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| import streamlit as st | |
| from transformers import AutoTokenizer,AutoModelForSeq2SeqLM | |
| def load_model(input_complex_sentence,model): | |
| base_path = "flax-community/" | |
| model_path = base_path + model | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_path) | |
| tokenized_sentence = tokenizer(input_complex_sentence,return_tensors="pt") | |
| result = model.generate(tokenized_sentence['input_ids'],attention_mask = tokenized_sentence['attention_mask'],max_length=256,num_beams=5) | |
| generated_sentence = tokenizer.decode(result[0],skip_special_tokens=True) | |
| return generated_sentence | |
| def main(): | |
| st.title("Sentence Split in English using T5 Variants") | |
| st.write("Sentence Split is the task of **dividing a long Complex Sentence into Simple Sentences**") | |
| st.sidebar.title("Sentence Simplifier") | |
| model = st.sidebar.selectbox( | |
| "Please Choose the Model", | |
| ("t5-base-wikisplit","t5-v1_1-base-wikisplit", "byt5-base-wikisplit","t5-large-wikisplit")) | |
| st.sidebar.write(''' | |
| ## Applications: | |
| * Sentence Simplification | |
| * Data Augmentation | |
| * Sentence Rephrase | |
| ''') | |
| st.sidebar.write("[More Exploration](https://github.com/bhadreshpsavani/t5-sentence-split)") | |
| example = "Mary likes to play football in her freetime whenever she meets with her friends that are very nice people." | |
| input_complex_sentence = st.text_area("Please type a Complex Sentence to split",example) | |
| if st.button('Split'): | |
| with st.spinner("Spliting Sentence...🧠"): | |
| generated_sentence = load_model(input_complex_sentence, model) | |
| st.write(generated_sentence) | |
| if __name__ == "__main__": | |
| main() | |