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| import streamlit as st | |
| st.title("Correct Grammar with Transformers 🦄") | |
| st.write("") | |
| st.write("Input your text here!") | |
| default_value = "Mike and Anna is skiing" | |
| sent = st.text_area("Text", default_value, height = 50) | |
| num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=3, value=1, step=1) | |
| ### Run Model | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| torch_device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| tokenizer = T5Tokenizer.from_pretrained('deep-learning-analytics/GrammarCorrector') | |
| model = T5ForConditionalGeneration.from_pretrained('deep-learning-analytics/GrammarCorrector').to(torch_device) | |
| def correct_grammar(input_text,num_return_sequences=num_return_sequences): | |
| batch = tokenizer([input_text],truncation=True,padding='max_length',max_length=64, return_tensors="pt").to(torch_device) | |
| results = model.generate(**batch,max_length=64,num_beams=2, num_return_sequences=num_return_sequences, temperature=1.5) | |
| #answer = tokenizer.batch_decode(results[0], skip_special_tokens=True) | |
| return results | |
| ##Prompts | |
| results = correct_grammar(sent, num_return_sequences) | |
| generated_sequences = [] | |
| for generated_sequence_idx, generated_sequence in enumerate(results): | |
| # Decode text | |
| text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True) | |
| generated_sequences.append(text) | |
| st.write(generated_sequences) | |