language-model

NOTICE

This code is largely taken from the official PyTorch examples repository in comformance with its LICENSE.

Word-level language modeling RNN

This example trains a multi-layer RNN (Elman, GRU, or LSTM) on a language modeling task. By default, the training script uses the Wikitext-2 dataset, provided. The trained model can then be used by the generate script to generate new text.

python main.py --cuda --epochs 6        # Train a LSTM on Wikitext-2 with CUDA
python main.py --cuda --epochs 6 --tied # Train a tied LSTM on Wikitext-2 with CUDA
python main.py --cuda --tied            # Train a tied LSTM on Wikitext-2 with CUDA for 40 epochs
python generate.py                      # Generate samples from the trained LSTM model.

The model uses the nn.RNN module (and its sister modules nn.GRU and nn.LSTM) which will automatically use the cuDNN backend if run on CUDA with cuDNN installed.

During training, if a keyboard interrupt (Ctrl-C) is received, training is stopped and the current model is evaluated against the test dataset.

The main.py script accepts the following arguments:

optional arguments:
  -h, --help         show this help message and exit
  --data DATA        location of the data corpus
  --model MODEL      type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
  --emsize EMSIZE    size of word embeddings
  --nhid NHID        number of hidden units per layer
  --nlayers NLAYERS  number of layers
  --lr LR            initial learning rate
  --clip CLIP        gradient clipping
  --epochs EPOCHS    upper epoch limit
  --batch_size N     batch size
  --bptt BPTT        sequence length
  --dropout DROPOUT  dropout applied to layers (0 = no dropout)
  --decay DECAY      learning rate decay per epoch
  --tied             tie the word embedding and softmax weights
  --seed SEED        random seed
  --cuda             use CUDA
  --log-interval N   report interval
  --save SAVE        path to save the final model

With these arguments, a variety of models can be tested. As an example, the following arguments produce slower but better models:

python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40           
python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 --tied    
python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40        
python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 --tied