Update README.md

README.md

@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # cnn_news_summary_model_trained_on_reduced_data
 
-This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an CNN Daily mail dataset.
+This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an **CNN Daily mail** dataset.
 It achieves the following results on the evaluation set:
 - Loss: 1.6597
 - Rouge1: 0.2162
@@ -29,15 +29,34 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+**Base Model:** *t5-small*, which is a smaller version of the *T5 (Text-to-Text Transfer Transformer) model* developed by ***Google***.
+
+This model can be particularly useful if you need to quickly summarize large volumes of text, making it easier to digest and understand key information.
 
 ## Intended uses & limitations
 
-More information needed
+### 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:
+
+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)
 
 ## Training and evaluation data
 
-More information needed
+The model was trained using the Adam optimizer with a learning rate of **2e-05** over **2 epochs**.
 
 ## Training procedure