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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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+ tags:
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+ - mbart-50
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+ ---
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+
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+ # mBART-50 many to many multilingual machine translation
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+
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+
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+ 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.
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+
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+
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+ The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method.
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+
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+
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+ ```python
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+ from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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+
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+ article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
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+ article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
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+
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+ model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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+ tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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+
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+ # translate Hindi to French
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+ tokenizer.src_lang = "hi_IN"
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+ encoded_hi = tokenizer(article_hi, return_tensors="pt")
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+ generated_tokens = model.generate(
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+ **encoded_hi,
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+ forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"]
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+ )
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+ tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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+ # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire dans la Syrie."
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+
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+ # translate Arabic to English
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+ tokenizer.src_lang = "ar_AR"
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+ encoded_ar = tokenizer(article_ar, return_tensors="pt")
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+ generated_tokens = model.generate(
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+ **encoded_ar,
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+ forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"]
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+ )
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+ tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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+ # => "The Secretary-General of the United Nations says there is no military solution in Syria."
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+ ```
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+
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+
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+ See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions.
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+
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+
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+ ## Languages covered
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+ 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)
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+
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+
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+ ## BibTeX entry and citation info
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+ ```
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+ @article{tang2020multilingual,
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+ title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
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+ 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},
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+ year={2020},
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+ eprint={2008.00401},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```
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