This document provides an overview of the Seq2Seq implementation on our platform. Seq2Seq, or Sequence to Sequence models, are a class of models used in natural language processing for tasks such as machine translation and text summarization.

Features

  • End-to-End Training: The model is trained end-to-end, ensuring a seamless transition from input to output.
  • State-of-the-Art Models: Utilizes state-of-the-art neural network architectures for optimal performance.
  • Customizable: Users can fine-tune the model for their specific needs.

Usage

To interact with the Seq2Seq implementation, simply send a POST request to /projects/seq2seq-implementation/translate. Include the input text and the desired output language in the request body.

Example Request

{
  "input": "你好，世界",
  "output_language": "en"
}

Example Response

{
  "translation": "Hello, world"
}

Learn More

For more information on Seq2Seq models and their applications, check out our Deep Learning Guide.

Visual Representation

Here's a visual representation of a Seq2Seq model in action:

Seq2Seq Model