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Token Classification
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PyTorch
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bert
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test

This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on the conll2003 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7372
  • Precision: 0.7696
  • Recall: 0.8396
  • F1: 0.8031
  • Accuracy: 0.8847

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 2 1.9496 0.0 0.0 0.0 0.4889
No log 2.0 4 1.6137 0.0 0.0 0.0 0.4919
No log 3.0 6 1.3906 0.0 0.0 0.0 0.5650
No log 4.0 8 1.2273 0.0652 0.0481 0.0554 0.6856
No log 5.0 10 1.0565 0.2051 0.1711 0.1866 0.7125
No log 6.0 12 0.9150 0.5094 0.4332 0.4682 0.7540
No log 7.0 14 0.8051 0.5988 0.5187 0.5559 0.7679
No log 8.0 16 0.7151 0.6707 0.5989 0.6328 0.7763
No log 9.0 18 0.6334 0.6685 0.6364 0.6521 0.8086
No log 10.0 20 0.5693 0.6957 0.6845 0.6900 0.8201
No log 11.0 22 0.5192 0.7166 0.7166 0.7166 0.8363
No log 12.0 24 0.4736 0.7135 0.7326 0.7230 0.8524
No log 13.0 26 0.4448 0.6938 0.7754 0.7323 0.8555
No log 14.0 28 0.4280 0.7177 0.8021 0.7576 0.8586
No log 15.0 30 0.4179 0.7588 0.8075 0.7824 0.8663
No log 16.0 32 0.4214 0.7356 0.8182 0.7747 0.8593
No log 17.0 34 0.4070 0.7391 0.8182 0.7766 0.8616
No log 18.0 36 0.4112 0.7586 0.8235 0.7897 0.8724
No log 19.0 38 0.4530 0.7330 0.8075 0.7684 0.8693
No log 20.0 40 0.4719 0.7766 0.8182 0.7969 0.8732
No log 21.0 42 0.4886 0.7260 0.8075 0.7646 0.8632
No log 22.0 44 0.5007 0.7217 0.8182 0.7669 0.8701
No log 23.0 46 0.5169 0.7321 0.8182 0.7727 0.8762
No log 24.0 48 0.5531 0.7238 0.8128 0.7657 0.8724
No log 25.0 50 0.5895 0.7311 0.8289 0.7769 0.8655
No log 26.0 52 0.5482 0.7330 0.8075 0.7684 0.8778
No log 27.0 54 0.5361 0.7488 0.8128 0.7795 0.8832
No log 28.0 56 0.5378 0.7427 0.8182 0.7786 0.8847
No log 29.0 58 0.5543 0.7371 0.8396 0.7850 0.8824
No log 30.0 60 0.5564 0.7585 0.8396 0.7970 0.8839
No log 31.0 62 0.5829 0.7235 0.8396 0.7772 0.8724
No log 32.0 64 0.5974 0.7269 0.8396 0.7792 0.8716
No log 33.0 66 0.5750 0.7610 0.8342 0.7959 0.8839
No log 34.0 68 0.5887 0.7723 0.8342 0.8021 0.8878
No log 35.0 70 0.6219 0.7441 0.8396 0.7889 0.8747
No log 36.0 72 0.6676 0.7269 0.8396 0.7792 0.8632
No log 37.0 74 0.6517 0.7452 0.8289 0.7848 0.8693
No log 38.0 76 0.6346 0.7828 0.8289 0.8052 0.8862
No log 39.0 78 0.6239 0.7839 0.8342 0.8083 0.8855
No log 40.0 80 0.6360 0.7277 0.8289 0.775 0.8762
No log 41.0 82 0.6645 0.7336 0.8396 0.7830 0.8701
No log 42.0 84 0.6611 0.7406 0.8396 0.7870 0.8747
No log 43.0 86 0.6707 0.7488 0.8289 0.7868 0.8762
No log 44.0 88 0.6901 0.7277 0.8289 0.775 0.8709
No log 45.0 90 0.6911 0.7393 0.8342 0.7839 0.8709
No log 46.0 92 0.6540 0.7761 0.8342 0.8041 0.8878
No log 47.0 94 0.6381 0.7761 0.8342 0.8041 0.8916
No log 48.0 96 0.6285 0.7745 0.8449 0.8082 0.8885
