This is a course project for the course Natural Language Understanding at ETH Zürich. The code implements a simple LSTM language model, and could be used to calculate probability of a sentence and generate sentences given initial words as well.
# download corpus and pretrained word embeddings
./get_data.sh
# show help
python model.py -h
# training with default setting
python model.py --n_epoch 3
# using pretrained word embeddings
python model.py --n_epoch 3 --pretrained vec/pretrained_embedding.vec
# increasing hidden dimension
python model.py --n_epoch 3 --pretrained vec/pretrained_embedding.vec --state_dim 1024 --hidden_proj_dim 512
# sentence generation
python model.py --n_epoch 3 --pretrained vec/pretrained_embedding.vec --conti_corpus data/sentences.continuation
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