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albert-base-chinese-0407-ner

Token Classification
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PyTorch
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albert
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YAML Metadata Error: "language[0]" must only contain lowercase characters
YAML Metadata Error: "language[0]" with value "List of ISO 639-1 code for your language" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.

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
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