Skip to main content

Pytorch Implementation of the Multi-task Neural Machine Transliteration System

Project description

Multi-task-NMT

The Pytorch-based implementation of the Multi-task Neural Machine Transliteration system initially built for the undegraduate thesis at University of Edinburgh.


  1. Requirements
torchtext>=0.5.0
torchvision>=0.5.0
python-Levenshtein>=0.12.0  # for Minimum Edit Distance
pandas>=1.0.3
  1. Install package
pip install multi-nmt-lawhy

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

multi_task_nmt_lawhy-0.1.3-py3-none-any.whl (2.8 MB view details)

Uploaded Python 3

File details

Details for the file multi_task_nmt_lawhy-0.1.3-py3-none-any.whl.

File metadata

  • Download URL: multi_task_nmt_lawhy-0.1.3-py3-none-any.whl
  • Upload date:
  • Size: 2.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.7.3

File hashes

Hashes for multi_task_nmt_lawhy-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 137fcd6cd3b55572113815b9ddb8111c93b3c9c4def649a84b51675552382858
MD5 1f385ad2710d2ac7a220138b3e5c7094
BLAKE2b-256 38d7450264bfe88fa7af8bfcca7ef36d319df8610150452e8a1b7fd1ad72bbea

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page