bentrevett/pytorch-seq2seq Alternatives

Choose for the official PyTorch seq2seq tutorial, which is actively maintained and covers the same translation task.

PyTorch tutorials.

Choose when you need a production-grade PyTorch NMT training and inference toolkit rather than a tutorial.

Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch

Choose if you are locked into TensorFlow 1.x and want a minimal seq2seq example, but it is unmaintained since 2016.

Sequence to sequence learning using TensorFlow.

Choose for a Lua/Torch attention-based seq2seq implementation, but it is a dead ecosystem and lacks PyTorch tooling.

Sequence-to-sequence model with LSTM encoder/decoders and attention

Choose if you are committed to Keras/TensorFlow 1.x for NMT, though it predates modern TF2/PyTorch practices.

Neural Machine Translation with Keras

Choose for a TensorFlow NMT tutorial, but it is archived and tied to TF1.

TensorFlow Neural Machine Translation Tutorial

Choose if you need a TensorFlow-based sequence learning toolkit, but it is old and not PyTorch.

An open-source tool for sequence learning in NLP built on TensorFlow.

Choose for a legacy Lua Torch seq2seq toolkit, but it is archived and superseded by fairseq in PyTorch.

Facebook AI Research Sequence-to-Sequence Toolkit

Choose if you only need a Keras Transformer layer implementation, but it is narrower than a full seq2seq tutorial.

Transformer implemented in Keras

Choose if you want a PyTorch/torchtext NLP framework for building models, but it is unmaintained and not a tutorial.

基于Pytorch和torchtext的自然语言处理深度学习框架。

Choose if you need a standalone PyTorch Transformer implementation, but it lacks the full seq2seq training pipeline.

A PyTorch implementation of the Transformer model in "Attention is All You Need".