README.md · infgrad/stella-base-zh at a6d4ffac6c4e4c59fa8720a07c0498e5745860f9

metadata

tags:
  - mteb
model-index:
  - name: stella-base-zh
    results:
      - task:
          type: STS
        dataset:
          type: C-MTEB/AFQMC
          name: MTEB AFQMC
          config: default
          split: validation
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 49.34825050234731
          - type: cos_sim_spearman
            value: 51.74726338428475
          - type: euclidean_pearson
            value: 50.14955499038012
          - type: euclidean_spearman
            value: 51.74730359287025
          - type: manhattan_pearson
            value: 50.016703594410615
          - type: manhattan_spearman
            value: 51.63936364317057
      - task:
          type: STS
        dataset:
          type: C-MTEB/ATEC
          name: MTEB ATEC
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 52.26876163587667
          - type: cos_sim_spearman
            value: 52.818410137444374
          - type: euclidean_pearson
            value: 55.24925286208574
          - type: euclidean_spearman
            value: 52.818404507964686
          - type: manhattan_pearson
            value: 55.21236977375391
          - type: manhattan_spearman
            value: 52.80289117015117
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (zh)
          config: zh
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 40.245999999999995
          - type: f1
            value: 38.55443674287747
      - task:
          type: STS
        dataset:
          type: C-MTEB/BQ
          name: MTEB BQ
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 61.553652835163255
          - type: cos_sim_spearman
            value: 63.29065064027392
          - type: euclidean_pearson
            value: 62.000329557485
          - type: euclidean_spearman
            value: 63.290650638944825
          - type: manhattan_pearson
            value: 62.02786936153664
          - type: manhattan_spearman
            value: 63.32720383880146
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/CLSClusteringP2P
          name: MTEB CLSClusteringP2P
          config: default
          split: test
          revision: None
        metrics:
          - type: v_measure
            value: 39.71224230526474
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/CLSClusteringS2S
          name: MTEB CLSClusteringS2S
          config: default
          split: test
          revision: None
        metrics:
          - type: v_measure
            value: 36.55705201882987
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/CMedQAv1-reranking
          name: MTEB CMedQAv1
          config: default
          split: test
          revision: None
        metrics:
          - type: map
            value: 85.69418720521168
          - type: mrr
            value: 87.97444444444446
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/CMedQAv2-reranking
          name: MTEB CMedQAv2
          config: default
          split: test
          revision: None
        metrics:
          - type: map
            value: 86.46348358482606
          - type: mrr
            value: 88.81428571428572
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/CmedqaRetrieval
          name: MTEB CmedqaRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 23.721
          - type: map_at_10
            value: 35.428
          - type: map_at_100
            value: 37.438
          - type: map_at_1000
            value: 37.557
          - type: map_at_3
            value: 31.589
          - type: map_at_5
            value: 33.647
          - type: mrr_at_1
            value: 36.709
          - type: mrr_at_10
            value: 44.590999999999994
          - type: mrr_at_100
            value: 45.684999999999995
          - type: mrr_at_1000
            value: 45.732
          - type: mrr_at_3
            value: 42.331
          - type: mrr_at_5
            value: 43.532
          - type: ndcg_at_1
            value: 36.709
          - type: ndcg_at_10
            value: 41.858000000000004
          - type: ndcg_at_100
            value: 49.775999999999996
          - type: ndcg_at_1000
            value: 51.844
          - type: ndcg_at_3
            value: 37.067
          - type: ndcg_at_5
            value: 38.875
          - type: precision_at_1
            value: 36.709
          - type: precision_at_10
            value: 9.411999999999999
          - type: precision_at_100
            value: 1.5709999999999997
          - type: precision_at_1000
            value: 0.183
          - type: precision_at_3
            value: 21.154999999999998
