BradechiO/tds-bert-qa-squad

BERT Extractive QA on SQuAD v1.1

Intended Use

English Extractive Question Answering using the SQuAD v1.1 dataset, developed for an academic course assignment.

Model and Training Details

• Base Model: bert-base-uncased
• Dataset: rajpurkar/squad (Subsampled to 15k training examples).
• Adaptation Strategy: Full Fine-tuning with differentiated learning rates; random seed 42; 2 epochs; batch size 16.
• Optimizer Configuration: Discriminative learning rates (Encoder LR: 2e-5, QA Head LR: 1e-3).

Evaluation Results

Evaluation performed on the subsampled validation set (2,000 examples):

• Exact Match (EM): 60.70
• F1-Score: 71.74
• Validation Loss: 1.319787

References

• Rajpurkar et al. (2016), SQuAD: 100,000+ Questions for Machine Comprehension of Text.
• Devlin et al. (2019), BERT: Pre-training of Deep Bidirectional Transformers.