readme: ## Model description
  T5 model trained for Grammar Correction. This model corrects grammatical mistakes in input sentences
  
  ### Dataset Description
  The T5-base model has been trained on C4_200M dataset. 
  ### Model in Action 🚀
  ```
  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
    
  ```
  
  ### Example 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.']
  ```
  
  Model Developed by [Priya-Dwivedi](https://www.linkedin.com/in/priyanka-dwivedi-6864362)