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--- |
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language: |
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- multilingual |
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- ar |
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- cs |
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- de |
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- en |
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- es |
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- et |
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- fi |
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- fr |
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- gu |
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- hi |
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- it |
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- ja |
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- kk |
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- ko |
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- lt |
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- lv |
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- my |
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- ne |
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- nl |
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- ro |
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- ru |
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- si |
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- tr |
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- vi |
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- zh |
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- af |
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- az |
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- bn |
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- fa |
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- he |
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- hr |
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- id |
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- ka |
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- km |
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- mk |
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- ml |
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- mn |
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- mr |
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- pl |
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- ps |
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- pt |
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- sv |
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- sw |
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- ta |
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- te |
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- th |
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- tl |
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- uk |
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- ur |
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- xh |
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- gl |
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- sl |
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license: mit |
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tags: |
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- mbart-50 |
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--- |
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# Knight-errant |
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Knight is a text style transfer model for knight-errant style. This model is for Chinese Knight-errant style transfer. |
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paper link: [To be a Knight-errant Novel Master: Knight-errant Style Transfer via Contrastive Learning](https://openreview.net/forum?id=FDw2hdpiWNO) |
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```python |
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#inference |
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast |
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model = MBartForConditionalGeneration.from_pretrained("Anonymous-TST/knight-errant-TST-zh") |
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tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="zh_CN", tgt_lang="zh_CN") |
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model.cuda() |
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model.eval() |
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article_1 = "jinyong: 接下来会发生什么？" |
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batch = tokenizer(article_1, return_tensors="pt",return_token_type_ids=False, truncation=True, max_length=64, padding=True).to('cuda') |
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translated_tokens = model.generate(**batch,max_length=64) |
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decoded = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=True) |
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print(decoded) |
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# 欲知后事如何? |
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``` |
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``` |
