This post-processor takes care of adding the special tokens needed by a Bert model:
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.
( sep cls trim_offsets = True add_prefix_space = True )
Parameters
Tuple[str, int]
) —
A tuple with the string representation of the SEP token, and its id
Tuple[str, int]
) —
A tuple with the string representation of the CLS token, and its id
bool
, optional, defaults to True
) —
Whether to trim the whitespaces from the produced offsets.
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:
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
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:
[CLS] Hello there [SEP]
[CLS] My name is Anthony [SEP] What is my name? [SEP]
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:
type_id == 0
: $A
or $B
sequence == A
: $0
, $1
, $2
, …$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
):
str
is provided, the whitespace is used as delimiter between tokensList[str]
is provided, a list of tokensTokens (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:
str
=> The special token id, as specified in the TemplateList[int]
=> The associated IDsList[str]
=> The associated tokensThe given dict expects the provided ids
and tokens
lists to have
the same length.