harvardnlp/seq2seq-attn

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1281 stars · 277 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Torch (Lua) implementation of sequence-to-sequence models with LSTM encoder-decoders and optional attention for neural machine translation. It supports bidirectional encoders, character-level inputs via CNN+highway networks, and many NMT research extensions.

Use cases

• train a neural machine translation model
• build a sequence-to-sequence model with attention
• experiment with character-level NMT
• reproduce Luong attention results
• learn how seq2seq encoder-decoder models work

When to choose

• you need the original reference implementation of Luong attention for research
• you work with Torch/Lua and want a classic seq2seq baseline
• you want to study or extend NMT techniques like pruning or knowledge distillation

When to avoid

• you want a maintained, production-ready translation system - use OpenNMT instead
• your stack is PyTorch or TensorFlow
• you need modern transformer-based models

Facets

library · maturity maintenance

machine-learning nlp deep-learning machine-learning deep-learning lua seq2seq attention lstm neural-machine-translation torch encoder-decoder character-level natural-language-processing gpu linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="harvardnlp/seq2seq-attn")