Dmeta-embedding-zh / README_zh.md
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metadata
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
icon

Dmeta-embedding

English | 中文

用法 | 评测(可复现) | FAQ | 联系 | 版权(免费商用)

重磅更新:

  • 2024.02.07, 发布了基于 Dmeta-embedding 模型的 Embedding API 产品,现已开启内测,点击申请即可免费获得 4 亿 tokens 使用额度,可编码大约 GB 级别汉字文本。

    • 我们的初心。既要开源优秀的技术能力,又希望大家能够在实际业务中使用起来,用起来的技术才是好技术、能落地创造价值的技术才是值得长期投入的。帮助大家解决业务落地最后一公里的障碍,让大家把 Embedding 技术低成本的用起来,更多去关注自身的商业和产品服务,把复杂的技术部分交给我们。
    • 申请和使用。点击申请,填写一个表单即可,48小时之内我们会通过 [email protected] 给您答复邮件。Embedding API 为了兼容大模型技术生态,使用方式跟 OpenAI 一致,具体用法我们将在答复邮件中进行说明。
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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,支持使用 Transformers/Sentence-Transformers/Langchain 等工具加载推理。

优势特点如下:

  • 多任务、场景泛化性能优异,目前已取得 MTEB 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, Langchain, 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 来加载推理:

pip install -U sentence-transformers
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 来加载推理:

pip install -U langchain
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 框架来加载推理:

pip install -U transformers
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

MTEB Chinese:

榜单数据集由智源研究院团队(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 数元灵 768 67.51 70.41 64.09 88.92 70 67.17 50.96
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 智源 1024 64.53 70.46 56.25 81.6 69.13 65.84 48.99
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) OpenAI 1536 53.02 52.0 43.35 69.56 64.31 54.28 45.68
text2vec-base 个人 768 47.63 38.79 43.41 67.41 62.19 49.45 37.66
text2vec-large 个人 1024 47.36 41.94 44.97 70.86 60.66 49.16 30.02

FAQ

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?

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。

2. 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。

3. 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。

4. 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!

Contact

您如果在使用过程中,遇到任何问题,欢迎前往讨论区建言献策。

您也可以联系我们:赵中昊 [email protected], 肖文斌 [email protected], 孙凯 [email protected]

同时我们也开通了微信群,可扫码加入我们(人数超200了,先加管理员再拉进群),一起共建 AIGC 技术生态!

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。