          - type: precision_at_5
            value: 15.184000000000001
          - type: recall_at_1
            value: 23.721
          - type: recall_at_10
            value: 51.714000000000006
          - type: recall_at_100
            value: 84.60600000000001
          - type: recall_at_1000
            value: 98.414
          - type: recall_at_3
            value: 37.091
          - type: recall_at_5
            value: 42.978
      - task:
          type: PairClassification
        dataset:
          type: C-MTEB/CMNLI
          name: MTEB Cmnli
          config: default
          split: validation
          revision: None
        metrics:
          - type: cos_sim_accuracy
            value: 73.61395069152135
          - type: cos_sim_ap
            value: 81.65459344597652
          - type: cos_sim_f1
            value: 75.66718995290425
          - type: cos_sim_precision
            value: 68.4918529746116
          - type: cos_sim_recall
            value: 84.5218611176058
          - type: dot_accuracy
            value: 73.61395069152135
          - type: dot_ap
            value: 81.64596407363373
          - type: dot_f1
            value: 75.66718995290425
          - type: dot_precision
            value: 68.4918529746116
          - type: dot_recall
            value: 84.5218611176058
          - type: euclidean_accuracy
            value: 73.61395069152135
          - type: euclidean_ap
            value: 81.6546013070452
          - type: euclidean_f1
            value: 75.66718995290425
          - type: euclidean_precision
            value: 68.4918529746116
          - type: euclidean_recall
            value: 84.5218611176058
          - type: manhattan_accuracy
            value: 73.51773902585688
          - type: manhattan_ap
            value: 81.57345451483191
          - type: manhattan_f1
            value: 75.7393958530681
          - type: manhattan_precision
            value: 68.87442572741195
          - type: manhattan_recall
            value: 84.12438625204582
          - type: max_accuracy
            value: 73.61395069152135
          - type: max_ap
            value: 81.6546013070452
          - type: max_f1
            value: 75.7393958530681
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/CovidRetrieval
          name: MTEB CovidRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 73.551
          - type: map_at_10
            value: 81.513
          - type: map_at_100
            value: 81.734
          - type: map_at_1000
            value: 81.73700000000001
          - type: map_at_3
            value: 80.27300000000001
          - type: map_at_5
            value: 81.017
          - type: mrr_at_1
            value: 73.762
          - type: mrr_at_10
            value: 81.479
          - type: mrr_at_100
            value: 81.699
          - type: mrr_at_1000
            value: 81.702
          - type: mrr_at_3
            value: 80.33
          - type: mrr_at_5
            value: 80.999
          - type: ndcg_at_1
            value: 73.867
          - type: ndcg_at_10
            value: 84.711
          - type: ndcg_at_100
            value: 85.714
          - type: ndcg_at_1000
            value: 85.803
          - type: ndcg_at_3
            value: 82.244
          - type: ndcg_at_5
            value: 83.514
          - type: precision_at_1
            value: 73.867
          - type: precision_at_10
            value: 9.557
          - type: precision_at_100
            value: 1.001
          - type: precision_at_1000
            value: 0.101
          - type: precision_at_3
            value: 29.505
          - type: precision_at_5
            value: 18.377
          - type: recall_at_1
            value: 73.551
          - type: recall_at_10
            value: 94.521
          - type: recall_at_100
            value: 99.05199999999999
          - type: recall_at_1000
            value: 99.789
          - type: recall_at_3
            value: 87.777
          - type: recall_at_5
            value: 90.83200000000001
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/DuRetrieval
          name: MTEB DuRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 26.230999999999998
          - type: map_at_10
            value: 80.635
          - type: map_at_100
            value: 83.393
          - type: map_at_1000
            value: 83.431
          - type: map_at_3
            value: 55.717000000000006
          - type: map_at_5
            value: 70.387
          - type: mrr_at_1
            value: 90.75
          - type: mrr_at_10
            value: 93.569
          - type: mrr_at_100
            value: 93.648
          - type: mrr_at_1000
            value: 93.65
          - type: mrr_at_3
            value: 93.27499999999999
          - type: mrr_at_5
            value: 93.482
          - type: ndcg_at_1
            value: 90.75
          - type: ndcg_at_10
            value: 87.801
          - type: ndcg_at_100
            value: 90.44
          - type: ndcg_at_1000
            value: 90.776
          - type: ndcg_at_3
            value: 86.556
          - type: ndcg_at_5
            value: 85.468
          - type: precision_at_1
            value: 90.75
          - type: precision_at_10
            value: 42.08
          - type: precision_at_100
            value: 4.816
          - type: precision_at_1000
            value: 0.49
          - type: precision_at_3
            value: 77.60000000000001
          - type: precision_at_5
            value: 65.49000000000001
          - type: recall_at_1
            value: 26.230999999999998
          - type: recall_at_10
            value: 89.00200000000001
          - type: recall_at_100
            value: 97.866
          - type: recall_at_1000
            value: 99.569
          - type: recall_at_3
            value: 57.778
          - type: recall_at_5
            value: 74.895
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/EcomRetrieval
          name: MTEB EcomRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 47.599999999999994
          - type: map_at_10
            value: 57.296
          - type: map_at_100
            value: 58.011
          - type: map_at_1000
            value: 58.028
          - type: map_at_3
            value: 54.300000000000004
          - type: map_at_5
            value: 56.21000000000001
          - type: mrr_at_1
            value: 47.599999999999994
          - type: mrr_at_10
            value: 57.296
          - type: mrr_at_100
            value: 58.011
          - type: mrr_at_1000
            value: 58.028
          - type: mrr_at_3
            value: 54.300000000000004
          - type: mrr_at_5
            value: 56.21000000000001
          - type: ndcg_at_1
            value: 47.599999999999994
          - type: ndcg_at_10
            value: 62.458000000000006
          - type: ndcg_at_100
            value: 65.589
          - type: ndcg_at_1000
            value: 66.059
          - type: ndcg_at_3
            value: 56.364000000000004
          - type: ndcg_at_5
            value: 59.815
          - type: precision_at_1
            value: 47.599999999999994
          - type: precision_at_10
            value: 7.89
          - type: precision_at_100
            value: 0.928
          - type: precision_at_1000
            value: 0.097
          - type: precision_at_3
            value: 20.767
          - type: precision_at_5
            value: 14.14
          - type: recall_at_1
            value: 47.599999999999994
          - type: recall_at_10
            value: 78.9
          - type: recall_at_100
            value: 92.80000000000001
          - type: recall_at_1000
            value: 96.6
          - type: recall_at_3
            value: 62.3
          - type: recall_at_5
            value: 70.7
      - task:
          type: Classification
        dataset:
          type: C-MTEB/IFlyTek-classification
          name: MTEB IFlyTek
          config: default
          split: validation
          revision: None
        metrics:
          - type: accuracy
            value: 47.46440938822624
          - type: f1
            value: 34.587004997852524
      - task:
          type: Classification
        dataset:
          type: C-MTEB/JDReview-classification
          name: MTEB JDReview
          config: default
          split: test
          revision: None
        metrics:
          - type: accuracy
            value: 84.9906191369606
          - type: ap
            value: 52.31309789960497
          - type: f1
            value: 79.55556102310072
      - task:
          type: STS
        dataset:
          type: C-MTEB/LCQMC
          name: MTEB LCQMC
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 69.80872804636063
          - type: cos_sim_spearman
            value: 75.83290476813391
          - type: euclidean_pearson
            value: 74.09865882324753
          - type: euclidean_spearman
            value: 75.83290698376118
          - type: manhattan_pearson
            value: 74.0616102379577
          - type: manhattan_spearman
            value: 75.81278969865738
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/MMarcoRetrieval
          name: MTEB MMarcoRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 65.029
          - type: map_at_10
            value: 74.39
          - type: map_at_100
