The BERT models trained on Japanese text.
There are models with two different tokenization methods:
To use MecabTokenizer, you should pip install transformers["ja"] (or pip install -e .["ja"] if you install
from source) to install dependencies.
See details on cl-tohoku repository.
Example of using a model with MeCab and WordPiece tokenization:
>>> import torch
>>> from transformers import AutoModel, AutoTokenizer
>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
>>> ## Input Japanese Text
>>> line = "吾輩は猫である。"
>>> inputs = tokenizer(line, return_tensors="pt")
>>> print(tokenizer.decode(inputs["input_ids"][0]))
[CLS] 吾輩 は 猫 で ある 。 [SEP]
>>> outputs = bertjapanese(**inputs)Example of using a model with Character tokenization:
>>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese-char")
>>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-char")
>>> ## Input Japanese Text
>>> line = "吾輩は猫である。"
>>> inputs = tokenizer(line, return_tensors="pt")
>>> print(tokenizer.decode(inputs["input_ids"][0]))
[CLS] 吾 輩 は 猫 で あ る 。 [SEP]
>>> outputs = bertjapanese(**inputs)Tips:
This model was contributed by cl-tohoku.
( vocab_file spm_file = None do_lower_case = False do_word_tokenize = True do_subword_tokenize = True word_tokenizer_type = 'basic' subword_tokenizer_type = 'wordpiece' never_split = None unk_token = '[UNK]' sep_token = '[SEP]' pad_token = '[PAD]' cls_token = '[CLS]' mask_token = '[MASK]' mecab_kwargs = None sudachi_kwargs = None jumanpp_kwargs = None **kwargs )
Parameters
str) —
Path to a one-wordpiece-per-line vocabulary file.
str, optional) —
Path to SentencePiece file (generally has a .spm or .model
extension) that contains the vocabulary.
bool, optional, defaults to True) —
Whether to lower case the input. Only has an effect when do_basic_tokenize=True.
bool, optional, defaults to True) —
Whether to do word tokenization.
bool, optional, defaults to True) —
Whether to do subword tokenization.
str, optional, defaults to "basic") —
Type of word tokenizer. Choose from [“basic”, “mecab”, “sudachi”, “jumanpp”].
str, optional, defaults to "wordpiece") —
Type of subword tokenizer. Choose from [“wordpiece”, “character”, “sentencepiece”,].
dict, optional) —
Dictionary passed to the MecabTokenizer constructor.
dict, optional) —
Dictionary passed to the SudachiTokenizer constructor.
dict, optional) —
Dictionary passed to the JumanppTokenizer constructor.
Construct a BERT tokenizer for Japanese text.
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to: this superclass for more information regarding those methods.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
List[int]) —
List of IDs to which the special tokens will be added.
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. A BERT sequence has the following format:
[CLS] X [SEP][CLS] A [SEP] B [SEP]Converts a sequence of tokens (string) in a single string.
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
)
→
List[int]
Parameters
List[int]) —
List of IDs.
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. A BERT sequence
pair mask has the following format:
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).
(
token_ids_0: typing.List[int]
token_ids_1: typing.Optional[typing.List[int]] = None
already_has_special_tokens: bool = False
)
→
List[int]
Parameters
List[int]) —
List of IDs.
List[int], optional) —
Optional second list of IDs for sequence pairs.
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.