Perplexity Calculator
Calculate and understand perplexity scores for language model evaluation
Calculate Perplexity
PPL = 2H = 22.5 = 5.66
Result
5.66
Excellent
Model predicts next tokens very accurately
Perplexity
5.6569
Cross-Entropy (bits)
2.5000
Model Benchmarks (WikiText-2)
Lower perplexity = better. Values are approximate and may vary by evaluation method.
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What is Perplexity?
Perplexity is a measurement of how well a language model predicts a text sample. Intuitively, it represents "how surprised" the model is by the text. Lower perplexity means the model predicts the text more accurately.
A perplexity of 10 means the model is as confused as if it had to choose uniformly among 10 options at each step. A perplexity of 1 would mean perfect prediction.
The Formula
PPL = 2H(p)
PPL = Perplexity
H(p) = Cross-entropy loss in bits
Alternatively: PPL = exp(H(p)) when using natural log loss
Perplexity Scale
| Range | Quality | Meaning |
|---|---|---|
| <10 | Excellent | State-of-the-art LLMs |
| 10-20 | Very Good | Strong language models |
| 20-50 | Good | Older or smaller LLMs |
| 50-100 | Fair | Basic models, specialized domains |
| >100 | Poor | Untrained or domain mismatch |
Important Caveats
Perplexity is dataset-specific. A model may have low perplexity on one dataset but high on another. Always compare models on the same evaluation set with the same tokenizer.
Frequently Asked Questions
Why base 2 vs base e?
Both are valid. Base 2 gives cross-entropy in bits (common in information theory). Base e (natural log) is often used in deep learning frameworks. Just be consistent when comparing models.
How do I calculate perplexity for my model?
Run your model on a test dataset, collect the cross-entropy loss, then PPL = exp(loss). Most ML frameworks provide this automatically during evaluation.
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