Exploring Use Cases of LLMs
Explore the practical applications of LLMs in tasks like text classification and image captioning.
We'll cover the following...
In the previous example, you saw how to generate text using GPT-2. Now, we’ll explore a variety of use cases, highlighting different tasks and the models best suited for each one.
In this lesson, we’ve used cardiffnlp/twitter-roberta-base-sentiment for text sentiment analysis and nlpconnect/vit-gpt2-image-captioning for image captioning. Feel free to explore other models or experiment with additional use cases to broaden your understanding!
Text classification
Imagine you’re working for a popular e-commerce platform where thousands of customers frequently leave product reviews, making manual analysis time-consuming and inefficient. To streamline the process, you decide to implement sentiment analysis using the cardiffnlp/twitter-roberta-base-sentiment model, a pretrained RoBERTa variant fine-tuned for text classification tasks. This model automatically processes reviews, categorizing them as positive, negative, or neutral, allowing your team to efficiently gain valuable customer insights without the need for manual intervention. This can be used for social media monitoring, brand reputation management, political sentiment analysis, market research, customer support ticket analysis, content ...