PyTorch implementation of deep text classification models including:
- WordCNN : Convolutional Neural Networks for Sentence Classification
- CharCNN : Character-level Convolutional Networks for Text Classification
- VDCNN : Very Deep Convolutional Networks for Text Classification
- QRNN : Quasi-Recurrent Neural Networks
- Python 3.5+
- PyTorch 0.3
- gensim 3.2
- tqdm
- requests
To begin, you will need to download datasets as follows:
$ python download_dataset.py all
You can also download a specific dataset by specifying its name instead of all. Available datasets are MR, SST-1, SST-2, ag_news, sogou_news, dbpedia, yelp_review_full, yelp_review_polarity, yahoo_answers, amazon_review_full, and amazon_review_polarity
To download word vectors, run the following:
$ python download_wordvector.py word2vec
$ python download_wordvector.py glove
To train WordCNN with rand mode:
$ python main.py --dataset MR WordCNN --mode rand --vector_size 128 --epochs 300
To train WordCNN with multichannel mode:
$ python main.py --dataset MR WordCNN --mode multichannel --wordvec_mode word2vec --epochs 300
Available modes are rand, static, non-static, and multichannel
To train CharCNN with small mode:
$ python main.py --dataset MR CharCNN --mode small --epochs 300
To train CharCNN with large mode:
$ python main.py --dataset MR CharCNN --mode large --epochs 300
To train VDCNN with depth = 29:
$ python main.py --dataset MR VDCNN --depth 29
To train QRNN with four layers:
$ python main.py --dataset MR QRNN --wordvec_mode glove --num_layers 4 --epochs 300
You can train a multinomial logistic regression with TF-IDF features as a benchmark.
$ python tf-idf.py --dataset MR
Refer to python main.py --help and python main.py {WordCNN, CharCNN, VDCNN, QRNN} --help for full description of how to use.
Results are reported as follows: Test accuracy reproduced here (Test accuracy reported by the paper)
To find the settings for experiments, refer to experiments.sh.
| MR | SST_1 | SST_2 | ag_news | sogu_news | db_pedia | yelp_review_full | yelp_review_polarity | yahoo_answer | amazon_review_full | amazon_review_polarity | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| WordCNN (rand) | 69.4 (76.1) | (45.0) | (82.7) | 88.3 | 92.5 | ||||||
| WordCNN (static) | (81.0) | (45.5) | (86.8) | ||||||||
| WordCNN (non-static) | (81.5) | (48.0) | (87.2) | ||||||||
| WordCNN (multichannel) | (81.1) | (47.4) | (88.1) | ||||||||
| CharCNN (small) | |||||||||||
| CharCNN (large) | |||||||||||
| VDCNN (29-layers) | |||||||||||
| QRNN (k=2) | (91.4) | ||||||||||
| QRNN (k=4) | (91.1) |