Update README.md

README.md

@@ -18,11 +18,11 @@ CentralBankRoBERTA is a large language model. It combines an economic agent clas
 
 #### Overview
 
-The AudienceClassifier model is designed to classify the target audience of a given text. It can determine whether the text is adressing **households**, **firms**, **the financial sector**, **the government** or **the central bank** itself. This model is based on the RoBERTa architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.
+The AgentClassifier model is designed to classify the target agent of a given text. It can determine whether the text is adressing **households**, **firms**, **the financial sector**, **the government** or **the central bank** itself. This model is based on the RoBERTa architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.
 
 #### Intended Use
 
-The AudienceClassifier model is intended to be used for the analysis of central bank communications where content categorization based on target audiences is essential. 
+The AgentClassifier model is intended to be used for the analysis of central bank communications where content categorization based on target agents is essential. 
 
 #### Performance
 
@@ -38,12 +38,12 @@ You can use these models in your own applications by leveraging the Hugging Face
 ```python
 from transformers import pipeline
 
-# Load the AudienceClassifier model
-audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier")
+# Load the AgentClassifier model
+agent_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-agent-classifier")
 
-# Perform audience classification
-audience_result = audience_classifier("We used our liquidity tools to make funding available to banks that might need it.")
-print("Audience Classification:", audience_result[0]['label'])
+# Perform agent classification
+agent_result = agent_classifier("We used our liquidity tools to make funding available to banks that might need it.")
+print("Agent Classification:", agent_result[0]['label'])
 ```
 
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