            value: 74.734
          - type: map_at_1000
            value: 74.74300000000001
          - type: map_at_3
            value: 72.52
          - type: map_at_5
            value: 73.724
          - type: mrr_at_1
            value: 67.192
          - type: mrr_at_10
            value: 74.95100000000001
          - type: mrr_at_100
            value: 75.25500000000001
          - type: mrr_at_1000
            value: 75.263
          - type: mrr_at_3
            value: 73.307
          - type: mrr_at_5
            value: 74.355
          - type: ndcg_at_1
            value: 67.192
          - type: ndcg_at_10
            value: 78.22200000000001
          - type: ndcg_at_100
            value: 79.76299999999999
          - type: ndcg_at_1000
            value: 80.018
          - type: ndcg_at_3
            value: 74.656
          - type: ndcg_at_5
            value: 76.697
          - type: precision_at_1
            value: 67.192
          - type: precision_at_10
            value: 9.513
          - type: precision_at_100
            value: 1.027
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_3
            value: 28.204
          - type: precision_at_5
            value: 18.009
          - type: recall_at_1
            value: 65.029
          - type: recall_at_10
            value: 89.462
          - type: recall_at_100
            value: 96.418
          - type: recall_at_1000
            value: 98.409
          - type: recall_at_3
            value: 80.029
          - type: recall_at_5
            value: 84.882
      - 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: 65.56489576328177
          - type: f1
            value: 63.37174551232159
      - 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: 71.4862138533961
          - type: f1
            value: 71.171374964826
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/MedicalRetrieval
          name: MTEB MedicalRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 48.6
          - type: map_at_10
            value: 54.92700000000001
          - type: map_at_100
            value: 55.528
          - type: map_at_1000
            value: 55.584
          - type: map_at_3
            value: 53.55
          - type: map_at_5
            value: 54.379999999999995
          - type: mrr_at_1
            value: 48.8
          - type: mrr_at_10
            value: 55.028999999999996
          - type: mrr_at_100
            value: 55.629
          - type: mrr_at_1000
            value: 55.684999999999995
          - type: mrr_at_3
            value: 53.65
          - type: mrr_at_5
            value: 54.48
          - type: ndcg_at_1
            value: 48.6
          - type: ndcg_at_10
            value: 57.965999999999994
          - type: ndcg_at_100
            value: 61.043000000000006
          - type: ndcg_at_1000
            value: 62.624
          - type: ndcg_at_3
            value: 55.132000000000005
          - type: ndcg_at_5
            value: 56.621
          - type: precision_at_1
            value: 48.6
          - type: precision_at_10
            value: 6.75
          - type: precision_at_100
            value: 0.823
          - type: precision_at_1000
            value: 0.095
          - type: precision_at_3
            value: 19.900000000000002
          - type: precision_at_5
            value: 12.659999999999998
          - type: recall_at_1
            value: 48.6
          - type: recall_at_10
            value: 67.5
          - type: recall_at_100
            value: 82.3
          - type: recall_at_1000
            value: 94.89999999999999
          - type: recall_at_3
            value: 59.699999999999996
          - type: recall_at_5
            value: 63.3
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/Mmarco-reranking
          name: MTEB MMarcoReranking
          config: default
          split: dev
          revision: None
        metrics:
          - type: map
            value: 29.196130696027474
          - type: mrr
            value: 28.43730158730159
      - task:
          type: Classification
        dataset:
          type: C-MTEB/MultilingualSentiment-classification
          name: MTEB MultilingualSentiment
          config: default
          split: validation
          revision: None
        metrics:
          - type: accuracy
            value: 72.48333333333333
          - type: f1
            value: 72.00258522357558
      - task:
          type: PairClassification
        dataset:
          type: C-MTEB/OCNLI
          name: MTEB Ocnli
          config: default
          split: validation
          revision: None
        metrics:
          - type: cos_sim_accuracy
