sim_spearman
value: 78.75806756061169
- type: euclidean_pearson
value: 78.47917601852879
- type: euclidean_spearman
value: 78.75807199272622
- type: manhattan_pearson
value: 78.40207586289772
- type: manhattan_spearman
value: 78.6911776964119
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 24.75987466552363
- type: mrr
value: 23.40515873015873
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.026999999999994
- type: map_at_10
value: 67.50699999999999
- type: map_at_100
value: 67.946
- type: map_at_1000
value: 67.96600000000001
- type: map_at_3
value: 65.503
- type: map_at_5
value: 66.649
- type: mrr_at_1
value: 60.20100000000001
- type: mrr_at_10
value: 68.271
- type: mrr_at_100
value: 68.664
- type: mrr_at_1000
value: 68.682
- type: mrr_at_3
value: 66.47800000000001
- type: mrr_at_5
value: 67.499
- type: ndcg_at_1
value: 60.20100000000001
- type: ndcg_at_10
value: 71.697
- type: ndcg_at_100
value: 73.736
- type: ndcg_at_1000
value: 74.259
- type: ndcg_at_3
value: 67.768
- type: ndcg_at_5
value: 69.72
- type: precision_at_1
value: 60.20100000000001
- type: precision_at_10
value: 8.927999999999999
- type: precision_at_100
value: 0.9950000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 25.883
- type: precision_at_5
value: 16.55
- type: recall_at_1
value: 58.026999999999994
- type: recall_at_10
value: 83.966
- type: recall_at_100
value: 93.313
- type: recall_at_1000
value: 97.426
- type: recall_at_3
value: 73.342
- type: recall_at_5
value: 77.997
- 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: 71.1600537995965
- type: f1
value: 68.8126216609964
- 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: 73.54068594485541
- type: f1
value: 73.46845879869848
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 54.900000000000006
- type: map_at_10
value: 61.363
- type: map_at_100
value: 61.924
- type: map_at_1000
value: 61.967000000000006
- type: map_at_3
value: 59.767
- type: map_at_5
value: 60.802
- type: mrr_at_1
value: 55.1
- type: mrr_at_10
value: 61.454
- type: mrr_at_100
value: 62.016000000000005
- type: mrr_at_1000
value: 62.059
- type: mrr_at_3
value: 59.882999999999996
- type: mrr_at_5
value: 60.893
- type: ndcg_at_1
value: 54.900000000000006
- type: ndcg_at_10
value: 64.423
- type: ndcg_at_100
value: 67.35900000000001
- type: ndcg_at_1000
value: 68.512
- type: ndcg_at_3
value: 61.224000000000004
- type: ndcg_at_5
value: 63.083
- type: precision_at_1
value: 54.900000000000006
- type: precision_at_10
value: 7.3999999999999995
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.8
- type: precision_at_5
value: 13.98
- type: recall_at_1
value: 54.900000000000006
- type: recall_at_10
value: 74
- type: recall_at_100
value: 88.2
- type: recall_at_1000
value: 97.3
- type: recall_at_3
value: 65.4
- type: recall_at_5
value: 69.89999999999999
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 75.15666666666667
- type: f1
value: 74.8306375354435
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 83.10774228478614
- type: cos_sim_ap
value: 87.17679348388666
- type: cos_sim_f1
value: 84.59302325581395
- type: cos_sim_precision
value: 78.15577439570276
- type: cos_sim_recall
value: 92.18585005279832
- type: dot_accuracy
value: 83.10774228478614
- type: dot_ap
value: 87.17679348388666
- type: dot_f1
value: 84.59302325581395
- type: dot_precision
value: 78.15577439570276
- type: dot_recall
value: 92.18585005279832
- type: euclidean_accuracy
value: 83.10774228478614
- type: euclidean_ap
value: 87.17679348388666
- type: euclidean_f1
value: 84.59302325581395
