Migrate doc files to Markdown. (#24376)
* Rename index.mdx to index.md * With saved modifs * Address review comment * Treat all files * .mdx -> .md * Remove special char * Update utils/tests_fetcher.py Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr> --------- Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>
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Sylvain Gugger
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docs/source/en/model_doc/marian.md
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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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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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# MarianMT
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<div class="flex flex-wrap space-x-1">
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<a href="https://huggingface.co/models?filter=marian">
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
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</a>
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<a href="https://huggingface.co/spaces/docs-demos/opus-mt-zh-en">
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
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</a>
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</div>
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**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)
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and assign @patrickvonplaten.
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Translations should be similar, but not identical to output in the test set linked to in each model card.
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Tips:
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- A framework for translation models, using the same models as BART.
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## Implementation Notes
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- Each model is about 298 MB on disk, there are more than 1,000 models.
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- The list of supported language pairs can be found [here](https://huggingface.co/Helsinki-NLP).
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- 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.
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- All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented
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in a model card.
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- The 80 opus models that require BPE preprocessing are not supported.
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- The modeling code is the same as [`BartForConditionalGeneration`] with a few minor modifications:
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- static (sinusoid) positional embeddings (`MarianConfig.static_position_embeddings=True`)
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- no layernorm_embedding (`MarianConfig.normalize_embedding=False`)
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- the model starts generating with `pad_token_id` (which has 0 as a token_embedding) as the prefix (Bart uses
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`<s/>`),
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- Code to bulk convert models can be found in `convert_marian_to_pytorch.py`.
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- This model was contributed by [sshleifer](https://huggingface.co/sshleifer).
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## Naming
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- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`
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- 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
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code {code}".
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- Codes formatted like `es_AR` are usually `code_{region}`. That one is Spanish from Argentina.
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- The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second
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group use a combination of ISO-639-5 codes and ISO-639-2 codes.
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## Examples
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- Since Marian models are smaller than many other translation models available in the library, they can be useful for
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fine-tuning experiments and integration tests.
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- [Fine-tune on GPU](https://github.com/huggingface/transformers/blob/master/examples/legacy/seq2seq/train_distil_marian_enro.sh)
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## Multilingual Models
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- All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`:
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- If a model can output multiple languages, and you should specify a language code by prepending the desired output
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language to the `src_text`.
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- 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).
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- Note that if a model is only multilingual on the source side, like `Helsinki-NLP/opus-mt-roa-en`, no language
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codes are required.
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New multi-lingual models from the [Tatoeba-Challenge repo](https://github.com/Helsinki-NLP/Tatoeba-Challenge)
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require 3 character language codes:
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```python
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>>> from transformers import MarianMTModel, MarianTokenizer
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>>> src_text = [
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... ">>fra<< this is a sentence in english that we want to translate to french",
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... ">>por<< This should go to portuguese",
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... ">>esp<< And this to Spanish",
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... ]
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>>> model_name = "Helsinki-NLP/opus-mt-en-roa"
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>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
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>>> print(tokenizer.supported_language_codes)
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['>>zlm_Latn<<', '>>mfe<<', '>>hat<<', '>>pap<<', '>>ast<<', '>>cat<<', '>>ind<<', '>>glg<<', '>>wln<<', '>>spa<<', '>>fra<<', '>>ron<<', '>>por<<', '>>ita<<', '>>oci<<', '>>arg<<', '>>min<<']
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>>> model = MarianMTModel.from_pretrained(model_name)
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>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
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>>> [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
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["c'est une phrase en anglais que nous voulons traduire en français",
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'Isto deve ir para o português.',
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'Y esto al español']
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```
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Here is the code to see all available pretrained models on the hub:
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```python
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from huggingface_hub import list_models
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model_list = list_models()
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org = "Helsinki-NLP"
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model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
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suffix = [x.split("/")[1] for x in model_ids]
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old_style_multi_models = [f"{org}/{s}" for s in suffix if s != s.lower()]
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```
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## Old Style Multi-Lingual Models
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These are the old style multi-lingual models ported from the OPUS-MT-Train repo: and the members of each language
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group:
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```python no-style
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['Helsinki-NLP/opus-mt-NORTH_EU-NORTH_EU',
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'Helsinki-NLP/opus-mt-ROMANCE-en',
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'Helsinki-NLP/opus-mt-SCANDINAVIA-SCANDINAVIA',
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'Helsinki-NLP/opus-mt-de-ZH',
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'Helsinki-NLP/opus-mt-en-CELTIC',
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'Helsinki-NLP/opus-mt-en-ROMANCE',
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'Helsinki-NLP/opus-mt-es-NORWAY',
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'Helsinki-NLP/opus-mt-fi-NORWAY',
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'Helsinki-NLP/opus-mt-fi-ZH',
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'Helsinki-NLP/opus-mt-fi_nb_no_nn_ru_sv_en-SAMI',
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'Helsinki-NLP/opus-mt-sv-NORWAY',
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'Helsinki-NLP/opus-mt-sv-ZH']
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GROUP_MEMBERS = {
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'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
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'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'],
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'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
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'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
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'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
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'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
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'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
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}
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```
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Example of translating english to many romance languages, using old-style 2 character language codes
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```python
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>>> from transformers import MarianMTModel, MarianTokenizer
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>>> src_text = [
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... ">>fr<< this is a sentence in english that we want to translate to french",
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... ">>pt<< This should go to portuguese",
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... ">>es<< And this to Spanish",
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... ]
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>>> model_name = "Helsinki-NLP/opus-mt-en-ROMANCE"
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>>> tokenizer = MarianTokenizer.from_pretrained(model_name)
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>>> model = MarianMTModel.from_pretrained(model_name)
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>>> translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
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>>> tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
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["c'est une phrase en anglais que nous voulons traduire en français",
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'Isto deve ir para o português.',
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'Y esto al español']
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```
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## Documentation resources
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- [Translation task guide](../tasks/translation)
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- [Summarization task guide](../tasks/summarization)
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- [Causal language modeling task guide](../tasks/language_modeling)
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## MarianConfig
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[[autodoc]] MarianConfig
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## MarianTokenizer
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[[autodoc]] MarianTokenizer
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- build_inputs_with_special_tokens
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## MarianModel
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[[autodoc]] MarianModel
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- forward
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## MarianMTModel
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[[autodoc]] MarianMTModel
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- forward
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## MarianForCausalLM
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[[autodoc]] MarianForCausalLM
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- forward
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## TFMarianModel
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[[autodoc]] TFMarianModel
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- call
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## TFMarianMTModel
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[[autodoc]] TFMarianMTModel
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- call
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## FlaxMarianModel
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[[autodoc]] FlaxMarianModel
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- __call__
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## FlaxMarianMTModel
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[[autodoc]] FlaxMarianMTModel
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- __call__
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