bert-srb-ner

This model was trained from scratch on the wikiann dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3561
  • Precision: 0.8909
  • Recall: 0.9082
  • F1: 0.8995
  • Accuracy: 0.9547

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: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.3907 1.0 625 0.2316 0.8255 0.8314 0.8285 0.9259
0.2091 2.0 1250 0.1920 0.8598 0.8731 0.8664 0.9420
0.1562 3.0 1875 0.1833 0.8608 0.8820 0.8713 0.9441
0.0919 4.0 2500 0.1985 0.8712 0.8886 0.8798 0.9476
0.0625 5.0 3125 0.2195 0.8762 0.8923 0.8842 0.9485
0.0545 6.0 3750 0.2320 0.8706 0.9004 0.8852 0.9495
0.0403 7.0 4375 0.2459 0.8817 0.8957 0.8887 0.9505
0.0269 8.0 5000 0.2603 0.8813 0.9021 0.8916 0.9516
0.0193 9.0 5625 0.2916 0.8812 0.8949 0.8880 0.9500
0.0162 10.0 6250 0.2938 0.8814 0.9025 0.8918 0.9520
0.0134 11.0 6875 0.3330 0.8809 0.8961 0.8885 0.9497
0.0076 12.0 7500 0.3141 0.8840 0.9025 0.8932 0.9524
0.0069 13.0 8125 0.3292 0.8819 0.9065 0.8940 0.9535
0.0053 14.0 8750 0.3454 0.8844 0.9018 0.8930 0.9523
0.0038 15.0 9375 0.3519 0.8912 0.9061 0.8986 0.9539
0.0034 16.0 10000 0.3437 0.8894 0.9038 0.8965 0.9539
0.0024 17.0 10625 0.3518 0.8896 0.9072 0.8983 0.9543
0.0018 18.0 11250 0.3572 0.8877 0.9072 0.8973 0.9543
0.0015 19.0 11875 0.3554 0.8910 0.9081 0.8994 0.9549
0.0011 20.0 12500 0.3561 0.8909 0.9082 0.8995 0.9547

Framework versions

  • Transformers 4.9.2
  • Pytorch 1.9.0
  • Datasets 1.11.0
  • Tokenizers 0.10.1
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