            value: 65.13264753654575
          - type: cos_sim_ap
            value: 70.52831936800807
          - type: cos_sim_f1
            value: 71.35353535353535
          - type: cos_sim_precision
            value: 57.787958115183244
          - type: cos_sim_recall
            value: 93.24181626187962
          - type: dot_accuracy
            value: 65.13264753654575
          - type: dot_ap
            value: 70.52828597418102
          - type: dot_f1
            value: 71.35353535353535
          - type: dot_precision
            value: 57.787958115183244
          - type: dot_recall
            value: 93.24181626187962
          - type: euclidean_accuracy
            value: 65.13264753654575
          - type: euclidean_ap
            value: 70.52828597418102
          - type: euclidean_f1
            value: 71.35353535353535
          - type: euclidean_precision
            value: 57.787958115183244
          - type: euclidean_recall
            value: 93.24181626187962
          - type: manhattan_accuracy
            value: 64.8077964266378
          - type: manhattan_ap
            value: 70.39954487476643
          - type: manhattan_f1
            value: 71.2270200940573
          - type: manhattan_precision
            value: 59.84195402298851
          - type: manhattan_recall
            value: 87.96198521647307
          - type: max_accuracy
            value: 65.13264753654575
          - type: max_ap
            value: 70.52831936800807
          - type: max_f1
            value: 71.35353535353535
      - task:
          type: Classification
        dataset:
          type: C-MTEB/OnlineShopping-classification
          name: MTEB OnlineShopping
          config: default
          split: test
          revision: None
        metrics:
          - type: accuracy
            value: 90.34
          - type: ap
            value: 87.79622626876444
          - type: f1
            value: 90.32357430051181
      - task:
          type: STS
        dataset:
          type: C-MTEB/PAWSX
          name: MTEB PAWSX
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 27.9175458105215
          - type: cos_sim_spearman
            value: 32.024302491613014
          - type: euclidean_pearson
            value: 33.01780461609846
          - type: euclidean_spearman
            value: 32.024301939183374
          - type: manhattan_pearson
            value: 32.94874897942371
          - type: manhattan_spearman
            value: 31.902283210178012
      - task:
          type: STS
        dataset:
          type: C-MTEB/QBQTC
          name: MTEB QBQTC
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 36.288219964332754
          - type: cos_sim_spearman
            value: 36.46838652731507
          - type: euclidean_pearson
            value: 35.11414028811812
          - type: euclidean_spearman
            value: 36.468386523814104
          - type: manhattan_pearson
            value: 35.20922826624027
          - type: manhattan_spearman
            value: 36.55349180906185
      - 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: 66.18186265837434
          - type: cos_sim_spearman
            value: 67.52365178443915
          - type: euclidean_pearson
            value: 65.46342439169497
          - type: euclidean_spearman
            value: 67.52365178443915
          - type: manhattan_pearson
            value: 67.3476263677961
          - type: manhattan_spearman
            value: 69.09476240936812
      - task:
          type: STS
        dataset:
          type: C-MTEB/STSB
          name: MTEB STSB
          config: default
          split: test
          revision: None
        metrics:
          - type: cos_sim_pearson
            value: 72.53864906415339
          - type: cos_sim_spearman
            value: 72.63037820118355
          - type: euclidean_pearson
            value: 72.42255276991672
          - type: euclidean_spearman
            value: 72.63037820118355
          - type: manhattan_pearson
            value: 72.36324244766192
          - type: manhattan_spearman
            value: 72.58609772740323
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/T2Reranking
          name: MTEB T2Reranking
          config: default
          split: dev
          revision: None
        metrics:
          - type: map
            value: 66.45708148192449
          - type: mrr
            value: 76.08372693469173
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/T2Retrieval
          name: MTEB T2Retrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 26.436999999999998
          - type: map_at_10
            value: 74.516
          - type: map_at_100
