Update README.md

README.md

@@ -180,7 +180,7 @@ The model also was evaluated on a held-out test set.
 
 #### Metrics
 The evaluation metrics include precision, recall, F1 score, and accuracy, which are standard for token classification tasks. These metrics provide a comprehensive understanding of the model's ability to identify scientific terms.
-```
+```python
 metric = evaluate.load("seqeval")
 ```
 
@@ -189,14 +189,14 @@ On the test set, the model achieved an accuracy of 98.34% with a F1 score of 0.9
 
 ## Citation
 **BibTeX:**
-@misc{your_model,
+@misc{scibert-NER-finetuned-improved,
   author = {JonyC},
   title = {SciBERT for Scientific Term Detection},
   year = {2025},
-  url = {https://huggingface.co/your-username/scibert-ner}
+  url = {https://huggingface.co/JonyC/scibert-NER-finetuned-improved}
 }
 **APA:**
-JonyC. (2025). SciBERT for Scientific Term Detection. Hugging Face. https://huggingface.co/your-username/scibert-ner
+JonyC. (2025). SciBERT for Scientific Term Detection. Hugging Face. https://huggingface.co/JonyC/scibert-NER-finetuned-improved
 
 Author: JonyC