Tokenization is the process of breaking down a larger body of text into smaller units called tokens.It is the process of creating a digital representation of a real thing. For example, "I love apples" → ["I", "love", "apples"]. Also, tokens are mappe...
Ever wondered how AI understands your messages? It all starts with tokenization - the process of breaking text into smaller, manageable pieces that computers can process! What is Tokenization? Imagine you're trying to teach a friend who doesn't speak...
Introduction In Natural Language Processing (NLP), one of the most classic problems is Sentiment Analysis, where the goal is to determine the emotion behind a text. In this project, I address this problem by using the IMDb Movie Reviews dataset aimin...
Part 1: Why Highlighting Matters and How Machines Learn Relevance The Problem: Finding Needles in Haystacks Imagine you're searching for "how to prevent memory leaks in Python" and you get back a 5,000-word article about Python programming. Somewhere...
We often face a classic dilemma: the most powerful models are also the most resource-hungry. A model like bert-base-uncased, with its 109 million parameters, can achieve best results but is often a non-starter for applications that demand low latency...
Web Demo: animerecbert.online I wanted to build an anime recommendation system and came across a GitHub repo that used BERT trained on the MovieLens dataset. The impressive results it achieved inspired me to adapt the approach for anime recommendatio...
Transformer Architecture in Generative AI 🤖 The transformer architecture is the foundation of many generative AI models, including language models like GPT and BERT. It consists of two main components: the encoder 📂 and the decoder. Key Components...
1 Introduction In e-commerce, product titles are often written by sellers in a detailed and descriptive manner, including brand names, places of origin, attributes, and other qualifiers. Unlike English, which uses spaces to separate words, Chinese la...
Emotions are messy. They don't just live in our words — they hide in our tone, our facial twitches, and the way we pause mid-sentence. That’s exactly why unimodal emotion recognition (just text or just audio) often falls short. So, for my most recent...
1 Introduction BERT is important in the world of NLP because it introduced a fundamentally better way for machines to understand human language by considering context from both directions—left and right—at the same time. The bidirectional approach en...
