--- license: cc-by-sa-4.0 datasets: - globis-university/aozorabunko-clean - oscar-corpus/OSCAR-2301 - Wikipedia - WikiBooks - CC-100 - allenai/c4 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-large) 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-large) 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-large) pre-trained on Japanese resources. The model has the following features: - Based on the well-known [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-large) 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-large' 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](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ます。 --- 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: 8 x 8 - Learning rate: 6.4e-5 - Maximum sequence length: 512 - Optimizer: AdamW - Learning rate scheduler: Linear schedule with warmup - Training steps: 2,000,000 - 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.