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license: cc-by-sa-4.0 |
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datasets: |
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- globis-university/aozorabunko-clean |
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- oscar-corpus/OSCAR-2301 |
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- Wikipedia |
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- WikiBooks |
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- CC-100 |
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- mC4 |
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language: |
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- ja |
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--- |
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# What’s this? |
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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。 |
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以下のような特徴を持ちます: |
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- 定評のある [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) を用いたモデル |
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- 日本語特化 |
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- 推論時に形態素解析器を用いない |
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- 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) |
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--- |
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This is a model based on [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) pre-trained on Japanese resources. |
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The model has the following features: |
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- Based on the well-known [DeBERTa V3](https://huggingface.co/microsoft/deberta-v3-base) model |
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- Specialized for the Japanese language |
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- Does not use a morphological analyzer during inference |
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- 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`) |
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# How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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model_name = 'globis-university/deberta-v3-japanese-base' |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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``` |
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# Tokenizer |
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[工藤氏によって示された手法](https://qiita.com/taku910/items/fbaeab4684665952d5a9)で学習した。 |
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以下のことを意識している: |
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- 推論時の形態素解析器なし |
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- トークンが単語 (`unidic-cwj-202302`) の境界を跨がない |
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- Hugging Faceで使いやすい |
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- 大きすぎない語彙数 |
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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。 |
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--- |
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The tokenizer is trained using [the method introduced by Kudo](https://qiita.com/taku910/items/fbaeab4684665952d5a9). |
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Key points include: |
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- No morphological analyzer needed during inference |
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- Tokens do not cross word boundaries (`unidic-cwj-202302`) |
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- Easy to use with Hugging Face |
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- Smaller vocabulary size |
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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. |
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# Data |
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| Dataset Name | Notes | File Size (with metadata) | Factor | |
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| ------------- | ----- | ------------------------- | ---------- | |
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| Wikipedia | 2023/07; [WikiExtractor](https://github.com/attardi/wikiextractor) | 3.5GB | x2 | |
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| Wikipedia | 2023/07; [cl-tohoku's method](https://github.com/cl-tohoku/bert-japanese/blob/main/make_corpus_wiki.py) | 4.8GB | x2 | |
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| WikiBooks | 2023/07; [cl-tohoku's method](https://github.com/cl-tohoku/bert-japanese/blob/main/make_corpus_wiki.py) | 43MB | x2 | |
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| Aozora Bunko | 2023/07; [globis-university/aozorabunko-clean](https://huggingface.co/globis-university/globis-university/aozorabunko-clean) | 496MB | x4 | |
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| CC-100 | ja | 90GB | x1 | |
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| mC4 | ja; extracted 10% of Wikipedia-like data using [DSIR](https://arxiv.org/abs/2302.03169) | 91GB | x1 | |
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| OSCAR 2023 | ja; extracted 20% of Wikipedia-like data using [DSIR](https://arxiv.org/abs/2302.03169) | 26GB | x1 | |
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# Training parameters |
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- Number of devices: 8 |
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- Batch size: 24 x 8 |
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- Learning rate: 1.92e-4 |
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- Maximum sequence length: 512 |
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- Optimizer: AdamW |
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- Learning rate scheduler: Linear schedule with warmup |
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- Training steps: 1,000,000 |
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- Warmup steps: 100,000 |
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- Precision: Mixed (fp16) |
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# Evaluation |
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| Model | JSTS | JNLI | JSQuAD | JCQA | |
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| ----- | ---- | ---- | ------ | ---- | |
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| ≤ small | | | | | |
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| [izumi-lab/deberta-v2-small-japanese](https://huggingface.co/izumi-lab/deberta-v2-small-japanese) | 0.890/0.846 | 0.880 | - | 0.737 | |
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| [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** | |
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| base | | | | | |
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| [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 | |
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| [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 | |
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| [izumi-lab/deberta-v2-base-japanese](https://huggingface.co/izumi-lab/deberta-v2-base-japanese) | 0.919/0.882 | 0.912 | - | 0.859 | |
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| [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** | - | |
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| [ku-nlp/deberta-v3-base-japanese](https://huggingface.co/ku-nlp/deberta-v3-base-japanese) | **0.927**/0.891 | **0.927** | 0.896/- | - | |
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| [**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** | |
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| large | | | | | |
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| [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 | |
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| [roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese) | **0.930**/**0.896** | 0.924 | 0.884/0.940 | **0.907** | |
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| [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 | |
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| [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 | - | |
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| [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 | |
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## License |
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CC BY SA 4.0 |
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## Acknowledgement |
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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。 |
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We used [ABCI](https://abci.ai/) for computing resources. Thank you. |
