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DeepNLP-models-Pytorch

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ: NLP with Deep Learning)

  • This is not for Pytorch beginners. If it is your first time to use Pytorch, I recommend these awesome tutorials.

  • If you're interested in DeepNLP, I strongly recommend you to work with this awesome lecture.

This material is not perfect but will help your study and research:) Please feel free to pull requests!!


Contents

Model Links
01. Skip-gram-Naive-Softmax [notebook / data / paper]
02. Skip-gram-Negative-Sampling [notebook / data / paper]
03. GloVe [notebook / data / paper]
04. Window-Classifier-for-NER [notebook / data / paper]
05. Neural-Dependancy-Parser [notebook / data / paper]
06. RNN-Language-Model [notebook / data / paper]
07. Neural-Machine-Translation-with-Attention [notebook / data / paper]
08. CNN-for-Text-Classification [notebook / data / paper]
09. Recursive-NN-for-Sentiment-Classification [notebook / data / paper]
10. Dynamic-Memory-Network-for-Question-Answering [notebook / data / paper]

Requirements

  • Python 3.5
  • Pytorch 0.2
  • nltk 3.2.2
  • gensim 2.2.0
  • sklearn_crfsuite

Getting started

git clone https://github.com/DSKSD/cs-224n-Pytorch.git

prepare dataset

cd script
chmod u+x prepare_dataset.sh
./prepare_dataset.sh

docker env

ubuntu 16.04 python 3.5.2 with various of ML/DL packages including tensorflow, sklearn, pytorch

docker pull dsksd/deepstudy:0.2

pip3 install docker-compose
cd script
docker-compose up -d

cloud setting

not yet

References

Author

Sungdong Kim / @DSKSD