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1-1.NNLM
1-1.NNLM
 
 
1-2.Word2Vec
1-2.Word2Vec
 
 
1-3.FastText
1-3.FastText
 
 
2-1.TextCNN
2-1.TextCNN
 
 
3-1.TextRNN
3-1.TextRNN
 
 
3-2.TextLSTM
3-2.TextLSTM
 
 
3-3.Bi-LSTM
3-3.Bi-LSTM
 
 
4-1.Seq2Seq
4-1.Seq2Seq
 
 
4-2.Seq2Seq(Attention)
4-2.Seq2Seq(Attention)
 
 
4-3.Bi-LSTM(Attention)
4-3.Bi-LSTM(Attention)
 
 
5-1.Transformer
5-1.Transformer
 
 
5-2.BERT
5-2.BERT
 
 
archive/tensorflow/v1
archive/tensorflow/v1
 
 
.gitignore
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CONTRIBUTING.md
CONTRIBUTING.md
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 

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nlp-tutorial

nlp-tutorial is a tutorial for who is studying NLP(Natural Language Processing) using Pytorch. Most of the models in NLP were implemented with less than 100 lines of code.(except comments or blank lines)

  • [08-14-2020] Old TensorFlow v1 code is archived in the archive folder. For beginner readability, only pytorch version 1.0 or higher is supported.

Curriculum - (Example Purpose)

1. Basic Embedding Model

  • 1-1. NNLM(Neural Network Language Model) - Predict Next Word
    • Paper - A Neural Probabilistic Language Model(2003)
    • Colab - NNLM.ipynb
  • 1-2. Word2Vec(Skip-gram) - Embedding Words and Show Graph
    • Paper - Distributed Representations of Words and Phrases and their Compositionality(2013)
    • Colab - Word2Vec.ipynb
  • 1-3. FastText(Application Level) - Sentence Classification
    • Paper - Bag of Tricks for Efficient Text Classification(2016)
    • Colab - FastText.ipynb

2. CNN(Convolutional Neural Network)

  • 2-1. TextCNN - Binary Sentiment Classification
    • Paper - Convolutional Neural Networks for Sentence Classification(2014)
    • TextCNN.ipynb

3. RNN(Recurrent Neural Network)

  • 3-1. TextRNN - Predict Next Step
    • Paper - Finding Structure in Time(1990)
    • Colab - TextRNN.ipynb
  • 3-2. TextLSTM - Autocomplete
    • Paper - LONG SHORT-TERM MEMORY(1997)
    • Colab - TextLSTM.ipynb
  • 3-3. Bi-LSTM - Predict Next Word in Long Sentence
    • Colab - Bi_LSTM.ipynb

4. Attention Mechanism

  • 4-1. Seq2Seq - Change Word
    • Paper - Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation(2014)
    • Colab - Seq2Seq.ipynb
  • 4-2. Seq2Seq with Attention - Translate
    • Paper - Neural Machine Translation by Jointly Learning to Align and Translate(2014)
    • Colab - Seq2Seq(Attention).ipynb
  • 4-3. Bi-LSTM with Attention - Binary Sentiment Classification
    • Colab - Bi_LSTM(Attention).ipynb

5. Model based on Transformer

  • 5-1. The Transformer - Translate
    • Paper - Attention Is All You Need(2017)
    • Colab - Transformer.ipynb, Transformer(Greedy_decoder).ipynb
  • 5-2. BERT - Classification Next Sentence & Predict Masked Tokens
    • Paper - BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding(2018)
    • Colab - BERT.ipynb

Dependencies

  • Python 3.5+
  • Pytorch 1.0.0+

Author

  • Tae Hwan Jung(Jeff Jung) @graykode
  • Author Email : [email protected]
  • Acknowledgements to mojitok as NLP Research Internship.

About

Natural Language Processing Tutorial for Deep Learning Researchers

www.reddit.com/r/MachineLearning/comments/amfinl/project_nlptutoral_repository_who_is_studying/

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attentionbertnatural-language-processingnlppaperpytorchtensorflowtransformertutorial

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