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Operators that split batched samples back into their constituent individual samples.
Distinct from Batch Aggregators: Distinct from Batch Aggregators: focuses on splitting batches into individuals rather than combining data points into summaries.
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Data-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines. The project distinguishes itself through a YAML-based data recipe sys
Provides operators to split batched samples back into individual samples.