A character-level language model using a GRU- or LSTM-based RNN, implemented with PyTorch
No installation is required to use char-parrot itself - simply clone this repository by running git clone https://github.com/cclaypool/char-parrot.git. However, before using char-parrot, some dependencies must be installed.
If you are using Linux, Python 3 is most likely already installed; if not, install it using your distribution's package manager. For other platforms, go to the Python official website to download and install Python 3. Ensure that the directory where Python 3 is installed is included in the PATH environment variable on your system.
Once Python is installed, head to the PyTorch official website for information on how to install the latest version of PyTorch.
tqdm is used to display progress bars during training. Install it using pip:
pip install tqdmNote that pip for Python 3 may be calledpip3 rather than pip, for example on Linux systems.
Note that you may need to replace python with python3 in the following commands, if, for example, you are using Linux and have both Python 2 and Python 3 installed.
From the char-parrot directory downloaded with git clone, run python train.py project_dir [options] to train a model, and python generate.py project_dir [options] to generate text based on a previously trained model. Run each script with the --help flag for detailed information on its usage.
project_dir must contain a model.ini model configuration file: see sample_project/model.ini for a commented example explaining each option.
The model will run on the GPU if available, unless force_cpu is set to True in hw.py.
Train a model based on a configuration stored in project/model.ini for 20 epochs, saving the model to project/save.pth after every epoch:
python train.py project -e 20 -s save.pthLoad the saved state project/save.pth and train for a further 10 epochs, saving the state to project/save.pth after every epoch:
python train.py project -e 10 -l save.pth -s save.pthGenerate 500 characters of text using the model whose state is saved in project/save.pth and whose configuration is stored in project/model.ini, using the seed phrase "once upon a time" and the sampling temperature 0.3:
python generate.py project -l save.pth -n 500 -s "once upon a time" -t 0.3