Sequence to Sequence Framework in PyTorch
nmtpytorch
Sequence to Sequence Framework in PyTorch
This project is not actively maintained so issues created are unlikely to be addressed in a timely way. If you are interested, there’s a recent fork of this repository called pysimt which includes Transformer-based architectures as well.
nmtpytorch allows training of various end-to-end neural architectures including
but not limited to neural machine translation, image captioning and automatic
speech recognition systems. The initial codebase was in Theano and was
inspired from the famous dl4mt-tutorial
codebase.
nmtpytorch received valuable contributions from the Grounded Sequence-to-sequence Transduction Team
of Frederick Jelinek Memorial Summer Workshop 2018:
Loic Barrault, Ozan Caglayan, Amanda Duarte, Desmond Elliott, Spandana Gella, Nils Holzenberger,
Chirag Lala, Jasmine (Sun Jae) Lee, Jindřich Libovický, Pranava Madhyastha,
Florian Metze, Karl Mulligan, Alissa Ostapenko, Shruti Palaskar, Ramon Sanabria, Lucia Specia and