|
--- |
|
language: ja |
|
thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png |
|
tags: |
|
- roberta |
|
- masked-lm |
|
- nlp |
|
license: mit |
|
datasets: |
|
- cc100 |
|
- wikipedia |
|
mask_token: "[MASK]" |
|
widget: |
|
- text: "[CLS]4年に1度[MASK]は開かれる。" |
|
--- |
|
|
|
# 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](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/) |
|
|
|
# How to load the model |
|
|
|
~~~~ |
|
from transformers import AutoTokenizer, AutoModelForMaskedLM |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-roberta-base", use_fast=False) |
|
tokenizer.do_lower_case = True # due to some bug of tokenizer config loading |
|
|
|
model = AutoModelForMaskedLM.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. |
|
|
|
## Note 3: Provide `position_ids` as an argument explicitly |
|
|
|
When `position_ids` are not provided for a `Roberta*` model, Huggingface's `transformers` will automatically construct it but start from `padding_idx` instead of `0` (see [issue](https://github.com/rinnakk/japanese-pretrained-models/issues/3) and function `create_position_ids_from_input_ids()` in Huggingface's [implementation](https://github.com/huggingface/transformers/blob/master/src/transformers/models/roberta/modeling_roberta.py)), which unfortunately does not work as expected with `rinna/japanese-roberta-base` since the `padding_idx` of the corresponding tokenizer is not `0`. So please be sure to constrcut the `position_ids` by yourself and make it start from position id `0`. |
|
|
|
## 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 = 5 |
|
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.LongTensor([token_ids]) |
|
|
|
# provide position ids explicitly |
|
position_ids = list(range(0, token_tensor.size(1))) |
|
print(position_ids) # output: [0, 1, 2, 3, 4, 5, 6, 7, 8] |
|
position_id_tensor = torch.LongTensor([position_ids]) |
|
|
|
# get the top 10 predictions of the masked token |
|
with torch.no_grad(): |
|
outputs = model(input_ids=token_tensor, position_ids=position_id_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 総会 |
|
1 サミット |
|
2 ワールドカップ |
|
3 フェスティバル |
|
4 大会 |
|
5 オリンピック |
|
6 全国大会 |
|
7 党大会 |
|
8 イベント |
|
9 世界選手権 |
|
""" |
|
~~~~ |
|
|
|
# Model architecture |
|
A 12-layer, 768-hidden-size transformer-based masked language model. |
|
|
|
# Training |
|
The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/) 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](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script. |
|
|
|
# How to cite |
|
```bibtex |
|
@misc{rinna-japanese-roberta-base, |
|
title = {rinna/japanese-roberta-base}, |
|
author = {Zhao, Tianyu and Sawada, Kei}, |
|
url = {https://huggingface.co/rinna/japanese-roberta-base} |
|
} |
|
|
|
@inproceedings{sawada2024release, |
|
title = {Release of Pre-Trained Models for the {J}apanese Language}, |
|
author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh}, |
|
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)}, |
|
month = {5}, |
|
year = {2024}, |
|
pages = {13898--13905}, |
|
url = {https://aclanthology.org/2024.lrec-main.1213}, |
|
note = {\url{https://arxiv.org/abs/2404.01657}} |
|
} |
|
``` |
|
|
|
# Licenese |
|
[The MIT license](https://opensource.org/licenses/MIT) |
|
|
