当我使用ner_tf 中的 TransformerNamedEntityRecognizerTF 微调MSRA_NER_BERT_BASE_ZH模型时，发现训练后的vocabs.json与微调之前不同。
后面我查看源码，发现keras_component.py中if finetune:条件后面只有load_weights, 没有load_vocabs,而代码前面有num_examples = self.build_vocab()导致vocab新建立了。代码如下图
而我对比torch_component.py中的finetune条件，调用了load(), load()中调用了load_vocabs()所以正确的。
代码如下图
It was designed this way to incorperate new tags introduced in your corpus. Otherwise you will need to implement weights loading for the classifier head like this:
Thanks for your answer! But this way to incorperate my new tags will change all tags order compared to before the fine-tuning and it mybe cause the all model parameters to largely updated although the classifier head is retrained.
Yes, you’re right. The classifier head will get trained from scratch but it might not be a critical issue. Because the number of parameters in classifier head is very small compared to the transformer encoder.
Ideally, you can implement the resize trick I mentioned before. Since I’m not actively maintaining the tensorflow versions, PRs are more than welcome.
Ok, thank you
