README.md

Relation Extraction v2

v2子项目源于Schlampig/OpenNRE_for_Chinese

Requirements

• Python>=3.5
• pytorch>=0.3.1
• scikit-learn>=0.18
• numpy
• jieba
• tqdm
• Flask(optional, if runing the server.py)

数据集

• source: The dataset used for this work is from BaiDu2019 Relation Extraction Competition, denoted as DuIE. Note that, rather than directly brought into OpenNRE_for_Chinese, DuIE should be first transformed to dataset DuNRE that has the correct format for the model.
  
• format of DuIE: a sample in DuIE is like:
  

sample = {"postag": [{"word": str, "pos": str}, {"word": str, "pos": str}, ...], 
          "text": str,
          "spo_list": [{"predicate": str, "object_type": str, "subject_type": str, "object": str, "subject": str}, 
                       {"predicate": str, "object_type": str, "subject_type": str, "object": str, "subject": str}, 
                       ...]}

• format of DuNRE: DuNRE contains three main datasets as follows (factually the same format as OpenNRE):

1. Sample dataset:
    [
        {
            'sentence': str (with space between word and punctuation),
            'head': {'word': str},
            'tail': {'word': str},
            'relation': str (the name of a class)
        },
        ...
    ]
2. Embeddings dataset:
    [
        {'word': str, 'vec': list of float},
        ...
    ]
            
3. Labels dataset:
    {
        'NA': 0 (it is necessary to denote NA as index 0),
        class_name_1 (str): 1,
        class_name_1 (str): 2,
        ...
    }

• example: a sample would be like:

    [
        {
            'sentence': '《 软件 体 的 生命周期 》 是 美国作家 特德·姜 的 作品 ， 2015 年 5 月 译林 出版社 出版 。 译者 张博然 等 。 ',
            'head': {'word': '特德·姜'},
            'tail': {'word': '软件体的生命周期'},
            'relation': ‘作者’)
        },
        ...
    ]

Command Line:

• generate DuNRE: detail steps could be seen here.
• prepare: transform DuNRE to numpy/pickle file for the model.

python prepare

• train: train, validate and save the model.

python learn

• predict: run the flask server, and predict new samples via Postman.

python predict

参考

• https://github.com/Schlampig/OpenNRE_for_Chinese