Create README.md

README.md

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+---
+tags:
+- text-classification
+- bert
+- financial-sentiment
+- natural-language-processing
+datasets:
+- FinancialSentimentAnalysis_NewsHeadlines
+license: apache-2.0
+model-index:
+- name: FinancialSentimentClassifier_NewsArticles
+  results:
+  - task:
+      name: Text Classification
+      type: text-classification
+    metrics:
+    - type: accuracy
+      value: 0.915
+      name: Accuracy
+    - type: f1
+      value: 0.908
+      name: F1 Score
+---
+
+# FinancialSentimentClassifier_NewsArticles
+
+## 📰 Overview
+
+This model is a fine-tuned **BERT (Bidirectional Encoder Representations from Transformers)** model specialized for classifying the sentiment of financial news headlines into three distinct categories: **POSITIVE**, **NEGATIVE**, and **NEUTRAL**. It is built to support automated monitoring of market-moving news and quantifying the immediate impact of reports on specific entities.
+
+## 🧠 Model Architecture
+
+The architecture is based on the robust `bert-base-uncased` checkpoint, fine-tuned for sequence classification.
+
+* **Base Model:** `bert-base-uncased`
+* **Classification Head:** A standard linear layer is added on top of the pooled output of BERT (the `[CLS]` token representation).
+* **Input Data:** Financial news headlines (English text).
+* **Output:** Logits corresponding to the three classes: Negative (0), Neutral (1), Positive (2).
+* **Training Dataset:** A proprietary dataset, **FinancialSentimentAnalysis_NewsHeadlines**, featuring headlines, entity mentions, and manually verified sentiment labels.
+
+## 🎯 Intended Use
+
+This model is primarily intended for research and industrial applications requiring high-precision financial text analysis:
+
+1.  **Automated Trading Signal Generation:** Classifying news sentiment to inform algorithmic trading strategies.
+2.  **Entity-Level Sentiment Tracking:** Monitoring sentiment specifically linked to companies or commodities mentioned in the headline (`Entity_Mentioned`).
+3.  **Risk Management:** Identifying sudden shifts in negative sentiment towards a portfolio.
+4.  **Academic Research:** Studying the correlation between news sentiment and market volatility.
+
+## ⚠️ Limitations
+
+1.  **Scope:** The model is highly specialized for *financial* news language. Performance may degrade when applied to general domain text (e.g., social media or political articles).
+2.  **Subtlety:** It may struggle with complex, nuanced, or ironic financial reporting where the sentiment is not explicitly stated but implied through market context (e.g., "analysts remain cautious").
+3.  **Language:** Trained exclusively on English headlines.
+4.  **Recency:** Its vocabulary and context were established up to its training date. New economic terminology or highly unique events may pose a challenge.