Update README.md

README.md

@@ -40,18 +40,24 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+In this project, we fine-tuned mT5small, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages.
+The model was fine-tuned on the electric patent corpus using a variety of techniques, including transfer learning, data augmentation, and hyperparameter tuning.
 
 ## Intended uses & limitations
 
-More information needed
+The fine-tuned model showed significant improvements in performance on the electric patent-specific tasks compared to the original pre-trained model.
+
+Note: This project is suitable for researchers who are working on electric patent, as it's fine-tuned on electric patents and it can be used for related NLP problems for electric patent and electric patent research.
 
 ## Training and evaluation data
+A subset of electric patents were used to fine-tune the model.
 
-More information needed
+The fine-tuned model was evaluated using the ROUGE metric on a variety of natural language processing tasks specific to the patent domain, including, named entity recognition, and summarization.
 
 ## Training procedure
 
+The model was fine-tuned on the electric patent corpus using a variety of techniques, including transfer learning, data augmentation, and hyperparameter tuning.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training: