Skip to content
Neural Relation Extraction implemented in PyTorch
Python
Branch: master
Clone or download
Latest commit 11bf7ce Nov 15, 2018
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
config big update Nov 14, 2018
models big update Nov 14, 2018
networks big update Nov 14, 2018
.gitignore Initial commit Aug 6, 2018
LICENSE Initial commit Aug 6, 2018
README.md Update README.md Nov 15, 2018
draw_plot.py big update Nov 14, 2018
gen_data.py big update Nov 14, 2018
test.py big update Nov 14, 2018
train.py big update Nov 14, 2018

README.md

OpenNRE-PyTorch

An open-source framework for neural relation extraction implemented in PyTorch.

Contributed by Shulin Cao, Tianyu Gao, Xu Han, Lumin Tang, Yankai Lin, Zhiyuan Liu

Overview

It is a PyTorch-based framwork for easily building relation extraction models. We divide the pipeline of relation extraction into four parts, which are embedding, encoder, selector and classifier. For each part we have implemented several methods.

  • Embedding
    • Word embedding
    • Position embedding
    • Concatenation method
  • Encoder
    • PCNN
    • CNN
  • Selector
    • Attention
    • Maximum
    • Average
  • Classifier
    • Softmax loss function
    • Output

All those methods could be combined freely.

We also provide fast training and testing codes. You could change hyper-parameters or appoint model architectures by using Python arguments. A plotting method is also in the package.

This project is under MIT license.

Requirements

  • Python (>=2.7)
  • PyTorch (==0.3.1)
  • CUDA (>=8.0)
  • Matplotlib (>=2.0.0)
  • scikit-learn (>=0.18)

Installation

  1. Install PyTorch
  2. Clone the OpenNRE repository:
git clone https://github.com/ShulinCao/OpenNRE-PyTorch
  1. Download NYT dataset from Google Drive
  2. Extract dataset to ./raw_data
unzip raw_data.zip

Dataset

NYT10 Dataset

NYT10 is a distantly supervised dataset originally released by the paper "Sebastian Riedel, Limin Yao, and Andrew McCallum. Modeling relations and their mentions without labeled text.". Here is the download link for the original data. You can download the NYT10 dataset from Google Drive. And the data details are as follows.

Training Data & Testing Data

Training data file and testing data file, containing sentences and their corresponding entity pairs and relations, should be in the following format

[
    {
        'sentence': 'Bill Gates is the founder of Microsoft .',
        'head': {'word': 'Bill Gates', 'id': 'm.03_3d', ...(other information)},
        'tail': {'word': 'Microsoft', 'id': 'm.07dfk', ...(other information)},
        'relation': 'founder'
    },
    ...
]

IMPORTANT: In the sentence part, words and punctuations should be separated by blank spaces.

Word Embedding Data

Word embedding data is used to initialize word embedding in the networks, and should be in the following format

[
    {'word': 'the', 'vec': [0.418, 0.24968, ...]},
    {'word': ',', 'vec': [0.013441, 0.23682, ...]},
    ...
]

Relation-ID Mapping Data

This file indicates corresponding IDs for relations to make sure during each training and testing period, the same ID means the same relation. Its format is as follows

{
    'NA': 0,
    'relation_1': 1,
    'relation_2': 2,
    ...
}

IMPORTANT: Make sure the ID of NA is always 0.

Quick Start

Process Data

python gen_data.py

The processed data will be stored in ./data

Train Model

python train.py --model_name pcnn_att

The arg model_name appoints model architecture, and pcnn_att is the name of one of our models. All available models are in ./models. About other arguments please refer to ./train.py. Once you start training, all checkpoints are stored in ./checkpoint.

Test Model

python test.py --model_name pcnn_att

Same usage as training. When finishing testing, the best checkpoint's corresponding pr-curve data will be stored in ./test_result.

Plot

python draw_plot.py PCNN_ATT

The plot will be saved as ./test_result/pr_curve.png. You could appoint several models in the arguments, like python draw_plot.py PCNN_ATT PCNN_ONE PCNN_AVE, as long as there are these models' results in ./test_result.

Build Your Own Model

Not only could you train and test existing models in our package, you could also build your own model or add methods to the four basic modules. When adding a new model, you could create a python file in ./models having the same name as the model and implement it like following:

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from networks.embedding import *
from networks.encoder import *
from networks.selector import *
from networks.classifier import *
from .Model import Model
class PCNN_ATT(Model):
  def __init__(self, config):
    super(PCNN_ATT, self).__init__(config)
    self.encoder = PCNN(config)
    self.selector = Attention(config, config.hidden_size * 3)

Then you can train, test and plot!

You can’t perform that action at this time.