AI / ML

The AI category ships the file shapes machine-learning pipelines actually pass around, real formats with documented schemas, all fully synthetic. The NLP set is JSON Lines training data: labelled sentiment, token-level NER with BIO tags, chat fine-tuning in both the OpenAI and Anthropic message shapes (linked as a conversion twin), Alpaca-style instruction tuning, extractive QA in the SQuAD v2 structure with an unanswerable question, abstractive summarization, and an English–Spanish parallel corpus. The embeddings set carries the same 24 texts as an L2-normalised 16-dimensional vector set in three twinned encodings (JSON, Apache Parquet, and a raw NumPy .npy matrix) for testing vector stores and loaders. The vision set renders one detection scene and annotates it three ways (COCO JSON, YOLO text, and Pascal-VOC XML) so you can diff annotation converters against a known image. The evaluation set has benchmark results, a confusion matrix as CSV and JSON twins, an ROC curve, and a scikit-learn-style classification report; there's a templated prompt library, and a genuinely-valid tiny safetensors weight file (sample values, not a trained model). Every dataset uses fixed seeds and invented names, companies, and places, no real people or data.

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Results start with a representative from each related test family. Use a card’s variant menu or the filters below to reach a specific format, profile, or failure case; every sample retains its own detail page and download.