readme: # Model Description:
  To create t5-base-c4jfleg model, T5-base model is fine-tuned on the [**JFLEG dataset**](https://huggingface.co/datasets/jfleg) and [**C4 200M dataset**](https://huggingface.co/datasets/liweili/c4_200m) by taking around 3000 examples from each with the objective of grammar correction.
  
  
  The original Google's [**T5-base**] model was pre-trained on [**C4 dataset**](https://huggingface.co/datasets/c4).
  
  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.
  
  # Prefix:
  The T-5 model use "grammar: " as the input text prefix for grammatical corrections.
  
  ## Usage :
  ```
  from transformers import pipeline
  
  checkpoint = "team-writing-assistant/t5-base-c4jfleg"
  model = pipeline("text2text-generation", model=checkpoint)
  
  text = "Speed of light is fastest then speed of sound"
  text = "grammar: " + text
  
  output = model(text)
  print("Result: ", output[0]['generated_text'])
  ```
  ```
  Result: Speed of light is faster than speed of sound.
  ```
  
  ## Other Examples :
  Input: My grammar are bad.   
  Output: My grammar is bad.
  
  Input: Who are the president?   
  Output: Who is the president?