alanwang93/seq2seq-pytorch

Seq2seq model with global attention implemented with PyTorch

Sequence to sequence implementation with PyTorch

Seq2seq model with

• global attention
• self-critical sequence training

Dependencies

• Python 3.6
• PyTorch 0.3
• Spacy 2.0.4
• Torchtext 0.2.3
• Numpy

You can install Torchtext following: https://stackoverflow.com/questions/42711144/how-can-i-install-torchtext

You need to install Spacy models specified in config.py (src_lang and trg_lang). Usually you can do this by running python -m spacy download en after installing Spacy.

Start training

1. create models, data and log folders in the root.
2. Prepare data files in data folder.
   • Prepare 6 files named as [train/test/valid].[src/trg], where each line in *.src is a source sentence, and in *.trg is a target sentence.
3. You can modify the configurations in config.py
4. Start training
   • python train.py --config <config_name> --exp <experiment_name> to train the model.

Options

• Define model settings in config.py and choose with --config.
• The model will use GPU if available, add --disable_cuda to use cpu explicitly.
• Use CUDA_VISIBLE_DEVICES=2 to choose GPU device. For example, CUDA_VISIBLE_DEVICES=1 python train.py --config chatbot_twitter.
• Add --resume to resume from a certain saved model, specified by --config and --exp.
• Add --early_stopping and set --patient <n> to enable early stopping, the training process will end if the validation loss doesn't decrease for n epochs, or max_epoch is reached. Without --early_stopping, we'll train the model for num_epoch epochs.
• Set --self_critical <p> to use hybrid loss.

TODO

Reference:

• OpenNMT-py: https://github.com/OpenNMT/OpenNMT-py
• Pytorch NMT tutorial: http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html (Note that the tutorial has some faults)
• torchtext: https://github.com/pytorch/text
• Effective Approaches to Attention-based Neural Machine Translation
• Neural Text Generation: A Practical Guide