人工智能项目资料-根据源码增添了模型预测功能以及预测结果导入neo4j图数据库的功能。.zip

# 写在前面 ​ 本人为完成研究生阶段的小作业，找到了源项目[CasRelPyTorch](https://github.com/Onion12138/CasRelPyTorch)。本项目修改自作者[Onion12138](https://github.com/Onion12138) 在2022年9月19日上传至Github的项目[CasRelPyTorch](https://github.com/Onion12138/CasRelPyTorch) ，本项目根据本人的需求修改部分代码以及增加了一些功能。如有任何侵权行为，请联系作者进行删除。 # 修改与增加的具体内容 ​ 作业的大概要求就是基于深度学习的方法完成实体关系抽取任务，并将抽取到的关系实体三元组输入到图数据库中（neo4j数据库）。 ​ 主要修改和增加功能的工作如下： - 修改了部分源码，在Run.py 中留出了模型训练、测试以及预测方法。 - 对部分主要的代码添加了注释等，尽量地提高代码可读性。 - 添加了模型预测方法。 - 增加了将文本（.txt文件）转换成模型输入数据格式（.json文件）代码dataGeneration.py。 - 增加了模型预测结果输出到图数据库neo4j的功能。 附百度数据集（源项目自带的数据集）训练的模型参数文件model.pt 如下： 链接：https://pan.baidu.com/s/1lTOU3tUMFU-rKk24phzi2w?pwd=evxg 提取码：evxg # CasRel Model Pytorch reimplement 3 The code is the PyTorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The [official code](https://github.com/weizhepei/CasRel) was written in keras. I have encountered a lot of troubles with the keras version, so I decided to rewrite the code in PyTorch. # Introduction I followed the previous work of [longlongman](https://github.com/longlongman/CasRel-pytorch-reimplement) and [JuliaSun623](https://github.com/JuliaSun623/CasRel_fastNLP). So I have to express sincere thanks to them. I made some changes in order to better apply to the Chinese Dataset. The changes I have made are listed: - I changed the tokenizer from HBTokenizer to BertTokenizer, so Chinese sentences are tokenized by single character. (Note that you don't need to worry about keras) - I substituted the original pretrained model with 'bert-base-chinese'. - I used fastNLP to build the datasets. - I changed the encoding and decoding methods in order to fit the Chinese Dataset. - I reconstruct the structure for readability. # Requirements - torch==1.8.0+cu111 - transformers==4.3.3 - fastNLP==0.6.0 - tqdm==4.59.0 - numpy==1.20.1 # Dataset I preprocessed the open-source dataset from Baidu. I did some cleaning, so the data given have 18 relation types. Some noisy data are eliminated. The data are in form of json. Take one as an example: ```json { "text": "陶喆的一首《好好说再见》推荐给大家，希望你们能够喜欢", "spo_list": [ { "predicate": "歌手", "object_type": "人物", "subject_type": "歌曲", "object": "陶喆", "subject": "好好说再见" } ] } ``` In fact the field object_type and subject_type are not used. If you have your own data, you can organize your data in the same format. # Usage 1、cmd命令行使用方式（原文的使用方式） ``` python Run.py [--arg] ``` [--arg]：可选参数，例如： ``` python Run.py --dataset=baidu ``` I have already set the default value of the model, but you can still set your own configuration in model/config.py 2、编译器中运行使用方式 此项目也可以在编译器中运行，在Run.py中给出了模型训练、模型测试以及模型预测三个功能函数，使用时只需修改 ``` parser.add_argument('--dataset', default='baidu', type=str, help='define your own dataset names') ``` 将上述代码中的default参数改写为自己的数据集即可。 # Results The best F1 score on test data is 0.78 with a precision of 0.80 and recall of 0.76. It is to my expectation although it may not reach its utmost. I have also trained the [SpERT](https://github.com/lavis-nlp/spert) model, and CasRel turns out to perform better. More experiments need to be carried out since there are slight differences in both criterion and datasets. # Experiences - Learning rate 1e-5 seems a good choice. If you change the learning rate, the model will be dramatically affected. - It shows little improvement when I substitute BERT with RoBERTa. - It is crucial to shuffle the datasets in order to avoid overfitting. # CasRelPyTorch-