pytorch mbart text2text-generation multilingual ar cs de en es et fi fr gu hi it ja kk ko lt lv my ne nl ro ru si tr vi zh af az bn fa he hr id ka km mk ml mn mr pl ps pt sv sw ta te th tl uk ur xh gl sl transformers mbart-50 license:mit autotrain_compatible
---
language: 
- multilingual
- ar 
- cs
- de
- en
- es
- et
- fi
- fr
- gu
- hi
- it
- ja
- kk
- ko
- lt
- lv
- my
- ne
- nl
- ro
- ru
- si
- tr
- vi
- zh
- af
- az
- bn
- fa
- he
- hr
- id
- ka
- km
- mk
- ml
- mn
- mr
- pl
- ps
- pt
- sv
- sw
- ta
- te
- th
- tl
- uk
- ur
- xh
- gl
- sl
license: mit
tags:
- mbart-50
---

# Knight-errant

Knight-errant is a test style transfer model for knight-errant style.




```python

#inference
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("Anonymous-TST/knight-errant-TST-zh")
tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="zh_CN", tgt_lang="zh_CN")

model.cuda()
model.eval()

article_1 = "jinyong: 接下来会发生什么？"
batch = tokenizer(article_1, return_tensors="pt",return_token_type_ids=False, truncation=True, max_length=64, padding=True).to('cuda')
translated_tokens = model.generate(**batch,max_length=64)
decoded = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True,  clean_up_tokenization_spaces=True)
print(decoded)
```




```