T5-base model fine-tuned on the JFLEG dataset with the objective of text2text-generation.

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.

For this task of grammar correction, we’ll use the prefix “grammar: “ to each of the input sentences.

Grammar: Your Sentence

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)