add some sample data

reproduction/train.py → HAN-document_classification/train.py

@@ -147,7 +147,7 @@ if __name__ == '__main__':
     import gensim
     from gensim import models
 
-    # train_word_vec()
+    train_word_vec()
 
     embed_model = Word2Vec.load('yelp.word2vec')
     embedding = Embedding_layer(embed_model.wv, embed_model.wv.vector_size)
@@ -158,6 +158,10 @@ if __name__ == '__main__':
     net = HAN(input_size=200, output_size=5, 
             word_hidden_size=50, word_num_layers=1, word_context_size=100,
             sent_hidden_size=50, sent_num_layers=1, sent_context_size=100)
-        
-    net.load_state_dict(torch.load('model.dict'))
+    try:
+        net.load_state_dict(torch.load('model.dict'))
+        print("last time trained model has loaded")
+    except Exception:
+        print("cannot load model, train the inital model")
+    
     train(net, dataset, num_epoch=5, batch_size=64, use_cuda=True)

reproduction/.gitignore

@@ -1 +0,0 @@
-*.pyc

reproduction/README.md

@@ -1,36 +0,0 @@
-## 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. The model will trained and autosaved in 'model.dict'
-```
-python train
-```
-3. Test the model.
-```
-python evaluate
-```