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---
license: cc-by-nc-sa-4.0
datasets:
- wikipedia
- cc100
language:
- ja
library_name: transformers
pipeline_tag: fill-mask
---
Japanese BERT-base (Nothing + WordPiece)
===
## How to load the tokenizer
Please download the dictionary file for Nothing + WordPiece from [our GitHub repository](https://github.com/hitachi-nlp/compare-ja-tokenizer/blob/public/data/dict/nothing_wordpiece.json).
Then you can load the tokenizer by specifying the path of the dictionary file to `dict_path`.
```python
from typing import Optional
from tokenizers import Tokenizer, NormalizedString, PreTokenizedString
from tokenizers.processors import BertProcessing
from tokenizers.pre_tokenizers import PreTokenizer
from transformers import PreTrainedTokenizerFast
# load a tokenizer
dict_path = /path/to/nothing_wordpiece.json
tokenizer = Tokenizer.from_file(dict_path)
tokenizer.post_processor = BertProcessing(
cls=("[CLS]", tokenizer.token_to_id('[CLS]')),
sep=("[SEP]", tokenizer.token_to_id('[SEP]'))
)
# convert to PreTrainedTokenizerFast
tokenizer = PreTrainedTokenizerFast(
tokenizer_object=tokenizer,
unk_token='[UNK]',
cls_token='[CLS]',
sep_token='[SEP]',
pad_token='[PAD]',
mask_token='[MASK]'
)
```
```python
# Test
test_str = "γγγ«γ‘γ―γη§γ―ε½’ζ
η΄ θ§£ζε¨γ«γ€γγ¦η η©Άγγγ¦γγΎγγ"
tokenizer.convert_ids_to_tokens(tokenizer(test_str).input_ids)
# -> ['[CLS]','γ','##γ','##γ«','##γ‘','##γ―','##γ','##η§','##γ―','##ε½’','##ζ
','##η΄ ','##解','##ζ','##ε¨','##γ«','##γ€','##γ','##γ¦','##η ','##η©Ά','##γ','##γ','##γ¦','##γ','##γΎ','##γ','##γ','[SEP]']
```
## How to load the model
```python
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("hitachi-nlp/bert-base_nothing-wordpiece")
```
**See [our repository](https://github.com/hitachi-nlp/compare-ja-tokenizer) for more details!**
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