deepke/example/ae/standard
xxupiano ea6321b97f Update README 2022-01-11 17:01:47 +08:00
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conf update readme 2021-10-24 14:18:09 +08:00
README.md Update README 2022-01-11 17:01:47 +08:00
README_CN.md Update README 2022-01-11 17:01:47 +08:00
predict.py fix bug 2021-12-12 14:33:09 +08:00
requirements.txt test 2021-09-16 14:30:03 +08:00
run.py add wandb 2021-11-30 21:39:29 +08:00

README.md

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.

    • 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)