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

python

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)

Society & Culture
Science & Mathematics
Health
Education & Reference
Computers & Internet
Sports
Business & Finance
Entertainment & Music
Family & Relationships
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

Koushim/distilbert-yahoo-answers-topic-classifier

DistilBERT Fine-Tuned on Yahoo Answers Topics