# MarianMT ## Overview [MarianMT](https://huggingface.co/papers/1804.00344) is a machine translation model trained with the Marian framework which is written in pure C++. The framework includes its own custom auto-differentiation engine and efficient meta-algorithms to train encoder-decoder models like BART. All MarianMT models are transformer encoder-decoders with 6 layers in each component, use static sinusoidal positional embeddings, don't have a layernorm embedding, and the model starts generating with the prefix `pad_token_id` instead of ``. You can find all the original MarianMT checkpoints under the [Language Technology Research Group at the University of Helsinki](https://huggingface.co/Helsinki-NLP/models?search=opus-mt) organization. > [!TIP] > This model was contributed by [sshleifer](https://huggingface.co/sshleifer). > > Click on the MarianMT models in the right sidebar for more examples of how to apply MarianMT to translation tasks. The example below demonstrates how to translate text using [`Pipeline`] or the [`AutoModel`] class. ```python import torch from transformers import pipeline pipeline = pipeline("translation_en_to_de", model="Helsinki-NLP/opus-mt-en-de", torch_dtype=torch.float16, device=0) pipeline("Hello, how are you?") ``` ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de", torch_dtype=torch.float16, attn_implementation="sdpa", device_map="auto") inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda") outputs = model.generate(**inputs, cache_implementation="static") print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to. ```python from transformers.utils.attention_visualizer import AttentionMaskVisualizer visualizer = AttentionMaskVisualizer("Helsinki-NLP/opus-mt-en-de") visualizer("Hello, how are you?") ``` ## Notes - MarianMT models are ~298MB on disk and there are more than 1000 models. Check this [list](https://huggingface.co/Helsinki-NLP) for supported language pairs. The language codes may be inconsistent. Two digit codes can be found [here](https://developers.google.com/admin-sdk/directory/v1/languages) while three digit codes may require further searching. - Models that require BPE preprocessing are not supported. - All model names use the following format: `Helsinki-NLP/opus-mt-{src}-{tgt}`. Language codes formatted like `es_AR` usually refer to the `code_{region}`. For example, `es_AR` refers to Spanish from Argentina. - If a model can output multiple languages, prepend the desired output language to `src_txt` as shown below. New multilingual models from the [Tatoeba-Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge) require 3 character language codes. ```py add code snippet here - Older multilingual models use 2 character language codes. ```py add code snippet here ## 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__