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 wapiti.
Wapiti is written in C and Ruby and requires a compiler with C99 support; it has been confirmed to work on Linux, macOS, and Windows.
$ [sudo] gem install wapiti
You can run the following examples starting the ruby interpreter (irb or pry) inside spec/fixtures directory.
Using a pattern and training data stored in a file:
require 'wapiti'
model = Wapiti.train('chtrain.txt', pattern: 'chpattern.txt')
#=> #<Wapiti::Model:0x0000010188f868>
model.labels
#=> ["B-ADJP", "B-ADVP", "B-CONJP" ...]
model.save('ch.mod')
#=> saves the model as 'ch.mod'
Alternatively, you can pass in the training data as a Wapiti::Dataset;
this class supports the default text format used by Wapiti as well as
additional formats (such as YAML or XML) and an API, to make it easier
to manage data sets used for input and training.
options = {threads:3, pattern: 'chpattern.txt'}
data_text = Wapiti::Dataset.open('chtrain.txt',tagged:true)
model2= Wapiti.train(data_text,options)
model2.labels
=> ["B-ADJP", "B-ADVP", "B-CONJP" ...]
options = {threads:3, pattern: 'chpattern_only_tag.txt'}
data_xml = Wapiti::Dataset.open('chtrain.xml')
#=> #<Wapiti::Dataset sequences={823}>
model3 = Wapiti.train(data_xml, options)
You can consult the Wapiti::Options.attribute_names class for a list of
supported configuration options and Wapiti::Options.algorithms for
all supported algorithms:
Use #valid? or #validate (which returns error messages) to make sure
your configuration is supported by Wapiti.
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.compact
model.save 'm2.mod'
#=> m2.mod file size 471K
model = Wapiti.load('m1.mod')
By calling #label on a Model instance you can add labels to a dataset:
model = Wapiti.load('ch.mod')
input = Wapiti::Dataset.open('chtrain.txt',tagged:true)
output = model.label(input)
The result is a new Wapiti::Dataset with the predicted labels for each
token. If your input data was already tagged, you can compare the input
and output datasets to evaluate your results:
output - input
# => new dataset of output sequences which are tagged differently than expected
If you pass a block to #label Wapiti will yield each token and the
corresponding label:
model.label input do |token, label|
[token.downcase, label.downcase]
end
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.
output_with_score = model.label input, score: true
# => Dataset where each token will include a score
output_with_score.first.map(&:score)
# => [5.950832716249245, 8.870883529621942, ...]
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 stats; however, you can reset the counters at any time, by calling
#reset_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 input, check: true
model.stats
=> {:token=>{:count=>19172, :errors=>36, :rate=>0.18777383684539956},
:sequence=>{:count=>823, :errors=>28, :rate=>3.402187120291616}}
For convenience, you can also use the #check method, which
will reset the counters, check your input, and return the stats.
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 2011-2023 Sylvester Keil. All rights reserved.
Copyright 2009-2013 CNRS. All rights reserved.
Wapiti-Ruby is distributed under a BSD-style license. See LICENSE for details.