marian.rst

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    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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        http://www.apache.org/licenses/LICENSE-2.0

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MarianMT
-----------------------------------------------------------------------------------------------------------------------

**Bugs:** If you see something strange, file a `Github Issue
<https://github.com/huggingface/transformers/issues/new?assignees=sshleifer&labels=&template=bug-report.md&title>`__
and assign @patrickvonplaten.

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 more than 1,000 models.
- The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
- 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 :class:`~transformers.BartForConditionalGeneration` with a few minor modifications:

    - static (sinusoid) positional embeddings (:obj:`MarianConfig.static_position_embeddings=True`)
    - no layernorm_embedding (:obj:`MarianConfig.normalize_embedding=False`)
    - the model starts generating with :obj:`pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
      :obj:`<s/>`),
- Code to bulk convert models can be found in ``convert_marian_to_pytorch.py``.
- This model was contributed by `sshleifer <https://huggingface.co/sshleifer>`__.

Tips
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- In Flax, it is highly advised to pass `early_stopping=True` to `generate`. *E.g.*:

    ::

          >>> from transformers import MarianTokenizer, FlaxMarianMTModel

          >>> model = FlaxMarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-en-de')
          >>> tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')

          >>> text = "My friends are cool but they eat too many carbs."
          >>> input_ids = tokenizer(text, max_length=64, return_tensors='jax').input_ids

          >>> # Marian has to make use of early_stopping=True
          >>> sequences = model.generate(input_ids, early_stopping=True, max_length=64, num_beams=2).sequences


Naming
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

- All model names use the following format: :obj:`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 :obj:`es_AR` are usually :obj:`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
  <https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_enro_teacher.sh>`__
- `Fine-tune on GPU with pytorch-lightning
  <https://github.com/huggingface/transformers/blob/master/examples/research_projects/seq2seq-distillation/train_distil_marian_no_teacher.sh>`__

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

- All model names use the following format: :obj:`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 :obj:`src_text`.
- You can see a models's supported language codes in its model card, under target constituents, like in `opus-mt-en-roa
  <https://huggingface.co/Helsinki-NLP/opus-mt-en-roa>`__.
- Note that if a model is only multilingual on the source side, like :obj:`Helsinki-NLP/opus-mt-roa-en`, no language
  codes are required.

New multi-lingual models from the `Tatoeba-Challenge repo <https://github.com/Helsinki-NLP/Tatoeba-Challenge>`__
require 3 character language codes:

.. code-block:: python

    >>> 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:

.. 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]
    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:

.. code-block:: python

    ['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


.. 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)

    >>> 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']



MarianConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MarianConfig
    :members:


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

.. autoclass:: transformers.MarianTokenizer
    :members: as_target_tokenizer


MarianModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MarianModel
    :members: forward


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

.. autoclass:: transformers.MarianMTModel
    :members: forward


MarianForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.MarianForCausalLM
    :members: forward


TFMarianModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFMarianModel
    :members: call


TFMarianMTModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFMarianMTModel
    :members: call


FlaxMarianModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.FlaxMarianModel
    :members: __call__


FlaxMarianMTModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.FlaxMarianMTModel
    :members: __call__