facebook_m2m100_418M
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Model card
The complete upstream card, rendered from this payload's README.md — the same hash-verified bytes the torrent distributes. Images and off-site links are removed; the original card on Hugging Face carries them.
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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magnet:?xt=urn:btih:ca48c10acf016191224cc6243cec9d334e18f7c4&dn=facebook_m2m100_418MOpen magnet in torrent client · infohash ca48c10acf016191224cc6243cec9d334e18f7c4
Files & hashes
| Path | Size | sha1 | sha256 |
|---|---|---|---|
| README.md | 4.5 KB (4,603 B) | 98b99edb1de68441bb0c87a9645c8f2e35ea34d0 | 1fd660f130aedc5ecf1796b47ab47d43d3c4b36541f5387b7adb3df75cb5dfdb |
| config.json | 908 B (908 B) | 93359f73052bad5dc918b1d53c1c75e89fd6a257 | df0ae43e4e4b0d7e3c97b7f447857a70ef6b6a2aa1f145cedbcc730d95f67134 |
| generation_config.json | 233 B (233 B) | ba941101cd3e90b113db16712f4a20372268e767 | aed76366507333ddbb8bd49960f23c82fe6446b3319a46a54befdb45324ccf61 |
| pytorch_model.bin | 1.80 GB (1,935,796,948 B) | bf634b99e94cd2c66c1f8495394eafdf80cc22e6 | d907ea45e4e4b9db163382a6674f6218b3c59566fe06d77f4055c208b4e87ed1 |
| rust_model.ot | 1.80 GB (1,935,781,288 B) | 364fbb6d75c44972d8351532e36351b9b1d1d5fc | f170f6a277d00b20144fa6dac6ecd781c5a5e66844c022244437dd2da3a83655 |
| sentencepiece.bpe.model | 2.3 MB (2,423,393 B) | b891f630544876fd639fa969bc076ce253ee796c | d8f7c76ed2a5e0822be39f0a4f95a55eb19c78f4593ce609e2edbc2aea4d380a |
| special_tokens_map.json | 1.1 KB (1,140 B) | c6c3195f9bb7856df9a3fdd0252bcf99ff209f5d | c1a4f86c3874d279ae1b2a05162858db5dd6c61665d84223ed886cbcff08fda6 |
| tokenizer_config.json | 298 B (298 B) | c0acb40bd22d2a992d273bb6a2b1c99c37613e4f | a53e6aa83da0b82565ed90c3849056307a9453843322ac5b8439ec4b9497fe48 |
| vocab.json | 3.5 MB (3,708,092 B) | 380e263bf4efc45fa72251fb19336a9593307bc4 | b6e77e474aeea8f441363aca7614317c06381f3eacfe10fb9856d5081d1074cc |
Cite this release
- Canonical URL
- https://aiseedbank.org/models/facebook_m2m100_418M/
- Slug
- facebook_m2m100_418M
- Infohash
- ca48c10acf016191224cc6243cec9d334e18f7c4
- License
- mit
- Signing key fingerprint
- 85a3b32c3712427b
Every file carries a locally computed sha256 — verify a download against the signed sums: facebook_m2m100_418M.SHA256SUMS (+ minisign signature).
Provenance
| Upstream repository | facebook/m2m100_418M |
|---|---|
| Revision (pinned) | 55c2e61bbf05dfb8d7abccdc3fae6fc8512fd636 |
| Fetched at | 2026-09-03T22:41:35Z |
| License at fetch | mit |
| Snapshot tool | huggingface · seedbank 0.1.0 |
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✓ 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