Transformer

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!