CBT4Rel

Reimplement of CasRel Using Torch

Paper "A Novel Cascade Binary Tagging Frameword for Relational Triple Extraction" ACL 2020

The Original code was written in keras

Requirements

• transformers 3.1.0
• torch 1.5.1+cu101
• tqdm

Dataset

Using WebNLG DataSet and I preprocess this dataset.

链接:https://pan.baidu.com/s/1RLgcRR1pRXCaBxR5NrA5AQ 密码:8nqn

This Data has been preprocessed

Usage

Get the pre-trained Bert Model

• Download the bert-base-uncased

• mkdir under data directory

  mkdir data/static
  mv {Default-Path}/bert-base-uncased data/static/

Note : Please following above shell command, do not use your own path

Train the Model and Test

python src/run.py

Result (updating)

The pure model performance

	Acc	Precision	Recall	F1-score
subject_start_point	0.994	0.943	0.964	0.953
subject_end_point	0.994	0.946	0.962	0.954
object_start_point	0.999	0.923	0.766	0.837
object_end_point	0.999	0.927	0.764	0.838

Decoding Part is not finished. QAQ

You can check the logger.py under the folder of log for more detail training process.

	Precision	Recall	F1-score
End2End	0.662	0.517	0.577

In end2end test, model dosen't perform good as it descriped in Paper.

I am still find the problem

Training Tip

! ! !

Set epoches a max value, and have enough patient

In my Training Experiment, I set epoches = 100.

• When epoch equals to 23, the subject tagger starts to recall

• When epoch equal to 31, the subject tagger starts to recall.

• Before this, in validation, the precision, recall and f1-score equal to zero.