Classify short texts with neural network.
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README.md

Smalltext

Classify short texts with neural network.

This gem is specifically created to classify small sentence/datasets using a supervised training algorithm. You can use this in place of Naive Bayes.

Installation

Requires Ruby >= 2.3.3

Add this line to your application's Gemfile:

gem 'smalltext'

And then execute:

$ bundle

Or install it yourself as:

$ gem install smalltext

Dependencies

Gem depends on Numo/NArray, Porter2Stemmer, Tokenizer, Croupier

Gem dependencies should be automatically installed.

Usage

Classification is easy to get started.

require 'smalltext'

s = Smalltext::Classifier.new

# Add your sentence using the `add_item` method
# Classifier#add_item(category, sentence)

s.add_item("schedule_list", "What time is my next session?")
s.add_item("schedule_list", "When is my next session?")
s.add_item("schedule_list", "What time is my next meeting?")
s.add_item("schedule_list", "Can you please show me my schedule?")
s.add_item("schedule_list", "Show me my schedule.")

s.add_item("greetings", "Hi")
s.add_item("greetings", "How are you doing?")
s.add_item("greetings", "have a nice day")
s.add_item("greetings", "good morning.")
s.add_item("greetings", "Whats up")
s.add_item("greetings", "Yo")

s.add_item("where_is", "Where is narkel bagan")
s.add_item("where_is", "show me the way to sasta sundar")
s.add_item("where_is", "where is the staircase")
s.add_item("where_is", "give me the direction to the parking lot")

s.add_item("weather", "Whats the weather")
s.add_item("weather", "Weather in Noida")
s.add_item("weather", "Is it raining")
s.add_item("weather", "Will it be hot tomorrow in Mumbai")
s.add_item("weather", "What is the maximum temperature today")

s.add_item("finance", "How many dollars is 17 euros?")
s.add_item("finance", "How much is 100 ruppees in US dollars")
s.add_item("finance", "How much is Starbucks stock?")
s.add_item("finance", "Tell me bitcoin exchange rate")
s.add_item("finance", "What is the value of ruppee")
s.add_item("finance", "Share price of Microsoft")

# Train a model using the Classifier#train method

s.train

# Test your trained model using the Classifier#classify(sentence) method

s.classify("give me the direction to moon")

# sentence: give me the direction to moon
# classification: [["where_is", 1.0]]

# => [["where_is", 1.0]]

You can also save your model. Use the Classifier#save_model(file_name)

s.save_model('intents.model')

To load a saved model use the Classifier#load_model(file_name)

s.load_model('intents.model')

s.classify("when is the next meeting")
# sentence: when is the next meeting
# classification: [["schedule_list", 0.9999189960209529]]

# => [["schedule_list", 0.9999189960209529]]

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/arjunmenon/smalltext. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the Smalltext project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

Credits

This gem is a Ruby port to the Medium article describing text classification in Python.

Roadmap

Goal of this gem is be an efficient tool for short texts classifications, where dataset is a constraint.

  • Implement word vectors. When dataset is sparse, we should leverage word vectors to map in categorizing unseen words.

  • More algorithm options Apart from neural networks, one can also switch and compare with different algorithms which mostly suits their needs.

Todo

  • Write Tests
  • Add support for batch training
  • Add SVD
  • Add support for word vectors. Need to try this method.
  • Add more classification algorithms.
  • Add benchmarking
  • Create model for known text datasets like Reuters, etc.