| # 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? |