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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: stella-base-zh-v3-1792d
  results:
  - task:
      type: STS
    dataset:
      type: C-MTEB/AFQMC
      name: MTEB AFQMC
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 54.5145388936202
    - type: cos_sim_spearman
      value: 59.223125058197134
    - type: euclidean_pearson
      value: 57.819377838734695
    - type: euclidean_spearman
      value: 59.22310494948463
    - type: manhattan_pearson
      value: 57.44029759610327
    - type: manhattan_spearman
      value: 58.88336250854381
  - task:
      type: STS
    dataset:
      type: C-MTEB/ATEC
      name: MTEB ATEC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 54.544243591344866
    - type: cos_sim_spearman
      value: 58.43052988038229
    - type: euclidean_pearson
      value: 62.1608405146189
    - type: euclidean_spearman
      value: 58.43052762862396
    - type: manhattan_pearson
      value: 61.88443779892169
    - type: manhattan_spearman
      value: 58.26899143609596
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_reviews_multi
      name: MTEB AmazonReviewsClassification (zh)
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.343999999999994
    - type: f1
      value: 44.46931958420461
  - task:
      type: STS
    dataset:
      type: C-MTEB/BQ
      name: MTEB BQ
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 68.52081000538426
    - type: cos_sim_spearman
      value: 70.44089935351529
    - type: euclidean_pearson
      value: 69.24671010626395
    - type: euclidean_spearman
      value: 70.44090281761693
    - type: manhattan_pearson
      value: 69.00737718109357
    - type: manhattan_spearman
      value: 70.24344902456502
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringP2P
      name: MTEB CLSClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 42.86119436460332
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/CLSClusteringS2S
      name: MTEB CLSClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 39.97521728440642
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv1-reranking
      name: MTEB CMedQAv1
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 88.34151862240452
    - type: mrr
      value: 90.40380952380953
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv2-reranking
      name: MTEB CMedQAv2
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 89.06288758814637
    - type: mrr
      value: 90.91285714285713
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/CmedqaRetrieval
      name: MTEB CmedqaRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 25.651000000000003
    - type: map_at_10
      value: 38.576
    - type: map_at_100
      value: 40.534
    - type: map_at_1000
      value: 40.64
    - type: map_at_3
      value: 34.016000000000005
    - type: map_at_5
      value: 36.675999999999995
    - type: mrr_at_1
      value: 39.06
    - type: mrr_at_10
      value: 47.278
    - type: mrr_at_100
      value: 48.272999999999996
    - type: mrr_at_1000
      value: 48.314
    - type: mrr_at_3
      value: 44.461
    - type: mrr_at_5
      value: 46.107
    - type: ndcg_at_1
      value: 39.06
    - type: ndcg_at_10
      value: 45.384
    - type: ndcg_at_100
      value: 52.796
    - type: ndcg_at_1000
      value: 54.55
    - type: ndcg_at_3
      value: 39.497
    - type: ndcg_at_5
      value: 42.189
    - type: precision_at_1
      value: 39.06
    - type: precision_at_10
      value: 10.17
    - type: precision_at_100
      value: 1.6179999999999999
    - type: precision_at_1000
      value: 0.184
    - type: precision_at_3
      value: 22.247
    - type: precision_at_5
      value: 16.529
    - type: recall_at_1
      value: 25.651000000000003
    - type: recall_at_10
      value: 56.82899999999999
    - type: recall_at_100
      value: 87.134
    - type: recall_at_1000
      value: 98.709
    - type: recall_at_3
      value: 39.461
    - type: recall_at_5
      value: 47.329
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/CMNLI
      name: MTEB Cmnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 83.1870114251353
    - type: cos_sim_ap
      value: 90.42393852164342
    - type: cos_sim_f1
      value: 84.10685985963323
    - type: cos_sim_precision
      value: 81.5229317533465
    - type: cos_sim_recall
      value: 86.85994856207621
