Update README.md

README.md

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 # DeUnCaser
 The output from Automated Speak Recognition software is usually uncased and without any punctation. This does not make a very readable text. 
 
-The DeUnCaser is a sequence-to-sequence byT5 model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns. It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer.
+The DeUnCaser is a sequence-to-sequence model that is reversing this process. It adds punctation, and capitalises the correct words. In some languages this means adding capital letters at start of sentences and on all proper nouns, in other languages, like German, it means capitalising the first letter of all nouns. It will also make attempts at adding hyphens and parentheses if this is making the meaning clearer.
 
-It is using based on the multi-lingual base model. However the current finetuning is only done on Norwegian. For other languages this will be mainly experimental. I will update it with support for other languages if there is any demand.
+It is using based on the multi-lingual T5 model. It is finetuned for 130,000 steps on a TPU v4-16 using T5X starting from the mT5.1.1 pretrained model. The finetuning scripts is based on up to 1,000,000 training examples (or as many as exists in OSCAR) from each of the 42 languages with Latin alphabet that is both part of OSCAR and the mT5 training set: Afrikaans, Albanian, Basque, Catalan, Cebuano, Czech, Danish, Dutch, English, Esperanto, Estonian, Finnish, French, Galician, German, Hungarian, Icelandic, Indonesian, Irish, Italian, Kurdish, Latin, Latvian, Lithuanian, Luxembourgish, Malagasy, Malay, Maltese, Norwegian Bokmål, Norwegian Nynorsk, Polish, Portuguese, Romanian, Slovak, Spanish, Swahili, Swedish, Turkish, Uzbek, Vietnamese, Welsh, West Frisian.
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+A Notebook for creating the training corpus is available [here](https://colab.research.google.com/drive/1bkH94z-0wIQP8Pz0qXFndhoQsokU-78x?usp=sharing).