Fake News Detection

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prerequisites
intermediate Python and PyTorch • basics of natural language processing
skills learned
loading and configuring pretrained RoBERTa model using Hugging Face • 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 RoBERTa variation of the BERT Transformer to detect occurrences of fake news in a data set. Political news can be tricky to validate for accuracy, as sources report the same events from different biased angles. RoBERTa uses different pre-training methods than traditional BERT and has hyperparameters that are highly optimized, meaning it tends to perform better than its predecessor. You’ll start out by loading the model using the Hugging Face library and training it to your data with PyTorch Lightning. The project will also include the implementation of training a custom tokenizer from scratch and using it to tokenize the data. A successful model will maximize positives, and so model evaluation will be based on your model having a high recall score.
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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