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README.md

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+Question Answering Model
+Overview
+This BERT-based model extracts answers from given context passages in response to questions. Fine-tuned on SQuAD-like datasets, it provides precise span-based answers for reading comprehension tasks.
+Model Architecture
+Utilizes BERT with 12 layers, 768 hidden units, and 12 attention heads, topped with a question answering head that predicts start and end tokens for answer spans.
+Intended Use
+Ideal for chatbots, search engines, or educational tools requiring factual extraction from text. It handles English queries and contexts up to 512 tokens.
+Limitations
+The model may fail on ambiguous questions, out-of-context queries, or non-English text. It assumes the answer is present in the context and could propagate biases from training data.