| language: | |
| - multilingual | |
| - af | |
| - am | |
| - ar | |
| - ast | |
| - az | |
| - ba | |
| - be | |
| - bg | |
| - bn | |
| - br | |
| - bs | |
| - ca | |
| - ceb | |
| - cs | |
| - cy | |
| - da | |
| - de | |
| - el | |
| - en | |
| - es | |
| - et | |
| - fa | |
| - ff | |
| - fi | |
| - fr | |
| - fy | |
| - ga | |
| - gd | |
| - gl | |
| - gu | |
| - ha | |
| - he | |
| - hi | |
| - hr | |
| - ht | |
| - hu | |
| - hy | |
| - id | |
| - ig | |
| - ilo | |
| - is | |
| - it | |
| - ja | |
| - jv | |
| - ka | |
| - kk | |
| - km | |
| - kn | |
| - ko | |
| - lb | |
| - lg | |
| - ln | |
| - lo | |
| - lt | |
| - lv | |
| - mg | |
| - mk | |
| - ml | |
| - mn | |
| - mr | |
| - ms | |
| - my | |
| - ne | |
| - nl | |
| - no | |
| - ns | |
| - oc | |
| - or | |
| - pa | |
| - pl | |
| - ps | |
| - pt | |
| - ro | |
| - ru | |
| - sd | |
| - si | |
| - sk | |
| - sl | |
| - so | |
| - sq | |
| - sr | |
| - ss | |
| - su | |
| - sv | |
| - sw | |
| - ta | |
| - th | |
| - tl | |
| - tn | |
| - tr | |
| - uk | |
| - ur | |
| - uz | |
| - vi | |
| - wo | |
| - xh | |
| - yi | |
| - yo | |
| - zh | |
| - zu | |
| license: mit | |
| tags: | |
| - small100 | |
| - translation | |
| datasets: | |
| - flores101 | |
| - gsarti/flores_101 | |
| - tico19 | |
| - gmnlp/tico19 | |
| - tatoeba | |
| # SMALL-100 Model | |
| SMaLL-100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in [this paper](https://arxiv.org/abs/2210.11621), and initially released in [this repository](https://github.com/alirezamshi/small100). | |
| The model architecture and config are the same as [M2M-100](https://huggingface.co/facebook/m2m100_418M/tree/main) implementation, but the tokenizer is modified to adjust language codes. So, you should load the tokenizer locally from [tokenization_small100.py](https://huggingface.co/alirezamsh/small100/blob/main/tokenization_small100.py) file for the moment. | |
| ``` | |
| from transformers import M2M100ForConditionalGeneration | |
| from tokenization_small100 import SMALL100Tokenizer | |
| hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।" | |
| chinese_text = "生活就像一盒巧克力。" | |
| model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100") | |
| tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100") | |
| # translate Hindi to French | |
| tokenizer.tgt_lang = "fr" | |
| encoded_hi = tokenizer(hi_text, return_tensors="pt") | |
| generated_tokens = model.generate(**encoded_hi) | |
| tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
| # => "La vie est comme une boîte de chocolat." | |
| # translate Chinese to English | |
| tokenizer.tgt_lang = "en" | |
| encoded_zh = tokenizer(chinese_text, return_tensors="pt") | |
| generated_tokens = model.generate(**encoded_zh) | |
| tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) | |
| # => "Life is like a box of chocolate." | |
| ``` | |
| Please refer to [original repository](https://github.com/alirezamshi/small100) for further details. | |
| # Languages Covered | |
| Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu) | |
| # Citation | |
| If you use this model for your research, please cite the following work: | |
| ``` | |
| @article{mohammadshahi2022small, | |
| title={SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages}, | |
| author={Mohammadshahi, Alireza and Nikoulina, Vassilina and Berard, Alexandre and Brun, Caroline and Henderson, James and Besacier, Laurent}, | |
| journal={arXiv preprint arXiv:2210.11621}, | |
| year={2022} | |
| } | |
| ``` |