    - type: dot_accuracy
      value: 83.1870114251353
    - type: dot_ap
      value: 90.41339758845682
    - type: dot_f1
      value: 84.10685985963323
    - type: dot_precision
      value: 81.5229317533465
    - type: dot_recall
      value: 86.85994856207621
    - type: euclidean_accuracy
      value: 83.1870114251353
    - type: euclidean_ap
      value: 90.42393581056393
    - type: euclidean_f1
      value: 84.10685985963323
    - type: euclidean_precision
      value: 81.5229317533465
    - type: euclidean_recall
      value: 86.85994856207621
    - type: manhattan_accuracy
      value: 82.77811184606134
    - type: manhattan_ap
      value: 90.18115714681704
    - type: manhattan_f1
      value: 83.75083130126357
    - type: manhattan_precision
      value: 79.62065331928345
    - type: manhattan_recall
      value: 88.33294365209258
    - type: max_accuracy
      value: 83.1870114251353
    - type: max_ap
      value: 90.42393852164342
    - type: max_f1
      value: 84.10685985963323
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/CovidRetrieval
      name: MTEB CovidRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 68.388
    - type: map_at_10
      value: 76.819
    - type: map_at_100
      value: 77.153
    - type: map_at_1000
      value: 77.16
    - type: map_at_3
      value: 74.98700000000001
    - type: map_at_5
      value: 76.101
    - type: mrr_at_1
      value: 68.599
    - type: mrr_at_10
      value: 76.844
    - type: mrr_at_100
      value: 77.168
    - type: mrr_at_1000
      value: 77.17500000000001
    - type: mrr_at_3
      value: 75.044
    - type: mrr_at_5
      value: 76.208
    - type: ndcg_at_1
      value: 68.599
    - type: ndcg_at_10
      value: 80.613
    - type: ndcg_at_100
      value: 82.017
    - type: ndcg_at_1000
      value: 82.19300000000001
    - type: ndcg_at_3
      value: 76.956
    - type: ndcg_at_5
      value: 78.962
    - type: precision_at_1
      value: 68.599
    - type: precision_at_10
      value: 9.336
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 27.678000000000004
    - type: precision_at_5
      value: 17.619
    - type: recall_at_1
      value: 68.388
    - type: recall_at_10
      value: 92.36
    - type: recall_at_100
      value: 98.52499999999999
    - type: recall_at_1000
      value: 99.895
    - type: recall_at_3
      value: 82.53399999999999
    - type: recall_at_5
      value: 87.355
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/DuRetrieval
      name: MTEB DuRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 25.1
    - type: map_at_10
      value: 77.71000000000001
    - type: map_at_100
      value: 80.638
    - type: map_at_1000
      value: 80.679
    - type: map_at_3
      value: 53.187
    - type: map_at_5
      value: 67.735
    - type: mrr_at_1
      value: 87.8
    - type: mrr_at_10
      value: 91.8
    - type: mrr_at_100
      value: 91.893
    - type: mrr_at_1000
      value: 91.89500000000001
    - type: mrr_at_3
      value: 91.51700000000001
    - type: mrr_at_5
      value: 91.704
    - type: ndcg_at_1
      value: 87.8
    - type: ndcg_at_10
      value: 85.55
    - type: ndcg_at_100
      value: 88.626
    - type: ndcg_at_1000
      value: 89.021
    - type: ndcg_at_3
      value: 83.94
    - type: ndcg_at_5
      value: 83.259
    - type: precision_at_1
      value: 87.8
    - type: precision_at_10
      value: 41.295
    - type: precision_at_100
      value: 4.781
    - type: precision_at_1000
      value: 0.488
    - type: precision_at_3
      value: 75.3
    - type: precision_at_5
      value: 64.13
    - type: recall_at_1
      value: 25.1
    - type: recall_at_10
      value: 87.076
    - type: recall_at_100
      value: 97.095
    - type: recall_at_1000
      value: 99.129
    - type: recall_at_3
      value: 56.013999999999996
    - type: recall_at_5
      value: 73.2
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/EcomRetrieval
      name: MTEB EcomRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 53.300000000000004
    - type: map_at_10
      value: 63.01
    - type: map_at_100
      value: 63.574
    - type: map_at_1000
      value: 63.587
    - type: map_at_3
      value: 60.783
    - type: map_at_5
      value: 62.098
    - type: mrr_at_1
      value: 53.300000000000004
    - type: mrr_at_10
      value: 63.01
    - type: mrr_at_100
      value: 63.574
