all kinds of text classification models and more with deep learning

中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RCNN, DCNN, DPCNN, VDCNN, CRNN, Bert, Xlnet, Albert, Attention, DeepMoji, HAN, 胶囊网络-CapsuleNet, Transformer-encode, Seq2seq, SWEM, LEAM, TextGCN

1st Place Solution for Zhihu Machine Learning Challenge . Implementation of various text-classification models.(知乎看山杯第一名解决方案)

Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN

Text classification models implemented in Keras, including: FastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, HAN, RCNN, RCNNVariant, etc.

多标签文本分类，多标签分类，文本分类, multi-label, classifier, text classification, BERT, seq2seq，attention, multi-label-classification

NLP 领域常见任务的实现，包括新词发现、以及基于pytorch的词向量、中文文本分类、实体识别、摘要文本生成、句子相似度判断、三元组抽取、预训练模型等。

情感分析、文本分类、词典、bayes、sentiment analysis、TextCNN、classification、tensorflow、BERT、CNN、text classification

1. Use BERT, ALBERT and GPT2 as tensorflow2.0's layer. 2. Implement GCN, GAN, GIN and GraphSAGE based on message passing.

all kinds of baseline models for long text classificaiton( text categorization)

TextClf ：基于Pytorch/Sklearn的文本分类框架，包括逻辑回归、SVM、TextCNN、TextRNN、TextRCNN、DRNN、DPCNN、Bert等多种模型，通过简单配置即可完成数据处理、模型训练、测试等过程。

OpenTextClassification is all you need for text classification! Open text classification for everyone, enjoy your NLP journey! 这可能是目前为止最全面的开源文本分类项目，支持中英双语、多种模型、多种任务。

Tensorflow2.3的文本分类项目，支持各种分类模型，支持相关tricks。

PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类

Using Bert/Roberta + LSTM/GRU/BiLSTM/TextCNN to do the sentiment analysis on the imdb datasets.

Implementation of papers for text classification task on SST-1/SST-2

LSTM，TextCNN，fastText情感分析，模型用 tf_serving 和 flask 部署成web应用

基于tensorflow2.0中的keras进行中文的文本分类，实验数据为中文新闻分类文本cnews数据集。

PyTorch repository for text categorization and NER experiments in Turkish and English.

Summary and comparison of Chinese classification models