Files

Alexander Whitestone 11cc14d707 init: Hermes config, skills, memories, cron

Sovereign backup of all Hermes Agent configuration and data.
Excludes: secrets, auth tokens, sessions, caches, code (separate repo).

Tracked:
- config.yaml (model, fallback chain, toolsets, display prefs)
- SOUL.md (Timmy personality charter)
- memories/ (persistent MEMORY.md + USER.md)
- skills/ (371 files — full skill library)
- cron/jobs.json (scheduled tasks)
- channel_directory.json (platform channels)
- hooks/ (custom hooks)

2026-03-14 14:42:33 -04:00

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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

Method	Speed	Recall	Use Case
Exact	Fastest	100%	Exact matches only
Fuzzy	Fast	~95%	Near-duplicates (recommended)
Semantic	Slow	~90%	Paraphrases, rewrites

Best practices

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

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