Update README.md

README.md

@@ -1,11 +1,23 @@
-# Model Description:
+## t5-base-fine-tuned-on-jfleg
+T5-base model fine-tuned on the JFLEG dataset with the objective of **text2text-generation**.
 
-This model is a T5-base model which is pre-trained on the C4 dataset and fine-tuned on the JFLEG dataset. T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
+# Model Description:
+T5 is an encoder-decoder model pre-trained with a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
+.T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g., for translation: translate English to German: …, for summarization: summarize: ….
 
 The T5 model was presented in **Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer** by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
 
 ## Pre-Processing:
-For this task of grammar correction, we’ll use the prefix “grammar: “ to each of the input sentences. This is done because T5 models are able to perform multiple tasks like translation and summarization with a single model, and a unique prefix is used for each task so that the model learns which task to perform.
+For this task of grammar correction, we’ll use the prefix “grammar: “ to each of the input sentences.
+```
+Grammar: Your Sentence
+
+```
+
+## How to use :
+You can use this model directly with the pipeline for text2text-generation.
+
+