Skip to content
This repository

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP

Train NLTK objects with zero code

branch: master
Octocat-spinner-32 docs corpus argument is first March 31, 2013
Octocat-spinner-32 nltk_trainer py3 classification updates April 21, 2014
Octocat-spinner-32 tests py3 classification updates April 21, 2014
Octocat-spinner-32 .gitignore ignore compiled python files October 26, 2013
Octocat-spinner-32 .hgignore initial docs July 31, 2011
Octocat-spinner-32 LICENSE apache license, initial setup.py, shebang in scripts February 26, 2011
Octocat-spinner-32 README.rst python versions, link to data install October 21, 2012
Octocat-spinner-32 analyze_chunked_corpus.py analyze chunked corpus works for v3 January 05, 2014
Octocat-spinner-32 analyze_chunker_coverage.py analyze chunker coverage working for v3 January 05, 2014
Octocat-spinner-32 analyze_classifier_coverage.py py3 classification updates April 21, 2014
Octocat-spinner-32 analyze_tagged_corpus.py analyze tagger coverage v3 January 05, 2014
Octocat-spinner-32 analyze_tagger_coverage.py analyze tagger coverage v3 January 05, 2014
Octocat-spinner-32 categorized_corpus2csv.py Correctly pull in the environment python January 28, 2013
Octocat-spinner-32 classify_corpus.py Correctly pull in the environment python January 28, 2013
Octocat-spinner-32 combine_classifiers.py Correctly pull in the environment python January 28, 2013
Octocat-spinner-32 requirements.txt fix numpy requirement December 22, 2012
Octocat-spinner-32 setup.py setup.py fixed October 10, 2012
Octocat-spinner-32 tag_phrases.py Correctly pull in the environment python January 28, 2013
Octocat-spinner-32 train_chunker.py node label for ieer February 23, 2014
Octocat-spinner-32 train_classifier.py py3 classification updates April 21, 2014
Octocat-spinner-32 train_tagger.py train tagger tagset option, v3 specific test script December 28, 2013
Octocat-spinner-32 translate_corpus.py Correctly pull in the environment python January 28, 2013
README.rst

NLTK Trainer

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

Requirements

You must have Python >=2.6 (but not 3.x) with argparse and NLTK 2.0 installed. NumPy, SciPy, and megam are recommended for training Maxent classifiers. 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. Every script also provides a --help option that describes all available parameters.

Something went wrong with that request. Please try again.