.gitattributes

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 *.tar.gz filter=lfs diff=lfs merge=lfs -text
 *.ot filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,46 +0,0 @@
----
-language: ja
-license: cc-by-sa-4.0
-datasets:
-- wikipedia
-widget:
-- text: 東北大学で[MASK]の研究をしています。
----
-
-# BERT base Japanese (IPA dictionary, whole word masking enabled)
-
-This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
-
-This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.
-Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
-
-The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0).
-
-## Model architecture
-
-The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
-
-## Training Data
-
-The model is trained on Japanese Wikipedia as of September 1, 2019.
-To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles.
-The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences.
-
-## Tokenization
-
-The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm.
-The vocabulary size is 32000.
-
-## Training
-
-The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps.
-
-For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
-
-## Licenses
-
-The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
-
-## Acknowledgments
-
-For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.

config.json

@@ -14,7 +14,6 @@
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
   "pad_token_id": 0,
-  "tokenizer_class": "BertJapaneseTokenizer",
   "type_vocab_size": 2,
   "vocab_size": 32000
 }

tokenizer_config.json

@@ -1,5 +0,0 @@
-{
-    "do_lower_case": false,
-    "word_tokenizer_type": "mecab",
-    "subword_tokenizer_type": "wordpiece"
-}