https://github.com/ssemeniuta/textvae

Theano code for experiments in the paper "A Hybrid Convolutional Variational Autoencoder for Text Generation."
https://github.com/ssemeniuta/textvae

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Theano code for experiments in the paper "A Hybrid Convolutional Variational Autoencoder for Text Generation."

• Host: GitHub
• URL: https://github.com/ssemeniuta/textvae
• Owner: ssemeniuta
• Created: 2017-02-07T22:19:35.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2018-10-05T18:54:16.000Z (about 6 years ago)
• Last Synced: 2024-08-02T05:12:02.621Z (3 months ago)
• Language: Python
• Homepage:
• Size: 30.3 KB
• Stars: 206
• Watchers: 11
• Forks: 45
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
####  A Hybrid Convolutional Variational Autoencoder for Text Generation.

Theano code for experiments in the paper [A Hybrid Convolutional Variational Autoencoder for Text Generation](https://arxiv.org/abs/1702.02390).

#### Preparation

First, run makedata.sh. This will download the ptb dataset, split, and preprocess it.

#### PTB Experiments

Files prefixed with ''lm_'' contain experiments on the ptb dataset. We provide scripts for training of non-VAE, baseline LSTM VAE, and our models and a script to greedily sample from a trained model. ''defs'' subfolder contains definitions of grid searches we have used to generate data for figures and tables in the paper. Running one search is done by:

```bash

python -u nn/scripts/grid_search.py -grid defs/gridname.json

```

To train our model on samples 60 characters long with alpha=0.2 run:

```bash

python -u lm_vae_lstm.py -alpha 0.2 -sample_size 60

```

#### Twitter Experiments

Code for these experiments is in files starting with ''twitter_''. We do not release the dataset we have used to train our model, but provide both a script to train one and [a pretrained model](https://www.dropbox.com/s/lv20o4gbkwvy097/pretrained.tar?dl=1). To use the script on custom data, create a file ''data/tweets.txt'' containing one data sample per line. By default, the first 10k samples will be used for validation and everything else for training, but no more than ~1M samples. In addition, it will only use tweets with up to 128 characters. This is done only for convenience when down- and upsampling. Training on tweets with up to 140 characters will require a little bit of care when handling spatial dimension.

#### License

MIT