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 text2text-generation.