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ByT5
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Overview
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The ByT5 model was presented in `ByT5: Towards a token-free future with pre-trained byte-to-byte models
<https://arxiv.org/abs/2105.13626>`_ by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir
Kale, Adam Roberts, Colin Raffel.

The abstract from the paper is the following:

*Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from
the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they
can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by
removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token
sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of
operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with
minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count,
training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level
counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on
tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of
pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our
experiments.*

This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The original code can be
found `here <https://github.com/google-research/byt5>`__.

ByT5's architecture is based on the T5v1.1 model, so one can refer to :doc:`T5v1.1's documentation page <t5v1.1>`. They
only differ in how inputs should be prepared for the model, see the code examples below.

Since ByT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.


Example
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ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:

.. code-block::

    from transformers import T5ForConditionalGeneration
    import torch

    model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')

    input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add 3 for special tokens
    labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + 3  # add 3 for special tokens

    loss = model(input_ids, labels=labels).loss # forward pass


For batched inference and training it is however recommended to make use of the tokenizer:

.. code-block::

    from transformers import T5ForConditionalGeneration, AutoTokenizer

    model = T5ForConditionalGeneration.from_pretrained('google/byt5-small')
    tokenizer = AutoTokenizer.from_pretrained('google/byt5-small')

    model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
    labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids

    loss = model(**model_inputs, labels=labels).loss # forward pass

ByT5Tokenizer
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.. autoclass:: transformers.ByT5Tokenizer

See :class:`~transformers.ByT5Tokenizer` for all details.