Create README.md

README.md

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+---
+tags:
+- sentiment-analysis
+- finance
+- roberta
+- text-classification
+datasets:
+- FinancialMarketNewsHeadlines
+license: apache-2.0
+---
+
+# 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
+
+```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}")