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bart-base-japanese-tobyoki-pairwise-wo_space

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update model card README.md

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@@ -16,12 +16,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ku-nlp/bart-base-japanese](https://huggingface.co/ku-nlp/bart-base-japanese) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.4647
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- - Rouge1: 9.203
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- - Rouge2: 1.326
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- - Rougel: 7.3269
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- - Rougelsum: 7.9726
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- - Gen Len: 25.78
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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- | 2.1248 | 1.0 | 800 | 1.9659 | 4.6735 | 1.3973 | 3.3399 | 3.6754 | 13.69 |
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- | 0.9881 | 2.0 | 1600 | 2.0469 | 4.4604 | 0.6802 | 3.3883 | 3.5799 | 13.09 |
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- | 0.7473 | 3.0 | 2400 | 2.3957 | 7.1848 | 1.4395 | 5.7287 | 6.234 | 22.42 |
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- | 0.5128 | 4.0 | 3200 | 2.4013 | 9.7241 | 1.7342 | 7.7361 | 8.3869 | 24.21 |
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- | 0.4115 | 5.0 | 4000 | 2.4647 | 9.203 | 1.326 | 7.3269 | 7.9726 | 25.78 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [ku-nlp/bart-base-japanese](https://huggingface.co/ku-nlp/bart-base-japanese) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.0850
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+ - Rouge1: 15.3454
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+ - Rouge2: 2.9489
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+ - Rougel: 10.7691
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+ - Rougelsum: 12.7028
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+ - Gen Len: 66.075
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
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+ | 2.1701 | 1.0 | 717 | 1.9507 | 12.6467 | 2.901 | 10.0035 | 11.1471 | 47.7375 |
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+ | 1.4042 | 2.0 | 1434 | 1.9519 | 11.9515 | 3.096 | 10.2259 | 10.8478 | 21.1375 |
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+ | 0.8952 | 3.0 | 2151 | 2.0323 | 15.5721 | 3.5875 | 10.6382 | 12.9346 | 76.35 |
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+ | 0.7489 | 4.0 | 2868 | 2.0724 | 15.5769 | 3.3042 | 11.0176 | 12.8107 | 63.7625 |
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+ | 0.5941 | 5.0 | 3585 | 2.0850 | 15.3454 | 2.9489 | 10.7691 | 12.7028 | 66.075 |
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  ### Framework versions