How to find My ML model is Overfitted?

🔍 What is Overfitting?

Overfitting is when your language model (LM) is too good at remembering the training data but bad at generalizing to new, unseen data 😬.

Think of it like this:

📘 Training Data = Your school textbook
🧠 Overfitted Model = A student who memorized every page but struggles with test questions that are worded differently.

🚨 Signs Your LM is Overfitted

1. 📉 Low Training Loss, 🚀 High Validation/Test Loss

• Your model is doing GREAT on the training set 😎 but performs poorly on validation/test data 😕.

✅ Training loss: Low
❌ Validation/Test loss: High

2. 🪞 Huge Gap Between Accuracy or Perplexity

If you’re tracking accuracy or perplexity (a measure for language models):

• Accuracy on training = 90%+ 🎯
• Accuracy on validation = 50%-60% 😐

That’s a big red flag 🚩

3. 📊 Your Loss Curve Looks Like This:

• Training loss keeps going down 📉
• Validation loss goes down first, then goes back up 📈

That’s a classic overfitting curve 🧠🔥

🛠️ How to Fix It?

✅ Use more data
✅ Regularization techniques like dropout 🕳️
✅ Early stopping ⏹️
✅ Smaller model if data is limited
✅ Data Augmentation (e.g., paraphrasing for text)