MarianMT

[![Models](https://img.shields.io/badge/All_model_pages-marian-blueviolet)](https://huggingface.co/models?filter=marian) [![Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/docs-demos/opus-mt-zh-en)

**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.

Tips:

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

## [](#implementation-notes)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 [BartForConditionalGeneration](/docs/transformers/v4.30.0/en/model_doc/bart#transformers.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](https://huggingface.co/sshleifer).
    

## [](#naming)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 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)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/legacy/seq2seq/train_distil_marian_enro.sh)

## [](#multilingual-models)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](https://huggingface.co/Helsinki-NLP/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](https://github.com/Helsinki-NLP/Tatoeba-Challenge) 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-multilingual-models)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)Documentation resources

-   [Translation task guide](../tasks/translation)
-   [Summarization task guide](../tasks/summarization)
-   [Causal language modeling task guide](../tasks/language_modeling)

## [](#transformers.MarianConfig)MarianConfig

### class transformers.MarianConfig

[](#transformers.MarianConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/configuration_marian.py#L34)

( vocab\_size = 58101decoder\_vocab\_size = Nonemax\_position\_embeddings = 1024encoder\_layers = 12encoder\_ffn\_dim = 4096encoder\_attention\_heads = 16decoder\_layers = 12decoder\_ffn\_dim = 4096decoder\_attention\_heads = 16encoder\_layerdrop = 0.0decoder\_layerdrop = 0.0use\_cache = Trueis\_encoder\_decoder = Trueactivation\_function = 'gelu'd\_model = 1024dropout = 0.1attention\_dropout = 0.0activation\_dropout = 0.0init\_std = 0.02decoder\_start\_token\_id = 58100scale\_embedding = Falsepad\_token\_id = 58100eos\_token\_id = 0forced\_eos\_token\_id = 0share\_encoder\_decoder\_embeddings = True\*\*kwargs )

This is the configuration class to store the configuration of a [MarianModel](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianModel). It is used to instantiate an Marian model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Marian [Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Examples:

```
>>> from transformers import MarianModel, MarianConfig

>>> 
>>> configuration = MarianConfig()

>>> 
>>> model = MarianModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.MarianTokenizer)MarianTokenizer

### class transformers.MarianTokenizer

[](#transformers.MarianTokenizer)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/tokenization_marian.py#L61)

( source\_spmtarget\_spmvocabtarget\_vocab\_file = Nonesource\_lang = Nonetarget\_lang = Noneunk\_token = '<unk>'eos\_token = '
'pad\_token = '<pad>'model\_max\_length = 512sp\_model\_kwargs: typing.Union\[typing.Dict\[str, typing.Any\], NoneType\] = Noneseparate\_vocabs = False\*\*kwargs )

Construct a Marian tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Examples:

```
>>> from transformers import MarianForCausalLM, MarianTokenizer

>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> src_texts = ["I am a small frog.", "Tom asked his teacher for advice."]
>>> tgt_texts = ["Ich bin ein kleiner Frosch.", "Tom bat seinen Lehrer um Rat."]  
>>> inputs = tokenizer(src_texts, text_target=tgt_texts, return_tensors="pt", padding=True)

>>> outputs = model(**inputs)  ```

#### build\_inputs\_with\_special\_tokens

[](#transformers.MarianTokenizer.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/tokenization_marian.py#L283)

( token\_ids\_0token\_ids\_1 = None )

Build model inputs from a sequence by appending eos\_token\_id.

## [](#transformers.MarianModel)MarianModel

### class transformers.MarianModel

[](#transformers.MarianModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1100)

( config: MarianConfig )

Parameters

-   [](#transformers.MarianModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Marian Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.MarianModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1184)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor\] = Nonedecoder\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_outputs: typing.Union\[typing.Tuple\[torch.Tensor\], transformers.modeling\_outputs.BaseModelOutput, NoneType\] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor\]\]\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.Seq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`

The [MarianModel](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, MarianModel

>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

>>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt")
>>> decoder_inputs = tokenizer(
...     "<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
...     return_tensors="pt",
...     add_special_tokens=False,
... )
>>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 26, 512]```

## [](#transformers.MarianMTModel)MarianMTModel

### class transformers.MarianMTModel

[](#transformers.MarianMTModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1286)

( config: MarianConfig )

Parameters

-   [](#transformers.MarianMTModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The Marian Model with a language modeling head. Can be used for summarization. This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

