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deep-text-classification-pytorch

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Deep neural network text classification papers in PyTorch

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# Deep Text Classification in PyTorch
PyTorch implementation of deep text classification models including:

- [WordCNN : Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882)
- [CharCNN : Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
- [VDCNN : Very Deep Convolutional Networks for Text Classification](https://arxiv.org/abs/1606.01781)
- [QRNN : Quasi-Recurrent Neural Networks](https://arxiv.org/abs/1611.01576)

## Requirements
- Python 3.5+
- [PyTorch 0.3](http://pytorch.org/)
- [gensim 3.2](https://github.com/RaRe-Technologies/gensim)
- [tqdm](https://github.com/tqdm/tqdm)
- [requests](https://github.com/requests/requests)

## Usage
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
```

#### WordCNN
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`

#### CharCNN
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
```

#### VDCNN
To train VDCNN with depth = 29:
```
$ python main.py --dataset MR VDCNN --depth 29
```

#### QRNN
To train QRNN with four layers:
```
$ python main.py --dataset MR QRNN --wordvec_mode glove --num_layers 4 --epochs 300
```

#### TF-IDF (benchmark)
You can train a multinomial logistic regression with TF-IDF features as a benchmark.
```
$ python tf-idf.py --dataset MR
```

#### Help
Refer to `python main.py --help` and `python main.py {WordCNN, CharCNN, VDCNN, QRNN} --help` for full description of how to use.

## Experiments
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) |                |                |                |                   |                    |                      |                    |              |                    |                        |

## References
- [Shawn1993's CNNs for Sentence Classification in PyTorch](https://github.com/Shawn1993/cnn-text-classification-pytorch)
- [ArdalanM's nlp-benchmarks](https://github.com/ArdalanM/nlp-benchmarks)
- [salesforce's Quasi-Recurrent Neural Network (QRNN) for PyTorch](https://github.com/salesforce/pytorch-qrnn)

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