F1 Score in NER: Precision, Recall & Evaluation 📊

Ever trained a Named Entity Recognition (NER) model and thought, “How do I really measure its performance?” Accuracy alone won’t cut it.

In this video, we dive into why the F1 Score is the gold standard for evaluating NER models—and how it balances precision and recall to give you real insight into your model’s effectiveness.

🔍 What You’ll Learn:

Why accuracy fails in NER tasks

How precision and recall work

What the F1 Score really tells you

The difference between token-level vs entity-level F1

When to use micro, macro, and weighted F1

Pro tips to boost your NER model's F1 score

We also break down the F1 formula, give practical examples, and share actionable tips to help you build more accurate, trustworthy NER systems using tools like spaCy, BERT, and RoBERTa.

💬 Have NER struggles or questions? Drop them in the comments below!
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