Skip to main content

Short Text Classification

Project description


A Modification of LibShortText and LIBLINEAR.

  • Uses Wissen Text Analyzer

  • Feature Selection

  • API Exported by Skitai App Engine

  • Win32 support (need MSVC)


git clone
cd haiku
python build install

Basic Usage

import haiku

model_path = "./golforbed"
analyzer =  haiku.StandardAnalyzer (max_term = 200, stem_level = 2, make_lower_case = 1)

trainset = [
    ('Golf', "cloudy cold calm"),
    ('Golf', "sunny warm"),
    ('Bed', "rainy hot"),
    ('Golf', "sunny hot windy"),
    ('Bed', "windy cloudy cold"),
    ('Bed', "rainy cloudy cold"),

# training
h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)
# pruning by document frequency and scoring by meth (FS_CF means category frequency) (data, mindf = 0, maxdf = 0, top = 0, meth = haiku.FS_CF)
# set training options: uni/bigram and feature representation
h.train (haiku.BIGRAM, haiku.FT_BIN)
h.close ()

# guessing
h = haiku.Haiku (model_path, haiku.CL_L2, analyzer)
h.load ()
print (h.guess ("sunny cold windy"))
h.close ()

Exporting API through Skitai App Engine

Place model data into app_root/resources/haikus/golforbed.

import haiku
import skitai

if __name__ == "__main__":

  pref = skitai.pref ()
  pref.config.resource_dir = skitai.joinpath ('resources')
  skitai.mount ("/", haiku, "app", pref) (port = 5005)

Go to

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

haiku-lst- (136.1 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page