#### forward

[](#transformers.MarianMTModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1413)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_input\_ids: typing.Optional\[torch.LongTensor\] = Nonedecoder\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonedecoder\_head\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_outputs: typing.Union\[typing.Tuple\[torch.Tensor\], transformers.modeling\_outputs.BaseModelOutput, NoneType\] = Nonepast\_key\_values: typing.Optional\[typing.Tuple\[typing.Tuple\[torch.FloatTensor\]\]\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonedecoder\_inputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.Seq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`

The [MarianMTModel](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianMTModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Pytorch version of marian-nmt’s transformer.h (c++). Designed for the OPUS-NMT translation checkpoints. Available models are listed [here](https://huggingface.co/models?search=Helsinki-NLP).

Examples:

```
>>> from transformers import AutoTokenizer, MarianMTModel

>>> src = "fr"  
>>> trg = "en"  

>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"
>>> model = MarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)

>>> sample_text = "où est l'arrêt de bus ?"
>>> batch = tokenizer([sample_text], return_tensors="pt")

>>> generated_ids = model.generate(**batch)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
"Where's the bus stop?"```

## [](#transformers.MarianForCausalLM)MarianForCausalLM

### class transformers.MarianForCausalLM

[](#transformers.MarianForCausalLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1557)

( config )

#### forward

[](#transformers.MarianForCausalLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_marian.py#L1590)

( input\_ids: LongTensor = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneencoder\_hidden\_states: typing.Optional\[torch.FloatTensor\] = Noneencoder\_attention\_mask: typing.Optional\[torch.FloatTensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonecross\_attn\_head\_mask: typing.Optional\[torch.Tensor\] = Nonepast\_key\_values: typing.Optional\[typing.List\[torch.FloatTensor\]\] = Noneinputs\_embeds: typing.Optional\[torch.FloatTensor\] = Nonelabels: typing.Optional\[torch.LongTensor\] = Noneuse\_cache: typing.Optional\[bool\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.modeling\_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`

Example:

```
>>> from transformers import AutoTokenizer, MarianForCausalLM

>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en")
>>> model = MarianForCausalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-en", add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> logits = outputs.logits
>>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size]
>>> list(logits.shape) == expected_shape
True```

## [](#transformers.TFMarianModel)TFMarianModel

### class transformers.TFMarianModel

[](#transformers.TFMarianModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_tf_marian.py#L1157)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFMarianModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

The bare MARIAN Model outputting raw hidden-states without any specific head on top. This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

-   having all inputs as keyword arguments (like PyTorch models), or
-   having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

-   a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
-   a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-   a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFMarianModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_tf_marian.py#L1169)

( input\_ids: tf.Tensor | None = Noneattention\_mask: tf.Tensor | None = Nonedecoder\_input\_ids: tf.Tensor | None = Nonedecoder\_attention\_mask: tf.Tensor | None = Nonedecoder\_position\_ids: tf.Tensor | None = Nonehead\_mask: tf.Tensor | None = Nonedecoder\_head\_mask: tf.Tensor | None = Nonecross\_attn\_head\_mask: tf.Tensor | None = Noneencoder\_outputs: tf.Tensor | None = Nonepast\_key\_values: Tuple\[Tuple\[tf.Tensor\]\] | None = Noneinputs\_embeds: tf.Tensor | None = Nonedecoder\_inputs\_embeds: tf.Tensor | None = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonetraining: bool = False\*\*kwargs ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqModelOutput) or `tuple(tf.Tensor)`

The [TFMarianModel](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.TFMarianModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFMarianModel
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = TFMarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.TFMarianMTModel)TFMarianMTModel

### class transformers.TFMarianMTModel

[](#transformers.TFMarianMTModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_tf_marian.py#L1262)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFMarianMTModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.

The MARIAN Model with a language modeling head. Can be used for summarization. This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

TensorFlow models and layers in `transformers` accept two formats as input:

-   having all inputs as keyword arguments (like PyTorch models), or
-   having all inputs as a list, tuple or dict in the first positional argument.

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

-   a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
-   a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
-   a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFMarianMTModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_tf_marian.py#L1300)

( input\_ids: tf.Tensor | None = Noneattention\_mask: tf.Tensor | None = Nonedecoder\_input\_ids: tf.Tensor | None = Nonedecoder\_attention\_mask: tf.Tensor | None = Nonedecoder\_position\_ids: tf.Tensor | None = Nonehead\_mask: tf.Tensor | None = Nonedecoder\_head\_mask: tf.Tensor | None = Nonecross\_attn\_head\_mask: tf.Tensor | None = Noneencoder\_outputs: Optional\[TFBaseModelOutput\] = Nonepast\_key\_values: Tuple\[Tuple\[tf.Tensor\]\] | None = Noneinputs\_embeds: tf.Tensor | None = Nonedecoder\_inputs\_embeds: tf.Tensor | None = Noneuse\_cache: Optional\[bool\] = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: tf.Tensor | None = Nonetraining: bool = False ) → [transformers.modeling\_tf\_outputs.TFSeq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSeq2SeqLMOutput) or `tuple(tf.Tensor)`

The [TFMarianMTModel](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.TFMarianMTModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

TF version of marian-nmt’s transformer.h (c++). Designed for the OPUS-NMT translation checkpoints. Available models are listed [here](https://huggingface.co/models?search=Helsinki-NLP).

Examples:

```
>>> from transformers import AutoTokenizer, TFMarianMTModel
>>> from typing import List

>>> src = "fr"  
>>> trg = "en"  
>>> sample_text = "où est l'arrêt de bus ?"
>>> model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}"

>>> model = TFMarianMTModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> batch = tokenizer([sample_text], return_tensors="tf")
>>> gen = model.generate(**batch)
>>> tokenizer.batch_decode(gen, skip_special_tokens=True)
"Where is the bus stop ?"```

## [](#transformers.FlaxMarianModel)FlaxMarianModel

### class transformers.FlaxMarianModel

[](#transformers.FlaxMarianModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_flax_marian.py#L1205)

( config: MarianConfiginput\_shape: typing.Tuple\[int\] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = True\*\*kwargs )

Parameters

-   [](#transformers.FlaxMarianModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
-   [](#transformers.FlaxMarianModel.dtype)**dtype** (`jax.numpy.dtype`, _optional_, defaults to `jax.numpy.float32`) — The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs).
    
    This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`.
    
    **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.**
    
    If you wish to change the dtype of the model parameters, see [to\_fp16()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and [to\_bf16()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).
    

The bare Marian Model transformer outputting raw hidden-states without any specific head on top. This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxMarianModel.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_flax_marian.py#L1140)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_input\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxSeq2SeqModelOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqModelOutput) or `tuple(torch.FloatTensor)`

The `FlaxMarianPreTrainedModel` forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, FlaxMarianModel

>>> tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state```

## [](#transformers.FlaxMarianMTModel)FlaxMarianMTModel

### class transformers.FlaxMarianMTModel

[](#transformers.FlaxMarianMTModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_flax_marian.py#L1289)

( config: MarianConfiginput\_shape: typing.Tuple\[int\] = (1, 1)seed: int = 0dtype: dtype = <class 'jax.numpy.float32'>\_do\_init: bool = True\*\*kwargs )

Parameters

-   [](#transformers.FlaxMarianMTModel.config)**config** ([MarianConfig](/docs/transformers/v4.30.0/en/model_doc/marian#transformers.MarianConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained) method to load the model weights.
-   [](#transformers.FlaxMarianMTModel.dtype)**dtype** (`jax.numpy.dtype`, _optional_, defaults to `jax.numpy.float32`) — The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs).
    
    This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`.
    
    **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.**
    
    If you wish to change the dtype of the model parameters, see [to\_fp16()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16) and [to\_bf16()](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16).
    

The MARIAN Model with a language modeling head. Can be used for translation. This model inherits from [FlaxPreTrainedModel](/docs/transformers/v4.30.0/en/main_classes/model#transformers.FlaxPreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

-   [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-   [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-   [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-   [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

#### \_\_call\_\_

[](#transformers.FlaxMarianMTModel.__call__)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/marian/modeling_flax_marian.py#L1140)

( input\_ids: ndarrayattention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_input\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_attention\_mask: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneposition\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Nonedecoder\_position\_ids: typing.Optional\[jax.\_src.numpy.ndarray.ndarray\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = Nonetrain: bool = Falseparams: dict = Nonedropout\_rng: PRNGKey = None ) → [transformers.modeling\_flax\_outputs.FlaxSeq2SeqLMOutput](/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput) or `tuple(torch.FloatTensor)`

The `FlaxMarianPreTrainedModel` forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, FlaxMarianMTModel

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

>>> sequences = model.generate(input_ids, max_length=64, num_beams=2).sequences

>>> outputs = tokenizer.batch_decode(sequences, skip_special_tokens=True)
>>> ```