| """ |
| 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" |
| ) |
| |
| |
| 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""" |
| |
| 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() |
| } |
| |
| |
| 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() |
|
|
