Update README.md

README.md

@@ -39,7 +39,7 @@ The model is a multi-class text classifier based on [sentence-transformers/all-m
 
 ## Intended uses & limitations
 
-The classifier assigns a class of 'NEGATIVE','TARGET_FREE', or 'NET-ZERO' to denote alignment with Net-Zero targets  in extracted passages from the documents. The intended use is for climate policy researchers and analysts seeking to automate the process of reviewing lengthy, non-standardized PDF documents to produce summaries and reports. 
+The classifier assigns a class of 'NEGATIVE','TARGET_FREE', or 'NET-ZERO' to denote **alignment with Net-Zero targets** in extracted passages from the documents. The intended use is for climate policy researchers and analysts seeking to automate the process of reviewing lengthy, non-standardized PDF documents to produce summaries and reports. 
 
 The performance of the classifier is very high. On training, the classifier exhibited very good overall performance (F1 ~ 0.9). This performance was evenly balanced between precise identification of true positive classifications (precision ~ 0.9) and a wide net to capture as many true positives as possible (recall ~ 0.9). When tested on real world unseen test data, the performance was still very high (F1 ~ 0.85). However, testing was based on a small out-of-sample dataset. Therefore classification performance will need to further evaluated on deployment.