Enhancing Ghanaian Political Tweet Engagement

Hyacinth Ampadu

0

Data Scientist

ML Engineer

AI Developer

Google Cloud Platform

Python

PyTorch

 Achieved a 27% perplexity decrease through unsupervised training(masked language modelling) using Distil RoBERTa to adapt to the Ghanaian political domain.
 Leveraging the domain-adapted model, developed a predictive model for predicting tweet engagement.
 Finetuned the GPT-3 model to optimize tweet virality by generating engaging variations for low-engagement tweets, ultimately yielding a 25% improvement in overall engagement metrics.
 Strategically deployed models on Google Cloud Platform: Utilized Cloud Run for serverless architecture, Employed Docker containers for efficient deployment, Collaborated cross-functionally for successful deployment and ensure ongoing stability.
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Posted Mar 18, 2024

Utilising AI by optimising DistilRoBERTa and GPT-3 to improve tweet engagement and reach larger audiences in the Ghanaian political domain

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Data Scientist

ML Engineer

AI Developer

Google Cloud Platform

Python

PyTorch

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