Building a Bigram Language Model From Scratch
📰 Medium · NLP
Learn to build a bigram language model from scratch and understand probabilities and negative log-likelihood in NLP
Action Steps
- Build a bigram language model using a tiny neural network
- Calculate probabilities of word sequences using the model
- Apply negative log-likelihood to evaluate the model's performance
- Generate new names using the trained model
- Compare the results with other language models
Who Needs to Know This
NLP engineers and data scientists can benefit from this tutorial to improve their language modeling skills and build more accurate models
Key Insight
💡 Bigram language models can be used to generate new text sequences, such as names, by predicting the next word based on the previous word
Share This
Build a bigram language model from scratch and learn about probabilities and negative log-likelihood in NLP #NLP #LanguageModeling
Key Takeaways
Learn to build a bigram language model from scratch and understand probabilities and negative log-likelihood in NLP
Full Article
Understanding probabilities, negative log-likelihood, and a tiny neural network by generating new names Continue reading on Medium »
DeepCamp AI