Question Answering Models

Summary

Models that extract or generate an answer to a question given a context passage — the core mechanism behind search-augmented and RAG-based assistants.

How to Learn This

1Run a pretrained extractive QA model (e.g. fine-tuned BERT on SQuAD) on real passages.
2Learn the difference between extractive QA and generative QA.
3Understand how this connects directly to the retrieval-augmented generation you'll build later.

Text Summarization

Sentiment Analysis App

More topics in Natural Language Processing

Text Preprocessing (Tokenization, Stemming, Lemmatization)Bag of Words & TF-IDF Word Embeddings (Word2Vec, GloVe)Named Entity Recognition Sentiment Analysis Hugging Face Transformers Library Pretrained NLP Models (BERT, RoBERTa)Sequence Classification Text Summarization

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