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Ecct

ECCT is a Python and PyTorch implementation of a Transformer-based soft decoder for linear error-correcting codes. It processes communication-channel outputs and uses adapted masked self-attention to model interactions among code bits.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ecct — 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 ECCT when you want a code-based research implementation for experiments in soft decoding of linear codes, especially the repository's stated POLAR training configuration.

Who it is for

  • Researchers exploring neural decoding for communication systems.
  • Developers and students experimenting with Transformer models for linear error-correcting codes.

Documented uses

  • Train an ECCT model for the documented POLAR(64,32) code configuration.
  • Use the implementation as a research baseline for Transformer-based soft decoding.
  • Study how channel-output reliability and relationships among code bits are represented.
Before you choose

Requirements & limits

The public GitHub repository is MIT licensed.

  • The README documents PyTorch as the installation requirement and provides a training command that targets GPU 0.
  • The repository describes a soft-decoding approach that combines channel-output processing with adapted masked self-attention.
  • The documented command covers training a specific POLAR configuration; the README does not detail model evaluation.

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 ECCT for the documented POLAR configuration

Start the repository's stated training configuration for a six-layer, dimension-32 ECCT on POLAR(64,32).

Before you start

  • The ECCT repository
  • PyTorch
  • GPU 0, as specified by the example command

Steps

  1. Install PyTorch.

  2. From the repository, run the command below.

    Documented command
    python Main.py --gpus=0 --N_dec=6 --d_model=32 --code_type=POLAR --code_n=64 --code_k=32

Expected outcome

The command starts the documented training configuration for a POLAR(64,32) code.

Guide limitations

  • The README does not specify additional environment setup or describe how to evaluate the resulting model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
yoniLc/ECCT · Official homepage title · retained in its source language
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