No log 49.0 98 0.6449 0.7692 0.8556 0.8101 0.8862
No log 50.0 100 0.6809 0.7442 0.8556 0.7960 0.8732
No log 51.0 102 0.6898 0.7395 0.8503 0.7910 0.8716
No log 52.0 104 0.6897 0.75 0.8503 0.7970 0.8762
No log 53.0 106 0.6714 0.7656 0.8556 0.8081 0.8855
No log 54.0 108 0.6612 0.7692 0.8556 0.8101 0.8855
No log 55.0 110 0.6583 0.7692 0.8556 0.8101 0.8855
No log 56.0 112 0.6648 0.7692 0.8556 0.8101 0.8855
No log 57.0 114 0.6757 0.7656 0.8556 0.8081 0.8832
No log 58.0 116 0.6803 0.7656 0.8556 0.8081 0.8839
No log 59.0 118 0.6834 0.7692 0.8556 0.8101 0.8862
No log 60.0 120 0.6889 0.7833 0.8503 0.8154 0.8878
No log 61.0 122 0.6963 0.7772 0.8396 0.8072 0.8862
No log 62.0 124 0.7057 0.7772 0.8396 0.8072 0.8862
No log 63.0 126 0.7212 0.7910 0.8503 0.8196 0.8862
No log 64.0 128 0.7334 0.7833 0.8503 0.8154 0.8824
No log 65.0 130 0.7398 0.7833 0.8503 0.8154 0.8801
No log 66.0 132 0.7400 0.7833 0.8503 0.8154 0.8809
No log 67.0 134 0.7345 0.7783 0.8449 0.8103 0.8855
No log 68.0 136 0.7270 0.79 0.8449 0.8165 0.8870
No log 69.0 138 0.7245 0.7839 0.8342 0.8083 0.8862
No log 70.0 140 0.7260 0.7868 0.8289 0.8073 0.8847
No log 71.0 142 0.7275 0.7817 0.8235 0.8021 0.8839
No log 72.0 144 0.7283 0.7778 0.8235 0.8000 0.8832
No log 73.0 146 0.7296 0.78 0.8342 0.8062 0.8847
No log 74.0 148 0.7344 0.7734 0.8396 0.8051 0.8832
No log 75.0 150 0.7314 0.7745 0.8449 0.8082 0.8824
No log 76.0 152 0.7299 0.7794 0.8503 0.8133 0.8832
No log 77.0 154 0.7282 0.7794 0.8503 0.8133 0.8839
No log 78.0 156 0.7252 0.7783 0.8449 0.8103 0.8839
No log 79.0 158 0.7216 0.7756 0.8503 0.8112 0.8855
No log 80.0 160 0.7194 0.7756 0.8503 0.8112 0.8870
No log 81.0 162 0.7191 0.7756 0.8503 0.8112 0.8878
No log 82.0 164 0.7201 0.7696 0.8396 0.8031 0.8862
No log 83.0 166 0.7211 0.7696 0.8396 0.8031 0.8862
No log 84.0 168 0.7222 0.7696 0.8396 0.8031 0.8862
No log 85.0 170 0.7220 0.7696 0.8396 0.8031 0.8862
No log 86.0 172 0.7239 0.7734 0.8396 0.8051 0.8870
No log 87.0 174 0.7291 0.7772 0.8396 0.8072 0.8847
No log 88.0 176 0.7344 0.7745 0.8449 0.8082 0.8824
No log 89.0 178 0.7373 0.7745 0.8449 0.8082 0.8824
No log 90.0 180 0.7391 0.7707 0.8449 0.8061 0.8832
No log 91.0 182 0.7403 0.7745 0.8449 0.8082 0.8824
No log 92.0 184 0.7412 0.7745 0.8449 0.8082 0.8832
No log 93.0 186 0.7417 0.7707 0.8449 0.8061 0.8832
No log 94.0 188 0.7402 0.7745 0.8449 0.8082 0.8839
No log 95.0 190 0.7389 0.7745 0.8449 0.8082 0.8847
No log 96.0 192 0.7381 0.7696 0.8396 0.8031 0.8839
No log 97.0 194 0.7377 0.7696 0.8396 0.8031 0.8847
No log 98.0 196 0.7374 0.7696 0.8396 0.8031 0.8847
No log 99.0 198 0.7372 0.7696 0.8396 0.8031 0.8847
No log 100.0 200 0.7372 0.7696 0.8396 0.8031 0.8847

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.1.0
  • Tokenizers 0.12.1
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Dataset used to train vegetable/test

Evaluation results

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