readme: ---
  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
  tags:
  - mbart-50
  ---
  
  # mBART-50 many to one multilingual machine translation
  
  
  This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper.
  The model can translate directly between any pair of 50 languages.
  
  
  ```python
  from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
  
  article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
  article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
  
  model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
  tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
  
  # translate Hindi to English
  tokenizer.src_lang = "hi_IN"
  encoded_hi = tokenizer(article_hi, return_tensors="pt")
  generated_tokens = model.generate(**encoded_hi)
  tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
  # => "The head of the UN says there is no military solution in Syria."
  
  # translate Arabic to English
  tokenizer.src_lang = "ar_AR"
  encoded_ar = tokenizer(article_ar, return_tensors="pt")
  generated_tokens = model.generate(**encoded_ar)
  tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
  # => "The Secretary-General of the United Nations says there is no military solution in Syria."
  ```
  
  
  See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions.
  
  
  ## Languages covered
  Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI)
  
  
  ## BibTeX entry and citation info
  ```
  @article{tang2020multilingual,
      title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
      author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
      year={2020},
      eprint={2008.00401},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
  }
  ```