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ByT5

docs/source/en/model_doc/byt5.md

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This model was released on 2021-05-28 and added to Hugging Face Transformers on 2021-06-01.

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ByT5

ByT5 is tokenizer-free version of the T5 model designed to works directly on raw UTF-8 bytes. This means it can process any language, more robust to noise like typos, and simpler to use because it doesn't require a preprocessing pipeline.

You can find all the original ByT5 checkpoints under the Google organization.

[!TIP] Refer to the T5 docs for more examples of how to apply ByT5 to different language tasks.

The example below demonstrates how to generate 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(
    "google/byt5-small"
)
model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-small",
    device_map="auto"
)

input_ids = tokenizer("summarize: Photosynthesis is the process by which plants, algae, and some bacteria convert light energy into chemical energy.", return_tensors="pt").to(model.device)

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
</hfoption> </hfoptions>

Quantization

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 torchao to only quantize the weights to int4.

python
# pip install torchao
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, TorchAoConfig


quantization_config = TorchAoConfig("int4_weight_only", group_size=128)

model = AutoModelForSeq2SeqLM.from_pretrained(
    "google/byt5-xl",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("google/byt5-xl")
input_ids = tokenizer("translate English to French: The weather is nice today.", return_tensors="pt").to(model.device)

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

Notes

  • It is recommended to use the tokenizer for batched inference and training.

  • The example below shows how to use the model without a tokenizer.

    python
    import torch
from transformers import AutoModelForSeq2SeqLM

model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-small", device_map="auto")

num_special_tokens = 3

input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
loss = model(input_ids, labels=labels).loss
loss.item()

  • ByT5 uses the top byte values (258, 257, etc.) for masking instead of sentinel tokens like {extra_id_0}.

    python
    # Example: character-level denoising with mask tokens
input_ids = tokenizer("The dog chases a ball in the park.").input_ids
masked_input = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
output = model.generate(masked_input, max_length=100)

ByT5Tokenizer

[[autodoc]] ByT5Tokenizer