| from transformers import AutoTokenizer, AutoModelWithLMHead |
| import gradio as grad |
| text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small") |
| mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small") |
|
|
|
|
| def text2text_summary(para): |
| initial_txt = para.strip().replace("\n","") |
| tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt") |
|
|
| tkn_ids = mdl.generate( |
| tkn_text, |
| max_length=250, |
| num_beams=5, |
| repetition_penalty=2.5, |
| |
| early_stopping=True |
| ) |
|
|
| response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True) |
| return response |
|
|
| para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph") |
| out=grad.Textbox(lines=1, label="Summary") |
| grad.Interface(text2text_summary, inputs=para, outputs=out).launch() |
|
|