            value: 78.29899999999999
          - type: map_at_1000
            value: 78.372
          - type: map_at_3
            value: 52.217
          - type: map_at_5
            value: 64.24
          - type: mrr_at_1
            value: 88.23
          - type: mrr_at_10
            value: 91.06400000000001
          - type: mrr_at_100
            value: 91.18
          - type: mrr_at_1000
            value: 91.184
          - type: mrr_at_3
            value: 90.582
          - type: mrr_at_5
            value: 90.88300000000001
          - type: ndcg_at_1
            value: 88.23
          - type: ndcg_at_10
            value: 82.511
          - type: ndcg_at_100
            value: 86.531
          - type: ndcg_at_1000
            value: 87.244
          - type: ndcg_at_3
            value: 83.987
          - type: ndcg_at_5
            value: 82.46900000000001
          - type: precision_at_1
            value: 88.23
          - type: precision_at_10
            value: 41.245
          - type: precision_at_100
            value: 4.987
          - type: precision_at_1000
            value: 0.515
          - type: precision_at_3
            value: 73.675
          - type: precision_at_5
            value: 61.71
          - type: recall_at_1
            value: 26.436999999999998
          - type: recall_at_10
            value: 81.547
          - type: recall_at_100
            value: 94.548
          - type: recall_at_1000
            value: 98.197
          - type: recall_at_3
            value: 54.056000000000004
          - type: recall_at_5
            value: 67.93
      - task:
          type: Classification
        dataset:
          type: C-MTEB/TNews-classification
          name: MTEB TNews
          config: default
          split: validation
          revision: None
        metrics:
          - type: accuracy
            value: 50.784
          - type: f1
            value: 48.89471168071432
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/ThuNewsClusteringP2P
          name: MTEB ThuNewsClusteringP2P
          config: default
          split: test
          revision: None
        metrics:
          - type: v_measure
            value: 63.19039347990962
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/ThuNewsClusteringS2S
          name: MTEB ThuNewsClusteringS2S
          config: default
          split: test
          revision: None
        metrics:
          - type: v_measure
            value: 55.357378578603225
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/VideoRetrieval
          name: MTEB VideoRetrieval
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 58.8
          - type: map_at_10
            value: 68.623
          - type: map_at_100
            value: 69.074
          - type: map_at_1000
            value: 69.085
          - type: map_at_3
            value: 66.767
          - type: map_at_5
            value: 67.972
          - type: mrr_at_1
            value: 58.699999999999996
          - type: mrr_at_10
            value: 68.573
          - type: mrr_at_100
            value: 69.024
          - type: mrr_at_1000
            value: 69.035
          - type: mrr_at_3
            value: 66.717
          - type: mrr_at_5
            value: 67.92200000000001
          - type: ndcg_at_1
            value: 58.8
          - type: ndcg_at_10
            value: 73.038
          - type: ndcg_at_100
            value: 75.16199999999999
          - type: ndcg_at_1000
            value: 75.422
          - type: ndcg_at_3
            value: 69.297
          - type: ndcg_at_5
            value: 71.475
          - type: precision_at_1
            value: 58.8
          - type: precision_at_10
            value: 8.67
          - type: precision_at_100
            value: 0.9650000000000001
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_3
            value: 25.533
          - type: precision_at_5
            value: 16.38
          - type: recall_at_1
            value: 58.8
          - type: recall_at_10
            value: 86.7
          - type: recall_at_100
            value: 96.5
          - type: recall_at_1000
            value: 98.5
          - type: recall_at_3
            value: 76.6
          - type: recall_at_5
            value: 81.89999999999999
      - task:
          type: Classification
        dataset:
          type: C-MTEB/waimai-classification
          name: MTEB Waimai
          config: default
          split: test
          revision: None
        metrics:
          - type: accuracy
            value: 86.61999999999999
          - type: ap
            value: 69.93149123197975
          - type: f1
            value: 84.99670691559903

