A fine-tuned BERT model with LoRA for financial sentiment analysis. This model classifies financial text into three categories: Negative, Neutral, and Positive.

Model Details

Base Model: bert-base-uncased
Fine-tuning: LoRA (Low-Rank Adaptation)
Classes: 3 (Negative, Neutral, Positive)
Domain: Financial text analysis
Language: English

Usage

Quick Start with Pipeline

python

from transformers import pipeline

# Load the model
classifier = pipeline(
    "text-classification", 
    model="Hananguyen12/LAPEFT-Financial-Sentiment-Analysis"
)

# Analyze sentiment
text = "The company reported strong quarterly earnings."
result = classifier(text)
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.9234}]

Advanced Usage

python

from transformers import BertTokenizer, BertForSequenceClassification
from peft import PeftModel

# 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("bert-base-uncased")

# Inference
text = "The quarterly results exceeded expectations."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1)

labels = ["NEGATIVE", "NEUTRAL", "POSITIVE"]
print(f"Predicted: {labels[predicted_class]}")

Model Performance

Optimized for financial text analysis
Efficient LoRA fine-tuning approach
Suitable for real-time sentiment analysis

Use Cases

Financial news sentiment analysis
Social media monitoring for financial content
Investment research and analysis
Risk assessment based on sentiment

Limitations

Trained primarily on English financial text
Performance may vary on non-financial content
Best suited for sentences and short paragraphs

Citation

bibtex

@misc{lapeft_financial_sentiment_2025,
  title={LAPEFT: Financial Sentiment Analysis with LoRA},
  author={Hananguyen12},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Hananguyen12/LAPEFT-Financial-Sentiment-Analysis}
}