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We wrote a Python script to calculate the perplexity of a sentence using the GPT-2 model from the Hugging Face Transformers library. The script imports necessary libraries, loads the model and tokenizer, and defines a function that tokenizes input text, computes the model's loss, and derives perplexity from that loss. We tested the function with an example sentence, outputting a perplexity value that indicates how predictable the model finds the text. This process helps evaluate the model's understanding of language based on its training.
code initializes the GPT-2 model and tokenizer, sets up the device for processing (GPU if available), encodes an input prompt ("Hi."), generates a text response using the model, and then decodes the output back to human-readable text for display.
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code initializes the GPT-2 model and tokenizer, sets up the device for processing (GPU if available), encodes an input prompt ("Hi."), generates a text response using the model, and then decodes the output back to human-readable text for display.