Update README.md

README.md

@@ -124,12 +124,13 @@ ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
 text = "Stealc malware targets browser cookies and passwords."
 entities = ner_pipeline(text)
 print(entities)
+```
 
 ## Training Details
 
 ### Training Objective and Procedure
 
-The `securebert2.0-ner` was fine-tuned for **token-level classification** on cybersecurity text using **Cross Entropy Loss**.  
+The `SecureBERT2.0-NER` was fine-tuned for **token-level classification** on cybersecurity text using **Cross Entropy Loss**.  
 Training focused on accurately classifying entity boundaries and types across five cybersecurity-specific categories: *Malware, Indicator, System, Organization,* and *Vulnerability*.
 
 The **AdamW** optimizer was used with a **linear learning rate scheduler**, and gradient clipping ensured stability during fine-tuning.
@@ -173,7 +174,7 @@ The model was fine-tuned on a **cybersecurity-specific NER corpus**, containing
 
 | Component | Description |
 |:-----------|:-------------|
-| GPUs Used | 8× NVIDIA A100 (80 GB) |
+| GPUs Used | 8× NVIDIA A100 |
 | Precision | Mixed precision (fp16) |
 | Batch Size | 8 per GPU |
 | Framework | Transformers (TensorFlow backend) |