Update README.md

README.md

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 # Web register classification (multilingual model)
 
-A multilingual web register classification model fine-tuned from XLM-RoBERTa-large. 
-
+Amultilingual web register classifier, fine-tuned from XLM-RoBERTa-large. 
+The model is trained with the multilingual CORE corpora across five languages (English, Finnish, French, Swedish, Turkish) to classify documents based on the [CORE taxonomy](https://www.cambridge.org/core/books/register-variation-online/D1D0F0E0BFEA077107F4686C357AA66B), detailed [here](https://turkunlp.org/register-annotation-docs/abbreviations). 
+The model demonstrates state-of-the-art performance in classifying web registers and achieves good zero-shot performance for additional languages.
+It is designed to support the development of open language models and for linguists analyzing register variation.
 ## Model Details
 
 ### Model Description
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 - **Repository:** https://github.com/TurkuNLP/pytorch-registerlabeling
 - **Paper:** Coming soon!
 
-## Uses
-
-This model is designed for classifying texts scraped from the unrestricted web into 25 pre-defined categories based on a hierarchical register taxonomy. 
-The taxonomy, based on the [CORE taxonomy](https://www.cambridge.org/core/books/register-variation-online/D1D0F0E0BFEA077107F4686C357AA66B), is detailed [here](https://turkunlp.org/register-annotation-docs/abbreviations). 
-It is trained on English, Finnish, French, Swedish, and Turkish, and performs well in zero-shot labeling for other languages. 
-It is designed to support the development of open language models and for linguists analyzing register variation.
-
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.