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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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
| Date | Stars |
|---|---|
| 2026-07-24 | 1812 |
| 2026-07-25 | 1812 |
| 2026-07-28 | 1812 |
| 2026-07-30 | 1812 |
| 2026-08-06 | 1812 |
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# [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)
- modExcerpt of 8,709 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7b9d7a7128da7d45, topic:nlp, topic:text-classification, desc:text classification