Update README.md

README.md

@@ -1,12 +1,21 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- en
+metrics:
+- f1
+- precision
+- recall
+- accuracy
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
+Here's the updated summary for your model card:
 
+This fine-tuned BERT model is a multilabel multiclass classifier designed to predict the genre of a movie based on its summary. It has been specifically trained to classify movies into one or more of the following genres: Drama, Action, Comedy, Animation, and Crime. The model leverages the capabilities of the BERT architecture to understand and interpret the nuances of movie summaries, providing accurate and potentially multiple genre predictions for each movie.
 
 
 ## Model Details
@@ -29,13 +38,32 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/Sinanmz/MIR]
 
 ## Uses
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Here's the "Uses" section for your model card:
+
+This BERT-based multilabel multiclass classifier is designed to predict the genre(s) of a movie based on its summary. It can be utilized in various applications, including but not limited to:
+
+- **Content Recommendation Systems:** Enhancing the accuracy of movie recommendation engines by predicting genres from summaries, allowing for better personalization.
+- **Movie Cataloging:** Assisting in the organization and tagging of movies in large databases or streaming platforms.
+- **Search Optimization:** Improving search results by classifying movies into multiple genres, thereby providing more relevant hits for user queries.
+- **Content Filtering:** Helping users find movies that match their preferences by identifying and categorizing movies into multiple genres.
+
+**Foreseeable Users:**
+
+- **Streaming Services:** To enhance content recommendation algorithms and search functionalities.
+- **Movie Database Administrators:** To automate the process of tagging and organizing movies.
+- **Developers:** Building applications that require genre classification from textual summaries.
+
+**Affected Parties:**
+
+- **Viewers/Consumers:** Benefiting from improved content recommendations and search results.
+- **Content Creators:** Gaining better visibility through accurate classification and tagging of their work.
+- **Platform Operators:** Improving user engagement and satisfaction with more personalized and accurate content delivery.
+
 
 ### Direct Use