Update README.md

README.md

@@ -1,12 +1,19 @@
 ---
 library_name: transformers
 pipeline_tag: summarization
+datasets:
+- gopalkalpande/bbc-news-summary
+language:
+- en
+metrics:
+- rouge
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+The T5_small_lecture_summarization model is a variant of the T5 (Text-to-Text Transfer Transformer) architecture,
+which is designed for summarization tasks
 
 
 ## Model Details
@@ -17,13 +24,20 @@ pipeline_tag: summarization
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
+- **Developed by:** [Hyun Lee]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** [Google/T5]
+
+- **Architecture:** The model is based on the T5 architecture, which employs a transformer-based neural network. Transformers have proven effective for various natural language processing (NLP) tasks due to their attention mechanisms and ability to capture contextual information.
+- **Task:** The primary purpose of this model is lecture summarization. Given a lecture or a longer text, it aims to generate a concise summary that captures the essential points. This can be valuable for students, researchers, or anyone seeking condensed information.
+- **Input Format:** The model expects input in a text-to-text format. Specifically, you provide a prompt (e.g., the lecture content) and specify the desired task (e.g., “summarize”). The model then generates a summary as the output.
+- **Fine-Tuning:** The Lucas-Hyun-Lee/T5_small_lecture_summarization model has likely undergone fine-tuning on lecture-specific data. During fine-tuning, it learns to optimize its parameters for summarization by minimizing a loss function.
+- **Model Size:** As the name suggests, this is a small-sized variant of T5. Smaller models are computationally efficient and suitable for scenarios where memory or processing power is limited.
+- **Performance:** The model’s performance depends on the quality and diversity of the training data, as well as the specific lecture content it encounters during fine-tuning.
+-  It should be evaluated based on metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores.
 
 ### Model Sources [optional]