entai2965/m2m100-1.2B-ctranslate2

From: https://huggingface.co./facebook/m2m100_1.2B

M2M100 1.2B

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

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}
}

How to download this model using python

• Install Python https://www.python.org/downloads/
• cmd
• python --version
• python -m pip install huggingface_hub
• python

import huggingface_hub
huggingface_hub.download_snapshot('entai2965/m2m100-1.2B-ctranslate2',local_dir='m2m100-1.2B-ctranslate2')

How to run this model

• https://opennmt.net/CTranslate2/guides/transformers.html#m2m-100
• cmd
• python -m pip install ctranslate2 transformers sentencepiece
• python

import ctranslate2
import transformers

translator = ctranslate2.Translator("m2m100-1.2B-ctranslate2", device="cpu")
tokenizer = transformers.AutoTokenizer.from_pretrained("m2m100-1.2B-ctranslate2",clean_up_tokenization_spaces=True)
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)))

How to run this model (batch syntax)

import os
import ctranslate2
import transformers

#set defaults
home_path=os.path.expanduser('~')
#model_path=home_path+'/Downloads/models/m2m100-418M-ctranslate2'
model_path=home_path+'/Downloads/models/m2m100-1.2B-ctranslate2'

#available languages list ->  https://huggingface.co./facebook/m2m100_1.2B   <-
source_language_code='ja'
target_language_code='es'

device='cpu'
#device='cuda'

#load data
string1='イキリカメラマン'
string2='おかあさん'
string3='人生はチョコレートの箱のようなものです。彼らは皆毒殺されています。'
list_to_translate=[string1,string2,string3]

#load model and tokenizer
translator=ctranslate2.Translator(model_path,device=device)
tokenizer=transformers.AutoTokenizer.from_pretrained(model_path,clean_up_tokenization_spaces=True)

#configure languages
tokenizer.src_lang=source_language_code
target_language_token=[tokenizer.lang_code_to_token[target_language_code]]

#encode
encoded_list=[]
for text in list_to_translate:
    encoded_list.append(tokenizer.convert_ids_to_tokens(tokenizer.encode(text)))

#translate
#https://opennmt.net/CTranslate2/python/ctranslate2.Translator.html?#ctranslate2.Translator.translate_batch
translated_list=translator.translate_batch(encoded_list, target_prefix=[target_language_token]*len(encoded_list))

#decode
for counter,tokens in enumerate(translated_list):
    translated_list[counter]=tokenizer.decode(tokenizer.convert_tokens_to_ids(tokens.hypotheses[0][1:]))

#output
for text in translated_list:
    print(text)

Functional programming version

import os
import ctranslate2
import transformers

#set defaults
home_path=os.path.expanduser('~')
#model_path=home_path+'/Downloads/models/m2m100-418M-ctranslate2'
model_path=home_path+'/Downloads/models/m2m100-1.2B-ctranslate2'

#available languages list ->  https://huggingface.co./facebook/m2m100_1.2B   <-
source_language_code='ja'
target_language_code='es'

device='cpu'
#device='cuda'

#load data
string1='イキリカメラマン'
string2='おかあさん'
string3='人生はチョコレートの箱のようなものです。彼らは皆毒殺されています。'
list_to_translate=[string1,string2,string3]

#load model and tokenizer
translator=ctranslate2.Translator(model_path,device=device)
tokenizer=transformers.AutoTokenizer.from_pretrained(model_path,clean_up_tokenization_spaces=True)
tokenizer.src_lang=source_language_code

#invoke witchcraft
translated_list=[tokenizer.decode(tokenizer.convert_tokens_to_ids(tokens.hypotheses[0][1:])) for tokens in translator.translate_batch([tokenizer.convert_ids_to_tokens(tokenizer.encode(i)) for i in list_to_translate], target_prefix=[[tokenizer.lang_code_to_token[target_language_code]]]*len(list_to_translate))]

#output
for text in translated_list:
    print(text)