https://github.com/japerk/nltk-trainer

Train NLTK objects with zero code
https://github.com/japerk/nltk-trainer

Last synced: 3 months ago
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Train NLTK objects with zero code

• Host: GitHub
• URL: https://github.com/japerk/nltk-trainer
• Owner: japerk
• License: apache-2.0
• Created: 2010-10-17T17:32:40.000Z (over 13 years ago)
• Default Branch: master
• Last Pushed: 2020-04-13T14:47:45.000Z (about 4 years ago)
• Last Synced: 2024-01-16T10:14:32.570Z (5 months ago)
• Language: Python
• Homepage: http://nltk-trainer.readthedocs.org/en/latest/
• Size: 229 KB
• Stars: 745
• Watchers: 64
• Forks: 227
• Open Issues: 4
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Lists

• awesome - nltk-trainer - Train NLTK objects with zero code (Python)
• awesome-stars - japerk/nltk-trainer - Train NLTK objects with zero code (Python)
• alex-mikhalev-awesome-stars - nltk-trainer - Train NLTK objects with zero code (Python)
• my-awesome-stars - japerk/nltk-trainer - Train NLTK objects with zero code (Python)
• my-awesome-stars - japerk/nltk-trainer - Train NLTK objects with zero code (Python)

README

        
NLTK Trainer

------------

NLTK Trainer exists to make training and evaluating NLTK objects as easy as possible.

Requirements

------------

The scripts with default arguments have been tested for compatibility with Python3.7 and NLTK 3.4.5. If something does not work for you, please `open an issue `_. Include the script with arguments and failure or exception output. To use the sklearn classifiers, you must also install `scikit-learn `_.

If you want to use any of the corpora that come with NLTK, you should `install the NLTK data `_.

Documentation

-------------

Documentation can be found at `nltk-trainer.readthedocs.org `_ (you can also find these documents in the `docs directory `_. Many of the scripts are covered in `Python 3 Text Processing with NLTK 3 Cookbook `_, and every script provides a ``--help`` option that describes all available parameters.

Using Trained Models

--------------------

The trained models are pickle files that by default are put into your ``nltk_data`` directory. You can load them using ``nltk.data.load``, for example::

    import nltk.data

    classifier = nltk.data.load('classifiers/movie_reviews_NaiveBayes.pickle')

You now have a NLTK classifier object you can work with.