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michaelfeil/
ct2fast-m2m100_1.2B

Name: ct2fast-m2m100_1.2B
Availability: InStock

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Fast-Inference with Ctranslate2

Speedup inference by 2x-8x using int8 inference in C++ quantized version of facebook/m2m100_1.2B

pip install hf_hub_ctranslate2>=2.0.6 ctranslate2>=3.13.0

python

from hf_hub_ctranslate2 import MultiLingualTranslatorCT2fromHfHub
from transformers import AutoTokenizer

model = MultiLingualTranslatorCT2fromHfHub(
    model_name_or_path="michaelfeil/ct2fast-m2m100_1.2B", device="cuda", compute_type="int8_float16",
    tokenizer=AutoTokenizer.from_pretrained(f"facebook/m2m100_1.2B")
)

outputs = model.generate(
    ["How do you call a fast Flamingo?", "Wie geht es dir?"],
    src_lang=["en", "de"],
    tgt_lang=["de", "fr"]
)

compute_type=int8_float16 for device="cuda" compute_type=int8 for device="cpu"

Converted 5/13/23 to Ctranslate2

bash

export ORG="facebook"
export NAME="m2m100_1.2B"
ct2-transformers-converter --model "$ORG/$NAME" --copy_files .gitattributes README.md generation_config.json sentencepiece.bpe.model  special_tokens_map.json tokenizer_config.json vocab.json --quantization float16

Alternative

python

import ctranslate2
import transformers

translator = ctranslate2.Translator("m2m100_1.2B")
tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/m2m100_1.2B")
tokenizer.src_lang = "en"

source = tokenizer.convert_ids_to_tokens(tokenizer.encode("Hello world!"))
target_prefix = [tokenizer.lang_code_to_token["de"]]
results = translator.translate_batch([source], target_prefix=[target_prefix])
target = results[0].hypotheses[0][1:]

print(tokenizer.decode(tokenizer.convert_tokens_to_ids(target)))

Original: 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

python

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}
}