    - type: mrr_at_1000
      value: 63.587
    - type: mrr_at_3
      value: 60.783
    - type: mrr_at_5
      value: 62.098
    - type: ndcg_at_1
      value: 53.300000000000004
    - type: ndcg_at_10
      value: 67.876
    - type: ndcg_at_100
      value: 70.434
    - type: ndcg_at_1000
      value: 70.753
    - type: ndcg_at_3
      value: 63.275000000000006
    - type: ndcg_at_5
      value: 65.654
    - type: precision_at_1
      value: 53.300000000000004
    - type: precision_at_10
      value: 8.32
    - type: precision_at_100
      value: 0.9480000000000001
    - type: precision_at_1000
      value: 0.097
    - type: precision_at_3
      value: 23.5
    - type: precision_at_5
      value: 15.260000000000002
    - type: recall_at_1
      value: 53.300000000000004
    - type: recall_at_10
      value: 83.2
    - type: recall_at_100
      value: 94.8
    - type: recall_at_1000
      value: 97.3
    - type: recall_at_3
      value: 70.5
    - type: recall_at_5
      value: 76.3
  - task:
      type: Classification
    dataset:
      type: C-MTEB/IFlyTek-classification
      name: MTEB IFlyTek
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 49.92689495959984
    - type: f1
      value: 37.784780470986625
  - task:
      type: Classification
    dataset:
      type: C-MTEB/JDReview-classification
      name: MTEB JDReview
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 86.26641651031895
    - type: ap
      value: 54.50750244841821
    - type: f1
      value: 80.94927946681523
  - task:
      type: STS
    dataset:
      type: C-MTEB/LCQMC
      name: MTEB LCQMC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 72.3980811478615
    - type: cos_sim_spearman
      value: 78.26906056425528
    - type: euclidean_pearson
      value: 77.87705501225068
    - type: euclidean_spearman
      value: 78.26905834518651
    - type: manhattan_pearson
      value: 77.77154630197
    - type: manhattan_spearman
      value: 78.1940918602169
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/Mmarco-reranking
      name: MTEB MMarcoReranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 27.48003475319453
    - type: mrr
      value: 26.400793650793652
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/MMarcoRetrieval
      name: MTEB MMarcoRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 64.373
    - type: map_at_10
      value: 73.604
    - type: map_at_100
      value: 73.953
    - type: map_at_1000
      value: 73.965
    - type: map_at_3
      value: 71.70100000000001
    - type: map_at_5
      value: 72.859
    - type: mrr_at_1
      value: 66.676
    - type: mrr_at_10
      value: 74.248
    - type: mrr_at_100
      value: 74.56099999999999
    - type: mrr_at_1000
      value: 74.572
    - type: mrr_at_3
      value: 72.59100000000001
    - type: mrr_at_5
      value: 73.592
    - type: ndcg_at_1
      value: 66.676
    - type: ndcg_at_10
      value: 77.417
    - type: ndcg_at_100
      value: 79.006
    - type: ndcg_at_1000
      value: 79.334
    - type: ndcg_at_3
      value: 73.787
    - type: ndcg_at_5
      value: 75.74
    - type: precision_at_1
      value: 66.676
    - type: precision_at_10
      value: 9.418
    - type: precision_at_100
      value: 1.0210000000000001
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 27.832
    - type: precision_at_5
      value: 17.736
    - type: recall_at_1
      value: 64.373
    - type: recall_at_10
      value: 88.565
    - type: recall_at_100
      value: 95.789
    - type: recall_at_1000
      value: 98.355
    - type: recall_at_3
      value: 78.914
    - type: recall_at_5
      value: 83.56
  - 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: 72.0544720914593
    - type: f1
      value: 69.61749470345791
  - 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: 75.30262273032953
    - type: f1
      value: 75.05097671215634
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/MedicalRetrieval
      name: MTEB MedicalRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 55.1
    - type: map_at_10
      value: 61.284000000000006
    - type: map_at_100
      value: 61.794000000000004
    - type: map_at_1000
      value: 61.838
    - type: map_at_3
      value: 59.75
    - type: map_at_5
      value: 60.64000000000001
    - type: mrr_at_1
      value: 55.300000000000004
    - type: mrr_at_10
      value: 61.38400000000001
    - type: mrr_at_100
