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:
-
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]
-
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.
-
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.
-
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):
- Input sentence in English: "I love NLP."
- Embedding layer creates word embeddings.
- Encoder processes the input, capturing relationships between words and their meanings.
- 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!
