FinancialNewsSentimentAnalyzer

FinancialNewsSentimentAnalyzer

Overview

This model is a RoBERTa-base sequence classification fine-tuned for analyzing the sentiment of financial market news headlines. It classifies headlines into one of three categories: Positive, Negative, or Neutral. The model is specifically optimized for short, impactful text snippets common in financial reporting.

Model Architecture

The core architecture is based on the RoBERTa-base pre-trained language model.

1. Pre-trained Base: RoBERTa (Robustly Optimized BERT Pretraining Approach) provides robust feature extraction from the input text.
2. Classification Head: A standard linear classification layer is added on top of the pooled output of the final RoBERTa hidden layer.
3. Output: The model outputs logits corresponding to the three sentiment classes: Negative (0), Neutral (1), and Positive (2).

Intended Use

This model is intended for:

• Algorithmic Trading Signals: Providing real-time sentiment input to inform trading strategies.
• Market Monitoring: Automatically categorizing and filtering large streams of financial news.
• Research: Analyzing market reaction to specific events or company announcements.

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "your_username/FinancialNewsSentimentAnalyzer" # Replace with actual hub path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

headline = "Tesla Stock Surges 8% Following Unexpectedly Strong Q3 Delivery Numbers"
inputs = tokenizer(headline, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_class_id = logits.argmax().item()
predicted_label = model.config.id2label[predicted_class_id]

print(f"Headline: {headline}")
print(f"Predicted Sentiment: {predicted_label}")