Keras-TextClassification
Install(安装)
pip install Keras-TextClassificationstep2: download and unzip the dir of 'data.rar', 地址: https://pan.baidu.com/s/1I3vydhmFEQ9nuPG2fDou8Q 提取码: rket
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
-
- 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
-
- 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
- TextCNN： Convolutional Neural Networks for Sentence Classification
- charCNN-kim： Character-Aware Neural Language Models
- charCNN-zhang: Character-level Convolutional Networks for Text Classification
- TextRNN： Recurrent Neural Network for Text Classification with Multi-Task Learning
- RCNN： Recurrent Convolutional Neural Networks for Text Classification
- DCNN: A Convolutional Neural Network for Modelling Sentences
- DPCNN: Deep Pyramid Convolutional Neural Networks for Text Categorization
- VDCNN: Very Deep Convolutional Networks
- CRNN: A C-LSTM Neural Network for Text Classification
- DeepMoji: Using millions of emojio ccurrences to learn any-domain represent ations for detecting sentiment, emotion and sarcasm
- SelfAttention: Attention Is All You Need
- HAN: Hierarchical Attention Networks for Document Classification
- CapsuleNet: Dynamic Routing Between Capsules
- Transformer(encode or decode): Attention Is All You Need
- Bert: BERT: Pre-trainingofDeepBidirectionalTransformersfor LanguageUnderstanding
- Xlnet: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- Albert: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- RoBERTa: RoBERTa: A Robustly Optimized BERT Pretraining Approach
- ELECTRA: ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
- TextGCN: Graph Convolutional Networks for Text Classification
参考/感谢
- 文本分类项目: https://github.com/mosu027/TextClassification
- 文本分类看山杯: https://github.com/brightmart/text_classification
- Kashgari项目: https://github.com/BrikerMan/Kashgari
- 文本分类Ipty : https://github.com/lpty/classifier
- keras文本分类: https://github.com/ShawnyXiao/TextClassification-Keras
- keras文本分类: https://github.com/AlexYangLi/TextClassification
- CapsuleNet模型: https://github.com/bojone/Capsule
- transformer模型: https://github.com/CyberZHG/keras-transformer
- keras_albert_model: https://github.com/TinkerMob/keras_albert_model
训练简单调用:
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!