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This project implements a distributed K-means clustering algorithm using a custom-built MapReduce framework. It is designed to handle potentially large datasets by distributing the clustering workload across multiple processes or machines. Uses gRPC for the communication between mapper, reducer, master
SMASHED is a toolkit designed to apply transformations to samples in datasets, such as fields extraction, tokenization, prompting, batching, and more. Supports datasets from Huggingface, torchdata iterables, or simple lists of dictionaries.