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Seq2Seq Couplet

Seq2Seq Couplet is a Python and TensorFlow project that trains a sequence-to-sequence deep-learning model on couplet data and can run a trained model as a web service for generating Chinese couplets.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Seq2Seq Couplet — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to explore or build a Chinese couplet-generation workflow when you can supply a dataset and configure the documented Python training and serving scripts.

Who it is for

  • People exploring deep-learning approaches to Chinese couplet generation.
  • Developers who want to train a couplet model and configure its documented web-service entry point.

Documented uses

  • Train a seq2seq model using couplet data.
  • Configure a trained model to serve couplet-generation requests.
  • Try the linked hosted couplet demo.
Before you choose

Requirements & limits

  • The documented prerequisites are Python 3.6, TensorFlow and a dataset.
  • Training is configured through file locations and hyperparameters in couplet.py.
  • The README says training outputs loss and BLEU score in TensorBoard.
  • Serving requires configuring the vocabulary file and model directory.
  • The project lists an AGPL-3.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train and serve a couplet model

Configure and train the model, then set up its documented web-service entry point.

Before you start

  • Python 3.6
  • TensorFlow
  • A dataset; the README links to a couplet dataset project
  • Vocabulary file and model directory for serving

Steps

  1. Edit couplet.py to configure file locations and hyperparameters, then run python couplet.py to train.

  2. For a custom dataset, add and as the first two lines of the vocabulary file.

  3. After training, configure vocab_file and model_dir in server.py, then run python server.py to start the web service.

  4. Alternatively, build the image with the repository Dockerfile and run it with Docker, mounting the correct model file paths into the container.

Expected outcome

A trained couplet model and a configured web service intended to generate couplets.

Guide limitations

  • The README does not provide detailed installation instructions or specify an API.
  • The README reports one training example on an Nvidia GTX-1080 taking about four days; this is not a runtime guarantee.
Documentation 1 ↗Documentation 2 ↗
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
wb14123/seq2seq-couplet · Official homepage title · retained in its source language
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