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| import gradio as gr | |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("mt5") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("mt5") | |
| model.resize_token_embeddings(len(tokenizer)) | |
| model.eval() | |
| def translate(hakubun): | |
| input_ids = tokenizer.encode(hakubun, return_tensors="pt", max_length=20, truncation=True) | |
| output = model.generate(input_ids) | |
| kakikudashi = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return kakikudashi | |
| title = "Kanbun-LM" | |
| description = "Gradio Demo for Kanbun-LM. Upload a hakubun then you can earn get its kanbun. Texts other than Tang poetry may not be translated correctly.<br>" \ | |
| "書き下し文生成のデモです。白文を入力し、翻訳された書き下し文を得ることができます。唐詩以外の漢文は正しく翻訳されない可能性があります。" | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2305.12759' target='_blank'>arXiv</a></p> " \ | |
| "<p style='text-align: center'><a href='https://github.com/nlp-waseda/Kanbun-LM' target='_blank'>Github Repo</a></p>" | |
| examples = [['春眠不覚暁'], | |
| ['処処聞啼鳥'], | |
| ['洛陽親友如相問'], | |
| ['一片氷心在玉壺']] | |
| demo = gr.Interface(fn=translate, inputs="text", outputs="text", | |
| title=title, description=description, article=article, examples=examples, allow_flagging=False) | |
| demo.launch() | |