raykyn/romanesco

romanesco

A vanilla recurrent neural network (RNN) language model. Supports model training, text scoring, and text generation.

Installation

Make sure you have an NVIDIA GPU at your disposal, with all drivers and CUDA installed. Make sure you also have python >= 3.5, pip and git installed, and run

git clone https://github.com/laeubli/romanesco.git
cd romanesco
pip install --user -e .

If you have sudo privileges and prefer to install romanesco for all users on your system, omit the --user flag. The -e flag installs the app in “editable mode”, meaning you can change source files (such as romanesco/const.py) at any time.

Model training

Models are trained from a single plaintext file with one sentence per line. Symbols – e.g., words or characters – are delimited by blanks.

Example input (word-level):

I love the people of Iowa .
So that 's the way it is .
Very simple .

Example input (character-level):

I <blank> l o v e <blank> t h e <blank> p e o p l e <blank> o f <blank> I o w a .
S o <blank> t h a t &apos; s <blank> t h e <blank> w a y <blank> i t <blank> i s .
V e r y <blank> s i m p l e .

romanesco doesn't preprocess training data. If you want to train a model on lowercased input, for example, you'll need to lowercase the training data yourself.

To train a model from corpus.train.txt using GPU 0, run

CUDA_VISIBLE_DEVICES=0 romanesco train corpus.train.txt

By default, the trained model and vocabulary will be stored in a directory called model, and logs (for monitoring with Tensorboard) in logs. You can use custom destinations through the -m and -l command line arguments, respectively. Folders will be created if they don't exist.

Some hyperparameters can be adjusted from the command line; run romanesco train -h for details. Other hyperparameters are currently hardcoded in romanesco/const.py.

Scoring

Once you've trained a model, you can use it to score texts. romanesco will calculate the perplexity of a text given a trained model. Lower is better: if you've trained a model on TV subtitles, it will typically assign lower scores to other TV subtitles than, say, an article from the New York Times.

To score my-article.txt using GPU 0, run

CUDA_VISIBLE_DEVICES=0 romanesco score my-article.txt

This assumes there is a folder called model in your current working directory, containing a model trained with romanesco (see above). If your model is stored somewhere else, use the -m command line argument.

For further options, run romanesco score -h.

Sampling

A trained model can be used to generate new text resembling the original training data. To generate a text with length 200 (numer of symbols), run

CUDA_VISIBLE_DEVICES=0 romanesco sample 200

This assumes there is a folder called model in your current working directory, containing a model trained with romanesco (see above). If your model is stored somewhere else, use the -m command line argument.

For further options, run romanesco sample -h.