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*This model was released on 2020-10-22 and added to Hugging Face Transformers on 2020-11-17 and contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

# mT5

[mT5](https://huggingface.co/papers/2010.11934) leverages a unified text-to-text format and scale to achieve state-of-the-art results across various multilingual NLP tasks. It was pre-trained on a Common Crawl-based dataset covering 101 languages. The model demonstrates superior performance on multilingual benchmarks and includes a technique to mitigate accidental translation in zero-shot scenarios. mT5 requires fine-tuning for specific tasks and does not benefit from task prefixes during single-task fine-tuning, unlike the original T5. Google provides several variants of mT5, ranging from small to XXL sizes.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text2text-generation", model="csebuetnlp/mT5_multilingual_XLSum", dtype="auto")
pipeline("""
Plants are remarkable organisms that produce their own food using a method called photosynthesis.
This process involves converting sunlight, carbon dioxide, and water into glucose, which provides energy for growth.
Plants play a crucial role in sustaining life on Earth by generating oxygen and serving as the foundation of most ecosystems.
"""
)
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/mT5_multilingual_XLSum", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/mT5_multilingual_XLSum")

text="""
Plants are remarkable organisms that produce their own food using a method called photosynthesis.
This process involves converting sunlight, carbon dioxide, and water into glucose, which provides energy for growth.
Plants play a crucial role in sustaining life on Earth by generating oxygen and serving as the foundation of most ecosystems.
"""
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfopton>
</hfoptions>

## Usage tips

- Fine-tune mT5 for downstream tasks. The model was only pretrained on the [mc4 dataset](https://huggingface.co/datasets/mc4), which doesn't include task-specific training.

## MT5Config

[[autodoc]] MT5Config

## MT5Tokenizer

[[autodoc]] MT5Tokenizer

See [`T5Tokenizer`] for all details.

## MT5TokenizerFast

[[autodoc]] MT5TokenizerFast

See [`T5TokenizerFast`] for all details.

## MT5Model

[[autodoc]] MT5Model

## MT5ForConditionalGeneration

[[autodoc]] MT5ForConditionalGeneration

## MT5EncoderModel

[[autodoc]] MT5EncoderModel

## MT5ForSequenceClassification

[[autodoc]] MT5ForSequenceClassification

## MT5ForTokenClassification

[[autodoc]] MT5ForTokenClassification

## MT5ForQuestionAnswering

[[autodoc]] MT5ForQuestionAnswering

