standard

Easy Start

English | 简体中文

Requirements

python == 3.8

• torch == 1.5
• hydra-core == 1.0.6
• tensorboard == 2.4.1
• matplotlib == 3.4.1
• scikit-learn == 0.24.1
• transformers == 3.4.0
• jieba == 0.42.1
• deepke

Download Code

git clone https://github.com/zjunlp/DeepKE.git
cd DeepKE/example/ae/standard

Install with Pip

• Create and enter the python virtual environment.
• Install dependencies: pip install -r requirements.txt.

Train and Predict

• Dataset

  • Download the dataset to this directory.

    wget 120.27.214.45/Data/ae/standard/data.tar.gz
    tar -xzvf data.tar.gz

  • The dataset is stored in data/origin:

    • train.csv: Training set
    • valid.csv : Validation set
    • test.csv: Test set
    • attribute.csv: Attribute types

• Training

  • Parameters for training are in the conf folder and users can modify them before training.This task supports multi card training. Modify trian.yaml's parameter use_multi_gpu to true, gpu_ids set to the selected gpus. The first card is the main card for calculation, which requires a little more memory.show_plot set to visualize the loss of the current epoch.The default value is False.

  • If using LM, modify lm_file to use the local model.

  • Logs for training are in the log folder and the trained model is saved in the checkpoints folder.

  python run.py

• Prediction

  python predict.py

Models

1. CNN
2. RNN
3. Capsule
4. GCN
5. Transformer
6. Pre-trained Model (BERT)