PS4_LLM-MODEL

The results from fine-tuning the T5 model for text summarization depend heavily on dataset quality, hyperparameter choices, and the training process itself. Typically, achieving higher ROUGE scores and generating human-like summaries are indications of successful fine-tuning. These metrics and qualitative assessments collectively provide insights into the model's performance and its potential for real-world applications such as custom chatbots or automated summarization tools.