Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ: NLP with Deep Learning)
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This is not for Pytorch beginners. If it is your first time to use Pytorch, I recommend these awesome tutorials.
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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!!
| 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] |
- Python 3.5
- Pytorch 0.2+
- nltk 3.2.2
- gensim 2.2.0
- sklearn_crfsuite
git clone https://github.com/DSKSD/cs-224n-Pytorch.git
cd script
chmod u+x prepare_dataset.sh
./prepare_dataset.sh
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
not yet
Sungdong Kim / @DSKSD