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ch05/01_main-chapter-code/ch05.ipynb
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@ -743,7 +743,7 @@
"id": "71ae26dd-d77e-41fd-b924-6bd103dd4ee7",
"metadata": {},
"source": [
"- The perplexity is often considered more interpretable because it can be understood as the effective vocabulary size that the model is uncertain about at each step (in the example above, that'd be 47,678 words or tokens)\n",
"- The perplexity is often considered more interpretable because it can be understood as the effective vocabulary size that the model is uncertain about at each step (in the example above, that'd be 48,725 words or tokens)\n",
"- In other words, perplexity provides a measure of how well the probability distribution predicted by the model matches the actual distribution of the words in the dataset\n",
"- Similar to the loss, a lower perplexity indicates that the model predictions are closer to the actual distribution"
]