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import gradio as gr |
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from transformers import PreTrainedTokenizerFast, BartForConditionalGeneration |
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model_name = "ainize/kobart-news" |
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tokenizer = PreTrainedTokenizerFast.from_pretrained(model_name) |
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model = BartForConditionalGeneration.from_pretrained(model_name) |
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def summ(txt): |
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input_ids=tokenizer.encode(txt, return_tensors="pt") |
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summary_text_ids=model.generate( |
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input_ids=input_ids, |
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bos_token_id=model.config.bos_token_id, |
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eos_token_id=model.config.eos_token_id, |
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length_penalty=2.0, |
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max_length=142, |
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min_length=56, |
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num_beams=4) |
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return tokenizer.decode(summary_text_ids[0],skip_special_tokens=True) |
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interface=gr.Interface(summ,[gr.Textbox(label="origina text")],[gr.Textbox(label="summary")]) |
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interface.launch() |