README.md

---
language: en
license: mit
datasets:
  - jfleg
tags:
  - grammar-correction
  - t5
  - english
pipeline_tag: text2text-generation
widget:
  - text: "correct grammar: She dont like to eat vegetables but she like fruits."
  - text: "correct grammar: They goes to the store every day."
  - text: "correct grammar: He have been working here for five years."
---

# Grammar Correction Model

This model is fine-tuned to correct grammatical errors in English text. It's based on T5 and specifically trained on essay correction data.

## Model Description

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

## Usage

```python
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

- 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}}
}
```