Hate Speech Detection

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prerequisites
intermediate Python and PyTorch • basics of natural language processing
skills learned
loading and configuring pretrained ALBERT model using HuggingFace • building and training a text classifier using PyTorch Lightning • validating the performance of the model and quantifying the results
Rohan Khilnani
1 week · 5-7 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll use the ALBERT variation of the BERT Transformer to detect occurrences of hate speech in a data set. The ALBERT model uses fewer parameters than BERT, making it more suitable to the unstructured and slang-heavy text of social media. You’ll load this powerful pretrained model using the Hugging Face library and fine-tune it for your specific needs with PyTorch Lightning. As falsely tagging hate speech can be a big problem, the success of your model will involve calculating and optimizing its precision score. Your final product will run as a notebook on a GPU in the Google Colab environment.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Rohan Khilnani
Rohan Khilnani is a data scientist at Optum, United Health Group. He has filed two patents in the field of natural language processing and has also published a research paper on LSTMs with Attention at the COLING conference in 2018.

prerequisites

This liveProject is for intermediate Python and NLP practitioners who are interested in implementing pretrained BERT architectures, and customizing them to solve real-world NLP problems. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Intermediate PyTorch
  • Basics of Google Colab
TECHNIQUES
  • Basics of machine learning
  • Basics of neural networks
  • Basics of natural language processing

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