ccks-2021-task2-baseline 90+

provide a baseline for ccks-2021-task2(address parsing)

Simple way to run for ccks2021 task2

1. prepare bert_pretrained model and revised '--model_name_or_path'

2. prepare bigram and char embedding and revised pretrain_unigram_path,pretrain_bigram_path

3. prepare dataset and revised '--data_dir'

4. run on colab (main.ipynb). turn debug on False before run main.

5. run on local device(main.py). turn debug on False before run main.

Stucture

The main module contains the follow files:

• The load_data.py Text process -> read a file and convert it to a format for model (fastNLP package).

• model.py Build Model -> char,bigram and bert embedding + Bi-LSTM + CRF model and other model can be added.

• pipeline.py contain two classes. Trainer is for training process. Tester is for testing process which contains model predict and evaluation.

• main.py main file to run on local device.

• config.py

• data folder contains files(train.conll,dev.conll,test.conll).

Other

• 1.one can add some trick to import prediction performance. For example model average,Pseudo label,model stacking. Details can be seenBDCI top1 scheme.
• 2.model_name_or_path contains pretrained bert model files (.bin,.json,.txt) which can be downloaded Chinese-BERT-wwm for chinese text (also support other language).
• 3.char and bigram embedding can be downloaded from Flat
• 4.Flat model achieves 87.93, cross validate:88.73, pseudo-labelling: 89.73
• 5.char-bigram-bilstm 86.88
• 6.biaffine-ner, 81.63
• 7.our provided model(char-bigram-bert-bilstm-crf) 90+