Updated README.md.

README.md

@@ -15,20 +15,48 @@ widget:
 
 ## Model description
 
-This is a Japanese RoBERTa model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
+This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
 
 ## 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/roberta-base-japanese")
-model=AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-base-japanese")
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese")
+model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-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 use this model for fine-tuning on downstream tasks.
+
 ## Tokenization
 
 The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in advance. Each word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece).
 
 ## Vocabulary
 
+The vocabulary consists of 32000 subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+
 ## Training procedure
+
+This model was trained on Japanese Wikipedia 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
+- mixed_precision_training: Native AMP
+
+## Performance on JGLUE
+
+coming soon