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@@ -60,7 +60,7 @@ We first segmented texts in the corpora into words using [Juman++](https://githu
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  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).
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  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.
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- The training took three weeks using 8 NVIDIA A100-SXM4-40GB GPUs.
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  The following hyperparameters were used during pre-training:
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  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).
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  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.
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+ The training took 33 hours using 8 NVIDIA A100-SXM4-40GB GPUs.
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  The following hyperparameters were used during pre-training:
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