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 # Amazon Fake Product Review Detection
 
 ## Overview
-This project tackles the challenge of detecting fake reviews on Amazon, differentiating between genuine reviews written by humans and those generated by a GPT-2 model. This task not only addresses a significant issue impacting consumer trust and business ethics on online platforms but also explores the capabilities of generative models in both crafting and identifying synthetic text. Inspired by the GROVER model's self-detection premise, this work expands upon prior research by Salminen et al., 2021, which utilized GPT-2 for the generation and RoBERTa for the detection of synthetic reviews.
+This project tackles the challenge of detecting fake reviews on Amazon, differentiating between genuine reviews written by humans and those generated by a GPT-2 model. This task not only addresses a significant issue impacting consumer trust and business ethics on online platforms but also explores the capabilities of generative models in both crafting and identifying synthetic text. Inspired by the GROVER model's self-detection premise, this work expands upon prior research by Salminen et al., 2022, which utilized GPT-2 for the generation and RoBERTa for the detection of synthetic reviews.
 
 ## Dataset
 The dataset comprises 40,000 reviews, balanced between original human-written and fake computer-generated entries, and was created from the work of Salminen et al., 2021. These reviews span various product categories, providing a rich basis for training and evaluating the model's performance. The data includes: