See https://github.com/pytorch/examples/tree/master/word_language_model for the original code this repository is based on!
This example trains a multi-layer RNN (Elman, GRU, or LSTM) or Transformer 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 main.py --cuda --epochs 6 --model Transformer --lr 5
# Train a Transformer model on Wikitext-2 with CUDA.
python generate.py # Generate samples from the trained LSTM model.
python generate.py --cuda --model Transformer
# Generate samples from the trained Transformer model.The model uses the nn.RNN module (and its sister modules nn.GRU and nn.LSTM) or Transformer module (nn.TransformerEncoder and nn.TransformerEncoderLayer) 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 network (RNN_TANH, RNN_RELU, LSTM, GRU, Transformer)
--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)
--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
--onnx-export ONNX_EXPORT
path to export the final model in onnx format
--nhead NHEAD the number of heads in the encoder/decoder of the transformer model
--dry-run verify the code and the modelWith 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 
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent