Zhibin Lu

This is a named entity recognizer based on BERT Model(pytorch-pretrained-BERT) and CRF.

Someone construct model with BERT, LSTM and CRF, like this BERT-BiLSTM-CRF-NER, but in theory, the BERT mechanism has replaced the role of LSTM, so I think LSTM is redundant.

For the performance, BERT+CRF is always a little better than single BERT in my experience.

• python 3.6
• pytorch 1.0.0
• pytorch-pretrained-bert 0.4.0

The NER_BERT_CRF.py include 2 model:

• model 1:
  • This is just a pretrained BertForTokenClassification, For a comparision with my BERT-CRF model
• model 2:
  • A pretrained BERT with CRF model.
• data set
  • CoNLL-2003

• NER_labels = ['X', '[CLS]', '[SEP]', 'O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC']
• max_seq_length = 180
• batch_size = 32
• learning_rate = 5e-5
• weight_decay = 1e-5
• learning_rate for CRF and FC: 8e-5
• weight_decay for CRF and FC: 5e-6
• total_train_epochs = 20
• bert_model_scale = 'bert-base-cased'
• do_lower_case = False

• Bert paper
  • F1-Score on valid data: 96.4 %
  • F1-Score on test data: 92.4 %
• BertForTokenClassification (epochs = 15)
  • Accuracy on valid data: 99.10 %
  • Accuracy on test data: 98.11 %
  • F1-Score on valid data: 96.18 %
  • F1-Score on test data: 92.17 %
• Bert+CRF (epochs = 16)
  • Accuracy on valid data: 99.10 %
  • Accuracy of test data: 98.14 %
  • F1-Score on valid data: 96.23 %
  • F1-Score on test data: 92.29 %

• Bert paper
• Bert with PyTorch implementation
• ericput/Bert-ner
• CoNLL-2003 data set
• Kyubyong/bert_ner