pere
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nb-nn-translation

Inference Endpoints

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pere commited on Jul 18, 2021

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@@ -26,7 +26,7 @@ Apart from some word-list based engines, there are not any working off-the-shelf
 ## Pretraining a T5-base
 There is an [mt5](https://huggingface.co/google/mt5-base) that includes Norwegian. Unfortunately a very small part of this is Nynorsk; there is only around 1GB Nynorsk text in mC4. Despite this, the mt5 also gives a BLEU score above 80. During the project we extracted all available Nynorsk text from the [Norwegian Colossal Corpus](https://github.com/NBAiLab/notram/blob/master/guides/corpus_v2_summary.md) at the National Library of Norway, and matched it (by material type i.e. book, newspapers and so on) with an equal amount of Bokmål. The corpus collection is described [here](https://github.com/NBAiLab/notram/blob/master/guides/nb_nn_balanced_corpus.md) and the total size is 19GB.
-## Finetuning - RECORD BLEU-SCORE 88.16 🎉
 Training for [30] epochs with a learning rate of [7e-4], a batch size of [32] and a max source and target length of [512] fine tuning reached a SACREBLEU score of [87.94] at training and a test score of [**88.16**] after training.
 ## How to use the model

 ## Pretraining a T5-base
 There is an [mt5](https://huggingface.co/google/mt5-base) that includes Norwegian. Unfortunately a very small part of this is Nynorsk; there is only around 1GB Nynorsk text in mC4. Despite this, the mt5 also gives a BLEU score above 80. During the project we extracted all available Nynorsk text from the [Norwegian Colossal Corpus](https://github.com/NBAiLab/notram/blob/master/guides/corpus_v2_summary.md) at the National Library of Norway, and matched it (by material type i.e. book, newspapers and so on) with an equal amount of Bokmål. The corpus collection is described [here](https://github.com/NBAiLab/notram/blob/master/guides/nb_nn_balanced_corpus.md) and the total size is 19GB.
+## Finetuning - BLEU-SCORE 88.16 🎉
 Training for [30] epochs with a learning rate of [7e-4], a batch size of [32] and a max source and target length of [512] fine tuning reached a SACREBLEU score of [87.94] at training and a test score of [**88.16**] after training.
 ## How to use the model