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Tasfiya025/
FinancialNewsSentimentAnalyzer

Name: FinancialNewsSentimentAnalyzer
Availability: InStock

FinancialMarketNewsHeadlines

apache-2.0

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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.

Pre-trained Base: RoBERTa (Robustly Optimized BERT Pretraining Approach) provides robust feature extraction from the input text.
Classification Head: A standard linear classification layer is added on top of the pooled output of the final RoBERTa hidden layer.
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

python

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}")

Tasfiya025/FinancialNewsSentimentAnalyzer