enginex-mlu370-any2any/transformers/docs/source/en/model_doc/mt5.md

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*This model was released on 2020-10-22 and added to Hugging Face Transformers on 2020-11-17.*

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# mT5

[mT5](https://huggingface.co/papers/2010.11934) is a multilingual variant of [T5](./t5), training on 101 languages. It also incorporates a new "accidental translation" technique to prevent the model from incorrectly translating predictions into the wrong language.

You can find all the original [mT5] checkpoints under the [mT5](https://huggingface.co/collections/google/mt5-release-65005f1a520f8d7b4d039509) collection.

> [!TIP]
> This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).
>
> Click on the mT5 models in the right sidebar for more examples of how to apply mT5 to different language tasks.

The example below demonstrates how to summarize text with [`Pipeline`], [`AutoModel`], and from the command line.

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

```python
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text2text-generation",
    model="csebuetnlp/mT5_multilingual_XLSum",
    dtype=torch.float16,
    device=0
)
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">

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

tokenizer = AutoTokenizer.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum",
    dtype=torch.float16,
    device_map="auto",
)

input_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."""
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers CLI">

```bash
echo -e "Plants are remarkable organisms that produce their own food using a method called photosynthesis." | transformers run --task text2text-generation --model csebuetnlp/mT5_multilingual_XLSum --device 0
```

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.

```python
import torch
from transformers import BitsAndBytesConfig, AutoModelForSeq2SeqLM, AutoTokenizer

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type="nf4"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum",
    dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum"
)
input_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."""
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Notes

- mT5 must be fine-tuned for downstream tasks because it was only pretrained on the [mc4](https://huggingface.co/datasets/mc4) dataset.

## 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