# ByT5

## Overview

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, refer to [T5v1.1's documentation page](t5v1.1) for the API reference. 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.

## Usage example

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

thon

>>> from transformers import T5ForConditionalGeneration

>>> import torch

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

>>> num_special_tokens = 3

>>> # Model has 3 special tokens which take up the input ids 0,1,2 of ByT5.

>>> # => Need to shift utf-8 character encodings by 3 before passing ids to model.

>>> 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()

2.66

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

thon

>>> 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_dict = tokenizer(

     ["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt"

 )

>>> labels = labels_dict.input_ids

>>> loss = model(**model_inputs, labels=labels).loss

>>> loss.item()

17.9

Similar to [T5](t5), ByT5 was trained on the span-mask denoising task. However, 

since the model works directly on characters, the pretraining task is a bit 

different. Let's corrupt some characters of the 

input sentence `"The dog chases a ball in the park."` and ask ByT5 to predict them 

for us.

thon

>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")

>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-base")

>>> input_ids_prompt = "The dog chases a ball in the park."

>>> input_ids = tokenizer(input_ids_prompt).input_ids

>>> # Note that we cannot add "{extra_id_}" to the string directly

>>> # as the Byte tokenizer would incorrectly merge the tokens

>>> # For ByT5, we need to work directly on the character level

>>> # Contrary to T5, ByT5 does not use sentinel tokens for masking, but instead

>>> # uses final utf character ids.

>>> # UTF-8 is represented by 8 bits and ByT5 has 3 special tokens.

>>> # => There are 2**8+2 = 259 input ids and mask tokens count down from index 258.

>>> # => mask to "The dog [258]a ball [257]park."

>>> input_ids = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])

>>> input_ids

tensor([[ 87, 107, 104,  35, 103, 114, 106,  35, 258,  35, 100,  35, 101, 100, 111, 111, 257,  35, 115, 100, 117, 110,  49,   1]])

>>> # ByT5 produces only one char at a time so we need to produce many more output characters here -> set `max_length=100`.

>>> output_ids = model.generate(input_ids, max_length=100)[0].tolist()

>>> output_ids

[0, 258, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118, 257,  35, 108, 113,  35, 119, 107, 104,  35, 103, 108, 118, 102, 114, 256, 108, 113,  35, 119, 107, 104, 35, 115, 100, 117, 110,  49,  35,  87, 107, 104,  35, 103, 114, 106, 35, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118,  35, 100,  35, 101, 100, 111, 111,  35, 108, 113, 255,  35, 108, 113,  35, 119, 107, 104,  35, 115, 100, 117, 110,  49]

>>> # ^- Note how 258 descends to 257, 256, 255

>>> # Now we need to split on the sentinel tokens, let's write a short loop for this

>>> output_ids_list = []

>>> start_token = 0

>>> sentinel_token = 258

>>> while sentinel_token in output_ids:

     split_idx = output_ids.index(sentinel_token)

     output_ids_list.append(output_ids[start_token:split_idx])

     start_token = split_idx

     sentinel_token -= 1

>>> output_ids_list.append(output_ids[start_token:])

>>> output_string = tokenizer.batch_decode(output_ids_list)

>>> output_string

['', 'is the one who does', ' in the disco', 'in the park. The dog is the one who does a ball in', ' in the park.']

## ByT5Tokenizer

[[autodoc]] ByT5Tokenizer

See [`ByT5Tokenizer`] for all details.