# Kaggle-Quora-Insincere-Questions-Classification
[Kaggle新赛](https://www.kaggle.com/c/quora-insincere-questions-classification)-基于BERT的fine-tuning方案baseline
submit.csv是测试集的预测结果
代码和项目[基于BERT的中文tagging](https://github.com/zhpmatrix/bert-sequence-tagging)一样，仅提供关键fine-tuning代码和运行脚本
基于bert的验证集的结果：
|class| precision | recall | f1-score |
|-----| ------ | ------ | ------ |
|0| 0.98 | 0.98 | 0.98 |
|1| 0.65 | 0.62 | 0.63 |
|micro avg| 0.96 | 0.96 | 0.96 |
|macro avg| 0.81 | 0.80 | 0.81 |
|weighted avg| 0.96 | 0.96 | 0.96 |
基于tensor2tensor的验证集结果：
|class| precision | recall | f1-score |
|-----| ------ | ------ | ------ |
|0| 0.98 | 0.96 | 0.96 |
|1| 0.23 | 0.19 | 0.21 |
|micro avg| 0.92 | 0.92 | 0.92 |
|macro avg| 0.59 | 0.57 | 0.58 |
|weighted avg| 0.91 | 0.92 | 0.91 |
Kaggle新赛(baseline)-基于BERT的fine-tuning方案+基于tensor2tensor的Transformer Encoder方案.zip （12个子文件） 
SJT-code bert 
submit.csv 1.24MB run.sh 704B run_classifier.py 32KB README.md 900B tensor2tensor utils util.py 2KB __pycache__ util.cpython-36.pyc 2KB cmd.sh 770B decoder 0.lc 49KB script __init__.py 25B my_problem.py 1KB __pycache__ __init__.cpython-36.pyc 171B my_problem.cpython-36.pyc 2KB资源评论
