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MADLAD-400 models were released in the paper [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](MADLAD-400: A Multilingual And Document-Level Large Audited Dataset).

The abstract from the paper is the following:

We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models 1 available to the research community.

This model was added by Juarez Bochi. The original checkpoints can be found here.

This is a machine translation model that supports many low-resource languages, and that is competitive with models that are significantly larger.

One can directly use MADLAD-400 weights without finetuning the model:

>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/madlad400-3b-mt")
>>> tokenizer = AutoTokenizer.from_pretrained("google/madlad400-3b-mt")

>>> inputs = tokenizer("<2pt> I love pizza!", return_tensors="pt")
>>> outputs = model.generate(**inputs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Eu amo pizza!']

Google has released the following variants:

The original checkpoints can be found here.

Refer to T5’s documentation page for all API references, code examples, and notebooks. For more details regarding training and evaluation of the MADLAD-400, refer to the model card.