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| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| import gradio as gr | |
| model_name = "deep-learning-analytics/wikihow-t5-small" | |
| text2text_token = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelWithLMHead.from_pretrained(model_name) | |
| def text2text_summary(para): | |
| initial_text = para.strip().replace("\n","") | |
| token_text = text2text_token.encode(initial_text, return_tensors="pt") | |
| token_ids = model.generate( | |
| token_text, | |
| max_length=250, | |
| num_beams=5, | |
| repetition_penalty=2.5, | |
| early_stopping=True ) | |
| response = text2text_token.decode(token_ids[0], skip_special_tokens=True) | |
| return response | |
| # UX | |
| in_para = gr.Textbox(lines=10, label="Input paragraph", placeholder="Place your paragraph to summarize here...") | |
| out = gr.Textbox(lines=1, label="Summary") | |
| gr.Interface(text2text_summary, inputs=in_para, outputs=out).launch() |