- type: euclidean_precision
value: 78.15577439570276
- type: euclidean_recall
value: 92.18585005279832
- type: manhattan_accuracy
value: 82.67460747157553
- type: manhattan_ap
value: 86.94296334435238
- type: manhattan_f1
value: 84.32327166504382
- type: manhattan_precision
value: 78.22944896115628
- type: manhattan_recall
value: 91.4466737064414
- type: max_accuracy
value: 83.10774228478614
- type: max_ap
value: 87.17679348388666
- type: max_f1
value: 84.59302325581395
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 93.24999999999999
- type: ap
value: 90.98617641063584
- type: f1
value: 93.23447883650289
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.071417937737856
- type: cos_sim_spearman
value: 45.049199344455424
- type: euclidean_pearson
value: 44.913450096830786
- type: euclidean_spearman
value: 45.05733424275291
- type: manhattan_pearson
value: 44.881623825912065
- type: manhattan_spearman
value: 44.989923561416596
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.38238052689359
- type: cos_sim_spearman
value: 42.61949690594399
- type: euclidean_pearson
value: 40.61261500356766
- type: euclidean_spearman
value: 42.619626605620724
- type: manhattan_pearson
value: 40.8886109204474
- type: manhattan_spearman
value: 42.75791523010463
- 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: 62.10977863727196
- type: cos_sim_spearman
value: 63.843727112473225
- type: euclidean_pearson
value: 63.25133487817196
- type: euclidean_spearman
value: 63.843727112473225
- type: manhattan_pearson
value: 63.58749018644103
- type: manhattan_spearman
value: 63.83820575456674
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 79.30616496720054
- type: cos_sim_spearman
value: 80.767935782436
- type: euclidean_pearson
value: 80.4160642670106
- type: euclidean_spearman
value: 80.76820284024356
- type: manhattan_pearson
value: 80.27318714580251
- type: manhattan_spearman
value: 80.61030164164964
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.26242871142425
- type: mrr
value: 76.20689863623174
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.240999999999996
- type: map_at_10
value: 73.009
- type: map_at_100
value: 76.893
- type: map_at_1000
value: 76.973
- type: map_at_3
value: 51.339
- type: map_at_5
value: 63.003
- type: mrr_at_1
value: 87.458
- type: mrr_at_10
value: 90.44
- type: mrr_at_100
value: 90.558
- type: mrr_at_1000
value: 90.562
- type: mrr_at_3
value: 89.89
- type: mrr_at_5
value: 90.231
- type: ndcg_at_1
value: 87.458
- type: ndcg_at_10
value: 81.325
- type: ndcg_at_100
value: 85.61999999999999
- type: ndcg_at_1000
value: 86.394
- type: ndcg_at_3
value: 82.796
- type: ndcg_at_5
value: 81.219
- type: precision_at_1
value: 87.458
- type: precision_at_10
value: 40.534
- type: precision_at_100
value: 4.96
- type: precision_at_1000
value: 0.514
- type: precision_at_3
value: 72.444
- type: precision_at_5
value: 60.601000000000006
- type: recall_at_1
value: 26.240999999999996
- type: recall_at_10
value: 80.42
- type: recall_at_100
value: 94.118
- type: recall_at_1000
value: 98.02199999999999
- type: recall_at_3
value: 53.174
- type: recall_at_5
value: 66.739
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 52.40899999999999
- type: f1
value: 50.68532128056062
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 65.57616085176686
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 58.844999922904925
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 58.4
- type: map_at_10
value: 68.64
- type: map_at_100
value: 69.062
- type: map_at_1000
value: 69.073
