T5-Fine-NQ: Fine-Tuned T5 Model on Natural Questions ShortQA

Model Overview

• Model Architecture: T5-base
• Task: Question Answering (Short-form answers)
• Dataset: lucadiliello/naturalquestionsshortqa (80/20 train/test split)
• Evaluation Metrics: Exact Match (EM), F1-score
• Pretrained Checkpoint: mrm8488/t5-base-finetuned-squadv2
• Framework: Hugging Face Transformers + PyTorch

Evaluation Results

Results are after 10 epochs of fine-tuning on GPU.