Update README.md

README.md

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 # 🧠 Open Multi-Label ASJC Classification
 
-We present the first **multi-label classification model incorporating the All Science Journal Classification (ASJC) taxonomy**, designed for fine-grained classification of scientific documents.
+first open, multi-label,
+implementation of the ASJC taxonomy
+
+We present the first **multi-label classification model** that builds on the ASJC taxonomy and credibly classifies individual documents, including those published in general science or interdisciplinary
+journals.   
 
 ## 👥 Team
 - **Michael Gusenbauer** – Johannes Kepler University Linz | ORCID: [https://orcid.org/0000-0001-7768-2351](https://orcid.org/0000-0001-7768-2351)
@@ -20,13 +25,13 @@ We present the first **multi-label classification model incorporating the All Sc
 - **Thomas Ströhle** – Universität Innsbruck | ORCID: [https://orcid.org/0000-0002-1954-6412](https://orcid.org/0000-0002-1954-6412)
   
 ## 🎯 Purpose
-Traditional ASJC classification is limited by incomplete sources, journal-level labels, and single-label assignments. This project provides:  
+Traditional ASJC classification approaches are limited by incomplete sources, journal-level labels, or single-label assignments. This project provides:  
 - **Multi-label classification across 307 subjects**  
 - Fine-tuned **SciBERT model** trained on Crossref metadata  
 - Methods for **collection-level analysis** (researcher portfolios, institutions, datasets)
 
 ## ✨ Features
-- High performance: weighted F1-score 0.892 (307 subjects) | 0.934 (26 parent subjects)  
+- High performance 
 - Works with or without source title metadata  
 - Open, reproducible, and ready for research use