Update README.md

README.md

@@ -50,6 +50,40 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 3
 
+
+### Usage
+```
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+import torch
+
+model = AutoModelForTokenClassification.from_pretrained('kiendt/phobert-ner-address')
+tokenizer = AutoTokenizer.from_pretrained('kiendt/phobert-ner-address')
+
+label_list = ['B_PRO', 'B_CITY', 'NUMBER_TYPE', 'B_DIST', 'TO_TYPE', 'B_STREET', 'I_PRO', 'I_DIST', 'PRO_TYPE', 'OTHER', 'I_STREET', 'B_WARD', 'STREET_TYPE', 'I_CITY', 'CITY_TYPE', 'O', 'NUMBER', 'WARD_TYPE', 'I_WARD', 'DIST_TYPE', 'TO']
+
+id2label = {i: label for i, label in enumerate(label_list)}
+
+def predict_entities(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    predictions = torch.argmax(logits, dim=-1)
+    predicted_labels = [id2label[label.item()] for label in predictions[0]]
+
+    tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
+
+    print("\nTokens and Predicted Labels:")
+    print(f"{'Token':<15} {'Predicted Label'}")
+    print("-" * 40)
+    for token, label in zip(tokens, predicted_labels):
+        print(f"{token:<15} {label}")
+
+predict_entities("Km 1 đường Nguyễn Văn Linh, PHƯỜNG PHÚC ĐỒNG, QUẬN LONG BIÊN, THÀNH PHỐ HÀ NỘI")
+```
+
+
 ### Training results