Keras-TextClassification
# [Keras-TextClassification](https://github.com/yongzhuo/Keras-TextClassification) [](https://pypi.org/project/Keras-TextClassification/) [](https://travis-ci.com/yongzhuo/Keras-TextClassification) [](https://pypi.org/project/Keras-TextClassification/) [](https://github.com/yongzhuo/Keras-TextClassification/stargazers) [](https://github.com/yongzhuo/Keras-TextClassification/network/members) [](https://gitter.im/yongzhuo/Keras-TextClassification?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) # Install(安装) ```bash pip install Keras-TextClassification ``` ```python step2: download and unzip the dir of 'data.rar', 地址: 链接：https://pan.baidu.com/s/1pIDzGaGXCZ7cjng1XU_kPA 提取码：w6ps 压缩包密码: 2022 cover the dir of data to anaconda, like '/anaconda/3.5.1/envs/tensorflow13/Lib/site-packages/keras_textclassification/data' step3: goto # Train&Usage(调用) and Predict&Usage(调用) ``` # keras_textclassification（代码主体,未完待续...） - Electra-fineture(todo) - Albert-fineture - Xlnet-fineture - Bert-fineture - FastText - TextCNN - charCNN - TextRNN - TextRCNN - TextDCNN - TextDPCNN - TextVDCNN - TextCRNN - DeepMoji - SelfAttention - HAN - CapsuleNet - Transformer-encode - SWEM - LEAM - TextGCN(todo) # run(运行, 以FastText为例) - 1. 进入keras_textclassification/m01_FastText目录， - 2. 训练: 运行 train.py, 例如: python train.py - 3. 预测: 运行 predict.py, 例如: python predict.py - 说明: 默认不带pre train的random embedding，训练和验证语料只有100条，完整语料移步下面data查看下载 # run(多标签分类/Embedding/test/sample实例) - bert,word2vec,random样例在test/目录下, 注意word2vec(char or word), random-word, bert(chinese_L-12_H-768_A-12)未全部加载,需要下载 - multi_multi_class/目录下以text-cnn为例进行多标签分类实例，转化为multi-onehot标签类别，分类则取一定阀值的类 - sentence_similarity/目录下以bert为例进行两个句子文本相似度计算,数据格式如data/sim_webank/目录下所示 - predict_bert_text_cnn.py - tet_char_bert_embedding.py - tet_char_bert_embedding.py - tet_char_xlnet_embedding.py - tet_char_random_embedding.py - tet_char_word2vec_embedding.py - tet_word_random_embedding.py - tet_word_word2vec_embedding.py # keras_textclassification/data - 数据下载 ** github项目中只是上传部分数据，需要的前往链接: https://pan.baidu.com/s/1I3vydhmFEQ9nuPG2fDou8Q 提取码: rket - baidu_qa_2019（百度qa问答语料，只取title作为分类样本，17个类，有一个是空''，已经压缩上传） - baike_qa_train.csv - baike_qa_valid.csv - byte_multi_news（今日头条2018新闻标题多标签语料，1070个标签，fate233爬取, 地址为: [byte_multi_news](https://github.com/fate233/toutiao-multilevel-text-classfication-dataset)） -labels.csv -train.csv -valid.csv - embeddings - chinese_L-12_H-768_A-12/(取谷歌预训练好点的模型,已经压缩上传, keras-bert还可以加载百度版ernie(需转换，[https://github.com/ArthurRizar/tensorflow_ernie](https://github.com/ArthurRizar/tensorflow_ernie)), 哈工大版bert-wwm(tf框架，[https://github.com/ymcui/Chinese-BERT-wwm](https://github.com/ymcui/Chinese-BERT-wwm)) - albert_base_zh/(brightmart训练的albert, 地址为https://github.com/brightmart/albert_zh) - chinese_xlnet_base_L-12_H-768_A-12/(哈工大预训练的中文xlnet模型[https://github.com/ymcui/Chinese-PreTrained-XLNet],12层) - term_char.txt(已经上传, 项目中已全, wiki字典, 还可以用新华字典什么的) - term_word.txt(未上传, 项目中只有部分, 可参考词向量的) - w2v_model_merge_short.vec(未上传, 项目中只有部分, 词向量, 可以用自己的) - w2v_model_wiki_char.vec(已上传百度网盘, 项目中只有部分, 自己训练的维基百科字向量, 可以用自己的) - model - fast_text/预训练模型存放地址 # 项目说明 - 1. 