Delete bert/bert-base-japanese-v3 directory

bert/bert-base-japanese-v3/README.md

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----
-license: apache-2.0
-datasets:
-- cc100
-- wikipedia
-language:
-- ja
-widget:
-- text: 東北大学で[MASK]の研究をしています。
----
-
-# BERT base Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
-
-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 Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), 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/).
-
-## 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 the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
-For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
-The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
-
-For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
-
-## Tokenization
-
-The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
-The vocabulary size is 32768.
-
-We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
-
-## Training
-
-We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
-For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
-
-For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
-
-## Licenses
-
-The pretrained models are distributed under the Apache License 2.0.
-
-## Acknowledgments
-
-This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.

bert/bert-base-japanese-v3/config.json

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-{
-    "architectures": [
-        "BertForPreTraining"
-    ],
-    "attention_probs_dropout_prob": 0.1,
-    "hidden_act": "gelu",
-    "hidden_dropout_prob": 0.1,
-    "hidden_size": 768,
-    "initializer_range": 0.02,
-    "intermediate_size": 3072,
-    "layer_norm_eps": 1e-12,
-    "max_position_embeddings": 512,
-    "model_type": "bert",
-    "num_attention_heads": 12,
-    "num_hidden_layers": 12,
-    "pad_token_id": 0,
-    "type_vocab_size": 2,
-    "vocab_size": 32768
-}