1. Jacob Perkins
  2. nltk-trainer

Source

nltk-trainer /

Filename Size Date modified Message
docs
nltk_trainer
tests
10 B
ignore compiled python files
46 B
initial docs
11.4 KB
apache license, initial setup.py, shebang in scripts
1.3 KB
links to text-processing.com & NLTK 3 cookbook
3.3 KB
analyze chunked corpus works for v3
3.9 KB
analyze chunker coverage working for v3
6.8 KB
py3 classification updates
3.6 KB
analyze tagger coverage v3
5.5 KB
analyze tagger coverage v3
2.3 KB
Correctly pull in the environment python
5.6 KB
tagset argument for train_chunker, remove babelfish references
2.1 KB
Correctly pull in the environment python
52 B
fix numpy requirement
1.4 KB
include all nltk_trainer packages
2.8 KB
Correctly pull in the environment python
7.6 KB
tagset argument for train_chunker, remove babelfish references
16.9 KB
Cleaner method using os.path.split
10.8 KB
train tagger tagset option, v3 specific test script

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. 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.