Update README.md

Code example updated

README.md

@@ -78,19 +78,47 @@ tags:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
-tokenizer = AutoTokenizer.from_pretrained("boltuix/bert-ner-model") 🎨
-model = AutoModelForTokenClassification.from_pretrained("boltuix/bert-ner-model") 🤖
-text = "Big Managers on Campus" 📝
-inputs = tokenizer(text, return_tensors="pt") 🚀
-outputs = model(**inputs) 🌟
-predictions = outputs.logits.argmax(dim=-1) 🎯
-tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0]) ✨
-labels = [label_list[p] for p in predictions[0]] 🎨
-print(list(zip(tokens, labels))) 📊
+import torch
+
+# Load tokenizer and model from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained("boltuix/bert-ner")
+model = AutoModelForTokenClassification.from_pretrained("boltuix/bert-ner")
+
+# Sample input text
+text = "Barack Obama visited Microsoft headquarters in Seattle."
+
+# Tokenize the input
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Get predictions
+predictions = outputs.logits.argmax(dim=-1)
+
+# Convert token IDs to tokens
+tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
+
+# Get the label map from model config
+label_map = model.config.id2label
+
+# Map predicted IDs to labels
+labels = [label_map[p.item()] for p in predictions[0]]
+
+# Print token-label pairs (skip special tokens like [CLS], [SEP])
+for token, label in zip(tokens, labels):
+    if token not in tokenizer.all_special_tokens:
+        print(f"{token:15} → {label}")
+
 ```
 
 ### ✨ Output
-💥 "Big Managers on Campus" → 🔹🔹🔹🔹
+barack          → B-PERSON
+obama           → I-PERSON
+visited         → O
+microsoft       → B-ORG
+headquarters    → O
+in              → O
+seattle         → B-GPE
+.               → O
 
 ## 🏋️ Training Details