Update README.md

README.md

@@ -67,41 +67,28 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 # Define the custom label mapping
 custom_label_mapping = {
-    0: 'O',        # Outside of any named entity
-    1: 'B-PER',    # Beginning of a person's name
-    2: 'I-PER',    # Inside a person's name
-    3: 'B-ORG',    # Beginning of an organization's name
-    4: 'I-ORG',    # Inside an organization's name
-    5: 'B-LOC',    # Beginning of a location's name
-    6: 'I-LOC',    # Inside a location's name
+    0: 'O',        
+    1: 'B-PER',    
+    2: 'I-PER',    
+    3: 'B-ORG',    
+    4: 'I-ORG',    
+    5: 'B-LOC',    
+    6: 'I-LOC',    
 }
 
 ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
 
-# Input text
-text = "Donald trabaja en Twitter"
+# Sample Spanish text
+text = "Elon Musk vive en Estados Unidos y es dueño de Space X, Tesla y Starlink"
 
 raw_results = ner_pipeline(text)
 
-results_with_labels = []
 for entity in raw_results:
     label_index = int(entity['entity'].split('_')[1])
     
     entity_label = custom_label_mapping.get(label_index, "UNKNOWN")
-    
-    entity_with_label = {
-        "entity": entity_label,  # Use the mapped label
-        "word": entity["word"],
-        "start": entity["start"],
-        "end": entity["end"],
-        "score": entity["score"],
-    }
-    results_with_labels.append(entity_with_label)
-
-# Print the final results
-print("NER Results Labels:")
-for result in results_with_labels:
-    print(result)
+    print(f"Word: {entity['word']}, Label: {entity_label}")
+
 ```
 
 ## Training procedure