docs

README.md

@@ -1,3 +1,91 @@
 ---
-license: cc-by-4.0
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+datasets:
+  - cc100
+  - mc4
+  - oscar
+  - wikipedia
+  - izumi-lab/cc100-ja
+  - izumi-lab/mc4-ja-filter-ja-normal
+  - izumi-lab/oscar2301-ja-filter-ja-normal
+  - izumi-lab/wikipedia-ja-20230720
+  - izumi-lab/wikinews-ja-20230728
+
+widget:
+- text: 東京大学で[MASK]の研究をしています。 
+
 ---
+
+# DeBERTa V2 small Japanese
+
+This is a [DeBERTaV2](https://github.com/microsoft/DeBERTa) model pretrained on Japanese texts.
+The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/releases/tag/v2.2.1).
+
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("izumi-lab/deberta-v2-base-japanese")
+model = AutoModelForMaskedLM.from_pretrained("izumi-lab/deberta-v2-base-japanese")
+...
+```
+
+
+## Tokenization
+
+The model uses a sentencepiece-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using [sentencepiece](https://github.com/google/sentencepiece).
+
+
+## Training Data
+
+We used the following corpora for pre-training:
+
+- [Japanese portion of CC-100](https://huggingface.co/datasets/izumi-lab/cc100-ja)
+- [Japanese portion of mC4](https://huggingface.co/datasets/izumi-lab/mc4-ja-filter-ja-normal)
+- [Japanese portion of OSCAR2301](izumi-lab/oscar2301-ja-filter-ja-normal)
+- [Japanese Wikipedia as of July 20, 2023](https://huggingface.co/datasets/izumi-lab/wikipedia-ja-20230720)
+- [Japanese Wikinews as of July 28, 2023](https://huggingface.co/datasets/izumi-lab/wikinews-ja-20230728)
+
+
+## Training Parameters
+
+- learning_rate: 6e-4
+- total_train_batch_size: 2,016
+- max_seq_length: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup
+- training_steps: 1,000,000
+- warmup_steps: 100,000
+- precision: BF16
+
+
+## Fine-tuning on General NLU tasks
+
+We evaluate our model with the average of five seeds.  
+
+
+| Model                                                                     | JSTS             | JNLI      | JCommonsenseQA |
+|---------------------------------------------------------------------------|------------------|-----------|----------------|
+|                                                                           | Pearson/Spearman | acc       | acc            |
+| **DeBERTaV2 small**                                                       | **0.890/0.846**  | **0.880** | **0.737**      |
+| [UTokyo BERT small](https://huggingface.co/izumi-lab/bert-small-japanese) | 0.889/0.841      | 0.841     | 0.715          |
+
+
+## Citation
+
+TBA
+
+
+## Licenses
+
+The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+
+
+## Acknowledgments
+
+This work was supported in part by JSPS KAKENHI Grant Number JP21K12010, and the JST-Mirai Program Grant Number JPMJMI20B1, Japan.