t5-base-fine-tuned-on-jfleg

t5-base-fine-tuned-on-jfleg

T5-base model fine-tuned on the JFLEG dataset 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 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.