Metadata-Version: 2.0
Name: chClassifier
Version: 0.6.4
Summary: Neural Network to classify short strings
Home-page: https://github.com/ekatek/char-classify
Author: Ekate Kuznetsova
Author-email: char-classify@technekate.com
License: MIT
Keywords: deep-learning machine-learning string classifier
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Text Processing
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Requires-Dist: chainer
Requires-Dist: numpy

Char Classifier
---------

Given a short phrase, train a neural network to classify it based on the characters that it contains.

The trainer takes in a file of phrases (separated by new line) and a file of target values (separated by new line).

To train on the sample data (parts of speech classification):

```
import chClassifier
p = chClassifier.Trainer("sample/words", "sample/parts-of-speech")
p.train([100, 100], 20, 100)
p.save("sample", "example-run")
```

This will save a trained model with the tag 'example-run' in the sample directory. To use that model, run:

```
import chClassifier
k = chClassifier.Classifier("sample", "example-run")
print k.classify("dog")
```

This will return an array of tuples of original label + likelyhood that the label is correct, like so:

```
>> print k.classify("dog")
[(u'VB', 0.050349433), (u'NN', 3.8027303)]
```

The higher the number, the more sure we are of the classification (`dog` is definitely a noun, for example, and probably not a verb). 


