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  - text: 中央疫情指揮中心臨時記者會宣布全院區為紅區，擴大隔離，但鄭文燦早在七十二小時前就主張，只要是先前在桃園醫院住院、轉院的患者與陪病家屬，都要居家隔離
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license: gpl-3.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: albert-base-chinese-0407-ner results: []

albert-base-chinese-0407-ner

This model is a fine-tuned version of ckiplab/albert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0948
  • Precision: 0.8603
  • Recall: 0.8871
  • F1: 0.8735
  • Accuracy: 0.9704

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
1.3484 0.05 500 0.5395 0.1841 0.1976 0.1906 0.8465
0.3948 0.09 1000 0.2910 0.6138 0.7113 0.6590 0.9263
0.2388 0.14 1500 0.2030 0.6628 0.7797 0.7165 0.9414
0.1864 0.18 2000 0.1729 0.7490 0.7935 0.7706 0.9498
0.1754 0.23 2500 0.1641 0.7415 0.7869 0.7635 0.9505
0.1558 0.28 3000 0.1532 0.7680 0.8002 0.7838 0.9530
0.1497 0.32 3500 0.1424 0.7865 0.8282 0.8068 0.9555
0.1488 0.37 4000 0.1373 0.7887 0.8111 0.7997 0.9553
0.1361 0.42 4500 0.1311 0.7942 0.8382 0.8156 0.9590
0.1335 0.46 5000 0.1264 0.7948 0.8423 0.8179 0.9596
0.1296 0.51 5500 0.1242 0.8129 0.8416 0.8270 0.9603
0.1338 0.55 6000 0.1315 0.7910 0.8588 0.8235 0.9586
0.1267 0.6 6500 0.1193 0.8092 0.8399 0.8243 0.9609
0.1207 0.65 7000 0.1205 0.8021 0.8469 0.8239 0.9601
0.1214 0.69 7500 0.1201 0.7969 0.8489 0.8220 0.9605
0.1168 0.74 8000 0.1134 0.8087 0.8607 0.8339 0.9620
0.1162 0.78 8500 0.1127 0.8177 0.8492 0.8331 0.9625
0.1202 0.83 9000 0.1283 0.7986 0.8550 0.8259 0.9580
0.1135 0.88 9500 0.1101 0.8213 0.8572 0.8389 0.9638
0.1121 0.92 10000 0.1097 0.8190 0.8588 0.8384 0.9635
0.1091 0.97 10500 0.1088 0.8180 0.8521 0.8347 0.9632
0.1058 1.02 11000 0.1085 0.8136 0.8716 0.8416 0.9630
0.0919 1.06 11500 0.1079 0.8309 0.8566 0.8436 0.9646
0.0914 1.11 12000 0.1079 0.8423 0.8542 0.8482 0.9656
0.0921 1.15 12500 0.1109 0.8312 0.8647 0.8476 0.9646
0.0926 1.2 13000 0.1240 0.8413 0.8488 0.8451 0.9637
0.0914 1.25 13500 0.1040 0.8336 0.8666 0.8498 0.9652
0.0917 1.29 14000 0.1032 0.8352 0.8707 0.8526 0.9662
0.0928 1.34 14500 0.1052 0.8347 0.8656 0.8498 0.9651
0.0906 1.38 15000 0.1032 0.8399 0.8619 0.8507 0.9662
0.0903 1.43 15500 0.1074 0.8180 0.8708 0.8436 0.9651
0.0889 1.48 16000 0.0990 0.8367 0.8713 0.8537 0.9670
0.0914 1.52 16500 0.1055 0.8508 0.8506 0.8507 0.9661
0.0934 1.57 17000 0.0979 0.8326 0.8740 0.8528 0.9669
0.0898 1.62 17500 0.1022 0.8393 0.8615 0.8502 0.9668
0.0869 1.66 18000 0.0962 0.8484 0.8762 0.8621 0.9682
0.089 1.71 18500 0.1008 0.8447 0.8714 0.8579 0.9674
0.0927 1.75 19000 0.0986 0.8379 0.8749 0.8560 0.9673
0.0883 1.8 19500 0.0965 0.8518 0.8749 0.8632 0.9688
0.0965 1.85 20000 0.0937 0.8412 0.8766 0.8585 0.9682
0.0834 1.89 20500 0.0920 0.8451 0.8862 0.8652 0.9687
0.0817 1.94 21000 0.0943 0.8439 0.8800 0.8616 0.9686
0.088 1.99 21500 0.0927 0.8483 0.8762 0.8620 0.9683
0.0705 2.03 22000 0.0993 0.8525 0.8783 0.8652 0.9690
0.0709 2.08 22500 0.0976 0.8610 0.8697 0.8653 0.9689
0.0655 2.12 23000 0.0997 0.8585 0.8665 0.8625 0.9683
0.0656 2.17 23500 0.0966 0.8569 0.8822 0.8694 0.9695
0.0698 2.22 24000 0.0955 0.8604 0.8775 0.8689 0.9696
0.065 2.26 24500 0.0971 0.8614 0.8780 0.8696 0.9697
0.0653 2.31 25000 0.0959 0.8600 0.8787 0.8692 0.9698
0.0685 2.35 25500 0.1001 0.8610 0.8710 0.8659 0.9690
0.0684 2.4 26000 0.0969 0.8490 0.8877 0.8679 0.9690
0.0657 2.45 26500 0.0954 0.8532 0.8832 0.8680 0.9696
0.0668 2.49 27000 0.0947 0.8604 0.8793 0.8698 0.9695
0.0644 2.54 27500 0.0989 0.8527 0.8790 0.8656 0.9696
0.0685 2.59 28000 0.0955 0.8596 0.8772 0.8683 0.9700
0.0702 2.63 28500 0.0937 0.8585 0.8837 0.8709 0.9700
0.0644 2.68 29000 0.0946 0.8605 0.8830 0.8716 0.9702
0.065 2.72 29500 0.0953 0.8617 0.8822 0.8719 0.9701
0.063 2.77 30000 0.0943 0.8597 0.8848 0.8721 0.9701
0.0638 2.82 30500 0.0941 0.8619 0.8846 0.8731 0.9702
0.066 2.86 31000 0.0942 0.8608 0.8847 0.8726 0.9701
0.0589 2.91 31500 0.0952 0.8632 0.8836 0.8733 0.9704
0.0568 2.95 32000 0.0948 0.8603 0.8871 0.8735 0.9704

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6