Principle:ChenghaoMou Text dedup Dataset Loading
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, NLP |
| Last Updated | 2026-02-14 21:00 GMT |
Overview
A unified data loading abstraction that normalizes diverse input sources into an indexed HuggingFace Dataset for downstream deduplication.
Description
Dataset Loading addresses the problem of heterogeneous data sources in deduplication pipelines. Text corpora may be stored as local Parquet/CSV/JSON files, as pre-built HuggingFace Dataset directories, or on the HuggingFace Hub. This principle provides a single loading interface that: (1) dispatches to the correct loader based on the input configuration type, (2) adds a deterministic internal index column for document tracking, and (3) returns a standardized Dataset object that all downstream pipeline steps can consume.
The internal index column (') is critical because deduplication algorithms need stable document identifiers for cluster assignment and duplicate tracking that persist across map/filter operations.
Usage
Use this principle at the start of any deduplication pipeline, immediately after configuration loading. It is the bridge between raw data storage and the algorithm-specific fingerprinting and clustering steps.
Theoretical Basis
The loading strategy uses polymorphic dispatch based on configuration type:
# Abstract loading logic (NOT real implementation)
match config.input:
case LocalHFDatasetInputConfig:
ds = load_from_disk(path) # Pre-built Dataset directory
case LocalInputConfig:
ds = load_dataset(format, path) # Parquet/CSV/JSON files
ds = add_index_column(ds, "__INDEX__") # Deterministic indexing