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@@ -97,16 +97,17 @@ Training code was from the Google's Jax/Flax based [t5x framework](https://githu
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  Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length of 128 bytes.
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- When fine-tuned on those datasets, this model (the fourth row of the table) achieves the following accuracy results compared to our other T5 models and their parameter counts:
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  | | Model parameters | Yle News accuracy | Eduskunta accuracy |
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  |-------------------------------------------------------|------------------|---------------------|----------------------|
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  |Finnish-NLP/t5-tiny-nl6-finnish | 31 million |92.80 |69.07 |
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  |Finnish-NLP/t5-mini-nl8-finnish | 72 million |93.89 |71.43 |
 
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  |Finnish-NLP/t5-small-nl24-finnish | 260 million |**94.68** |74.90 |
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  |Finnish-NLP/byt5-base-finnish | 582 million |92.33 |73.13 |
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  |Finnish-NLP/t5-base-nl36-finnish | 814 million |94.40 |**75.97** |
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- |Finnish-NLP/t5-large-nl36-finnish | 1425 million |TBA |TBA |
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  Fine-tuning Google's multilingual mT5 models on the same datasets we can clearly see that our monolingual Finnish T5 models achieve much better results on Finnish text classification:
 
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  Evaluation was done by fine-tuning the model on a downstream text classification task with two different labeled Finnish datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Classification fine-tuning was done with a sequence length of 128 bytes.
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+ When fine-tuned on those datasets, this model (the fifth row of the table) achieves the following accuracy results compared to our other T5 models and their parameter counts:
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  | | Model parameters | Yle News accuracy | Eduskunta accuracy |
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  |-------------------------------------------------------|------------------|---------------------|----------------------|
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  |Finnish-NLP/t5-tiny-nl6-finnish | 31 million |92.80 |69.07 |
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  |Finnish-NLP/t5-mini-nl8-finnish | 72 million |93.89 |71.43 |
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+ |Finnish-NLP/t5-small-nl16-finnish | 184 million |94.46 |74.00 |
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  |Finnish-NLP/t5-small-nl24-finnish | 260 million |**94.68** |74.90 |
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  |Finnish-NLP/byt5-base-finnish | 582 million |92.33 |73.13 |
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  |Finnish-NLP/t5-base-nl36-finnish | 814 million |94.40 |**75.97** |
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+ |Finnish-NLP/t5-large-nl36-finnish | 1425 million |94.17 |73.50 |
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  Fine-tuning Google's multilingual mT5 models on the same datasets we can clearly see that our monolingual Finnish T5 models achieve much better results on Finnish text classification: