The Wapiti-Ruby gem provides a wicked fast linear-chain CRF (Conditional Random Fields) API for sequence segmentation and labelling; it is based on the codebase of Thomas Lavergne's awesome wapiti.


Wapiti-Ruby is written in C and Ruby and requires a compiler with C99 support (e.g., gcc); the gem has been confirmed to work with MRI 1.9, 1.8.7, and Rubinius.



$ [sudo] gem install wapiti

Creating a Model

Using a pattern and training data stored in a file:

model = Wapiti.train('train.txt', :pattern => 'pattern.txt')
=> #<Wapiti::Model:0x0000010188f868>
=> ["B-ADJP", "B-ADVP", "B-CONJP" ...]
=> # saves the model as 'ch.mod'

Alternatively, you can pass in the training data as an array; the array should contain one array for each sequence of training data.

data = []
data << ['Confidence NN B-NP', 'in IN B-PP', 'the DT B-NP', 'pound NN I-NP', '. . O']
model = Wapiti.train(data, options)

You can consult the Wapiti::Options class for a list of supported configuration options and algorithms:

=> [:algorithm, :check, :compact, :convergence_window, :development_data,
:jobsize, :label, :max_iterations, :maxent, :pattern, :posterior, :rho1,
:rho2, :score, :sparse, :stop_epsilon, :stop_window, :threads]
=> ["l-bfgs", "sgd-l1", "bcd", "rprop", "rprop+", "rprop-", "auto"]

Use #valid? or #validate (which returns error messages) to make sure your configuration is supported by Wapiti.

You can pass options either as an options hash or by adding a block to the method invocation:

model = Wapiti::Model.train(data) do |config|
  config.pattern = 'pattern.txt'
  threads = 4

Before saving your model you can use compact to reduce the model's size:

model.save 'm1.mod'
=> # m1.mod file size 1.8M
model.save 'm2.mod'
=> # m2.mod file size 471K

Loading existing Models

model = Wapiti::Model.load('m1.mod')


By calling #label on a Model instance you can add labels to your sequence data:

model = Wapiti.load('m2.mod')
=> [[["Confidence NN B-NP", "B-NP"], ["in IN B-PP", "B-PP"] ... ]

The result is an array of sequence arrays; each sequence array consists of the original token and feature string (when using test data, the final feature is usually the expected label) and the label calculated by Wapiti.

As with training data, you can pass in data either by filename or as a Ruby Array:

model.label [['Confidence NN', 'in IN', 'the DT', 'pound NN', '. .']]
=> [[["Confidence NN", "B-NP"], ["in IN", "B-PP"], ["the DT", "B-NP"],
["pound NN", "I-NP"], [". .", "O"]]]

If you pass a block to #label Wapiti will yield each token and the corresponding label:

model.label [['Confidence NN', 'in IN', 'the DT', 'pound NN', '. .']] do |token, label|
  [token.downcase, label.downcase]
=> [[["confidence nn", "b-np"], ["in in", "b-pp"], ["the dt", "b-np"],
["pound nn", "i-np"], [". .", "o"]]]

Note that if you set the :score option (either in the Model's #options or when calling #label), the score for each label will be appended to each token/label tuple as a floating point number or passed as a third argument to the passed-in block.

model.label [['Confidence NN']], :score => true
=> [[["Confidence NN", "B-NP", 4.642034838737357]]]

Similarly, if you set the :nbest option to a value greater than one, Wapiti will append more label and, optionally, score values to each tuple.

model.label [['Confidence NN']], :score => true, :nbest => 3, :skip_tokens => true
=> [[["B-NP", 4.642034838737357, "B-VP", 1.7040256847206927, "B-ADJP", 0.7636429298060177]]]

Note how we also suppressed the output of the token string using the :skip_tokens option.


By setting the :check option you can tell Wapiti to keep statistics during the labelling phase (for the statistics to be meaningful you obviously need to provide input data that is already labelled). Wapiti does not reset the counters during consecutive calls to #label to allow you to collect accumulative date; however, you can reset the counters at any time, by calling #clear_counters.

After calling #label with the :check options set and appropriately labelled input, you can access the statistics via #statistics (the individual values are also available through the associated attribute readers).

model.label 'test.txt', :check => true
=> {:tokens=>{:total=>1896, :errors=>137, :rate=>0.0007225738396624472},
:sequences=>{:total=>77, :errors=>50, :rate=>0.006493506493506494}}


If you're using Wapiti-Ruby for research purposes, please use the following citation of the original wapiti package:

  author    = {Lavergne, Thomas and Capp\'{e}, Olivier and Yvon, Fran\c{c}ois},
  title     = {Practical Very Large Scale {CRFs}},
  booktitle = {Proceedings the 48th Annual Meeting of the Association for
              Computational Linguistics (ACL)},
  month     = {July},
  year      = {2010},
  location  = {Uppsala, Sweden},
  publisher = {Association for Computational Linguistics},
  pages     = {504--513},
  url       = {http://www.aclweb.org/anthology/P10-1052}

If you're profiting from any of the Wapiti-Ruby specific features you are welcome to also refer back to the Wapiti-Ruby homepage.


The Wapiti-Ruby source code is hosted on GitHub. You can check out a copy of the latest code using Git:

$ git clone https://github.com/inukshuk/wapiti-ruby.git

If you've found a bug or have a question, please open an issue on the Wapiti-Ruby issue tracker. Or, for extra credit, clone the Wapiti-Ruby repository, write a failing example, fix the bug and submit a pull request.


Copyright 2011 Sylvester Keil. All rights reserved.

Copyright 2009-2011 CNRS. All rights reserved.

Wapiti-Ruby is distributed under a BSD-style license. See LICENSE for details.