- type: map_at_3
value: 66.567
- type: map_at_5
value: 67.89699999999999
- type: mrr_at_1
value: 58.4
- type: mrr_at_10
value: 68.64
- type: mrr_at_100
value: 69.062
- type: mrr_at_1000
value: 69.073
- type: mrr_at_3
value: 66.567
- type: mrr_at_5
value: 67.89699999999999
- type: ndcg_at_1
value: 58.4
- type: ndcg_at_10
value: 73.30600000000001
- type: ndcg_at_100
value: 75.276
- type: ndcg_at_1000
value: 75.553
- type: ndcg_at_3
value: 69.126
- type: ndcg_at_5
value: 71.519
- type: precision_at_1
value: 58.4
- type: precision_at_10
value: 8.780000000000001
- type: precision_at_100
value: 0.968
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 25.5
- type: precision_at_5
value: 16.46
- type: recall_at_1
value: 58.4
- type: recall_at_10
value: 87.8
- type: recall_at_100
value: 96.8
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 76.5
- type: recall_at_5
value: 82.3
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.21000000000001
- type: ap
value: 69.17460264576461
- type: f1
value: 84.68032984659226
license: apache-2.0
language:
- zh
- en
---
<div align="center">
<img src="logo.png" alt="icon" width="100px"/>
</div>
<h1 align="center">Dmeta-embedding</h1>
<h4 align="center">
<p>
<a href=#usage>用法</a> |
<a href="#evaluation">评测</a> |
<a href=#faq>FAQ</a> |
<a href="#contact">联系</a> |
<a href="#license">版权（免费商用）</a>
<p>
</h4>
Dmeta-embedding 是一款跨领域、跨任务、开箱即用的中文 Embedding 模型，适用于搜索、问答、智能客服、LLM+RAG 等各种业务场景。
优势特点如下：
- 多任务、场景泛化性能优异，目前已取得 [MTEB](https://huggingface.co/spaces/mteb/leaderboard) 中文榜单第二成绩（2024.01.25）
- 模型参数大小仅 400MB，对比参数量超过 GB 级模型，可以极大降低推理成本
- 支持上下文窗口长度达到 1024，对于长文本检索、RAG 等场景更适配
## Usage
目前模型支持通过 [Sentence-Transformers](#sentence-transformers), [Langchain](#langchain), [Huggingface Transformers](#huggingface-transformers) 等主流框架进行推理，具体用法参考各个框架的示例。
### Sentence-Transformers
Dmeta-embedding 模型支持通过 [sentence-transformers](https://www.SBERT.net) 来加载推理：
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
model = SentenceTransformer('DMetaSoul/Dmeta-embedding')
embs1 = model.encode(texts1, normalize_embeddings=True)
embs2 = model.encode(texts2, normalize_embeddings=True)
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
示例输出如下：
```
查询文本：胡子长得太快怎么办？
相似文本：胡子长得快怎么办？，打分：0.9535336494445801
相似文本：怎样使胡子不浓密！，打分：0.6776421070098877
相似文本：香港买手表哪里好，打分：0.2297907918691635
相似文本：在杭州手机到哪里买，打分：0.11386542022228241
查询文本：在香港哪里买手表好
相似文本：香港买手表哪里好，打分：0.9843372106552124
相似文本：在杭州手机到哪里买，打分：0.45211508870124817
相似文本：胡子长得快怎么办？，打分：0.19985519349575043
相似文本：怎样使胡子不浓密！，打分：0.18558596074581146
```
### Langchain
Dmeta-embedding 模型支持通过 LLM 工具框架 [langchain](https://www.langchain.com/) 来加载推理：
```
pip install -U langchain
```
```python
import torch
import numpy as np
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "DMetaSoul/Dmeta-embedding"
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
)
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
embs1 = model.embed_documents(texts1)
embs2 = model.embed_documents(texts2)
embs1, embs2 = np.array(embs1), np.array(embs2)
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
### HuggingFace Transformers
Dmeta-embedding 模型支持通过 [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) 框架来加载推理：
```
pip install -U transformers
```
```python
import torch
from transformers import AutoTokenizer, AutoModel
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)
def cls_pooling(model_output):
return model_output[0][:, 0]
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"]
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"]
tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/Dmeta-embedding')
model = AutoModel.from_pretrained('DMetaSoul/Dmeta-embedding')
model.eval()
with torch.no_grad():
inputs1 = tokenizer(texts1, padding=True, truncation=True, return_tensors='pt')
inputs2 = tokenizer(texts2, padding=True, truncation=True, return_tensors='pt')
model_output1 = model(**inputs1)
model_output2 = model(**inputs2)
embs1, embs2 = cls_pooling(model_output1), cls_pooling(model_output2)
embs1 = torch.nn.functional.normalize(embs1, p=2, dim=1).numpy()
embs2 = torch.nn.functional.normalize(embs2, p=2, dim=1).numpy()
# 计算两两相似度
similarity = embs1 @ embs2.T
print(similarity)
# 获取 texts1[i] 对应的最相似 texts2[j]
for i in range(len(texts1)):
scores = []
for j in range(len(texts2)):
scores.append([texts2[j], similarity[i][j]])
scores = sorted(scores, key=lambda x:x[1], reverse=True)
print(f"查询文本：{texts1[i]}")
for text2, score in scores:
print(f"相似文本：{text2}，打分：{score}")
print()
```
## Evaluation
Dmeta-embedding 模型在 [MTEB 中文榜单](https://huggingface.co/spaces/mteb/leaderboard)取得开源第一的成绩（2024.01.25，Baichuan 榜单第一、未开源），具体关于评测数据和代码可参考 MTEB 官方[仓库](https://github.com/embeddings-benchmark/mteb)。
**MTEB Chinese**:
该[榜单数据集](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB)由智源研究院团队（BAAI）收集整理，包含 6 个经典任务共计 35 个中文数据集，涵盖了分类、检索、排序、句对、STS 等任务，是目前 Embedding 模型全方位能力评测的全球权威榜单。
| Model | Vendor | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------------------------------------------------------------------------------- | ------ |:-------------------:|:-----:|:---------:|:-----:|:------------------:|:--------------:|:---------:|:----------:|
| [Dmeta-embedding](https://huggingface.co/DMetaSoul/Dmeta-embedding) | 数元灵 | 1024 | 67.51 | 70.41 | 64.09 | 88.92 | 70 | 67.17 | 50.96 |
| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | 阿里达摩院 | 1024 | 66.72 | 72.49 | 57.82 | 84.41 | 71.34 | 67.4 | 53.07 |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 智源 | 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) | 智源 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | OpenAI | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 个人 | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 个人 | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
## FAQ
<details>
<summary>1. 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？</summary>
<!-- ### 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？ -->
简单来说，模型优异的泛化能力来自于预训练数据的广泛和多样，以及模型优化时面向多任务场景设计了不同优化目标。
具体来说，技术要点有：
1）首先是大规模弱标签对比学习。业界经验表明开箱即用的语言模型在 Embedding 相关任务上表现不佳，但由于监督数据标注、获取成本较高，因此大规模、高质量的弱标签学习成为一条可选技术路线。通过在互联网上论坛、新闻、问答社区、百科等半结构化数据中提取弱标签，并利用大模型进行低质过滤，得到 10 亿级别弱监督文本对数据。
2）其次是高质量监督学习。我们收集整理了大规模开源标注的语句对数据集，包含百科、教育、金融、医疗、法律、新闻、学术等多个领域共计 3000 万句对样本。同时挖掘难负样本对，借助对比学习更好的进行模型优化。
3）最后是检索任务针对性优化。考虑到搜索、问答以及 RAG 等场景是 Embedding 模型落地的重要应用阵地，为了增强模型跨领域、跨场景的效果性能，我们专门针对检索任务进行了模型优化，核心在于从问答、检索等数据中挖掘难负样本，借助稀疏和稠密检索等多种手段，构造百万级难负样本对数据集，显著提升了模型跨领域的检索性能。
</details>
<details>
<summary>2. 模型可以商用吗？</summary>
<!-- ### 模型可以商用吗 -->
我们的开源模型基于 Apache-2.0 协议，完全支持免费商用。
</details>
<details>
<summary>3. 如何复现 MTEB 评测结果？</summary>
<!-- ### 如何复现 MTEB 评测结果？ -->
我们在模型仓库中提供了脚本 mteb_eval.py，您可以直接运行此脚本来复现我们的评测结果。
</details>
<details>
<summary>4. 后续规划有哪些？</summary>
<!-- ### 后续规划有哪些？ -->
我们将不断致力于为社区提供效果优异、推理轻量、多场景开箱即用的 Embedding 模型，同时我们也会将 Embedding 逐步整合到目前已经的技术生态中，跟随社区一起成长！
</details>
## Contact
您如果在使用过程中，遇到任何问题，欢迎前往[讨论区](https://huggingface.co/DMetaSoul/Dmeta-embedding/discussions)建言献策。
您也可以联系我们：赵中昊 <zhongh@dmetasoul.com>, 肖文斌 <xiaowenbin@dmetasoul.com>, 孙凯 <sunkai@dmetasoul.com>
## License
Dmeta-embedding 模型采用 Apache-2.0 License，开源模型可以进行免费商用私有部署。 |