Sentence Classification in TensorFlow

This project is roughly an exact TensorFlow implementation of Yoon Kim's paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). His original Theano code can be found here. Alternate to this, you can look at Denny Britz's TensorFlow implementation, here.

Setup

1. Download Google's word2vec embeddings and place them inside data/w2v/. This is a large file (~ 3.5G). You may git clone this.
2. Ensure you have a working tensorflow or tensorflow-gpu (version 1.0+). Additional dependencies include yaml, bunch and cPickle.
3. Pre-process the data by using,

cd data
chmod +x process_sst2.sh process_sst2_sentence.sh
./process_sst2.sh
./process_sst2_sentence.sh

4. Run python train.py to train the model, and run python train.py --mode test to evaluate the model.

Model Configuration

The model hyperparameters and mode (nonstatic, static and rand) are configured via YAML files inside config/. All hyperparameters (except batch_size) are identical to those reported in the paper. You may change the training directory via the --job_id parameter, and the random seed using --seed. Look at config/arguments.py for more details.

Results

All results have been averaged across 10 random seeds. All reported results are on the SST2 dataset. The models were trained on Titan X GPUs.

Model	Dataset	Average	Std	Range
Kim14 - nonstatic	Phrases	87.2	-	-
Our Kim14 - nonstatic	Phrases	87.06	0.34	86.55 - 87.70
Our Kim14 - nonstatic	Sentences	85.92	0.68	84.79 - 86.99
Kim14 - rand	Phrases	82.7	-	-
Our Kim14 - rand	Phrases	84.53	0.38	84.01 - 85.01
Our Kim14 - rand	Sentences	79.65	0.89	77.76 - 81.00
Kim14 - static	Phrases	86.8	-	-
Our Kim14 - static	Phrases	86.10	0.58	85.23 - 86.82
Our Kim14 - static	Sentences	85.18	0.66	83.69 - 86.32

Contributing

Feel free to add Issues and PRs (for the existing issues). It should be fairly easy to understand the code.