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mT5

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 is a multilingual variant of 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 collection.

[!TIP] This model was contributed by 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="AutoModel">
python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum",
    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> </hfoptions>

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

The example below uses bitsandbytes to only quantize the weights to int4.

python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "csebuetnlp/mT5_multilingual_XLSum",
    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 dataset.

MT5Config

[[autodoc]] MT5Config

MT5Model

[[autodoc]] MT5Model

MT5ForConditionalGeneration

[[autodoc]] MT5ForConditionalGeneration

MT5EncoderModel

[[autodoc]] MT5EncoderModel

MT5ForSequenceClassification

[[autodoc]] MT5ForSequenceClassification

MT5ForTokenClassification

[[autodoc]] MT5ForTokenClassification

MT5ForQuestionAnswering

[[autodoc]] MT5ForQuestionAnswering