构建了base基类(网络(graph)、向量嵌入(词、字、句子embedding)),后边的具体模型继承它们，代码简单 - 2. keras_layers存放一些常用的layer, conf存放项目数据、模型的地址, data存放数据和语料, data_preprocess为数据预处理模块, # 模型与论文paper题与地址 * FastText: [Bag of Tricks for Efficient Text Classification](https://arxiv.org/abs/1607.01759) * TextCNN： [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882) * charCNN-kim： [Character-Aware Neural Language Models](https://arxiv.org/abs/1508.06615) * charCNN-zhang: [Character-level Convolutional Networks for Text Classification](https://arxiv.org/pdf/1509.01626.pdf) * TextRNN： [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://www.ijcai.org/Proceedings/16/Papers/408.pdf) * RCNN： [Recurrent Convolutional Neural Networks for Text Classification](http://www.nlpr.ia.ac.cn/cip/~liukang/liukangPageFile/Recurrent%20Convolutional%20Neural%20Networks%20for%20Text%20Classification.pdf) * DCNN: [A Convolutional Neural Network for Modelling Sentences](https://arxiv.org/abs/1404.2188) * DPCNN: [Deep Pyramid Convolutional Neural Networks for Text Categorization](https://www.aclweb.org/anthology/P17-1052) * VDCNN: [Very Deep Convolutional Networks](https://www.aclweb.org/anthology/E17-1104) * CRNN: [A C-LSTM Neural Network for Text Classification](https://arxiv.org/abs/1511.08630) * DeepMoji: [Using millions of emojio ccurrences to learn any-domain represent ations for detecting sentiment, emotion and sarcasm](https://arxiv.org/abs/1708.00524) * SelfAttention: [Attention Is All You Need](https://arxiv.org/abs/1706.03762) * HAN: [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf) * CapsuleNet: [Dynamic Routing Between Capsules](https://arxiv.org/pdf/1710.09829.pdf) * Transformer(encode or decode): [Attention Is All You Need](https://arxiv.org/abs/1706.03762) * Bert: [BERT: Pre-trainingofDeepBidirectionalTransformersfor LanguageUnderstanding]() * Xlnet: [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) * Albert: [ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS](https://arxiv.org/pdf/1909.11942.pdf) * RoBERTa: [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) * ELECTRA: [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB) * TextGCN: [Graph Convolutional Networks for Text Classification](https://arxiv.org/abs/1809.05679) # 参考/感谢 * 文本分类项目: [https://github.com/mosu027/TextClassification](https://github.com/mosu027/TextClassification) * 文本分类看山杯: [https://github.com/brightmart/text_classification](https://github.com/brightmart/text_classification) * Kashgari项目: [https://github.com/BrikerMan/Kashgari](https://github.com/BrikerMan/Kashgari) * 文本分类Ipty : [https://github.com/lpty/classifier](https://github.com/lpty/classifier) * keras文本分类: [https://github.com/ShawnyXiao/TextClassification-Keras](https://github.com/ShawnyXiao/TextClassification-Keras) * keras文本分类: [https://github.com/AlexYangLi/TextClassification](https://github.com/AlexYangLi/TextClassification) * CapsuleNet模型: [https://github.com/bojone/Capsule](https://github.com/bojone/Capsule) * transformer模型: [https://github.com/CyberZHG/keras-transformer](https://github.com/CyberZHG/keras-transformer) * keras_albert_model: [https://github.com/TinkerMob/keras_albert_model](https://github.com/TinkerMob/keras_albert_model) # 训练简单调用: ```python from keras_textclassification import train train(graph='TextCNN', # 必填, 算法名, 可选"ALBERT","BERT","XLNET","FASTTEXT","TEXTCNN","CHARCNN", # "TEXTRNN","RCNN","DCNN","DPCNN","VDCNN","CRNN","DEEPMOJI", # "SELFATTENTION", "HAN","CAPSULE","TRANSFORMER" label=17, # 必填, 类别数, 训练集和测试集合必须一样 path_train_data=None, # 必填, 训练数据文件, csv格式, 必须含'label,ques'头文件, 详见keras_textclassification/data path_dev_data=None, # 必填, 测试数据文件, csv格式, 必须含'label,ques'头文件, 详见keras_textclassification/data rate=1, # 可填, 训练数据选取比例 hyper_parameters=None) # 可填, json格式, 超参数, 默认embedding为'char','random' ``` # Reference For citing this work, you can refer to the present GitHub project. For example, with BibTeX: ``` @misc{Keras-TextClassification, howpublished = {\url{https://github.com/yongzhuo/Keras-TextClassification}}, title = {Keras-TextClassification}, author = {Yongzhuo Mo}, publisher = {GitHub}, year = {2019} } ``` *希望对你有所帮助!