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To create t5-base-c4jfleg model, T5-base model is fine-tuned on the JFLEG dataset and C4 200M dataset 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.
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.
The T-5 model use "grammar: " as the input text prefix for grammatical corrections.
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['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?
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