---
language: ja
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
mask_token: "[MASK]"
widget:
- text: "京都大学で自然言語処理を[MASK]する。"
---

# ku-nlp/roberta-large-japanese-char-wwm

## Model description

This is a Japanese RoBERTa large model pre-trained on Japanese Wikipedia and the Japanese portion of CC-100.
This model is trained with character-level tokenization and whole word masking.

## How to use

You can use this model for masked language modeling as follows:

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained('ku-nlp/roberta-large-japanese-char-wwm')
model = AutoModelForMaskedLM.from_pretrained('ku-nlp/roberta-large-japanese-char-wwm')

sentence = '京都大学で自然言語処理を[MASK]する。'
encoding = tokenizer(sentence, return_tensors='pt')
...
```

You can fine-tune this model on downstream tasks.

## Tokenization

There is no need to tokenize texts in advance, and you can give raw texts to the tokenizer.
The texts are tokenized into character-level tokens by [sentencepiece](https://github.com/google/sentencepiece).

## Vocabulary

The vocabulary consists of 18,377 tokens including all characters that appear in the training corpus.

## Training procedure

This model was trained on Japanese Wikipedia (as of 20220220) and the Japanese portion of CC-100. It took a month using 8-16 NVIDIA A100 GPUs.

The following hyperparameters were used during pre-training:

- learning_rate: 5e-5
- per_device_train_batch_size: 38
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 4864
- max_seq_length: 512
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear schedule with warmup
- training_steps: 500000
- warmup_steps: 10000

## Acknowledgments

This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of Large-Scale Japanese Language Models".
For training models, we used the mdx: a platform for the data-driven future.