transformers/docs/source/en/model_doc/mbart.md

mBART

mBART is a multilingual machine translation model that pretrains the entire translation model (encoder-decoder) unlike previous methods that only focused on parts of the model. The model is trained on a denoising objective which reconstructs the corrupted text. This allows mBART to handle the source language and the target text to translate to.

mBART-50 is pretrained on an additional 25 languages.

You can find all the original mBART checkpoints under the AI at Meta organization.

Tip

Click on the mBART models in the right sidebar for more examples of applying mBART to different language tasks.

The example below demonstrates how to translate text with [Pipeline] or the [AutoModel] class.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="translation",
    model="facebook/mbart-large-50-many-to-many-mmt",
    device=0,
    torch_dtype=torch.float16,
    src_lang="en_XX",
    tgt_lang="fr_XX",
)
print(pipeline("UN Chief Says There Is No Military Solution in Syria"))

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

article_en = "UN Chief Says There Is No Military Solution in Syria"

model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")

tokenizer.src_lang = "en_XX"
encoded_hi = tokenizer(article_en, return_tensors="pt").to("cuda")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"], cache_implementation="static")
print(tokenizer.batch_decode(generated_tokens, skip_special_tokens=True))

Notes

• You can check the full list of language codes via tokenizer.lang_code_to_id.keys().

• mBART requires a special language id token in the source and target text during training. The source text format is X [eos, src_lang_code] where X is the source text. The target text format is [tgt_lang_code] X [eos]. The bos token is never used. The [~PreTrainedTokenizerBase._call_] encodes the source text format passed as the first argument or with the text keyword. The target text format is passed with the text_label keyword.

• Set the decoder_start_token_id to the target language id for mBART.

  import torch
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  
  model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-en-ro", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
  tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX")
  
  article = "UN Chief Says There Is No Military Solution in Syria"
  inputs = tokenizer(article, return_tensors="pt")
  
  translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
  tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
  

• mBART-50 has a different text format. The language id token is used as the prefix for the source and target text. The text format is [lang_code] X [eos] where lang_code is the source language id for the source text and target language id for the target text. X is the source or target text respectively.

• Set the eos_token_id as the decoder_start_token_id for mBART-50. The target language id is used as the first generated token by passing forced_bos_token_id to [~GenerationMixin.generate].

  import torch
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  
  model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
  tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
  
  article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."
  tokenizer.src_lang = "ar_AR"
  
  encoded_ar = tokenizer(article_ar, return_tensors="pt")
  generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
  tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
  

MBartConfig

autodoc MBartConfig

MBartTokenizer

autodoc MBartTokenizer - build_inputs_with_special_tokens

MBartTokenizerFast

autodoc MBartTokenizerFast

MBart50Tokenizer

autodoc MBart50Tokenizer

MBart50TokenizerFast

autodoc MBart50TokenizerFast

MBartModel

autodoc MBartModel

MBartForConditionalGeneration

autodoc MBartForConditionalGeneration

MBartForQuestionAnswering

autodoc MBartForQuestionAnswering

MBartForSequenceClassification

autodoc MBartForSequenceClassification

MBartForCausalLM

autodoc MBartForCausalLM - forward

TFMBartModel

autodoc TFMBartModel - call

TFMBartForConditionalGeneration

autodoc TFMBartForConditionalGeneration - call

FlaxMBartModel

autodoc FlaxMBartModel - call - encode - decode

FlaxMBartForConditionalGeneration

autodoc FlaxMBartForConditionalGeneration - call - encode - decode

FlaxMBartForSequenceClassification

autodoc FlaxMBartForSequenceClassification - call - encode - decode

FlaxMBartForQuestionAnswering

autodoc FlaxMBartForQuestionAnswering - call - encode - decode