      value: 61.894000000000005
    - type: mrr_at_1000
      value: 61.938
    - type: mrr_at_3
      value: 59.85
    - type: mrr_at_5
      value: 60.74
    - type: ndcg_at_1
      value: 55.1
    - type: ndcg_at_10
      value: 64.345
    - type: ndcg_at_100
      value: 67.148
    - type: ndcg_at_1000
      value: 68.36
    - type: ndcg_at_3
      value: 61.182
    - type: ndcg_at_5
      value: 62.808
    - type: precision_at_1
      value: 55.1
    - type: precision_at_10
      value: 7.3999999999999995
    - type: precision_at_100
      value: 0.8789999999999999
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 21.767
    - type: precision_at_5
      value: 13.86
    - type: recall_at_1
      value: 55.1
    - type: recall_at_10
      value: 74
    - type: recall_at_100
      value: 87.9
    - type: recall_at_1000
      value: 97.5
    - type: recall_at_3
      value: 65.3
    - type: recall_at_5
      value: 69.3
  - task:
      type: Classification
    dataset:
      type: C-MTEB/MultilingualSentiment-classification
      name: MTEB MultilingualSentiment
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 76.21666666666667
    - type: f1
      value: 76.03732395559548
  - task:
      type: PairClassification
    dataset:
      type: C-MTEB/OCNLI
      name: MTEB Ocnli
      config: default
      split: validation
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 81.8083378451543
    - type: cos_sim_ap
      value: 85.43050139514027
    - type: cos_sim_f1
      value: 83.25969563082965
    - type: cos_sim_precision
      value: 77.79816513761469
    - type: cos_sim_recall
      value: 89.54593453009504
    - type: dot_accuracy
      value: 81.8083378451543
    - type: dot_ap
      value: 85.43050139514027
    - type: dot_f1
      value: 83.25969563082965
    - type: dot_precision
      value: 77.79816513761469
    - type: dot_recall
      value: 89.54593453009504
    - type: euclidean_accuracy
      value: 81.8083378451543
    - type: euclidean_ap
      value: 85.43050139514027
    - type: euclidean_f1
      value: 83.25969563082965
    - type: euclidean_precision
      value: 77.79816513761469
    - type: euclidean_recall
      value: 89.54593453009504
    - type: manhattan_accuracy
      value: 81.53762858689767
    - type: manhattan_ap
      value: 84.90556637024838
    - type: manhattan_f1
      value: 82.90258449304174
    - type: manhattan_precision
      value: 78.30985915492957
    - type: manhattan_recall
      value: 88.0675818373812
    - type: max_accuracy
      value: 81.8083378451543
    - type: max_ap
      value: 85.43050139514027
    - type: max_f1
      value: 83.25969563082965
  - task:
      type: Classification
    dataset:
      type: C-MTEB/OnlineShopping-classification
      name: MTEB OnlineShopping
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 93.53
    - type: ap
      value: 91.62070655043128
    - type: f1
      value: 93.51908163199477
  - task:
      type: STS
    dataset:
      type: C-MTEB/PAWSX
      name: MTEB PAWSX
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 38.451787103814375
    - type: cos_sim_spearman
      value: 43.97299462643919
    - type: euclidean_pearson
      value: 43.63298716626501
    - type: euclidean_spearman
      value: 43.973080252178576
    - type: manhattan_pearson
      value: 43.37465277323481
    - type: manhattan_spearman
      value: 43.71981281220414
  - task:
      type: STS
    dataset:
      type: C-MTEB/QBQTC
      name: MTEB QBQTC
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 37.75882451277358
    - type: cos_sim_spearman
      value: 40.0244327844802
    - type: euclidean_pearson
      value: 38.11050875514246
    - type: euclidean_spearman
      value: 40.02440987254504
    - type: manhattan_pearson
      value: 38.03186803221696
    - type: manhattan_spearman
      value: 39.757452890246775
  - 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: 65.9133992390713
    - type: cos_sim_spearman
      value: 66.4894937647578
    - type: euclidean_pearson
      value: 66.19047142189935
    - type: euclidean_spearman
      value: 66.4894937647578
    - type: manhattan_pearson
      value: 66.6960935896136
    - type: manhattan_spearman
      value: 66.88179996508133
  - task:
      type: STS
    dataset:
      type: C-MTEB/STSB
      name: MTEB STSB
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 80.55099417946924
