Tokenizers documentation

Post-processors

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# Post-processors

Python
Rust
Node

## BertProcessing

### class tokenizers.processors.BertProcessing

( sep cls )

Parameters

• sep (Tuple[str, int]) — A tuple with the string representation of the SEP token, and its id
• cls (Tuple[str, int]) — A tuple with the string representation of the CLS token, and its id

This post-processor takes care of adding the special tokens needed by a Bert model:

• a SEP token
• a CLS token

## ByteLevel

### class tokenizers.processors.ByteLevel

( trim_offsets = True )

Parameters

• trim_offsets (bool) — Whether to trim the whitespaces from the produced offsets.

This post-processor takes care of trimming the offsets.

By default, the ByteLevel BPE might include whitespaces in the produced tokens. If you don’t want the offsets to include these whitespaces, then this PostProcessor must be used.

## RobertaProcessing

### class tokenizers.processors.RobertaProcessing

( sep cls trim_offsets = True add_prefix_space = True )

Parameters

• sep (Tuple[str, int]) — A tuple with the string representation of the SEP token, and its id
• cls (Tuple[str, int]) — A tuple with the string representation of the CLS token, and its id
• trim_offsets (bool, optional, defaults to True) — Whether to trim the whitespaces from the produced offsets.
• add_prefix_space (bool, optional, defaults to True) — Whether the add_prefix_space option was enabled during pre-tokenization. This is relevant because it defines the way the offsets are trimmed out.

This post-processor takes care of adding the special tokens needed by a Roberta model:

• a SEP token
• a CLS token

It also takes care of trimming the offsets. By default, the ByteLevel BPE might include whitespaces in the produced tokens. If you don’t want the offsets to include these whitespaces, then this PostProcessor should be initialized with trim_offsets=True

## TemplateProcessing

### class tokenizers.processors.TemplateProcessing

( single pair special_tokens )

Parameters

• single (Template) — The template used for single sequences
• pair (Template) — The template used when both sequences are specified
• special_tokens (Tokens) — The list of special tokens used in each sequences

Provides a way to specify templates in order to add the special tokens to each input sequence as relevant.

Let’s take BERT tokenizer as an example. It uses two special tokens, used to delimitate each sequence. [CLS] is always used at the beginning of the first sequence, and [SEP] is added at the end of both the first, and the pair sequences. The final result looks like this:

• Single sequence: [CLS] Hello there [SEP]
• Pair sequences: [CLS] My name is Anthony [SEP] What is my name? [SEP]

With the type ids as following:

[CLS]   ...   [SEP]   ...   [SEP]
0      0      0      1      1

You can achieve such behavior using a TemplateProcessing:

TemplateProcessing(
single="[CLS] $0 [SEP]", pair="[CLS]$A [SEP] $B:1 [SEP]:1", special_tokens=[("[CLS]", 1), ("[SEP]", 0)], ) In this example, each input sequence is identified using a $ construct. This identifier lets us specify each input sequence, and the type_id to use. When nothing is specified, it uses the default values. Here are the different ways to specify it:

• Specifying the sequence, with default type_id == 0: $A or $B
• Specifying the type_id with default sequence == A: $0, $1, $2, … • Specifying both: $A:0, \$B:1, …

The same construct is used for special tokens: <identifier>(:<type_id>)?.

Warning: You must ensure that you are giving the correct tokens/ids as these will be added to the Encoding without any further check. If the given ids correspond to something totally different in a Tokenizer using this PostProcessor, it might lead to unexpected results.

Types:

Template (str or List):

• If a str is provided, the whitespace is used as delimiter between tokens
• If a List[str] is provided, a list of tokens

Tokens (List[Union[Tuple[int, str], Tuple[str, int], dict]]):

• A Tuple with both a token and its associated ID, in any order

• A dict with the following keys:

• “id”: str => The special token id, as specified in the Template
• “ids”: List[int] => The associated IDs
• “tokens”: List[str] => The associated tokens

The given dict expects the provided ids and tokens lists to have the same length.