stella model

stella是一个通用的中文文本编码模型，目前有两个版本：base 和 large，2个版本的模型均支持1024的输入长度。

完整的训练思路和训练过程已记录在博客，欢迎阅读讨论。

训练数据：

开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本
在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据

训练方法：

对比学习损失函数
带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例)
EWC(Elastic Weights Consolidation)[4]
cosent loss[5]
每一种类型的数据一个迭代器，分别计算loss进行更新

初始权重：
stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position embedding使用层次分解位置编码[7]进行初始化。
感谢商汤科技研究院开源的piccolo系列模型。

stella is a general-purpose Chinese text encoding model, currently with two versions: base and large, both of them support input lengths of 1024.

The training data mainly includes:

Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater than 512.
A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM.

The loss functions mainly include:

Contrastive learning loss function
Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives)
EWC (Elastic Weights Consolidation)
cosent loss

Model weight initialization:
stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the 512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding.

Training strategy:
One iterator for each type of data, separately calculating the loss.

Metric

C-MTEB leaderboard

stella模型在C-MTEB[8]的结果，评测脚本请参见博客。

Model Name	Model Size (GB)	Dimension	Sequence Length	Average (35)	Classification (9)	Clustering (4)	Pair Classification (2)	Reranking (4)	Retrieval (8)	STS (8)
stella-large-zh	0.65	1024	1024	64.54	67.62	48.65	78.72	65.98	71.02	58.3
stella-base-zh	0.2	768	1024	64.16	67.77	48.7	76.09	66.95	71.07	56.54
piccolo-large-zh	0.65	1024	512	64.11	67.03	47.04	78.38	65.98	70.93	58.02
bge-large-zh	1.3	1024	512	63.96	68.32	48.39	78.94	65.11	71.52	54.98
piccolo-base-zh	0.2	768	512	63.66	66.98	47.12	76.61	66.68	71.2	55.9
bge-large-zh-no-instruct	1.3	1024	512	63.4	68.58	50.01	76.77	64.9	70.54	53
[bge-base-zh	0.41	768	512	62.8	67.07	47.64	77.5	64.91	69.53	54.12

Evaluation for long text

经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的，更致命的是那些长度大于512的文本，其重点都在前半部分这里以CMRC2018的数据为例说明这个问题：

question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？

passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推......

passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。
简言之，现有数据集的2个问题：
1）长度大于512的过少
2）即便大于512，对于检索而言也只需要前512的文本内容
导致无法准确评估模型的长文本编码能力。

为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是：

CMRC2018，通用百科
CAIL，法律阅读理解
DRCD，繁体百科，已转简体
Military，军工问答
Squad，英文阅读理解，已转中文
Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9]

处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing

评测指标为Recall@5, 结果如下：

Dataset	piccolo-base-zh	piccolo-large-zh	bge-base-zh	bge-large-zh	stella-base-zh	stella-large-zh
CMRC2018	94.34	93.82	91.56	93.12	96.08	95.56
CAIL	28.04	33.64	31.22	33.94	34.62	37.18
DRCD	78.25	77.9	78.34	80.26	86.14	84.58
Military	76.61	73.06	75.65	75.81	83.71	80.48
Squad	91.21	86.61	87.87	90.38	93.31	91.21
Multifieldqa_zh	81.41	83.92	83.92	83.42	79.9	80.4
Average	74.98	74.83	74.76	76.15	78.96	78.24

注意： 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。

Usage

本模型是在piccolo基础上训练的，因此用法和piccolo完全一致。
注意：在stella中instruction里的冒号是英文冒号, 即查询: 和结果: 。

在sentence-transformer库中的使用方法：

# 对于短对短数据集，下面是通用的使用方式
from sentence_transformers import SentenceTransformer

sentences = ["数据1", "数据2"]
model = SentenceTransformer('infgrad/stella-base-zh')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# 如果是短对长数据集，推荐添加instruction，来帮助模型更好地进行检索。
# 注意instruction里的是英文的冒号

直接使用transformers库：

from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize

model = AutoModel.from_pretrained('infgrad/stella-base-zh')
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh')
sentences = ["数据1", "数据ABCDEFGH"]
batch_data = tokenizer(
    batch_text_or_text_pairs=sentences,
    padding="longest",
    return_tensors="pt",
    max_length=1024,
    truncation=True,
)
attention_mask = batch_data["attention_mask"]
model_output = model(**batch_data)
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
vectors = normalize(vectors, norm="l2", axis=1, )
print(vectors.shape)  # 2,768

Training Detail

硬件： 单卡A100-80GB

环境： torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing

学习率： 1e-6

batch_size： base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例

数据量： 约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b

ToDoList

评测的稳定性： 评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，基本上可以忽略不计，不影响评测结论。
但是不完全一样还是比较难理解的，本人试了bge和piccolo系列的模型都存在这个问题，个人猜测可能和使用的库、batch_size等环境有关。

更高质量的长文本训练和测试数据： 训练数据多是用13b模型构造的，肯定会存在噪声。测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。

OOD的性能： 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, 它们的效果均比不上BM25。

infgrad
/

stella-base-zh