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
- 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 %
