BERT-for-Sequence-Labeling-and-Text-Classification

• BERT is used for sequence annotation and text categorization template code to facilitate BERT for more tasks. Welcome to use this BERT template to solve more NLP tasks, and then share your results and code here.
• 这是使用BERT进行序列注释和文本分类的模板代码，方便大家将BERT用于更多任务。欢迎使用这个BERT模板解决更多NLP任务，然后在这里分享你的结果和代码。

Template Code Usage Method

1. Move google's BERT code to file bert (I've prepared a copy for you.);
2. Download google's BERT pretrained model and unzip then to file pretrained_model;
3. Run Code! You can change task_name and output_dir.

python run_text_classification.py \
--task_name=Snips \
--do_train=true \
--do_eval=true \
--data_dir=data/snips_Intent_Detection_and_Slot_Filling \
--vocab_file=pretrained_model/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=pretrained_model/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=pretrained_model/uncased_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=./output/snips_Intent_Detection/

File Structure

BERT-for-Sequence-Labeling-and-Text-Classification |____ bert store google's BERT code |____ data store task data set |____ output store model output |____ pretrained_model store BERT pretrained model |____ run_sequence_labeling.py for Sequence Labeling Task |____ run_text_classification.py for Text Classification Task |____ run_sequence_labeling_and_text_classification.py for join task (come soon!)
|____ tf_metrics.py for evaluation model

Task

Welcome to add!

Task name	Explain	data source
CoNLL-2003 named entity recognition	NER
Atis Joint Slot Filling and Intent Prediction		https://github.com/MiuLab/SlotGated-SLU/tree/master/data/atis
Snips Joint Slot Filling and Intent Prediction		https://github.com/MiuLab/SlotGated-SLU/tree/master/data/snips
GLUE