A 175 KB PDF Carries a Small AG News Classifier Inside Its Metadata

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A developer has packaged a news-topic classifier into a 175 KB PDF, storing 500 labeled AG News examples as base64url-encoded JSON in the file's metadata. The visible page is a model card, and a browser viewer parses the embedded data and trains the model on the fly. The 500-example coreset reaches 78.3% accuracy on an embedded 400-row test set and 74.3% on a fresh set. The author notes the approach is a bag-of-words Complement Naive Bayes model rather than a large language model, and that the training labels came from a 1.5B-parameter model. The project is presented as a demonstration of where small models can live and how they can travel as ordinary documents.

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