Skip to content

Latest commit

 

History

History
93 lines (71 loc) · 2.89 KB

README.md

File metadata and controls

93 lines (71 loc) · 2.89 KB
Raw

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 m.lankinen@iki.fi.

Pre-requirements

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 https://github.com/Theano/archive/rel-0.8.2.zip
pip install https://github.com/fchollet/keras/archive/1.0.5.zip
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:

./run_c2w2c.sh

Running WordLSTM model and use example data from data folder:

./run_word_lstm.sh

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

License

MIT