readme: ## t5-base-fine-tuned-on-jfleg
  T5-base model fine-tuned on the [**JFLEG dataset**](https://huggingface.co/datasets/jfleg) with the objective of **text2text-generation**.
  
  # 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**](https://arxiv.org/pdf/1910.10683.pdf) 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.
  ```
  Grammar: Your Sentence
  
  ```
  
  ## How to use :
  You can use this model directly with the pipeline for detecting and correcting grammatical mistakes.
  
  ```
  from transformers import pipeline
  
  model_checkpoint = "Modfiededition/t5-base-fine-tuned-on-jfleg"
  model = pipeline("text2text-generation", model=model_checkpoint)
  text = "I am write on AI"
  output = model(text)
  ```
  Result(s)
  ```
  I am writing on AI.
  ```
  
  
  
  
  