README.md

---
license: cc-by-sa-4.0
datasets:
- globis-university/aozorabunko-clean
- oscar-corpus/OSCAR-2301
- Wikipedia
- WikiBooks
- CC-100
- mC4
language:
- ja
---

# What’s this?
日本語リソースで学習した [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) モデルです。

以下のような特徴を持ちます:

- 定評のある [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) を用いたモデル
- 日本語特化
- 推論時に形態素解析器を用いない
- 単語境界をある程度尊重する (`の都合上` や `の判定負けを喫し` のような複数語のトークンを生じさせない)

---
This is a model based on [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) pre-trained on Japanese resources.

The model has the following features:
- Based on the well-known [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) model
- Specialized for the Japanese language
- Does not use a morphological analyzer during inference
- Respects word boundaries to some extent (does not produce tokens spanning multiple words like `の都合上` or `の判定負けを喫し`)

# How to use
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification

model_name = 'globis-university/deberta-v3-japanese-base'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
```

# Tokenizer
[工藤氏によって示された手法](https://qiita.com/taku910/items/fbaeab4684665952d5a9)で学習した。

以下のことを意識している:

- 推論時の形態素解析器なし
- トークンが単語の境界を跨がない (辞書: `unidic-cwj-202302`)
- Hugging Faceで使いやすい
- 大きすぎない語彙数

本家の DeBERTa V3 は大きな語彙数で学習されていることに特徴があるが、反面埋め込み層のパラメータ数が大きくなりすぎる ([microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) モデルの場合で埋め込み層が全体の 54%) ことから、本モデルでは小さめの語彙数を採用している。

---
The tokenizer is trained using [the method introduced by Kudo](https://qiita.com/taku910/items/fbaeab4684665952d5a9).

Key points include:
- No morphological analyzer needed during inference
- Tokens do not cross word boundaries (dictionary: `unidic-cwj-202302`)
- Easy to use with Hugging Face
- Smaller vocabulary size

Although the original DeBERTa V3 is characterized by a large vocabulary size, which can result in a significant increase in the number of parameters in the embedding layer (for the [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) model, the embedding layer accounts for 54% of the total), this model adopts a smaller vocabulary size to address this.

# Data
| Dataset Name  | Notes | File Size (with metadata) | Factor |
| ------------- | ----- | ------------------------- | ---------- |
| Wikipedia     | 2023/07; [WikiExtractor](https://github.com/attardi/wikiextractor) | 3.5GB | x2 |
| Wikipedia     | 2023/07; [cl-tohoku's method](https://github.com/cl-tohoku/bert-japanese/blob/main/make_corpus_wiki.py) | 4.8GB | x2 |
| WikiBooks     | 2023/07; [cl-tohoku's method](https://github.com/cl-tohoku/bert-japanese/blob/main/make_corpus_wiki.py) | 43MB | x2 |
| Aozora Bunko  | 2023/07; [globis-university/aozorabunko-clean](https://huggingface.co/globis-university/globis-university/aozorabunko-clean) | 496MB | x4 |
| CC-100        | ja | 90GB | x1 |
| mC4           | ja; extracted 10%, with Wikipedia-like focus via [DSIR](https://arxiv.org/abs/2302.03169) | 91GB | x1 |
| OSCAR 2023    | ja; extracted 10%, with Wikipedia-like focus via [DSIR](https://arxiv.org/abs/2302.03169) | 26GB | x1 |

# Training parameters
- Number of devices: 8
- Batch size: 24 x 8
- Learning rate: 1.92e-4
- Maximum sequence length: 512
- Optimizer: AdamW
- Learning rate scheduler: Linear schedule with warmup
- Training steps: 1,000,000
- Warmup steps: 100,000
- Precision: Mixed (fp16)

# Evaluation
| Model | #params | JSTS | JNLI | JSQuAD | JCQA |
| ----- | ------- | ---- | ---- | ------ | ---- |
| ≤ small | | | | | |
| [izumi-lab/deberta-v2-small-japanese](https://huggingface.co/izumi-lab/deberta-v2-small-japanese) | 17.8M | 0.890/0.846 | 0.880 | - | 0.737 |
| [globis-university/deberta-v3-japanese-xsmall](https://huggingface.co/globis-university/deberta-v3-japanese-xsmall) | 33.7M | **0.916**/**0.880** | **0.913** | **0.869**/**0.938** | **0.821** |
| base | | | | |
| [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3) | 111M | 0.919/0.881 | 0.907 | 0.880/0.946 | 0.848 |
| [nlp-waseda/roberta-base-japanese](https://huggingface.co/nlp-waseda/roberta-base-japanese) | 111M | 0.913/0.873 | 0.895 | 0.864/0.927 | 0.840 |
| [izumi-lab/deberta-v2-base-japanese](https://huggingface.co/izumi-lab/deberta-v2-base-japanese) | 110M | 0.919/0.882 | 0.912 | - | 0.859 |
| [ku-nlp/deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese) | 112M | 0.922/0.886 | 0.922 | **0.899**/**0.951** | - |
| [ku-nlp/deberta-v3-base-japanese](https://huggingface.co/ku-nlp/deberta-v3-base-japanese) | 160M | **0.927**/0.891 | **0.927** | 0.896/- | - |
| [**globis-university/deberta-v3-japanese-base**](https://huggingface.co/globis-university/deberta-v3-japanese-base) | 110M | 0.925/**0.895** | 0.921 | 0.890/0.950 | **0.886** |
| large | | | | | |
| [cl-tohoku/bert-large-japanese-v2](https://huggingface.co/cl-tohoku/bert-large-japanese-v2) | 337M | 0.926/0.893 | **0.929** | 0.893/0.956 | 0.893 |
| [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese) | 337M | **0.930**/**0.896** | 0.924 | 0.884/0.940 | **0.907** |
| [nlp-waseda/roberta-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512) | 337M | 0.926/0.892 | 0.926 | **0.918**/**0.963** | 0.891 |
| [ku-nlp/deberta-v2-large-japanese](https://huggingface.co/ku-nlp/deberta-v2-large-japanese) | 339M | 0.925/0.892 | 0.924 | 0.912/0.959 | - |
| [globis-university/deberta-v3-japanese-large](https://huggingface.co/globis-university/deberta-v3-japanese-large) | 352M | 0.928/**0.896** | 0.924 | 0.896/0.956 | 0.900 |

## License
CC BY SA 4.0

## Acknowledgement
計算リソースに [ABCI](https://abci.ai/) を利用させていただきました。ありがとうございます。

---
We used [ABCI](https://abci.ai/) for computing resources. Thank you.