详见关系抽取指导手册
数据集属于网上公开数据集（origin_data文件夹下relation2id;test;train）
vec.txt:谷歌公开词向量集
模型了参照paper文件夹下两篇论文（Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification，Neural Relation Extraction with Selective Attention over Instances）;亦包含对应的总结
人工智能-项目实践-自注意力机制-通过BiGRU+注意力机制对关系进行自动抽取
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Relationship-extraction-master-master.zip （52个子文件） 
Relationship-extraction-master-master 
model_train.py 7KB initial.py 12KB base helper common.py 2KB constant.py 677B __pycache__ args.cpython-37.pyc 1KB common.cpython-37.pyc 2KB constant.cpython-37.pyc 599B args.py 3KB paper 知识抽取.xmind 3.05MB
Designing an adaptive attention mechanism for relation classification.pdf 1.99MB Neural Relation Extraction with Selective Attention over Instances.pdf 295KB Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.pdf 554KB logs main.log 453KB train_loss 
temp.txt 2B data model_id.txt 4B model checkpoint 209B data_util.py 3KB
关系抽取指导手册.docx 169KB dataset_info.py 1KB origin_data pre.txt 74B test.txt 16KB relation2id.txt 119B .idea misc.xml 185B Relationship-extraction-master.iml 395B modules.xml 312B encodings.xml 185B workspace.xml 9KB inspectionProfiles profiles_settings.xml 174B out allprob_iter_8600.npy 4KB allprob_iter_9500.npy 4KB allprob_iter_9100.npy 4KB allprob_iter_8300.npy 4KB allprob_iter_8800.npy 4KB allprob_iter_8500.npy 4KB allprob_iter_9000.npy 4KB allprob_iter_8100.npy 4KB allprob_iter_9200.npy 4KB allprob_iter_8200.npy 4KB allprob_iter_8900.npy 4KB allprob_iter_9400.npy 4KB allprob_iter_9300.npy 4KB allprob_iter_8700.npy 4KB allprob_iter_8000.npy 4KB allprob_iter_8400.npy 4KB result.csv 30B allprob_iter_9700.npy 4KB allprob_iter_9600.npy 4KB allprob_iter_9800.npy 4KB model_predict.py 4KB README.md 389B network.py 17KB model_test.py 5KB
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