Grammarcorrector

🚀 Grammar Correction T5 Model

A T5 model trained for grammar correction, capable of identifying and rectifying grammatical errors in input sentences.

🚀 Quick Start

This T5 model is specifically designed for grammar correction. It can effectively correct grammatical mistakes in input sentences.

✨ Features

• Trained for grammar correction, offering high - accuracy error identification and correction.
• Utilizes the T5 - base architecture, which has strong generalization ability.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = 'deep-learning-analytics/GrammarCorrector'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(torch_device)

def correct_grammar(input_text,num_return_sequences):
  batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device)
  translated = model.generate(**batch,max_length=64,num_beams=num_beams, num_return_sequences=num_return_sequences, temperature=1.5)
  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
  return tgt_text
  

Advanced Usage

text = 'He are moving here.'
print(correct_grammar(text, num_return_sequences=2))
['He is moving here.', 'He is moving here now.']

Another example

text = 'Cat drinked milk'
print(correct_grammar(text, num_return_sequences=2))
['Cat drank milk.', 'Cat drink milk.']

📚 Documentation

Model Information

📄 License

No license information is provided in the original document.

Model Developed by Priya - Dwivedi