Hugging Face's logo

globis-university
/
deberta-v3-japanese-xsmall

Token Classification
Transformers
PyTorch
Japanese
deberta-v2
Inference Endpoints
Model card Files Community
1
Edit model card

What’s this?

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)

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)

How to use 

from transformers import AutoTokenizer, AutoModelForTokenClassification

model_name = 'globis-university/deberta-v3-japanese-xsmall'
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で学習しました。

以下のことを意識しています:

  • 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りますが、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 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ます。

注意点として、 xsmall 、 base 、 large の 3 つのモデルのうち、前者二つは unigram アルゴリズムで学習しているが、 large モデルのみ BPE アルゴリズムで学習している。 深い理由はなく、 large モデルのみ語彙サイズを増やすために独立して学習を行ったが、なぜか unigram アルゴリズムでの学習がうまくいかなかったことが原因である。 原因の探究よりモデルの完成を優先して、 BPE アルゴリズムに切り替えた。

The tokenizer is trained using the method introduced by Kudo.

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 model, the embedding layer accounts for 54% of the total), this model adopts a smaller vocabulary size to address this.

Note that, among the three models: xsmall, base, and large, the first two were trained using the unigram algorithm, while only the large model was trained using the BPE algorithm. The reason for this is simple: while the large model was independently trained to increase its vocabulary size, for some reason, training with the unigram algorithm was not successful. Thus, prioritizing the completion of the model over investigating the cause, we switched to the BPE algorithm.

Data

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
CC-100 ja 90GB x1
mC4 ja; extracted 10%, with Wikipedia-like focus via DSIR 91GB x1
OSCAR 2023 ja; extracted 10%, with Wikipedia-like focus via DSIR 26GB x1

Training parameters

  • Number of devices: 8
  • Batch size: 48 x 8
  • Learning rate: 3.84e-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)
  • Vocabulary size: 32,000

Evaluation

Model #params JSTS JNLI JSQuAD JCQA
≤ small
izumi-lab/deberta-v2-small-japanese 17.8M 0.890/0.846 0.880 - 0.737
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 111M 0.919/0.881 0.907 0.880/0.946 0.848
nlp-waseda/roberta-base-japanese 111M 0.913/0.873 0.895 0.864/0.927 0.840
izumi-lab/deberta-v2-base-japanese 110M 0.919/0.882 0.912 - 0.859
ku-nlp/deberta-v2-base-japanese 112M 0.922/0.886 0.922 0.899/0.951 -
ku-nlp/deberta-v3-base-japanese 160M 0.927/0.891 0.927 0.896/- -
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 337M 0.926/0.893 0.929 0.893/0.956 0.893
nlp-waseda/roberta-large-japanese 337M 0.930/0.896 0.924 0.884/0.940 0.907
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 339M 0.925/0.892 0.924 0.912/0.959 -
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 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 for computing resources. Thank you.

Downloads last month
413
Inference Examples
Token Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Datasets used to train globis-university/deberta-v3-japanese-xsmall

Collection including globis-university/deberta-v3-japanese-xsmall