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@@ -23,10 +23,8 @@ The results on test set.
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  | Sequence-to-sequence model with attention | 26.10
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  | Neural Phrase-based Machine Translation [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 27.69
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  | Neural Phrase-based Machine Translation + LM [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 28.07
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- | t5-en-vi-small (pretraining, without training data) | **28.46** (cased)
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- | t5-en-vi-small (pretraining, without training data) | **29.23** (uncased)
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- |t5-en-vi-small (fineturning with training data) | **31.74** (cased)
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- |t5-en-vi-small (fineturning with training data) | **32.54** (uncased)
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  #### Example Using
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  | Sequence-to-sequence model with attention | 26.10
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  | Neural Phrase-based Machine Translation [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 27.69
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  | Neural Phrase-based Machine Translation + LM [Huang et. al. (2017)](https://arxiv.org/abs/1706.05565) | 28.07
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+ | t5-en-vi-small (pretraining, without training data) | **28.46** (cased) / **29.23** (uncased)
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+ |t5-en-vi-small (fineturning with training data) | **31.74** (cased) / **32.54** (uncased)
 
 
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  #### Example Using
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