
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)

This pretrained model is almost the same as [cl-tohoku/bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2) but do not need `fugashi` or `unidic_lite`.
The only difference is in `word_tokenzer_type` property (specify `basic` instead of `mecab`) in `tokenizer_config.json`.

{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"]}
transformers pytorch bert fill-mask ja dataset:wikipedia license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)

This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'.

# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)

This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'.
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)

This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'.
passage: TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)

This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'.