This repository demonstrates a step-by-step implementation of the self-attention mechanism using PyTorch. It provides a simple example to illustrate how attention scores are calculated and how they help in understanding relationships between words in a sentence.
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Word Embeddings:
Utilizes Word2Vec to generate word embeddings for a sample sentence. -
Query, Key, and Value Vectors:
Explains how to create Query, Key, and Value vectors using learnable weight matrices, similar to how these concepts are used in the Transformer architecture. -
Attention Score Calculation:
Shows how to compute attention scores using the dot product of Query and Key vectors, then scale and normalize them using softmax. -
Context Vectors:
Computes context vectors for each word in the sentence, summarizing its understanding by integrating information from all the other words. -
Intuitive Analogies:
Uses database analogies and detailed comments throughout the code to explain the fundamentals of self-attention in natural language processing (NLP).
- Clone the Repository:
git clone https://github.com/yourusername/Self-Attention-Demo.git cd Self-Attention-Demo - **Run the Script: Execute the self_attention.py script to see how self-attention is applied step-by-step:
python self_attention.py
- **Analyze the Output: The script will print the context vectors for each word in the example sentence, showing how each word's representation changes after considering its relationship with other words.
This project breaks down complex concepts like Query, Key, and Value in the self-attention mechanism, making it easier to understand how modern NLP models like Transformers work under the hood.
- A comprehensive implementation of self-attention using PyTorch.
- Detailed comments and explanations to guide you through the process.
- Analogies to help relate self-attention to everyday concepts, making it beginner-friendly.