Update README.md

README.md

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 **[🚀 Click here to try the Interactive Demo!](https://huggingface.co/spaces/wi-lab/lwm-interactive-demo)**
 
-LWM is a powerful pre-trained model developed as a **universal feature extractor** for wireless channels. As the world's first foundation model crafted for this domain, LWM leverages transformer architectures to extract refined representations from simulated datasets, such as DeepMIMO and Sionna, and real-world wireless data.
+LWM is a powerful **pre-trained** model developed as a **universal feature extractor** for wireless channels. As the world's first foundation model crafted for this domain, LWM leverages transformer architectures to extract refined representations from simulated datasets, such as DeepMIMO and Sionna, and real-world wireless data.
 
 
 ### What Does LWM Offer?
 
-The LWM model’s structure is based on transformers, allowing it to capture both fine-grained and global dependencies within channel data. Unlike traditional models that are limited to specific tasks, LWM employs a self-supervised approach through our proposed technique, Masked Channel Modeling (MCM). This method trains the model on unlabeled data by predicting masked channel segments, enabling it to learn intricate relationships between antennas and subcarriers. Utilizing bidirectional attention, LWM interprets the full context by attending to both preceding and succeeding channel segments, resulting in embeddings that encode comprehensive spatial information, making them applicable to a variety of scenarios.
+The LWM model’s structure is based on transformers, allowing it to capture both **fine-grained and global dependencies** within channel data. Unlike traditional models that are limited to specific tasks, LWM employs a **self-supervised** approach through our proposed technique, Masked Channel Modeling (MCM). This method trains the model on unlabeled data by predicting masked channel segments, enabling it to learn intricate relationships between antennas and subcarriers. Utilizing **bidirectional attention**, LWM interprets the full context by attending to both preceding and succeeding channel segments, resulting in embeddings that encode comprehensive spatial information, making them applicable to a variety of scenarios.
 
 ### What does LWM offer?
 
-LWM provides a universal feature extraction framework that can be applied across diverse wireless communication and sensing tasks. It is built to handle complex wireless environments, capturing channel characteristics in a way that facilitates robust performance across different scenarios and conditions.
+LWM provides a universal feature extraction framework that can be applied across diverse **wireless communication and sensing** tasks. It is built to handle complex wireless environments, capturing channel characteristics in a way that facilitates robust performance across different scenarios and conditions.
 
 Trained on hundreds of thousands of wireless channel samples, LWM has been designed to generalize across varied environments—from dense urban areas to synthetic setups, ensuring its adaptability and consistency across a broad spectrum of wireless tasks.
 
 ### How is LWM used?
 
-LWM is designed to be easily integrated into downstream applications as a source of high-quality embeddings that encapsulate complex channel features. By feeding raw wireless channel data into the pre-trained model, users obtain embeddings that capture essential spatial relationships and interactions within the channel environment.
+LWM is designed to be easily integrated into downstream applications as a source of high-quality **embeddings** that encapsulate complex channel features. By feeding raw wireless channel data into the pre-trained model, users obtain embeddings that capture essential spatial relationships and interactions within the channel environment.
 
 These embeddings provide a versatile and contextualized representation of wireless data, which can be leveraged across different applications. By utilizing the pre-trained model in this way, users can reduce the need for extensive labeled data while benefiting from embeddings that retain the critical properties of the original channel.