Awesome

DeepNLP-models-Pytorch

Pytorch implementations of various Deep NLP models in <a href="http://web.stanford.edu/class/cs224n/">cs-224n(Stanford Univ: NLP with Deep Learning)</a>

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.
- <a href="http://web.stanford.edu/class/cs224n/syllabus.html">cs-224n-slides</a>
- <a href="https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6">cs-224n-videos</a>

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

<hr>

Model	Links
01. <strong>Skip-gram-Naive-Softmax</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/01.Skip-gram-Naive-Softmax.ipynb">notebook</a> / data / <a href="https://arxiv.org/abs/1301.3781">paper</a>]
02. <strong>Skip-gram-Negative-Sampling</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/02.Skip-gram-Negative-Sampling.ipynb">notebook</a> / data / <a href="http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf">paper</a>]
03. <strong>GloVe</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/03.GloVe.ipynb">notebook</a> / data / <a href="https://nlp.stanford.edu/pubs/glove.pdf">paper</a>]
04. <strong>Window-Classifier-for-NER</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/04.Window-Classifier-for-NER.ipynb">notebook</a> / data / paper]
05. <strong>Neural-Dependancy-Parser</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/05.Neural-Dependancy-Parser.ipynb">notebook</a> / <a href="https://github.com/rguthrie3/DeepDependencyParsingProblemSet/tree/master/data">data</a> / <a href="http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf">paper</a>]
06. <strong>RNN-Language-Model</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/06.RNN-Language-Model.ipynb">notebook</a> / <a href="https://github.com/tomsercu/lstm/tree/master/data">data</a> / <a href="https://arxiv.org/pdf/1504.00941.pdf">paper</a>]
07. <strong>Neural-Machine-Translation-with-Attention</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/07.Neural-Machine-Translation-with-Attention.ipynb">notebook</a> / <a href="http://www.manythings.org/anki/">data</a> / <a href="https://arxiv.org/pdf/1409.0473.pdf">paper</a>]
08. <strong>CNN-for-Text-Classification</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/08.CNN-for-Text-Classification.ipynb">notebook</a> / <a href="http://cogcomp.org/Data/QA/QC">data</a> / <a href="http://www.aclweb.org/anthology/D14-1181">paper</a>]
09. <strong>Recursive-NN-for-Sentiment-Classification</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/09.Recursive-NN-for-Sentiment-Classification.ipynb">notebook</a> / <a href="https://nlp.stanford.edu/sentiment/index.html">data</a> / <a href="https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf">paper</a>]
10. <strong>Dynamic-Memory-Network-for-Question-Answering</strong>	[<a href="https://nbviewer.jupyter.org/github/DSKSD/DeepNLP-models-Pytorch/blob/master/notebooks/10.Dynamic-Memory-Network-for-Question-Answering.ipynb">notebook</a> / <a href="https://research.fb.com/downloads/babi/">data</a> / <a href="https://arxiv.org/abs/1506.07285">paper</a>]

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

<a href="https://github.com/spro/practical-pytorch">practical-pytorch</a>
<a href="https://github.com/rguthrie3/DeepLearningForNLPInPytorch">DeepLearningForNLPInPytorch</a>
<a href="https://github.com/yunjey/pytorch-tutorial">pytorch-tutorial</a>
<a href="https://github.com/pytorch/examples/">pytorch-examples</a>

Author

Sungdong Kim / <a href="https://github.com/DSKSD">@DSKSD</a>