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C2W2C Language Model

The implementation of the language model from my Master's Thesis. If you are interested in getting the paper, please send me email to


In order to run the model, the following pre-requirements must be satisfied.

  1. Install Python 2.7.x, pip and virtualenv

  2. Create new virtual environment and install the following packages:

# Theano and keras and their deps
pip install numpy scipy pyyaml
pip install
pip install
pip install Cython
pip install h5py

NOTE: If you are using OSX, these native packages must be installed before you can install the actual Python packages (using homebrew)

# if gfortran is missing
brew install gcc 
brew tap homebrew/science
brew install hdf5

Running the model

Running C2W2C model and use example data from data folder:


Running WordLSTM model and use example data from data folder:


NOTE: If you are using OS X and XCode 7.x, you may need to install older version of XCode and set $DEVELOPER_DIR environment variable to point to older installation (see example from run script).

Available options

Usage: ./run <args>

C2W2C language model

optional arguments:
  -h, --help            show this help message and exit
  --training filename   Training dataset filename
  --test filename       Validation dataset filename
  --data-limit training:validation, -l training:validation
                        Limit data size to the given rows (e.g. "10:1")
  --batch-size n        Number of samples is single training batch
  --learning-rate num, -r num
  --num-epoch n, -e n   Number of epoch to run
  --load-weights filename
                        File containing the initial model weights
  --save-weights filename
                        Filename where model weights will be saved
  --max-word-length n, -w n
                        Maximum word length (longer words will be truncated)
  --d_C n               Character features vector size
  --d_W n               Word features vector size
  --d_Wi n              Intermediate word LSTM state dimension
  --d_L n               Language model state dimension
  --d_D n               W2C Decoder state dimension
  --gen-text n          Generate N sample sentences after each epoch
  --test-only, -T       Run only PP test and (optional) text generation
  --mode c2w2c|word     Select which mode to run

Example data

Example data files are taken from Europarl V7 Finnish Corpus and pre-processed with Apache OpenNLP tokenizer and Finnish tokenizing model.

  • training.txt : 700 sentences / 15k tokens
  • validation.txt : 30 sentences / 600 tokens




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