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+## Introduction
+This is the implementation of [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf) paper in PyTorch.
+* Dataset is 600k documents extracted from [Yelp 2018](https://www.yelp.com/dataset) customer reviews
+* Use [NLTK](http://www.nltk.org/) and [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/) to tokenize documents and sentences
+* Both CPU & GPU support
+* The best accuracy is 71%, reaching the same performance in the paper
+
+## Requirement
+* python 3.6
+* pytorch = 0.3.0
+* numpy
+* gensim
+* nltk
+* coreNLP
+
+## Parameters
+According to the paper and experiment, I set model parameters:
+|word embedding dimension|GRU hidden size|GRU layer|word/sentence context vector dimension|
+|---|---|---|---|
+|200|50|1|100|
+
+And the training parameters:
+|Epoch|learning rate|momentum|batch size|
+|---|---|---|---|
+|3|0.01|0.9|64|
+
+## Run
+1. Prepare dataset. Download the [data set](https://www.yelp.com/dataset), and unzip the custom reviews as a file. Use preprocess.py to transform file into data set foe model input.
+2. Train the model. Word enbedding of train data in 'yelp.word2vec'. The model will trained and autosaved in 'model.dict'
+```
+python train
+```
+3. Test the model.
+```
+python evaluate
+```
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