Principle:Huggingface Datasets Dataset From Generator Construction
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 18:00 GMT |
Overview
Creating datasets from Python generator functions enables lazy, memory-efficient dataset construction from arbitrary data sources.
Description
Generator-based dataset construction allows users to define a Python generator function that yields individual examples (dictionaries), and the library handles batching, serialization, and Arrow table creation. This approach is ideal for data that is too large to fit in memory at once, or data that is produced incrementally (e.g., reading from files, APIs, or databases). The generator is executed and its output is cached to disk as an Arrow dataset, making subsequent access fast. Multiprocessing is supported by passing shardable keyword arguments via gen_kwargs and setting num_proc greater than 1.
Usage
Use generator-based construction when your data source is large, streaming, or requires custom processing logic that is most naturally expressed as a Python generator. This is the recommended approach for creating datasets from custom file formats, web APIs, or any source where the full dataset cannot or should not be materialized in memory at once.
Theoretical Basis
The generator pattern decouples data production from data consumption. The library wraps the user-provided generator in an internal dataset builder that reads yielded examples, encodes them according to the provided (or inferred) features, and writes them to cached Arrow files. A fingerprint system based on hashing the generator function and its arguments enables automatic cache reuse. The sharding mechanism splits list-type values in gen_kwargs across processes, enabling parallel data generation.