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Understanding NLP with Transformers

Explore how transformers revolutionized Natural Language Processing and what makes models like BERT and GPT tick

7 min read
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Natural Language Processing (NLP) has drastically improved in recent years, largely due to the emergence of Transformer models like BERT, GPT, and T5.


What is NLP?


NLP enables computers to understand, interpret, and generate human language. It powers tools like chatbots, translation engines, and voice assistants.


Why Transformers?


Transformers introduced the self-attention mechanism, allowing models to weigh the importance of words in context:


text

“I saw the man with the telescope.”

The model learns to associate “with the telescope” correctly depending on context.


Key Transformer Models


  • **BERT**: Bidirectional Encoder that understands context from both sides of a sentence.
  • **GPT**: Generative model trained to predict the next word in a sequence.
  • **T5**: Converts all NLP problems into a text-to-text format.

    • Applications


  • Sentiment analysis
  • Question answering
  • Summarization
  • Chatbots and assistants

    • Code Example (HuggingFace Transformers)


    python

    from transformers import pipeline

    qa = pipeline("question-answering")

    result = qa({

    'question': 'What is a transformer?',

    'context': 'Transformers are neural network architectures developed by Google in 2017.'

    })

    print(result['answer'])


    Transformers have made NLP accessible and powerful across industries.