Think Different - Dhiraj Patra
1 min readDec 30, 2023

The transformer architecture with its key components and examples:

Transformer: A deep learning architecture primarily used for natural language processing (NLP) tasks. It’s known for its ability to process long sequences of text, capture long-range dependencies, and handle complex language patterns.

Key Components:

  1. Embedding Layer:
  • Converts input words or tokens into numerical vectors, representing their meaning and relationships.
  • Example: [“I”, “love”, “NLP”] -> [0.25, 0.81, -0.34], [0.42, -0.15, 0.78], [-0.12, 0.54, -0.68]
  1. Encoder:
  • Processes the input sequence and extracts meaningful information.
  • Consists of multiple encoder blocks, each containing:
  • Multi-Head Attention: Allows the model to focus on different parts of the input sequence simultaneously, capturing relationships between words.
  • Feed Forward Network: Adds non-linearity and learns more complex patterns.
  • Layer Normalization: Helps stabilize training and improve convergence.
  1. Decoder:
  • Generates the output sequence, word by word, based on the encoded information.
  • Similar structure to the encoder, with additional components:
  • Masked Multi-Head Attention: Prevents the model from seeing future words during training, ensuring realistic generation.
  1. Positional Encoding:
  • Adds information about word order within the sequence, as transformers don’t have a built-in understanding of sequence.

Example Application (Machine Translation):

  1. Input sentence in English: “I love NLP.”
  2. Embedding layer creates word embeddings.
  3. Encoder processes the input, capturing relationships between words and their meanings.
  4. Decoder generates the output sentence in French: “J’adore le NLP.”

Other Applications:

  • Text summarization
  • Question answering
  • Text generation
  • Sentiment analysis
  • Machine translation
  • And more!



Think Different - Dhiraj Patra

I am a Software architect for AI, ML, IoT microservices cloud applications. Love to learn and share.