from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load model and tokenizer
model = T5ForConditionalGeneration.from_pretrained("Tegence/grammar-correction-model")
tokenizer = T5Tokenizer.from_pretrained("Tegence/grammar-correction-model")

# Prepare input (add the prefix "correct grammar: ")
incorrect_text = "She dont like to eat vegetables but she like fruits."
input_text = f"correct grammar: {incorrect_text}"

# Tokenize and generate
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(corrected_text)
# Expected output: "She doesn't like to eat vegetables but she likes fruits."

Limitations

Model Type: T5
Task: Grammar Correction
Training Data: Custom essay dataset with grammatical errors and corrections
Output: Grammatically corrected text

Works best with English text
Performance may vary for technical or domain-specific content
Very long or complex sentences may be challenging to correct

Citation

If you use this model in your research, please cite:

bibtex

@misc{grammar-correction-model,
  author = {AdmitEase},
  title = {Grammar Correction Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Tegence/grammar-correction-model}}
}