https://github.com/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification

Kaggle新赛(baseline)-基于BERT的fine-tuning方案+基于tensor2tensor的Transformer Encoder方案
https://github.com/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification

bert kaggle quora tensor2tensor transformer

Last synced: 6 days ago
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Kaggle新赛(baseline)-基于BERT的fine-tuning方案+基于tensor2tensor的Transformer Encoder方案

• Host: GitHub
• URL: https://github.com/zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification
• Owner: zhpmatrix
• Created: 2018-11-10T13:59:22.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2018-11-11T06:22:51.000Z (almost 6 years ago)
• Last Synced: 2024-08-02T08:09:54.265Z (3 months ago)
• Topics: bert, kaggle, quora, tensor2tensor, transformer
• Language: Python
• Homepage:
• Size: 652 KB
• Stars: 61
• Watchers: 5
• Forks: 28
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-transformer-nlp - zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification - Kaggle baseline—fine-tuning BERT and tensor2tensor based Transformer encoder solution. (Tasks / Classification)
• awesome-bert - zhpmatrix/Kaggle-Quora-Insincere-Questions-Classification - 基于BERT的fine-tuning方案+基于tensor2tensor的Transformer Encoder方案 (BERT classification task:)

README

        
# 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 |