|
--- |
|
language: ja |
|
license: cc-by-sa-4.0 |
|
datasets: |
|
- wikipedia |
|
- cc100 |
|
mask_token: "[MASK]" |
|
widget: |
|
- text: "早稲田大学で自然言語処理を[MASK]する。" |
|
--- |
|
|
|
# nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp |
|
|
|
## Model description |
|
|
|
This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100 with the maximum sequence length of 512. |
|
|
|
## 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-large-japanese-seq512-with-auto-jumanpp") |
|
model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-seq512-with-auto-jumanpp") |
|
|
|
sentence = '早稲田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 |
|
|
|
`BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). 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-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512). |
|
|
|
Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). |
|
|
|
## 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 from the checkpoint of [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). It took a week using eight NVIDIA A100 GPUs. |
|
|
|
The following hyperparameters were used during pretraining: |
|
- learning_rate: 6e-5 |
|
- distributed_type: multi-GPU |
|
- num_devices: 8 |
|
- total_train_batch_size: 4120 (max_seq_length=128), 4032 (max_seq_length=512) |
|
- max_seq_length: 512 |
|
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6 |
|
- lr_scheduler_type: linear |
|
- training_steps: 670000 (max_seq_length=128) + 70000 (max_seq_length=512) |
|
- warmup_steps: 10000 |
|
- mixed_precision_training: Native AMP |
|
|