    - type: cos_sim_spearman
      value: 83.05000687568048
    - type: euclidean_pearson
      value: 82.62744668792926
    - type: euclidean_spearman
      value: 83.05000687568048
    - type: manhattan_pearson
      value: 82.6543207325763
    - type: manhattan_spearman
      value: 83.06852715971705
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/T2Reranking
      name: MTEB T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 66.48634798223672
    - type: mrr
      value: 76.30158461488861
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/T2Retrieval
      name: MTEB T2Retrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 27.483999999999998
    - type: map_at_10
      value: 76.848
    - type: map_at_100
      value: 80.541
    - type: map_at_1000
      value: 80.607
    - type: map_at_3
      value: 54.111
    - type: map_at_5
      value: 66.46300000000001
    - type: mrr_at_1
      value: 90.045
    - type: mrr_at_10
      value: 92.552
    - type: mrr_at_100
      value: 92.642
    - type: mrr_at_1000
      value: 92.645
    - type: mrr_at_3
      value: 92.134
    - type: mrr_at_5
      value: 92.391
    - type: ndcg_at_1
      value: 90.045
    - type: ndcg_at_10
      value: 84.504
    - type: ndcg_at_100
      value: 88.23100000000001
    - type: ndcg_at_1000
      value: 88.85300000000001
    - type: ndcg_at_3
      value: 85.992
    - type: ndcg_at_5
      value: 84.548
    - type: precision_at_1
      value: 90.045
    - type: precision_at_10
      value: 41.91
    - type: precision_at_100
      value: 5.017
    - type: precision_at_1000
      value: 0.516
    - type: precision_at_3
      value: 75.15899999999999
    - type: precision_at_5
      value: 62.958000000000006
    - type: recall_at_1
      value: 27.483999999999998
    - type: recall_at_10
      value: 83.408
    - type: recall_at_100
      value: 95.514
    - type: recall_at_1000
      value: 98.65
    - type: recall_at_3
      value: 55.822
    - type: recall_at_5
      value: 69.868
  - task:
      type: Classification
    dataset:
      type: C-MTEB/TNews-classification
      name: MTEB TNews
      config: default
      split: validation
      revision: None
    metrics:
    - type: accuracy
      value: 53.196
    - type: f1
      value: 51.51679244513836
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringP2P
      name: MTEB ThuNewsClusteringP2P
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 67.87592101539063
  - task:
      type: Clustering
    dataset:
      type: C-MTEB/ThuNewsClusteringS2S
      name: MTEB ThuNewsClusteringS2S
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 62.4675464095125
  - task:
      type: Retrieval
    dataset:
      type: C-MTEB/VideoRetrieval
      name: MTEB VideoRetrieval
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 57.9
    - type: map_at_10
      value: 68.099
    - type: map_at_100
      value: 68.55499999999999
    - type: map_at_1000
      value: 68.566
    - type: map_at_3
      value: 66.4
    - type: map_at_5
      value: 67.46
    - type: mrr_at_1
      value: 57.9
    - type: mrr_at_10
      value: 68.099
    - type: mrr_at_100
      value: 68.55499999999999
    - type: mrr_at_1000
      value: 68.566
    - type: mrr_at_3
      value: 66.4
    - type: mrr_at_5
      value: 67.46
    - type: ndcg_at_1
      value: 57.9
    - type: ndcg_at_10
      value: 72.555
    - type: ndcg_at_100
      value: 74.715
    - type: ndcg_at_1000
      value: 75.034
    - type: ndcg_at_3
      value: 69.102
    - type: ndcg_at_5
      value: 71.004
    - type: precision_at_1
      value: 57.9
    - type: precision_at_10
      value: 8.63
    - type: precision_at_100
      value: 0.963
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 25.633
    - type: precision_at_5
      value: 16.3
    - type: recall_at_1
      value: 57.9
    - type: recall_at_10
      value: 86.3
    - type: recall_at_100
      value: 96.3
    - type: recall_at_1000
      value: 98.9
    - type: recall_at_3
      value: 76.9
    - type: recall_at_5
      value: 81.5
  - task:
      type: Classification
    dataset:
      type: C-MTEB/waimai-classification
      name: MTEB Waimai
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 87.27000000000001
    - type: ap
      value: 71.10883470119464
    - type: f1
      value: 85.76618863591946
license: mit
---

