Update README.md

README.md

@@ -35,24 +35,24 @@ This model can be particularly useful if you need to quickly summarize large vol
 
 ## Intended uses & limitations
 
-* Intended Use
+* ### Intended Use
 
-The model is designed for **text summarization**, which involves condensing long pieces of text into shorter, more digestible summaries. Here are some specific use cases:
+* The model is designed for **text summarization**, which involves condensing long pieces of text into shorter, more digestible summaries. Here are some specific use cases:
 
-News Summarization: Quickly summarizing news articles to provide readers with the main points.
-Document Summarization: Condensing lengthy reports or research papers into brief overviews.
-Content Curation: Helping content creators and curators to generate summaries for newsletters, blogs, or social media posts.
-Educational Tools: Assisting students and educators by summarizing academic texts and articles.
+**News Summarization:** Quickly summarizing news articles to provide readers with the main points.
+**Document Summarization**: Condensing lengthy reports or research papers into brief overviews.
+**Content Curation**: Helping content creators and curators to generate summaries for newsletters, blogs, or social media posts.
+**Educational Tools**: Assisting students and educators by summarizing academic texts and articles.
 
 * ### Limitations
 
 While the model is powerful, it does have some limitations:
 
-Accuracy: The summaries generated might not always capture all the key points accurately, especially for complex or nuanced texts.
-Bias: The model can inherit biases present in the training data, which might affect the quality and neutrality of the summaries.
-Context Understanding: It might struggle with understanding the full context of very long documents, leading to incomplete or misleading summaries.
-Language and Style: The model’s output might not always match the desired tone or style, requiring further editing.
-Data Dependency: Performance can vary depending on the quality and nature of the input data. It performs best on data similar to its training set (news articles)
+**Accuracy**: The summaries generated might not always capture all the key points accurately, especially for complex or nuanced texts.
+**Bias**: The model can inherit biases present in the training data, which might affect the quality and neutrality of the summaries.
+**Context Understanding**: It might struggle with understanding the full context of very long documents, leading to incomplete or misleading summaries.
+**Language and Style**: The model’s output might not always match the desired tone or style, requiring further editing.
+**Data Dependency**: Performance can vary depending on the quality and nature of the input data. It performs best on data similar to its training set (news articles)
 
 ## Training and evaluation data