Initial model upload - DistilBERT RSS Advertisement Detection

README.md

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+---
+license: mit
+language:
+- en
+tags:
+- text-classification
+- distilbert
+- advertisement-detection
+- rss
+- news
+- binary-classification
+pipeline_tag: text-classification
+---
+
+# DistilBERT RSS Advertisement Detection
+
+A DistilBERT-based model for classifying RSS article titles as advertisements or legitimate news content.
+
+## Model Description
+
+This model is fine-tuned from `distilbert-base-uncased` for binary text classification. It can distinguish between:
+- **Advertisement**: Promotional content, deals, sales, sponsored content
+- **News**: Legitimate news articles, editorial content, research findings
+
+## Intended Use
+
+- **Primary**: Filtering RSS feeds to separate advertisements from news
+- **Secondary**: Content moderation, spam detection, content categorization
+- **Research**: Text classification, advertisement detection studies
+
+## Performance
+
+- **Accuracy**: ~95%
+- **F1 Score**: ~94%
+- **Precision**: ~93%
+- **Recall**: ~94%
+
+## Training Data
+
+- **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}
+}
+```

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