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README.md
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@@ -102,7 +102,7 @@ We fine-tuned the following models and evaluated them on the [JGLUE](https://git
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We adjusted the learning rate and training epochs for each model and task in accordance with [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
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| Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
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| tohoku-nlp/bert-base-japanese-v3 | 0.957 | 0.914 | 0.876 | 0.906 | 0.878 | 0.946 | 0.849 |
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| tohoku-nlp/bert-large-japanese-v2| 0.959 | 0.916 | 0.877 | 0.901 | 0.884 | 0.951 | 0.867 |
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| ku-nlp/deberta-v3-base-japanese | 0.958 | 0.925 | 0.890 | 0.902 | 0.925 | 0.910 | 0.882 |
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We adjusted the learning rate and training epochs for each model and task in accordance with [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
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| Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
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| tohoku-nlp/bert-base-japanese-v3 | 0.957 | 0.914 | 0.876 | 0.906 | 0.878 | 0.946 | 0.849 |
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| tohoku-nlp/bert-large-japanese-v2| 0.959 | 0.916 | 0.877 | 0.901 | 0.884 | 0.951 | 0.867 |
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| ku-nlp/deberta-v3-base-japanese | 0.958 | 0.925 | 0.890 | 0.902 | 0.925 | 0.910 | 0.882 |
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