google/seq2seq

★ 5,629PythonAudience · researcherComplexity · 4/5LicenseSetup · hard

Mindmap

mindmap
  root((seq2seq))
    What it does
      Encoder-decoder AI
      Sequence transformation
      Research framework
    Applications
      Machine translation
      Summarization
      Chatbots
      Image captioning
    Tech
      Python
      TensorFlow
      Google research
    Use cases
      Academic reproduction
      NLP experiments
      Custom training

mindmap root((seq2seq)) What it does Encoder-decoder AI Sequence transformation Research framework Applications Machine translation Summarization Chatbots Image captioning Tech Python TensorFlow Google research Use cases Academic reproduction NLP experiments Custom training

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Train a machine translation model to convert text from one language to another using the encoder-decoder architecture.

USE CASE 2

Run experiments to reproduce or extend the 2017 Google research paper on neural machine translation architectures.

USE CASE 3

Build a text summarization model that reads long documents and outputs shorter summaries.

USE CASE 4

Experiment with a conversational AI model that generates responses to input messages.

Tech stack

PythonTensorFlow

Getting it running

Difficulty · hard Time to first run · 1day+

Academic research codebase, not production-ready, external documentation required and GPU strongly recommended.

Apache 2.0, use freely for any purpose including commercial, as long as you keep the copyright and license notice.

In plain English

This repository contains a general-purpose encoder-decoder framework built on TensorFlow, an AI development library. Encoder-decoder models are a class of AI architecture where one part of the model reads an input sequence and compresses it into a representation, and another part generates an output sequence from that representation. This approach is used for tasks where you need to transform one sequence of tokens into another. The framework was built to support multiple applications: translating text from one language to another, summarizing longer documents into shorter ones, building conversational systems that generate responses to input messages, and generating text descriptions of images. The README is brief and points to an external documentation site for setup and usage details. The code was originally published as the official implementation for a 2017 research paper called Massive Exploration of Neural Machine Translation Architectures, written by researchers at Google. That paper systematically tested a large number of design choices in translation models to understand which architectural decisions matter most. The repository is shared for academic reproducibility and general experimentation rather than as a production-ready library. This is not an officially supported Google product.

Copy-paste prompts

Prompt 1

Using the google/seq2seq framework, how do I train a basic English-to-French translation model from scratch with TensorFlow?

Prompt 2

I want to reproduce the experiments from the Massive Exploration of Neural Machine Translation Architectures paper. Walk me through setting up the google/seq2seq codebase.

Prompt 3

How do I adapt the google/seq2seq encoder-decoder framework to train a document summarization model instead of a translation model?

Prompt 4

What is an encoder-decoder architecture in plain terms, and how does google/seq2seq implement it for machine translation?

Prompt 5

I want to use google/seq2seq for a chatbot. How do I format conversational data as input-output sequence pairs for training?

Open on GitHub → Explain another repo

← google on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.