Koushim/distilbert-yahoo-answers-topic-classifier

DistilBERT Fine-Tuned on Yahoo Answers Topics

This is a fine-tuned DistilBERT model for topic classification on the Yahoo Answers Topics dataset. It classifies questions into one of 10 predefined categories like "Science & Mathematics", "Health", "Business & Finance", etc.

🧠 Model Details

• Base model: distilbert-base-uncased
• Task: Multi-class Text Classification (10 classes)
• Dataset: Yahoo Answers Topics
• Training samples: 50,000 (subset)
• Evaluation samples: 5,000 (subset)
• Metrics: Accuracy

🧪 How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("Koushim/distilbert-yahoo-answers")
model = AutoModelForSequenceClassification.from_pretrained("Koushim/distilbert-yahoo-answers")

text = "How do I improve my math skills for competitive exams?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)

predicted_class = outputs.logits.argmax(dim=1).item()
print("Predicted class:", predicted_class)

📊 Classes (Labels)

0. Society & Culture
1. Science & Mathematics
2. Health
3. Education & Reference
4. Computers & Internet
5. Sports
6. Business & Finance
7. Entertainment & Music
8. Family & Relationships
9. Politics & Government

📦 Training Details

• Optimizer: AdamW
• Learning rate: 2e-5
• Batch size: 16 (train), 32 (eval)
• Epochs: 3
• Weight decay: 0.01
• Framework: PyTorch + 🤗 Transformers

📁 Repository Structure

• config.json – Model config
• pytorch_model.bin – Trained model weights
• tokenizer.json, vocab.txt – Tokenizer files

✍️ Author

• Hugging Face Hub: Koushim
• Model trained using transformers.Trainer API

📄 License

Apache 2.0