language: ja
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
tags:
- ja
- japanese
- roberta
- masked-lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
widget:
- text: '[CLS]4年に1度[MASK]は開かれる。'
mask_token: '[MASK]'
japanese-roberta-base
This repository provides a base-sized Japanese RoBERTa model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.
How to load the model
NOTE: Use T5Tokenizer to initiate the tokenizer.
from transformers import T5Tokenizer, RobertaForMaskedLM
tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-roberta-base")
tokenizer.do_lower_case = True # due to some bug of tokenizer config loading
model = RobertaForMaskedLM.from_pretrained("rinna/japanese-roberta-base")
How to use the model for masked token prediction
Note 1: Use [CLS]
To predict a masked token, be sure to add a [CLS] token before the sentence for the model to correctly encode it, as it is used during the model training.
Note 2: Use [MASK] after tokenization
A) Directly typing [MASK] in an input string and B) replacing a token with [MASK] after tokenization will yield different token sequences, and thus different prediction results. It is more appropriate to use [MASK] after tokenization (as it is consistent with how the model was pretrained). However, the Huggingface Inference API only supports typing [MASK] in the input string and produces less robust predictions.
Example
Here is an example by to illustrate how our model works as a masked language model. Notice the difference between running the following code example and running the Huggingface Inference API.
# original text
text = "4年に1度オリンピックは開かれる。"
# prepend [CLS]
text = "[CLS]" + text
# tokenize
tokens = tokenizer.tokenize(text)
print(tokens) # output: ['[CLS]', '▁4', '年に', '1', '度', 'オリンピック', 'は', '開かれる', '。']
# mask a token
masked_idx = 6
tokens[masked_idx] = tokenizer.mask_token
print(tokens) # output: ['[CLS]', '▁4', '年に', '1', '度', '[MASK]', 'は', '開かれる', '。']
# convert to ids
token_ids = tokenizer.convert_tokens_to_ids(tokens)
print(token_ids) # output: [4, 1602, 44, 24, 368, 6, 11, 21583, 8]
# convert to tensor
import torch
token_tensor = torch.tensor([token_ids])
# get the top 10 predictions of the masked token
model = model.eval()
with torch.no_grad():
outputs = model(token_tensor)
predictions = outputs[0][0, masked_idx].topk(10)
for i, index_t in enumerate(predictions.indices):
index = index_t.item()
token = tokenizer.convert_ids_to_tokens([index])[0]
print(i, token)
"""
0 ワールドカップ
1 フェスティバル
2 オリンピック
3 サミット
4 東京オリンピック
5 総会
6 全国大会
7 イベント
8 世界選手権
9 パーティー
"""
Model architecture
A 12-layer, 768-hidden-size transformer-based masked language model.
Training
The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a masked language modelling objective on 8*V100 GPUs for around 15 days. It reaches ~3.9 perplexity on a dev set sampled from CC-100.
Tokenization
The model uses a sentencepiece-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.