| ## 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) |
