---
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")