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@@ -61,8 +61,9 @@ We tuned learning rate and training epochs for each model and task following [th
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  For tasks other than MARC-ja, the maximum length is short, so the attention_type was set to "original_full" and fine-tuning was performed for tasks other than MARC-ja. For MARC-ja, both "block_sparse" and "original_full" were used.
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- | Model |MARC-ja(block_sparse)/acc | MARC-ja(original_full)/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
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- |-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|------------|
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- | Waseda RoBERTa base | - | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 |
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- | Waseda RoBERTa large (seq512) | - |0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 |
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- | BigBird base | 0.959 |0.959 | 0.888 | 0.846 | 0.896 | 0.884 | 0.933 | 0.787 |
 
 
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  For tasks other than MARC-ja, the maximum length is short, so the attention_type was set to "original_full" and fine-tuning was performed for tasks other than MARC-ja. For MARC-ja, both "block_sparse" and "original_full" were used.
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+ | Model | MARC-ja(original_full)/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
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+ |-------------------------------|--------------|---------------|----------|-----------|-----------|------------|------------|
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+ | Waseda RoBERTa base | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 |
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+ | Waseda RoBERTa large (seq512) | 0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 |
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+ | BigBird base (original_full) |0.959 | 0.888 | 0.846 | 0.896 | 0.884 | 0.933 | 0.787 |
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+ | BigBird base (block_sparse) |0.959 | - | - | - | - | - | - |