Deduplication Guide

Complete guide to exact, fuzzy, and semantic deduplication.

Exact deduplication

Remove documents with identical content.

from nemo_curator.modules import ExactDuplicates

# Exact deduplication
exact_dedup = ExactDuplicates(
    id_field="id",
    text_field="text",
    hash_method="md5"  # or "sha256"
)

deduped = exact_dedup(dataset)

Performance: ~16× faster on GPU vs CPU

Fuzzy deduplication

Remove near-duplicate documents using MinHash + LSH.

from nemo_curator.modules import FuzzyDuplicates

fuzzy_dedup = FuzzyDuplicates(
    id_field="id",
    text_field="text",
    num_hashes=260,        # MinHash permutations (more = accurate)
    num_buckets=20,        # LSH buckets (more = faster, less recall)
    hash_method="md5",
    jaccard_threshold=0.8  # Similarity threshold
)

deduped = fuzzy_dedup(dataset)

Parameters:

• num_hashes: 128-512 (default 260)
• num_buckets: 10-50 (default 20)
• jaccard_threshold: 0.7-0.9 (default 0.8)

Performance: 16× faster on 8TB dataset (120h → 7.5h)

Semantic deduplication

Remove semantically similar documents using embeddings.

from nemo_curator.modules import SemanticDuplicates

semantic_dedup = SemanticDuplicates(
    id_field="id",
    text_field="text",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    embedding_batch_size=256,
    threshold=0.85,  # Cosine similarity threshold
    device="cuda"
)

deduped = semantic_dedup(dataset)

Models:

• all-MiniLM-L6-v2: Fast, 384 dims
• all-mpnet-base-v2: Better quality, 768 dims
• Custom models supported

Comparison

Best practices

1. Start with exact dedup - Remove obvious duplicates
2. Use fuzzy for large datasets - Best speed/quality trade-off
3. Semantic for high-value data - Expensive but thorough
4. GPU acceleration required - 10-16× speedup