Simple sentiment analysis experiment with Quasi-recurrent neural networks (QRNN) and IMDb dataset. Implemented with chainer. More detailed explanation could be found in my blog post: http://sergeiturukin.com/2017/03/15/qrnn-imdb.html
IMDb dataset could be downloaded here: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz 300-dimensional cased GloVe embeddings could be found here: http://nlp.stanford.edu/data/glove.6B.zip
Clone repo, download dataset and embeddings:
git clone https://github.com/rampage644/qrnn
cd qrnn
mkdir data/
cd data/
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
wget http://nlp.stanford.edu/data/glove.6B.zip
tar xfz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
unzip http://nlp.stanford.edu/data/glove.6B.zip
Preprocess the data (GloVe embeddings):
python3 glove_to_npy.py -d data/glove.840B.300d.txt --npy_output data/embeddings.npy --dict_output data/vocab.pckl --dict_whitelist data/aclImdb/imdb.vocab
Model relies on modified chainer version. Install it from sources https://github.com/jekbradbury/chainer/tree/raw-kernel. In case of problems follow my blog post (see link in above) to apply patch to current version of chainer (though you still need to install it from sources).
Train the model with default parameters:
python3 train.py -g0 -o results/ --dataset data/aclImdb/ --vocabulary data/vocab.pckl --embeddings data/embeddings.npy
With default parameters (only dropout set to 0.5) model achieves ~86% accuracy on validatio set. Hyperparameters tuning was not performed. Model suffers heavily from overfitting. 20 epoch on single NVIDIA Titan X Pascal GPU take ~10 minutes.
$ python3 train.py -g0 -o results/ --dataset data/aclImdb/ --vocabulary data/vocab.pckl --embeddings data/embeddings.npy --dropout 0.5
epoch main/loss validation/main/loss main/accuracy validation/main/accuracy
1 0.542545 0.415724 0.748241 0.815159
2 0.412443 0.371796 0.819378 0.838072
3 0.37779 0.348942 0.836295 0.854506
4 0.354687 0.470325 0.849848 0.800603
5 0.337171 0.336974 0.857885 0.863385
6 0.317884 0.39081 0.865153 0.839712
7 0.304117 0.350891 0.872921 0.853087
8 0.291776 0.330035 0.880759 0.860645
9 0.277024 0.365185 0.887127 0.854467
10 0.263624 0.404913 0.893514 0.835333
11 0.248957 0.365441 0.898472 0.862405
12 0.233838 0.383892 0.908221 0.856066
13 0.221411 0.380842 0.912828 0.860224
14 0.211432 0.423715 0.915187 0.848769
15 0.194838 0.442775 0.92195 0.851088
16 0.184106 0.469984 0.928222 0.851787
17 0.175725 0.472484 0.931701 0.861365
18 0.164538 0.518889 0.934638 0.849648
19 0.157166 0.46597 0.939738 0.850867
20 0.147039 0.549008 0.942138 0.858005