Source: 75+ RSS feeds from major tech news outlets
Articles: 1,600+ RSS articles
Labeled: 1,000+ manually labeled examples
Sources: TechCrunch, WIRED, The Verge, Ars Technica, OpenAI, Google AI, etc.

Usage

python

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", 
                     model="SoroushXYZ/distilbert-rss-ad-detection")

# Classify examples
examples = [
    "Apple Announces New iPhone with Advanced AI Features",
    "50% OFF - Limited Time Offer on Premium Headphones!",
    "Scientists Discover New Method for Carbon Capture",
    "Buy Now! Get Free Shipping on All Electronics Today Only!"
]

for text in examples:
    result = classifier(text)
    print(f"{text} -> {result[0]['label']} ({result[0]['score']:.3f})")

Model Architecture

Base Model: distilbert-base-uncased
Task: Binary text classification
Input: Text (max 128 tokens)
Output: Class probabilities (news, advertisement)

Training Details

Epochs: 3
Batch Size: 16
Learning Rate: 5e-5
Optimizer: AdamW
Framework: PyTorch + Transformers

Limitations

Trained primarily on tech news content
May not generalize well to other domains
Performance depends on title quality and clarity
Limited to English language content

Citation

If you use this model, please cite:

bibtex

@misc{distilbert-rss-ad-detection,
  title={DistilBERT RSS Advertisement Detection},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/SoroushXYZ/distilbert-rss-ad-detection}
}