Create README.md

README.md

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+---
+license: cc-by-sa-4.0
+datasets:
+- globis-university/aozorabunko-clean
+- oscar-corpus/OSCAR-2301
+language:
+- ja
+---
+
+# What’s this?
+This is a model based on DeBERTa V3 pre-trained on Japanese resources.
+
+The model has the following features:
+- Based on the well-known DeBERTa V3 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`)
+
+---
+
+日本語リソースで学習した DeBERTa V3 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 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)
+
+# 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
+The tokenizer is trained using the method demonstrated by Kudo.
+
+Key points include:
+- No morphological analyzer needed during inference
+- Tokens do not cross word boundaries
+- 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, this model adopts a smaller vocabulary size to address this.
+
+---
+
+工藤氏によって示された手法で学習した。
+
+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
+- 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ことから、本モデルでは小さめの語彙数を採用している。
+
+# Data
+| Dataset Name  | Notes | File Size (with metadata) | Times |
+| ------------- | ----- | ------------------------- | ---------- |
+| Wikipedia     | 2023/07; WikiExtractor | 3.5GB | x2 |
+| Wikipedia     | 2023/07; cl-tohoku's method | 4.8GB | x2 |
+| WikiBooks     | 2023/07; cl-tohoku's method | 43MB | x2 |
+| Aozora Bunko  | 2023/07; globis-university/aozorabunko-clean | 496MB | x4 |
+| CC-100        | ja | 90GB | x1 |
+| mC4           | ja; extracted 10% of Wikipedia-like data using DSIR | 91GB | x1 |
+| OSCAR 2023    | ja; extracted 20% of Wikipedia-like data using DSIR | 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 | JSTS | JNLI | JSQuAD | JCQA |
+| ----- | ---- | ---- | ------ | ---- |
+| ≤ small | | | | |
+| [izumi-lab/deberta-v2-small-japanese](https://huggingface.co/izumi-lab/deberta-v2-small-japanese) | 0.890/0.846 | 0.880 | - | 0.737 |
+| [globis-university/deberta-v3-japanese-xsmall](https://huggingface.co/globis-university/deberta-v3-japanese-xsmall) | 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) | 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) | 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) | 0.919/0.882 | 0.912 | - | 0.859 |
+| [ku-nlp/deberta-v2-base-japanese](https://huggingface.co/ku-nlp/deberta-v2-base-japanese) | 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) | 0.927/0.891 | 0.927 | 0.896/- | - |
+| [globis-university/deberta-v3-japanese-base](https://huggingface.co/globis-university/deberta-v3-japanese-base) | 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) | 0.926/0.893 | 0.929 | 0.893/0.956 | 0.893 |
+| [roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese) | 0.930/0.896 | 0.924 | 0.884/0.940 | 0.907 |
+| [roberta-large-japanese-seq512](https://huggingface.co/nlp-waseda/roberta-large-japanese-seq512) | 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) | 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) | 0.928/0.896 | 0.924 | 0.896/0.956 | 0.900 |
+
+## License
+CC BY SA 4.0
+
+## Acknowledgement
+We used ABCI for computing resources.
+
+---
+
+計算リソースにABCIを利用させていただきました。