diff --git a/examples/huggingface_demo.py b/examples/huggingface_demo.py
deleted file mode 100644
index b758436f..00000000
--- a/examples/huggingface_demo.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""
-HuggingFace Local Model Usage Demo (Bring Your Own Model)
-
-This script demonstrates how to use the 'semantica' library with local HuggingFace models
-for Named Entity Recognition (NER), Relation Extraction (RE), and Triplet Extraction.
-
-Prerequisites:
-    pip install transformers torch
-
-Usage:
-    python examples/huggingface_demo.py
-"""
-
-import sys
-import os
-
-# Add project root to path (for running from this dir)
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-from semantica.semantic_extract import NERExtractor, RelationExtractor, TripletExtractor, Entity
-
-def demo_ner():
-    print("\n" + "="*50)
-    print("NER Demo: Bring Your Own Model (BYOM)")
-    print("="*50)
-    
-    # 1. Initialize NERExtractor with HuggingFace method and a specific model
-    # Common models: "dslim/bert-base-NER", "dbmdz/bert-large-cased-finetuned-conll03-english"
-    model_name = "dslim/bert-base-NER" 
-    print(f"Initializing NERExtractor with model: {model_name}...")
-    
-    extractor = NERExtractor(
-        method="huggingface", 
-        model=model_name,
-        device="cpu"  # Use "cuda" for GPU
-    )
-    
-    text = "Steve Jobs founded Apple Inc. in Cupertino, California on April 1, 1976."
-    print(f"\nInput text: {text}")
-    
-    try:
-        # Note: This will download the model if not cached (approx 400MB)
-        print("Extracting entities (this may take a moment on first run)...")
-        entities = extractor.extract_entities(text)
-        
-        print(f"\nExtracted {len(entities)} entities:")
-        for ent in entities:
-            print(f"  - {ent.text:20} | Type: {ent.label:10} | Conf: {ent.confidence:.2f}")
-            
-    except Exception as e:
-        print(f"Extraction failed (missing dependencies?): {e}")
-
-
-def demo_relation():
-    print("\n" + "="*50)
-    print("Relation Extraction Demo: Local Model")
-    print("="*50)
-    
-    # 1. Initialize RelationExtractor
-    # Note: Relation extraction usually requires a SequenceClassification model 
-    # trained on relation datasets (e.g., TACRED, SemEval).
-    # For demo purposes, we'll use a generic placeholder or a widely used one.
-    model_name = "semantica/relation-model-v1" # This is hypothetical; replace with real model
-    print(f"Initializing RelationExtractor with method='huggingface'...")
-    
-    extractor = RelationExtractor(
-        method="huggingface",
-        model=model_name,
-        device="cpu"
-    )
-    
-    text = "Steve Jobs founded Apple Inc."
-    # Pre-defined entities are usually required for relation extraction
-    entities = [
-        Entity(text="Steve Jobs", label="PERSON", start_char=0, end_char=10), 
-        Entity(text="Apple Inc.", label="ORG", start_char=19, end_char=29)
-    ]
-    
-    print(f"\nInput text: {text}")
-    print(f"Entities: {[e.text for e in entities]}")
-    
-    try:
-        print("Extracting relations...")
-        # Note: This will fail if the model doesn't exist on HF Hub.
-        # In a real scenario, use a valid model ID like "some-user/bert-relation-extraction"
-        # For this demo, we just show the call structure.
-        relations = extractor.extract_relations(text, entities)
-        
-        print(f"\nExtracted {len(relations)} relations:")
-        for rel in relations:
-            print(f"  - {rel.subject.text} --[{rel.predicate}]--> {rel.object.text} (Conf: {rel.confidence:.2f})")
-            
-    except Exception as e:
-        print(f"Note: Relation extraction mock run (model download might fail or be skipped): {e}")
-
-
-def demo_triplet():
-    print("\n" + "="*50)
-    print("Triplet Extraction Demo: REBEL (Seq2Seq)")
-    print("="*50)
-    
-    # 1. Initialize TripletExtractor with REBEL model
-    # REBEL is a popular model for end-to-end triplet extraction
-    model_name = "Babelscape/rebel-large"
-    print(f"Initializing TripletExtractor with model: {model_name}...")
-    
-    extractor = TripletExtractor(
-        method="huggingface",
-        model=model_name,
-        device="cpu"
-    )
-    
-    text = "Apple was founded by Steve Jobs in 1976."
-    print(f"\nInput text: {text}")
-    
-    try:
-        print("Extracting triplets (this may take a moment)...")
-        triplets = extractor.extract_triplets(text)
-        
-        print(f"\nExtracted {len(triplets)} triplets:")
-        for triplet in triplets:
-            print(f"  - ({triplet.subject}, {triplet.predicate}, {triplet.object})")
-            
-    except Exception as e:
-        print(f"Extraction failed (missing dependencies?): {e}")
-
-if __name__ == "__main__":
-    print("Starting Semantica HuggingFace Usage Demo...")
-    print("Note: This script attempts to download models from Hugging Face Hub.")
-    print("Ensure you have an internet connection and 'transformers' installed.")
-    
-    # Run demos
-    # We wrap in try-except to ensure the script doesn't crash the whole session if one fails
-    try:
-        demo_ner()
-    except Exception as e:
-        print(f"NER Demo Error: {e}")
-        
-    try:
-        demo_relation()
-    except Exception as e:
-        print(f"Relation Demo Error: {e}")
-        
-    try:
-        demo_triplet()
-    except Exception as e:
-        print(f"Triplet Demo Error: {e}")