cjf9028 / finch

Deep NLP Examples in TensorFlow 深度学习与自然语言

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Why This Project?

  • simplifies code by using high-level TF interfaces: tf.layers, tf.data, tf.estimator;

  • produces lots of clean Notebooks to make life easy;

  • can be run on CPU in reasonable time;


Contents


Requirements

  • Python 3 and Jupyter Notebook are required

    (CPU User) $ pip3 install tensorflow sklearn scipy bunch tqdm wget
    
    (GPU User) $ pip3 install tensorflow-gpu sklearn scipy bunch tqdm wget
    

Word Embedding(词向量)

Text Classification(文本分类)

Text Generation(文本生成)

  • Language Model(语言模型)

  • Speech Recognition(语音识别)

Text Matching(文本匹配)

  • User-Item Matching     Folder

  • Question Matching     Folder

Sequence Labelling(序列标记)

  • POS Tagging(词性识别)

  • Chinese Segmentation(中文分词)

Sequence to Sequence(序列到序列)

  • Learning to Sort(机器排序)

  • Learning to Dialog(机器对话)

  • Learning to Reconstruct(重建句子)

  • Learning to Control(定向重建句子)

    • VAE + Discriminator     Folder

Question Answering(问题回答)

  • Memory Network(记忆网络)

    • End-to-End Memory Network     Notebook

    • Dynamic Memory Network     Notebook

Knowledge Graph(知识图谱)

TensorFlow Detail(TensorFlow 细节)

About

Deep NLP Examples in TensorFlow 深度学习与自然语言

License:MIT License


Languages

Language:Jupyter Notebook 85.7%Language:Python 14.3%Language:Shell 0.0%