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github.com/pfnet-research
pfgen-benchmark is a benchmark designed to evaluate Japanese text generation, specifically for pretrained models. Unlike conventional benchmarks that use templates containing instructions, this benchmark relies solely on numerous examples. By conveying expectations such as the question-answering nature of the task, responses of approximately 100 characters, and outputs resembling formal public doc
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This is an experimental Python package that reimplements AutoGBT using LightGBM and Optuna. AutoGBT is an automatically tuned machine learning classifier which won the first prize at NeurIPS'18 AutoML Challenge. AutoGBT has the following features: Automatic Hyperparameter Tuning: the hyperparameters of LightGBM are automatically optimized, Automatic Feature Engineering: simple feature engineering
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