April 2025

Have you ever wondered how computers understand that two sentences mean the same thing—or are similar—even if they don’t use the same words? Well, that’s the magic of sentence embeddings and cosine similarity. Let’s dive in, ditch the jargon, and keep it simple. What are Sentence Embeddings? Imagine you have a sentence: Hey bro, I am visiting Rome tomorrrow. Computers don’t understand natural language the way humans do. They need numbers. So, we convert this sentence into a […]

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In the world of machine learning, bigger isn’t always better — especially when it comes to deploying models in resource-constrained environments like mobile apps or real-time systems. Fortunately, Langformers offers an elegant solution: you can train a smaller model to mimic the embeddings of a large pretrained model. This technique is heavily inspired by previous works like Hinton et al., 2015, Reimers and Gurevych, 2020, and Lamsal et al., 2025. In this blog, we’ll dive into how to […]

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When working with search systems, getting the most relevant results is crucial — yet traditional vector search methods don’t always guarantee this. That’s where reranking comes into play, and with Langformers, implementing reranking has never been easier. In this article, we’ll discuss: What reranking is? Why it’s needed alongside vector search? How Langformers makes reranking simple and effective? A step-by-step example to get you started What is Reranking? Reranking is the process of reordering a list of retrieved […]

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In today’s data-driven world, finding relevant information quickly is crucial. Traditional keyword-based search engines often fall short when you need more context-aware retrieval. That’s where semantic search steps in — and Langformers makes it easier than ever to set up your own semantic search engine in just a few lines of code! This post will walk you through how to quickly set up semantic search with Langformers using popular vector databases like FAISS, ChromaDB, and Pinecone. What is […]

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When working with semantic search or designing sophisticated NLP pipelines such as RAG (Retrieval-augmented Generation), converting text into numerical vectors—a process called embedding—is often one of the very first steps. Embeddings capture the meaning and context of sentences in a way that machines can understand, enabling powerful applications like search engines, recommendation systems, and conversational AI. Langformers makes sentence embeddings incredibly simple. This guide will walk you through how to embed sentences and calculate textual similarity. Why Sentence […]

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Masked Language Models (MLMs) like BERT, RoBERTa, and MPNet have revolutionized the way we understand and process language. These models are foundational for tasks such as text classification, named-entity recognition (NER), and many other NLP applications where the entire input sequence matters. But what if you want to create your own MLM — tailored to your specific domain, like legal documents, medical texts, or tweets? Langformers makes this process straightforward, flexible, and efficient! In this guide, you’ll learn […]

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Training text classifiers has traditionally required a lot of setup, coding, and hyperparameter tuning. But thanks to Langformers, you can now fine-tune powerful Transformer models (encoder-only models / masked lanuage models) like BERT, RoBERTa, or MPNet for your custom classification tasks with just a few lines of code — all while keeping full flexibility and control. This guide will walk you through how to quickly set up and train a text classifier using Langformers, customize training settings, and […]

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