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bert/deberta-v2-large-japanese/README.md

@@ -0,0 +1,111 @@
+---
+language: ja
+license: cc-by-sa-4.0
+library_name: transformers
+tags:
+  - deberta
+  - deberta-v2
+  - fill-mask
+datasets:
+  - wikipedia
+  - cc100
+  - oscar
+metrics:
+  - accuracy
+mask_token: "[MASK]"
+widget:
+  - text: "京都 大学 で 自然 言語 処理 を [MASK] する 。"
+---
+
+# Model Card for Japanese DeBERTa V2 large
+
+## Model description
+
+This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the
+Japanese portion of OSCAR.
+
+## How to use
+
+You can use this model for masked language modeling as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese')
+model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese')
+
+sentence = '京都 大学 で 自然 言語 処理 を [MASK] する 。'  # input should be segmented into words by Juman++ in advance
+encoding = tokenizer(sentence, return_tensors='pt')
+...
+```
+
+You can also fine-tune this model on downstream tasks.
+
+## Tokenization
+
+The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in
+advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each
+word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece).
+
+## Training data
+
+We used the following corpora for pre-training:
+
+- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
+- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
+- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
+
+Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
+Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of
+CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
+
+## Training procedure
+
+We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
+Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC))
+and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
+
+We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model
+using [transformers](https://github.com/huggingface/transformers) library.
+The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs.
+
+The following hyperparameters were used during pre-training:
+
+- learning_rate: 1e-4
+- per_device_train_batch_size: 18
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 2,304
+- max_seq_length: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
+- lr_scheduler_type: linear schedule with warmup
+- training_steps: 300,000
+- warmup_steps: 10,000
+
+The accuracy of the trained model on the masked language modeling task was 0.799.
+The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
+
+## Fine-tuning on NLU tasks
+
+We fine-tuned the following models and evaluated them on the dev set of JGLUE.
+We tuned learning rate and training epochs for each model and task
+following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
+
+| Model                         | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
+|-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|
+| Waseda RoBERTa base           | 0.965       | 0.913        | 0.876         | 0.905    | 0.853     | 0.916     | 0.853      |
+| Waseda RoBERTa large (seq512) | 0.969       | 0.925        | 0.890         | 0.928    | 0.910     | 0.955     | 0.900      |
+| LUKE Japanese base*           | 0.965       | 0.916        | 0.877         | 0.912    | -         | -         | 0.842      |
+| LUKE Japanese large*          | 0.965       | 0.932        | 0.902         | 0.927    | -         | -         | 0.893      |
+| DeBERTaV2 base                | 0.970       | 0.922        | 0.886         | 0.922    | 0.899     | 0.951     | 0.873      |
+| DeBERTaV2 large               | 0.968       | 0.925        | 0.892         | 0.924    | 0.912     | 0.959     | 0.890      |
+
+*The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke).
+
+## Acknowledgments
+
+This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (
+JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of
+Large-Scale Japanese Language Models".
+For training models, we used the mdx: a platform for the data-driven future.