Machine Learning

The Roman Nepali Embedding Problem I spelled the same Nepali word four different ways and asked four open-source embedding models whether the spellings meant the same thing. The model with the prettiest-looking cosine gap wasn’t the one that actually worked — and a twenty-line preprocessing script beat all four of them without touching a single weight. This post is a small experiment with a strong conclusion: if you are shipping NLP for Nepali users today, the best thing you can do is not a bigger model — it’s a regex. ...

Introduction Transformers have revolutionized the field of machine learning, particularly in the domain of Natural Language Processing (NLP). Originally introduced in the paper “Attention Is All You Need” by Vaswani et al. in 2017, transformers have become the backbone of many state-of-the-art models, including BERT, GPT, and T5. In this blog, we will explore the basics of transformers, how they work, and why they have become so popular in recent years. ...