What are transformers in NLP?

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A Long Short-Term Memory (LSTM) network is an advanced type of Recurrent Neural Network (RNN) designed to overcome the limitations of traditional RNNs, especially the vanishing and exploding gradient problems. These problems make it difficult for standard RNNs to learn long-term dependencies in sequential data. LSTMs solve this using a special architecture with a cell state and gates that control the flow of information.

In Natural Language Processing (NLP), transformers are a deep learning architecture designed to handle sequential data (like text) more efficiently than traditional models such as RNNs or LSTMs.

🔑 Key Concepts of Transformers:

1. Self-Attention Mechanism

   • Core idea: Instead of processing words sequentially, transformers use attention to look at all words in a sentence at once.

   • Each word’s meaning is understood in relation to every other word.

   • Example: In “The cat sat on the mat”, the word “cat” attends to “sat” and “mat” for better context.

2. Encoder-Decoder Architecture

   • Encoder: Processes the input text and builds contextual representations.

   • Decoder: Uses those representations to generate output (like a translation).

   • Some models (e.g., BERT) use only encoders; others (e.g., GPT) use only decoders.

3. Positional Encoding

   • Since transformers don’t process words sequentially like RNNs, they add positional embeddings to keep track of word order.

⚙️ Why Transformers Are Important in NLP:

• Parallelization → Unlike RNNs, transformers process tokens simultaneously, making training much faster.

• Long-Range Dependencies → They capture relationships between words even if they’re far apart in a sentence.

• State-of-the-Art Performance → Power models like BERT, GPT, T5, RoBERTa, which dominate NLP tasks.

🚀 Applications:

• Machine Translation (Google Translate uses transformers).

• Text Summarization.

• Question Answering.

• Sentiment Analysis.

• Chatbots & LLMs

🔑Read More:

What is NLP?

What is the difference between stemming and lemmatization?

What are embeddings in NLP?

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