Update README.md

README.md

@@ -9,7 +9,7 @@ inference: false
 
 **slim-sa-ner** combines two of the most popular traditional classifier functions (**Sentiment Analysis** and **Named Entity Recognition**), and reimagines them as function calls on a specialized decoder-based LLM, generating output consisting of a python dictionary with keys corresponding to sentiment, and NER identifiers, such as people, organization, and place, e.g.:  
 
-    `{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],  
+    `{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],`  
      'place': ['..]}`
 
 This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.