Post-processors
译者:片刻小哥哥
项目地址:https://huggingface.apachecn.org/docs/tokenizers/api/post-processors
原始地址:https://huggingface.co/docs/tokenizers/api/post-processors
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 toTrue
) — Whether to trim the whitespaces from the produced offsets. - add_prefix_space
(
bool
, optional , defaults toTrue
) — 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 providedids
andtokens
lists to have the same length.
- “id”: