Update README.md

README.md

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 # Sentence Frame Classifier
 
-A RoBERTa-based model for detecting media frames at the sentence level. This model can classify sentences into 10 different frame categories and works across both news articles and reader comments.
+A RoBERTa-based model for detecting media frames at the sentence level. This model can classify sentences into 10 different frame categories and thus can be employed to detect frames across different text types (i.e. articles and comments, but also social media etc.).
 
 ## Model Description
 
 This model was trained to identify media frames in text at the sentence level. It's based on the Media Frame Corpus [*Card et al. 2015*](https://aclanthology.org/P15-2072.pdf) and extends to online discussion contexts [*Hartmann et al., 2019*](https://arxiv.org/pdf/1904.03969), making it suitable for analyzing both professional journalism and user-generated content.
+Although trained as a multi-class classifier, probabilities for all labels can be extracted, enabling its use in multi-label classification settings.
 
-**Key Features:**
-
-- Sentence-level frame classification
-- Cross-domain capability (news articles + comments)
-- 9 frame categories based on established political communication theory
-- Robust performance across different topics
 
 ## Frame Categories
 
-The model classifies sentences into these 9 frame categories:
+The model classifies sentences into these 10 frame categories:
 
 - Economic — Economic costs, benefits, or implications
 - Morality — Moral or ethical considerations
@@ -38,14 +33,12 @@ The model classifies sentences into these 9 frame categories:
 - Health and Safety — Health risks, safety concerns, or medical implications
 - Cultural Identity — Cultural values, traditions, or identity issues
 - Public Opinion — Public sentiment, polls, or popular support
-- Other/None
+- None/Other
 
 ## Performance
 
 - Macro F1: 0.66
-- Accuracy: 0.77
-- Cross-topic generalization: Robust performance across different topics
-- Validation: Human-validated on 600 sentences
+
 
 ## Usage