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A text classification engine using machine learning and designed as client-server architecture

Project description

OpenTC is a text classification engine using machine learning. It is designed as client-server architecture and uses python libraries scikit-learn and tensorflow for it’s machine learning algorithms. Currently following algorithms are supported:

  • Naive Bayes

  • Support Vector Machine

  • Convolutional Neural Network

In the future it will also support FastText from Facebookresearch.

The engine is running as a server listening on command and text to be classified. By default it listens on localhost port 3333, but it can be changed in the yaml configuration file.


  • Python 3.x

  • numpy

  • pyparsing

  • PyYAML

  • scikit-learn

  • scipy

  • tensorflow 1.x





The daemon listens for incoming connections on TCP socket and classify files or text string on demand. It reads the configuration from /etc/opentc/opentc.yml


The command uses a newline character as the delimiter. If opentcd doesn’t recognize the command, or the command doesn’t follow the requirements specified below, it will reply with an error message, but still wait for the next commands (this behaviour can be changed in the future).


Check the server’s state. It should reply with “PONG”.


Print the program version


Reload the engine


List the supported classifiers (at the moment there are three classifiers supported: Bayesian, Support Vector Machine and Convolutional Neural Network). It shows also the status of classifier, either True (enabled) or False (disabled).


Enabled or disabled the specific classifier


Classify text streams. It uses a new line character as delimiter for every sentences.


Classify file. It uses a new line character as delimiter for every sentences


Close the connection


  • Multilabel classification

  • Include FastText from Facebookresearch

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