If useful for you, maybe a star to encourage our work.

ERNIE , RoBerta based model for sentence similarity

For example:

387,支原体肺炎,支原体肺炎的症状及治疗方法是什么,肺炎衣原体与肺炎支原体有什么区别？,0
388,支原体肺炎,支原体肺炎的症状及治疗方法是什么,肺炎支原体培养及药敏的检验单怎么看？,0
389,支原体肺炎,支原体肺炎的症状及治疗方法是什么,小儿支原体与小儿支原体肺炎相同吗？,0
390,支原体肺炎,宝宝支原体肺炎感染的症状有哪些？,宝宝肺炎支原体感染的症状是什么？,1
391,支原体肺炎,宝宝支原体肺炎感染的症状有哪些？,宝宝支原体肺炎感染有什么症状？,1

• ERNIE 1.0
• Nadam with 2.0*1e-5 lr
• OHEM CE, with label smoothing
• cosine lr scheduler with warmup
• clean noise data by an overfitted model

• simply change the model

• add any 'word2vec' features

• split into multipiece data,get N bert,
  using multiple feature to train a tree based
  model, lightGBM, Xgboost...

• for those hard example, maybe add the nearest sentence
  (pair with label) for reference info, into bert

• pseudo label

• more open data(e.g ping an CHIP 2019)

• ...

• opencv-python
• pytorch >= 1.4
• pandas
• yacs
• sklearn

• download the ernie (128 length) model from https://github.com/nghuyong/ERNIE-Pytorch
• but using the config in this repo at pretrained/ernie/

you maye change the data path, have a look at train.py test.py

export PYTHONPATH=./
sh train_pipeline.sh

https://tianchi.aliyun.com/competition/entrance/231776/introduction?spm=5176.12281949.1003.4.21eb2448atCLQk