Update README.md

README.md

@@ -72,7 +72,7 @@ The model was trained using the Hugging Face `transformers` library. The trainin
 
 ## Evaluation Results
 
-The model was evaluated on a held-out test set from the CoNLL++-NER-AR dataset. Here are the key performance metrics:
+The model was evaluated on a held-out test set from the CoNLL-NER-AR dataset. Here are the key performance metrics:
 
 - **Precision:** 0.8547
 - **Recall:** 0.8633
@@ -89,8 +89,8 @@ You can load and use the model with the Hugging Face `transformers` library as f
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("your-username/AraBERT-NER")
-model = AutoModelForTokenClassification.from_pretrained("your-username/AraBERT-NER")
+tokenizer = AutoTokenizer.from_pretrained("MostafaAhmed98/AraBert-Arabic-NER-CoNLLpp")
+model = AutoModelForTokenClassification.from_pretrained("MostafaAhmed98/AraBert-Arabic-NER-CoNLLpp")
 
 # Create a NER pipeline
 ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)