**新闻 | News**

**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型,**专门针对检索和语义匹配任务,更多的考虑泛化性和私有通用测试集效果,向量维度可变,中英双语****[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型,支持**向量可变维度****[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。

**[2023-10-19]** 开源stella-base-en-v2 使用简单,**不需要任何前缀文本****[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单,**不需要任何前缀文本****[2023-09-11]** 开源stella-base-zh和stella-large-zh

欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型,并提出您的宝贵意见!

# 1 开源清单

本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型,同时开源全量160万对话重写数据集和20万的难负例的检索数据集。

**开源模型:**

| ModelName                                                                                                     | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score |   
|---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------|
| [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d)                     | 0.4GB     | 512       | 1792                | zh-CN    | 通用文本     | 67.96        |   
| [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d)                   | 1.3GB     | 512       | 1792                | zh-CN    | 通用文本     | 68.48        |   
| [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB     | 512       | 1792                | zh-CN    | **对话文本** | 不适用          |

**开源数据:**

1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万
2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万

上述数据集均使用LLM构造,欢迎各位贡献数据集。

# 2 使用方法

## 2.1 通用编码模型使用方法

直接SentenceTransformer加载即可:

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d")
# model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
vectors = model.encode(["text1", "text2"])
```

## 2.2 dialogue编码模型使用方法

**使用场景:**
**在一段对话中,需要根据用户语句去检索相关文本,但是对话中的用户语句存在大量的指代和省略,导致直接使用通用编码模型效果不好,
可以使用本项目的专门的dialogue编码模型进行编码**

**使用要点:**

1. 对dialogue进行编码时,dialogue中的每个utterance需要是如下格式:`"{ROLE}: {TEXT}"`,然后使用`[SEP]` join一下
2. 整个对话都要送入模型进行编码,如果长度不够就删掉早期的对话,**编码后的向量本质是对话中最后一句话的重写版本的向量!!**
3. 对话用stella-dialogue-large-zh-v3-1792d编码,被检索文本使用stella-large-zh-v3-1792d进行编码,所以本场景是需要2个编码模型的

如果对使用方法还有疑惑,请到下面章节阅读该模型是如何训练的。

使用示例:

```python
from sentence_transformers import SentenceTransformer

dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d")
general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
# dialogue = ["张三: 吃饭吗", "李四: 等会去"]
dialogue = ["A: 最近去打篮球了吗", "B: 没有"]
corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"]
last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True)
corpus_vectors = general_model.encode(corpus, normalize_embeddings=True)
# 计算相似度
sims = (last_utterance_vector * corpus_vectors).sum(axis=1)
print(sims)
```

# 3 通用编码模型训练技巧分享

## hard negative

难负例挖掘也是个经典的trick了,几乎总能提升效果

## dropout-1d

dropout已经是深度学习的标配,我们可以稍微改造下使其更适合句向量的训练。
我们在训练时会尝试让每一个token-embedding都可以表征整个句子,而在推理时使用mean_pooling从而达到类似模型融合的效果。
具体操作是在mean_pooling时加入dropout_1d,torch代码如下:

```python
vector_dropout = nn.Dropout1d(0.3)  # 算力有限,试了0.3和0.5 两个参数,其中0.3更优
last_hidden_state = bert_model(...)[0]
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
last_hidden = vector_dropout(last_hidden)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
```

# 4 dialogue编码模型细节

## 4.1 为什么需要一个dialogue编码模型?

参见本人历史文章:https://www.zhihu.com/pin/1674913544847077376

## 4.2 训练数据

单条数据示例:

```json
{
  "dialogue": [
    "A: 最近去打篮球了吗",
    "B: 没有"
  ],
  "last_utterance_rewrite": "B: 我最近没有去打篮球"
}
```

## 4.3 训练Loss

```
loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) )
```

dial_model就是要被训练的模型,本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的

existing_model就是现有训练好的**通用编码模型**,本人使用的是stella-large-zh-v3-1792d

已开源dialogue-embedding的全量训练数据,理论上可以复现本模型效果。

Loss下降情况:

<div align="center">
<img src="dial_loss.png" alt="icon" width="2000px"/>
</div>

## 4.4 效果

目前还没有专门测试集,本人简单测试了下是有效果的,部分测试结果见文件`dial_retrieval_test.xlsx`。

# 5 后续TODO

1. 更多的dial-rewrite数据
2. 不同EmbeddingDimensions的编码模型

# 6 FAQ

Q: 为什么向量维度是1792?\
A: 最初考虑发布768、1024,768+768,1024+1024,1024+768维度,但是时间有限,先做了1792就只发布1792维度的模型。理论上维度越高效果越好。

Q: 如何复现CMTEB效果?\
A: SentenceTransformer加载后直接用官方评测脚本就行,注意对于Classification任务向量需要先normalize一下

Q: 复现的CMTEB效果和本文不一致?\
A: 聚类不一致正常,官方评测代码没有设定seed,其他不一致建议检查代码或联系本人。

Q: 如何选择向量模型?\
A: 没有免费的午餐,在自己测试集上试试,本人推荐bge、e5和stella.

Q: 长度为什么只有512,能否更长?\
A: 可以但没必要,长了效果普遍不好,这是当前训练方法和数据导致的,几乎无解,建议长文本还是走分块。

Q: 训练资源和算力?\
A: 亿级别的数据,单卡A100要一个月起步