GithubHelp home page GithubHelp logo

martinthoma / lidtk Goto Github PK

View Code? Open in Web Editor NEW
18.0 3.0 7.0 468 KB

Language Identification Toolkit

License: MIT License

Python 99.38% Makefile 0.62%
language-identification python-3 python-3-5 mit-license language-identification-toolkit machine-learning nlp nlp-machine-learning

lidtk's Introduction

DOI PyPI version Python Support Build Status Code style: black GitHub last commit GitHub commits since latest release (by SemVer) CodeFactor

lidtk

lidtk - the language identification toolkit - was written in order to investigate the current state of language performance.

Installation

The recommended way to install clana is:

$ pip install lidtk --user

If you want the latest version:

$ git clone https://github.com/MartinThoma/lidtk.git; cd lidtk
$ pip install -e . --user

I recommend getting the WiLI-2018 dataset.

Usage

$ lidtk --help

Usage: lidtk [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  analyze-data           Utility function for the languages...
  analyze-unicode-block  Analyze how important a Unicode block is for...
  char-distrib           Use the character distribution language...
  cld2                   Use the CLD-2 language classifier.
  create-dataset         Create sharable dataset from downloaded...
  download               Download 1000 documents of each language.
  google-cloud           Use the CLD-2 language classifier.
  langdetect             Use the langdetect language classifier.
  langid                 Use the langid language classifier.
  map                    Map predictions to something known by WiLI
  nn                     Use a neural network classifier.
  textcat                Use the CLD-2 language classifier.
  tfidf_nn               Use the TfidfNNClassifier classifier.

For example:

$ lidtk cld2 predict --text 'This is a test.'
eng

The usual order is:

  1. lidtk download: Please use WiLI-2018 instead of downloading the dataset on your own.
  2. lidtk create-dataset: This step can be skipped if you use WiLI-2018
  3. lidtk analyze-unicode-block --start 0 --end 128
  4. lidtk tfidf_nn train vectorizer --config lidtk/classifiers/config/tfidf_nn.yaml
  5. lidtk tfidf_nn train vectorizer --config lidtk/classifiers/config/tfidf_nn.yaml
  6. lidtk tfidf_nn wili --config lidtk/classifiers/config/tfidf_nn.yaml

Or to use one directly:

$ lidtk cld2 predict --text 'This text is written in some language.'

eng

Development

Check tests with tox.

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.