Update README.md

README.md

@@ -8,25 +8,24 @@ language:
 ---
 
 # 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。
+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 を用いたモデル
+- 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
@@ -37,39 +36,38 @@ 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。
+[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
+- トークンが単語 (`unidic-cwj-202302`) 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。
 
+---
+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 (`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, this model adopts a smaller vocabulary size to address this.
+
 # Data
-| Dataset Name  | Notes | File Size (with metadata) | Times |
+| Dataset Name  | Notes | File Size (with metadata) | Factor |
 | ------------- | ----- | ------------------------- | ---------- |
-| 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 |
+| 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% of Wikipedia-like data using DSIR | 91GB | x1 |
-| OSCAR 2023    | ja; extracted 20% of Wikipedia-like data using DSIR | 26GB | x1 |
+| mC4           | ja; extracted 10% of Wikipedia-like data using [DSIR](https://arxiv.org/abs/2302.03169) | 91GB | x1 |
+| OSCAR 2023    | ja; extracted 20% of Wikipedia-like data using [DSIR](https://arxiv.org/abs/2302.03169) | 26GB | x1 |
 
 # Training parameters
 - Number of devices: 8
@@ -87,27 +85,26 @@ Although the original DeBERTa V3 is characterized by a large vocabulary size, wh
 | ----- | ---- | ---- | ------ | ---- |
 | ≤ 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 |
+| [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 |
+| [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 |
+| [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 |
+| [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](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。
 
 ---
-
-計算リソースにABCIを利用させていただきました。
+We used [ABCI](https://abci.ai/) for computing resources. Thank you.