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racasrel's Introduction

A Relation Aware Embedding Mechanism for Relation Extraction

some of our codes are from https://github.com/weizhepei/CasRel

Requirements

This repo was tested on Python 3.6 and Keras 2.2.4. The main requirements are:

  • tqdm
  • keras-bert==0.82.0
  • tensorflow-gpu == 1.13.1

Datasets

  1. Get datasets

    Download the two datasets above. Then decompress it under data/NYT/ or data/WebNLG/.

Usage

  1. Get pre-trained model BERT

    Download Google's pre-trained BERT model (BERT-Base, Cased). Then decompress it under pretrained_bert_models/.

  2. Train and select the model

    Specify the running mode and dataset at the command line

    python run.py --train=True --dataset=NYT

    The model weights that lead to the best performance on validation set will be stored in saved_weights/NYT/ or saved_weights/WebNLG/.

Baselines

racasrel's People

Contributors

lixiang20 avatar

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