Artificial Intelligence

    

Fine-Tuning a BERT Model for Email Classification: Leveraging Personal Gmail Inbox

Authors: Rafael Costa da Silva

This study aims to develop an effective model for classifying emails as wanted or unwanted using fine-tuned BERT models. The process involved downloading the Gmail inbox through Google Takeout and converting the data to Parquet format. A frequency distribution analysis of From emails was conducted, and the emails were manually classified. A final dataset was created with email subject, classification, and binary labels. The BERT-base-multilingualcased model was fine-tuned using about 10,000 observations for each category. The resulting models achieved an accuracy of 0.9429411764705883. The models are publicly available in Hugging Face's model repository

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Submission history

[v1] 2023-07-05 18:22:52

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