mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
Delete examples directory
This commit is contained in:
Mohd Kaif
@@ -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}")
|
||||
Reference in New Issue
Block a user