Tyrion - Word Embedding Autoencoder
An autoencoder to calculate word embeddings as mentioned in Lebret/Collobert paper 2015
Runs the model mentined in http://arxiv.org/abs/1412.4930 on set of text files stored in directory on GPU
Supports both theano and tensorflow implementation
With GPU support code is 5-8x faster than it's CPU version.
Tensorflow (or) Theano
To install the package follow the instructions mentioned below
Install from source
To install the package from source run setup.py file, it will install and include necessary files in system python directory
$ python setup.py install
Installation from pip (currently not supported !! )
Tyrion can also be install from pip. Run following command from console
$ pip install tyrion
You have an option to choose the backend to train the model on (theano/tensorflow). To select a backend, adjust the backend flag in config.json file supplied along with the code.
To set theano backend, change "backend" to 'th'.
To set tensorflow backend, change "backend" to 'tf'.
To set parameters of the model, like learning rate, context window size etc. change the appropriate fields in the config file.
To train the model on bunch of text files run following commnands in python console:
There is a sample data folder in project's folder, that can be used for sanity checking
python train.py CONFIG_PATH DATADIR_PATH
Different hyperparameters are set by default in training module, to set hyperparamters manually follow the instruction in the previous section. Some arguments explained are:
ContextSize is the contex window size used for constructing coocurence matrix
min_count is minimum threshold frequency for words, removes garbage words below this frequency
newdims is the dimension of embedding desired.
ntimes is number of epochs for training model
lr is the learning rate to drive optimization routine
To generate word embeddings and to find closest words to a word, use utils module in tyrion. Ex.
>> from tyrion import utils
>> embedding = utils.gen_embedding('word')
>> close_words = utils.closest_words('word',n=10)