This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Algiers, Algeria Chapter.

The problem

In the Algerian context, the development of NLP-based solutions is hampered by the general use of dialect in conversation on social media. Thus, there is a need to develop customized NLP solutions that can handle dialect-based text content. 

The goals

As the first NLP-related project for our local chapter, this challenge aims to build a model to classify hate speech content in the Algerian dialect, extracted from social media. At the same time, it will allow the participants to evaluate their knowledge with regards to NLP and machine /deep learning methods and language models and also learn new skills throughout the project in a collaborative environment with their peers.

The objectives of the project are:

• Extract data from social networks.
• Annotate the data and build the hate speech dataset.
• Explore the current state-of-the-art NLP techniques for the Algerian dialect.
• Build content classifiers using different techniques, Machine learning/ Deep learning, and transformers.
• Evaluate and compare the performances of the different models.
• Deployment of the selected classifier.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work