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LinkLinkWu/
Boss_Stock_News_Analysis

Name: Boss_Stock_News_Analysis
Availability: InStock

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🧠 FinancialBERT Sentiment Analysis (FinNews Binary)

This is a fine-tuned BERT model for binary sentiment classification of financial news headlines, adapted for real-time stock market sentiment prediction.

🔍 Model Details

Architecture: BERT (12-layer, 768-hidden, 12-heads)
Base model: ahmedrachid/FinancialBERT-Sentiment-Analysis
Fine-tuned task: Binary classification – Positive or Negative
Problem type: single_label_classification
Special tokens: [CLS], [SEP], [PAD], [MASK], [UNK]

Neutral headlines are mapped to Positive to simplify binary output.

🧾 Training Summary

Dataset: 5,000+ manually labeled financial news headlines
Tokenizer: Custom WordPiece tokenizer
Max sequence length: 128
Framework: Transformers v4.51.3 (PyTorch backend)
Output labels:
- LABEL_0 = Negative
- LABEL_1 = Positive

📈 Intended Use

Ideal for:

Real-time market sentiment dashboard
Trading signal pipelines
Event-driven NLP analysis

🚀 Usage (Example)

python

from transformers import pipeline

classifier = pipeline("text-classification", model="your-username/your-model-name")
classifier("Apple's Q4 earnings beat expectations amid strong iPhone sales")
# Output: [{'label': 'LABEL_1', 'score': 0.98}]

LinkLinkWu/Boss_Stock_News_Analysis