Update README.md

README.md

@@ -6,11 +6,11 @@ metrics:
 - accuracy
 ---
 
-# Model Card: POLLCHECK/Pollcheck-llama3-news-classifier
+# Model Card: POLLCHECK/Llama3-instruct-classifier
 
 ## Model Details
 
-**Model Name:** POLLCHECK/Pollcheck-llama3-news-classifier
+**Model Name:** POLLCHECK/Llama3-instruct-classifier
 
 **Model Description:** This is a fine-tuned llama3 model for news classification e.g. "biased" or "unbiased". In this particular task, the term 'biased' represents disinformation, propaganda, loaded language, negative associations, generalization, harm, hatred, satire 
 whereas 'unbiased' represents real news without the spread of misinformation, disinformation, and propaganda. The model can be used to identify potential bias in text, which is useful for applications in media analysis, content moderation, and research on bias in written communication.
@@ -46,7 +46,7 @@ from trl import setup_chat_format
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load the fine-tuned model and tokenizer
-model_name = "POLLCHECK/Pollcheck-llama3-news-classifier"  # Change this to the path of your fine-tuned model
+model_name = "POLLCHECK/Llama3-instruct-classifier"  # Change this to the path of your fine-tuned model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,