Building a Character-Level Bigram Language Model from Scratch with PyTorch
📰 Dev.to · Mohamed Heni
Learn to build a basic character-level bigram language model from scratch using PyTorch, understanding the fundamentals of neural language modeling
Action Steps
- Build a character-level bigram language model using PyTorch from scratch
- Implement a simple counting mechanism to track character sequences
- Use PyTorch to create and train the model
- Sample from the trained model to generate text
- Test and evaluate the performance of the model
Who Needs to Know This
This project is ideal for NLP engineers, AI researchers, or students looking to understand the basics of language modeling and PyTorch implementation. It can be a starting point for more complex NLP projects.
Key Insight
💡 Character-level bigram language models can be built using basic counting and sampling mechanisms, providing a foundational understanding of neural language modeling
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🤖 Build a character-level bigram language model from scratch with PyTorch! No attention, no transformers. Just counting and sampling. #PyTorch #NLP
Key Takeaways
Learn to build a basic character-level bigram language model from scratch using PyTorch, understanding the fundamentals of neural language modeling
Full Article
I built the simplest possible neural language model — a character-level bigram — from scratch with PyTorch. No attention, no transformers. Just counting and sampling.
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