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class NerDLDataLoader extends AnyRef

DataLoader for NerDLApproach with threaded prefetching.

This class provides an efficient way to load training data for NER models by:

  • Prefetching batches in background threads to overlap I/O with computation
  • Using a bounded queue to prevent excessive memory usage
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Instance Constructors

  1. new NerDLDataLoader(config: DataLoaderConfig = DataLoaderConfig())

    config

    Configuration for the data loader

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  6. def createIterator(dataset: Dataset[Row], inputCols: Seq[String], labelColumn: String): Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]

    Creates an iterator that prefetches and yields batches of NER training data from a Spark DataFrame.

    Creates an iterator that prefetches and yields batches of NER training data from a Spark DataFrame.

    The iterator uses background threads to prefetch batches while the main thread consumes them, improving throughput by overlapping I/O with computation.

    dataset

    Spark DataFrame containing the training data

    inputCols

    TOKEN and EMBEDDING type input columns

    labelColumn

    Column name containing the NER labels

    returns

    Iterator over batches, where each batch is an Array of (labels, embeddings) pairs

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  16. def shutdown(): Unit

    Shuts down the data loader and releases all resources.

    Shuts down the data loader and releases all resources.

    This method should be called when there is still data but the loader is no longer needed to prevent resource leaks. It's safe to call multiple times.

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