Transformers Power Question Answering Systems

Models like BERT and RoBERTa answer questions based on textual context using attention mechanisms.

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🤯 Did You Know (click to read)

BERT achieved human-level performance on the SQuAD 1.1 dataset, demonstrating the effectiveness of Transformers in reading comprehension.

The encoder generates contextual embeddings for passages, while a classifier head predicts the start and end of the answer span. Multi-head attention ensures that relevant contextual cues from the entire passage influence predictions.

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💥 Impact (click to read)

QA systems using Transformers provide fast, accurate information retrieval for education, customer support, and search engines.

Students and developers benefit from models that understand context, improving comprehension and AI-assisted learning.

Source

Devlin et al., 2018 - BERT

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