Fine-tuned the Flan-T5 model, achieving absolute percentage improvements of the instruct model over the original model:ROUGE1: 0.26%, ROUGE-L: 0.26%, ROUGE-Lsum: 0.26%.• Conducted prompt engineering using the Flan-T5 model and DialogSum dataset, exploring the impact of different prompts and comparing zero-shot and few-shot inferences, Defined training arguments and created a Seq2SeqTrainer instance for the model, utilizing a higher learning rate than full fine-tuning for enhanced performance.
Performing Prompt engineering on a dialogue summarization task using Flan-T5 and the dialogsum dataset. Exploring how different prompts affect the output of the model, and comparing zero-shot and few-shot inferences.
Project Overview :
