transformers/docs/source/en/model_doc/marian.md

MarianMT

Bugs: If you see something strange, file a Github Issue and assign @patrickvonplaten.

Translations should be similar, but not identical to output in the test set linked to in each model card.

Tips:

• A framework for translation models, using the same models as BART.

Implementation Notes

• Each model is about 298 MB on disk, there are more than 1,000 models.

• The list of supported language pairs can be found here.

• Models were originally trained by Jörg Tiedemann using the Marian C++ library, which supports fast training and translation.

• All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.

• The 80 opus models that require BPE preprocessing are not supported.

• The modeling code is the same as [BartForConditionalGeneration] with a few minor modifications:

  • static (sinusoid) positional embeddings (MarianConfig.static_position_embeddings=True)
  • no layernorm_embedding (MarianConfig.normalize_embedding=False)
  • the model starts generating with pad_token_id (which has 0 as a token_embedding) as the prefix (Bart uses <s/>),

• Code to bulk convert models can be found in convert_marian_to_pytorch.py.

• This model was contributed by sshleifer.

Naming

• All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt}
• The language codes used to name models are inconsistent. Two digit codes can usually be found here, three digit codes require googling "language code {code}".
• Codes formatted like es_AR are usually code_{region}. That one is Spanish from Argentina.
• The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second group use a combination of ISO-639-5 codes and ISO-639-2 codes.

Examples

• Since Marian models are smaller than many other translation models available in the library, they can be useful for fine-tuning experiments and integration tests.
• Fine-tune on GPU

Multilingual Models

• All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt}:
• If a model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text.
• You can see a models's supported language codes in its model card, under target constituents, like in opus-mt-en-roa.
• Note that if a model is only multilingual on the source side, like Helsinki-NLP/opus-mt-roa-en, no language codes are required.

New multi-lingual models from the Tatoeba-Challenge repo require 3 character language codes:

>>> from transformers import MarianMTModel, MarianTokenizer

>>> src_text = [
...     ">>fra<< this is a sentence in english that we want to translate to french",
...     ">>por<< This should go to portuguese",
...     ">>esp<< And this to Spanish",
... ]

>>> model_name = "Helsinki-NLP/opus-mt-en-roa"
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
>>> print(tokenizer.supported_language_codes)
['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']

>>> model = MarianMTModel.from_pretrained(model_name)
>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
>>> [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
["c'est une phrase en anglais que nous voulons traduire en français",
 'Isto deve ir para o português.',
 'Y esto al español']

Here is the code to see all available pretrained models on the hub:

from huggingface_hub import list_models

model_list = list_models()
org = "Helsinki-NLP"
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
suffix = [x.split("/")[1] for x in model_ids]
old_style_multi_models = [f"{org}/{s}" for s in suffix if s != s.lower()]

Old Style Multi-Lingual Models

These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language group:

['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
 'Helsinki-NLP/opus-mt-ROMANCE-en',
 'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
 'Helsinki-NLP/opus-mt-de-ZH',
 'Helsinki-NLP/opus-mt-en-CELTIC',
 'Helsinki-NLP/opus-mt-en-ROMANCE',
 'Helsinki-NLP/opus-mt-es-NORWAY',
 'Helsinki-NLP/opus-mt-fi-NORWAY',
 'Helsinki-NLP/opus-mt-fi-ZH',
 'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
 'Helsinki-NLP/opus-mt-sv-NORWAY',
 'Helsinki-NLP/opus-mt-sv-ZH']
GROUP_MEMBERS = {
 'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
 'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
 'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
 'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
 'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
}

Example of translating english to many romance languages, using old-style 2 character language codes

>>> from transformers import MarianMTModel, MarianTokenizer

>>> src_text = [
...     ">>fr<< this is a sentence in english that we want to translate to french",
...     ">>pt<< This should go to portuguese",
...     ">>es<< And this to Spanish",
... ]

>>> model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"
>>> tokenizer = MarianTokenizer.from_pretrained(model_name)

>>> model = MarianMTModel.from_pretrained(model_name)
>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
>>> tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
["c'est une phrase en anglais que nous voulons traduire en français", 
 'Isto deve ir para o português.',
 'Y esto al español']

Documentation resources

• Translation task guide
• Summarization task guide
• Causal language modeling task guide

MarianConfig

autodoc MarianConfig

MarianTokenizer

autodoc MarianTokenizer - build_inputs_with_special_tokens

MarianModel

autodoc MarianModel - forward

MarianMTModel

autodoc MarianMTModel - forward

MarianForCausalLM

autodoc MarianForCausalLM - forward

TFMarianModel

autodoc TFMarianModel - call

TFMarianMTModel

autodoc TFMarianMTModel - call

FlaxMarianModel

autodoc FlaxMarianModel - call

FlaxMarianMTModel

autodoc FlaxMarianMTModel - call