seq2seq-pytorch 0.1.2

Industrial-grade implementation of seq2seq algorithm based on Pytorch, integrated beam search algorithm.

seq2seq

Industrial-grade implementation of seq2seq algorithm based on Pytorch, integrated beam search algorithm.

seq2seq is based on other excellent open source projects, this project has the following highlights:

1. easy to train, predict and deploy;
2. lightweight implementation;
3. multitasking support (including dialogue generation and machine translation).

Model description

• Encoder: Bidirectional GRU
• Decoder: GRU with Attention Mechanism
• Bahdanau Attention: Neural Machine Translation by Jointly Learning to Align and Translate
• Luong Attention: Effective Approaches to Attention-based Neural Machine Translation
• Diversity Promoting Beam Search: A Simple, Fast Diverse Decoding Algorithm for Neural Generation

Install

seq2seq is dependent on PyTorch. Two ways to install:

Install seq2seq from Pypi:

pip install seq2seq-pytorch

Install seq2seq from the Github source:

git clone https://github.com/Chiang97912/seq2seq.git
cd seq2seq
python setup.py install

Usage

Train

from seq2seq.model import Seq2Seq

sources = ['...']
targets = ['...']
model = Seq2Seq('seq2seq-model', embed_size=256, hidden_size=512, lang4src='en', lang4tgt='en', device='cuda:0')
model.fit(sources, targets, epochs=20, batch_size=64)

Test

from seq2seq.model import Seq2Seq

model = Seq2Seq('seq2seq-model')
outputs = model.generate('...', beam_size=3, method='greedy')
print(outputs)

Dependencies

• python version 3.6
• pyTorch version 1.9.0
• torchtext version 0.3.1
• numpy version 1.19.5
• nltk version 3.5
• jieba version 0.42.1

References

• PyTorch Tutorial
• @spro/practical-pytorch
• @AuCson/PyTorch-Batch-Attention-Seq2seq
• @keon/seq2seq