initial readme.md(not perfect at all)

README.md

@@ -1,3 +1,64 @@
 ---
+language: ja
 license: cc-by-sa-4.0
+datasets:
+- wikipedia
+- cc100
+- oscar
+mask_token: "[MASK]"
+widget:
+- text: "早稲田 大学 で 自然 言語 処理 を [MASK] する 。"
 ---
+
+# nlp-waseda/roberta-base-japanese
+
+## Model description
+
+This is a Japanese BigBird base model pretrained on Japanese Wikipedia, the Japanese portion of CC-100 and the Japanese portion of oscar.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/bigbird-base-japanese")
+model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/bigbird-base-japanese")
+
+sentence = '早稲田 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can fine-tune this model on downstream tasks.
+
+## Tokenization
+
+The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece).
+
+`BertJapaneseTokenizer` now supports automatic `JumanppTokenizer` and `SentencepieceTokenizer`. You can use [this model](https://huggingface.co/nlp-waseda/roberta-base-japanese-with-auto-jumanpp) without any data preprocessing.
+
+## Vocabulary
+
+The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training procedure
+
+This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took a week using eight NVIDIA A100 GPUs.
+
+The following hyperparameters were used during pretraining:
+- learning_rate: 1e-4
+- per_device_train_batch_size: 256
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 4096
+- max_seq_length: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 700000
+- warmup_steps: 10000
+- mixed_precision_training: Native AMP
+
+## Performance on JGLUE
+
+See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.