
 
CPM
	
Overview
	
The CPM model was proposed in CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin,
Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen,
Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
The abstract from the paper is the following:
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3,
with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even
zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus
of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the
Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best
of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained
language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation,
cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many
NLP tasks in the settings of few-shot (even zero-shot) learning.
This model was contributed by canwenxu. The original implementation can be found
here: https://github.com/TsinghuaAI/CPM-Generate
Note: We only have a tokenizer here, since the model architecture is the same as GPT-2.
CpmTokenizer
	
class transformers.CpmTokenizer
<
source
>
(
vocab_file
do_lower_case = False
remove_space = True
keep_accents = False
bos_token = '<s>'
eos_token = '</s>'
unk_token = '<unk>'
sep_token = '<sep>'
pad_token = '<pad>'
cls_token = '<cls>'
mask_token = '<mask>'
additional_special_tokens = ['<eop>', '<eod>']
sp_model_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None
**kwargs
)
Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.
build_inputs_with_special_tokens
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLNet sequence has the following format:
single sequence: X <sep> <cls>
pair of sequences: A <sep> B <sep> <cls>
convert_tokens_to_string
<
source
>
(
tokens
)
Converts a sequence of tokens (strings for sub-words) in a single string.
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
sequence pair mask has the following format:
	Copied
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
get_special_tokens_mask
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
already_has_special_tokens (bool, optional, defaults to False) —
Whether or not the token list is already formatted with special tokens for the model.
Returns
List[int]
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer prepare_for_model method.
CpmTokenizerFast
	
class transformers.CpmTokenizerFast
<
source
>
(
vocab_file = None
tokenizer_file = None
do_lower_case = False
remove_space = True
keep_accents = False
bos_token = '<s>'
eos_token = '</s>'
unk_token = '<unk>'
sep_token = '<sep>'
pad_token = '<pad>'
cls_token = '<cls>'
mask_token = '<mask>'
additional_special_tokens = ['<eop>', '<eod>']
**kwargs
)
Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.
build_inputs_with_special_tokens
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs to which the special tokens will be added.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLNet sequence has the following format:
single sequence: X <sep> <cls>
pair of sequences: A <sep> B <sep> <cls>
create_token_type_ids_from_sequences
<
source
>
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters 
token_ids_0 (List[int]) —
List of IDs.
token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.
Returns
List[int]
List of token type IDs according to the given sequence(s).
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
sequence pair mask has the following format:
	Copied
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |
If token_ids_1 is None, this method only returns the first portion of the mask (0s).
←ConvBERT
CPMANT→
CPM
Overview
CpmTokenizer
CpmTokenizerFast
