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README.md

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 A lightweight approach to topic extraction leveraging the Bottleneck T5 autoencoder architecture with learned transformation matrices. This project provides three specialized transformation matrices for mapping content embeddings to topic embeddings across different domains.
 
-## TL;DR
-Transform content embeddings into topic embeddings using domain-specific 1024×1024 transformation matrices, trained on three distinct datasets. Built on top of the Bottleneck T5 architecture for efficient, training-free topic extraction.
+**TL;DR:** Transform content embeddings into topic embeddings using domain-specific 1024×1024 transformation matrices, trained on three distinct datasets. Built on top of the Bottleneck T5 architecture for efficient, training-free topic extraction.
 
 ## Motivation
 Large Language Models (LLMs) have become the go-to solution for many NLP tasks, including topic extraction and classification. However, they come with significant overhead: