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| # MarianMT | |
| <div class="flex flex-wrap space-x-1"> | |
| <a href="https://huggingface.co/models?filter=marian"> | |
| <img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet"> | |
| </a> | |
| <a href="https://huggingface.co/spaces/docs-demos/opus-mt-zh-en"> | |
| <img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"> | |
| </a> | |
| </div> | |
| **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 | |
| - 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`] 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 | |
| - 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 | |
| - 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 | |
| - 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: | |
| ```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: | |
| ```python | |
| 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: | |
| ```python no-style | |
| ['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 | |
| ```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'] | |
| ``` | |
| ## Documentation resources | |
| - [Translation task guide](../tasks/translation) | |
| - [Summarization task guide](../tasks/summarization) | |
| - [Causal language modeling task guide](../tasks/language_modeling) | |
| ## 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__ | |