| ## 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. | |
| ``` | |