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Usage examples for LAPEFT Financial Sentiment Analysis
"""
from transformers import pipeline, BertTokenizer, BertForSequenceClassification
from peft import PeftModel
import torch
def simple_usage():
"""Simple pipeline usage - recommended for most users"""
classifier = pipeline(
"text-classification",
model="Hananguyen12/LAPEFT-Financial-Sentiment-Analysis"
)
# Example texts
examples = [
"The company exceeded earnings expectations with strong revenue growth.",
"Market volatility continues with mixed signals from investors.",
"Bankruptcy filing has caused significant concern among stakeholders."
]
for text in examples:
result = classifier(text)[0]
print(f"Text: {text}")
print(f"Sentiment: {result['label']} (Confidence: {result['score']:.3f})")
print("-" * 50)
def advanced_usage():
"""Advanced usage with direct model access"""
# Load model components
base_model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=3)
model = PeftModel.from_pretrained(base_model, "Hananguyen12/LAPEFT-Financial-Sentiment-Analysis")
tokenizer = BertTokenizer.from_pretrained("Hananguyen12/LAPEFT-Financial-Sentiment-Analysis")
def predict_detailed(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
return {
"negative": probs[0][0].item(),
"neutral": probs[0][1].item(),
"positive": probs[0][2].item()
}
# Example
text = "The quarterly report shows promising growth indicators."
scores = predict_detailed(text)
print(f"Text: {text}")
print(f"Detailed scores: {scores}")
if __name__ == "__main__":
print("=== Simple Usage ===")
simple_usage()
print("\n=== Advanced Usage ===")
advanced_usage()
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