You can deploy this model and start earning money today!

QuantBridge/
energy-news-classifier

Views

No views yet

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")