MarianMT
----------------------------------------------------
**DISCLAIMER:** If you see something strange,
file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
@sshleifer. Translations should be similar, but not identical to, output in the test set linked to in each model card.

Implementation Notes
~~~~~~~~~~~~~~~~~~~~
- Each model is about 298 MB on disk, there are 1,000+ models.
- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
- The 1,000+ models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ 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``)
    - a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
    - no layernorm_embedding (``MarianConfig.normalize_embedding=False``)
    - the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix. (Bart uses <s/>)
- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``

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 <https://developers.google.com/admin-sdk/directory/v1/languages>`_, three digit codes require googling "language code {code}".
- Codes formatted like ``es_AR`` are usually ``code_{region}``. That one is spanish documents from Argentina.


Multilingual Models
~~~~~~~~~~~~~~~~~~~~

All  model names use the following format: ``Helsinki-NLP/opus-mt-{src}-{tgt}``:
    - if ``src`` is in all caps, the model supports multiple input languages, you can figure out which ones by looking at the model card, or the Group Members `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_ .
    - if ``tgt`` is in all caps, the 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 tokenizer's supported language codes in ``tokenizer.supported_language_codes``

Example of translating english to many romance languages, using language codes:

.. code-block:: python

    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)
    print(tokenizer.supported_language_codes)
    model = MarianMTModel.from_pretrained(model_name)
    translated = model.generate(**tokenizer.prepare_seq2seq_batch(src_text))
    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']

Sometimes, models were trained on collections of languages that do not resolve to a group. In this case, _ is used as a separator for src or tgt, as in ``'Helsinki-NLP/opus-mt-en_el_es_fi-en_el_es_fi'``. These still require language codes.
There are many supported regional language codes, like ``>>es_ES<<`` (Spain) and ``>>es_AR<<`` (Argentina), that do not seem to change translations. I have not found these to provide different results than just using ``>>es<<``.

For Example:
    - ``Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU``: translates from all NORTH_EU languages (see `mapping <https://gist.github.com/sshleifer/6d20e7761931b08e73c3219027b97b8a>`_) to all NORTH_EU languages. Use a special language code like ``>>de<<`` to specify output language.
    - ``Helsinki-NLP/opus-mt-ROMANCE-en``: translates from many romance languages to english, no codes needed since there is only 1 tgt language.



.. code-block:: python

    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']
    }

Code to see available pretrained models:

.. code-block:: python

    from transformers.hf_api import HfApi
    model_list = HfApi().model_list()
    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]
    multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]

MarianMTModel
~~~~~~~~~~~~~

Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
Model API is identical to BartForConditionalGeneration.
Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
This class inherits nearly all functionality from ``BartForConditionalGeneration``, see that page for method signatures.

MarianConfig
~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.MarianConfig
    :members:


MarianTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MarianTokenizer
    :members: prepare_seq2seq_batch




