What are transformers in NLP?

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In Natural Language Processing (NLP), Transformers are a type of deep learning model architecture designed to handle sequential data (like text) more efficiently than older models (RNNs, LSTMs). They were introduced in the paper “Attention is All You Need” (2017) by Vaswani et al.

Key Ideas:

  1. Self-Attention Mechanism – Instead of processing words one by one, transformers look at all words in a sentence at once and learn which words are important in relation to each other.

  2. Parallelization – Unlike RNNs, transformers process sequences in parallel, making training much faster.

  3. Contextual Understanding – Each word’s meaning is understood based on its context (e.g., “bank” in river bank vs money bank).

  4. Scalability – Transformers can be scaled into very large models (like GPT, BERT).

Applications in NLP:

  • Machine Translation (Google Translate uses it)

  • Text Classification (spam detection, sentiment analysis)

  • Question Answering (e.g., chatbots, search engines)

  • Text Generation (e.g., GPT models)

👉 In short, transformers revolutionized NLP by making models more accurate, scalable, and capable of understanding long-range dependencies in text.

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