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)
from transformers import pipeline
classifier = pipeline(
"text-classification",
model="QuantBridge/energy-news-classifier",
top_k=None
)
classifier("Oil supply disrupted due to geopolitical tensions")