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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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- transformers |
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- text-generation-inference |
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- code |
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- PyTorch |
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library_name: transformers |
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--- |
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# mBART-50 one to many multilingual machine translation |
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This model is a fine-tuned checkpoint of [TheBloke-Llama-2-13B](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML). `mbart-large-50-one-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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The model can translate English to other 49 languages mentioned below. |
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To translate into a target language, the target language id is forced as the first generated token. To force the |
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target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. |
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```python |
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast |
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article_en = "The head of the United Nations says there is no military solution in Syria" |
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model = MBartForConditionalGeneration.from_pretrained("SnypzZz/Llama2-13b-Language-translate") |
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tokenizer = MBart50TokenizerFast.from_pretrained("SnypzZz/Llama2-13b-Language-translate", src_lang="en_XX") |
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model_inputs = tokenizer(article_en, return_tensors="pt") |
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# translate from English to Hindi |
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generated_tokens = model.generate( |
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**model_inputs, |
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forced_bos_token_id=tokenizer.lang_code_to_id["hi_IN"] |
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) |
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tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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# => 'संयुक्त राष्ट्र के नेता कहते हैं कि सीरिया में कोई सैन्य समाधान नहीं है' |
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# translate from English to Chinese |
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generated_tokens = model.generate( |
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**model_inputs, |
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forced_bos_token_id=tokenizer.lang_code_to_id["zh_CN"] |
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) |
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tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) |
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# => '联合国首脑说,叙利亚没有军事解决办法' |
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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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## 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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## 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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``` |
