Principle:ChenghaoMou Text dedup Duplicate Removal And Output
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Deduplication |
| Last Updated | 2026-02-14 21:00 GMT |
Overview
A unified output stage that filters duplicate records from the dataset and persists both the deduplicated data and optional cluster mappings to disk.
Description
After any deduplication algorithm has assigned cluster labels or duplicate flags to documents, the final pipeline stage must: (1) filter out duplicates, keeping only cluster representatives or first-seen entries, (2) save the cleaned dataset to disk in HuggingFace Dataset format, (3) optionally persist the cluster mapping as a pickle file for downstream evaluation or analysis, and (4) clean up internal columns (', __CLUSTER__') unless explicitly requested to keep them.
This principle separates the output concern from the algorithm concern, allowing all four algorithms (MinHash, SimHash, Bloom Filter, Suffix Array) to share the same save logic.
Usage
Use this principle as the final step of any deduplication pipeline, after cluster assignment or duplicate flagging is complete.
Theoretical Basis
The output logic follows a common pattern:
# Abstract output logic (NOT real implementation)
if not skip_filtering:
dataset = dataset.filter(is_not_duplicate)
remove_internal_columns(dataset, ["__INDEX__", "__CLUSTER__"])
dataset.save_to_disk(output_dir)
if save_clusters:
pickle.dump(clusters, open("clusters.pickle", "wb"))