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  1. app.py +25 -0
  2. requirements.txt +4 -0
app.py ADDED
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+ from transformers import AutoTokenizer, AutoModelWithLMHead
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+ import gradio as grad
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+ text2text_tkn = AutoTokenizer.from_pretrained("deep-learning-analytics/wikihow-t5-small")
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+ mdl = AutoModelWithLMHead.from_pretrained("deep-learning-analytics/wikihow-t5-small")
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+
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+
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+ def text2text_summary(para):
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+ initial_txt = para.strip().replace("\n","")
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+ tkn_text = text2text_tkn.encode(initial_txt, return_tensors="pt")
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+
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+ tkn_ids = mdl.generate(
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+ tkn_text,
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+ max_length=250,
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+ num_beams=5,
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+ repetition_penalty=2.5,
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+
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+ early_stopping=True
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+ )
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+
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+ response = text2text_tkn.decode(tkn_ids[0], skip_special_tokens=True)
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+ return response
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+
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+ para=grad.Textbox(lines=10, label="Paragraph", placeholder="Copy paragraph")
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+ out=grad.Textbox(lines=1, label="Summary")
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+ grad.Interface(text2text_summary, inputs=para, outputs=out).launch()
requirements.txt ADDED
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+ gradio
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+ transformers
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+ torch
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+ transformers[sentencepiece]