conan1024hao's picture
support BertJapaneseTokenizer
00d40f3
|
raw
history blame
2.41 kB
metadata
language: ja
license: cc-by-sa-4.0
datasets:
  - wikipedia
  - cc100
mask_token: '[MASK]'
widget:
  - text: 早稲田 大学  自然 言語 処理  [MASK] する 

nlp-waseda/roberta-base-japanese

Model description

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:

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 fine-tune this model on downstream tasks.

Tokenization

BertJapaneseTokenizer now supports automatic tokenization for Juman++. However, if your dataset is large, you may take a long time since BertJapaneseTokenizer still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model nlp-waseda/roberta-base-japanese.

Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by sentencepiece.

Vocabulary

The vocabulary consists of 32000 tokens including words (JumanDIC) and subwords induced by the unigram language model of 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 of JGLUE.