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facebook_m2m100_418M

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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

M2M100 418M

M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper and first released in this repository.

The model that can directly translate between the 9,900 directions of 100 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.

Note: M2M100Tokenizer depends on sentencepiece, so make sure to install it before running the example.

To install sentencepiece run pip install sentencepiece

from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer

hi_text = "जीवन एक चॉकलेट बॉक्स की तरह है।"
chinese_text = "生活就像一盒巧克力。"

model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")

# translate Hindi to French
tokenizer.src_lang = "hi"
encoded_hi = tokenizer(hi_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.get_lang_id("fr"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "La vie est comme une boîte de chocolat."

# translate Chinese to English
tokenizer.src_lang = "zh"
encoded_zh = tokenizer(chinese_text, return_tensors="pt")
generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Life is like a box of chocolate."

See the model hub to look for more fine-tuned versions.

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)

BibTeX entry and citation info

@misc{fan2020englishcentric,
      title={Beyond English-Centric Multilingual Machine Translation}, 
      author={Angela Fan and Shruti Bhosale and Holger Schwenk and Zhiyi Ma and Ahmed El-Kishky and Siddharth Goyal and Mandeep Baines and Onur Celebi and Guillaume Wenzek and Vishrav Chaudhary and Naman Goyal and Tom Birch and Vitaliy Liptchinsky and Sergey Edunov and Edouard Grave and Michael Auli and Armand Joulin},
      year={2020},
      eprint={2010.11125},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Files & hashes

PathSizesha1sha256
README.md4.5 KB (4,603 B)98b99edb1de68441bb0c87a9645c8f2e35ea34d01fd660f130aedc5ecf1796b47ab47d43d3c4b36541f5387b7adb3df75cb5dfdb
config.json908 B (908 B)93359f73052bad5dc918b1d53c1c75e89fd6a257df0ae43e4e4b0d7e3c97b7f447857a70ef6b6a2aa1f145cedbcc730d95f67134
generation_config.json233 B (233 B)ba941101cd3e90b113db16712f4a20372268e767aed76366507333ddbb8bd49960f23c82fe6446b3319a46a54befdb45324ccf61
pytorch_model.bin1.80 GB (1,935,796,948 B)bf634b99e94cd2c66c1f8495394eafdf80cc22e6d907ea45e4e4b9db163382a6674f6218b3c59566fe06d77f4055c208b4e87ed1
rust_model.ot1.80 GB (1,935,781,288 B)364fbb6d75c44972d8351532e36351b9b1d1d5fcf170f6a277d00b20144fa6dac6ecd781c5a5e66844c022244437dd2da3a83655
sentencepiece.bpe.model2.3 MB (2,423,393 B)b891f630544876fd639fa969bc076ce253ee796cd8f7c76ed2a5e0822be39f0a4f95a55eb19c78f4593ce609e2edbc2aea4d380a
special_tokens_map.json1.1 KB (1,140 B)c6c3195f9bb7856df9a3fdd0252bcf99ff209f5dc1a4f86c3874d279ae1b2a05162858db5dd6c61665d84223ed886cbcff08fda6
tokenizer_config.json298 B (298 B)c0acb40bd22d2a992d273bb6a2b1c99c37613e4fa53e6aa83da0b82565ed90c3849056307a9453843322ac5b8439ec4b9497fe48
vocab.json3.5 MB (3,708,092 B)380e263bf4efc45fa72251fb19336a9593307bc4b6e77e474aeea8f441363aca7614317c06381f3eacfe10fb9856d5081d1074cc

Cite this release

Canonical URL
https://aiseedbank.org/models/facebook_m2m100_418M/
Slug
facebook_m2m100_418M
Infohash
ca48c10acf016191224cc6243cec9d334e18f7c4
License
mit
Signing key fingerprint
85a3b32c3712427b

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Provenance

Upstream repositoryfacebook/m2m100_418M
Revision (pinned)55c2e61bbf05dfb8d7abccdc3fae6fc8512fd636
Fetched at2026-09-03T22:41:35Z
License at fetchmit
Snapshot toolhuggingface · seedbank 0.1.0

Trackers

✓ verified · rehash-vs-hf-metadata at 2026-09-03T22:42:10Z

mit3.61 GB (3,877,716,903 bytes)transformerspytorchrustm2m_100text2text-generationmultilingualastcebiloendpoints_compatible97 languages (af, am, ar …)paper: 2010.11125