FinancialNewsSentimentClassifier_DistilBERT

FinancialNewsSentimentClassifier_DistilBERT

📰 Overview

This is a fine-tuned DistilBERT model optimized for Sequence Classification to analyze the sentiment of financial news headlines and short articles. It categorizes the text into three classes: Bullish, Neutral, and Bearish, providing a quantifiable measure of market outlook derived from textual data. The model was trained on a comprehensive dataset of news articles from major financial publications, labeled by human experts.

🧠 Model Architecture

This model is built upon the DistilBERT base uncased architecture, a smaller, faster, and lighter version of BERT.

• Base Model: distilbert-base-uncased
• Task: Sequence Classification (DistilBertForSequenceClassification)
• Input: Tokenized financial news headlines or short-form texts (max sequence length 512).
• Output: Logits for three classes:
  • 0: Bullish (Positive market sentiment)
  • 1: Neutral (No significant market impact)
  • 2: Bearish (Negative market sentiment)
• Training Details: Fine-tuned for 3 epochs with a batch size of 16 and AdamW optimizer. Achieved an F1-score of 0.89 on the validation set.

💡 Intended Use

• Quantitative Finance: Generating sentiment scores for stocks, sectors, or the entire market based on real-time news feeds.
• Algorithmic Trading: Using the sentiment output as an input feature for high-frequency trading models.
• Market Research: Tracking historical shifts in market sentiment towards specific companies or topics.
• News Filtering: Prioritizing news articles based on their potential market impact.

How to use

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis", 
    model="[YOUR_HF_USERNAME]/FinancialNewsSentimentClassifier_DistilBERT",
    tokenizer="distilbert-base-uncased"
)

# Example usage
result = classifier("Tesla stock surges 5% on better-than-expected Q4 earnings and new China factory plans.")
print(result) 
# Expected output: [{'label': 'Bullish', 'score': 0.98...}]