Elisabet Sulú

This repository contains the weights and tokenizer for bert-base-uncased fully fine-tuned on SQuAD v1.1 (Stanford Question Answering Dataset) for extractive question answering. The model was adapted using end-to-end full fine-tuning with a differential two-learning-rate optimizer scheme as part of an academic comparative study on BERT adaptation paradigms. The model was trained on a deterministic subsample of SQuAD v1.1 (rajpurkar/squad): - Task Head (QA outputs, 1,538 params): Learning rate = 1e-3 Evaluation was performed on the complete official SQuAD v1.1 validation split (10,570 examples, spanning 10,753 feature windows) using an optimized post-processing pipeline (top-15 start/end…