Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-classification
+tags:
+- transformers
+- text-classification
+- multi-label
+- energy
+---
+
+# Energy News Classifier
+
+## Overview
+
+This model is a multi-label text classification system designed to extract structured signals from unstructured news data.
+
+It focuses on identifying themes related to global energy systems, macroeconomic shifts, and geopolitical dynamics.
+
+The model is built on top of DistilBERT and fine-tuned for domain-aware classification of news headlines and articles.
+
+---
+
+## Motivation
+
+Energy is one of the most critical drivers of global systems.
+
+Changes in supply chains, geopolitical tensions, regulation, and trade flows directly impact:
+
+- Commodity markets  
+- Inflation cycles  
+- Global logistics  
+- Financial systems  
+
+Most traditional NLP models treat news as generic categories. This model instead focuses on extracting **signal-level intelligence** from news streams.
+
+---
+
+## Labels
+
+The model supports multi-label classification across:
+
+- energy  
+- politics  
+- trade  
+- stocks  
+- regulation  
+- shipping  
+- macro  
+- business  
+- technology  
+- risk  
+
+---
+
+## Model Details
+
+- Base Model: `distilbert-base-uncased`  
+- Task: Multi-label classification  
+- Framework: Hugging Face Transformers  
+- Output: Sigmoid probabilities  
+
+---
+
+## Usage — Transformers (Recommended)
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="QuantBridge/energy-news-classifier",
+    top_k=None
+)
+
+classifier("Oil supply disrupted due to geopolitical tensions")