11cc14d707
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)
88 lines
2.1 KiB
Markdown
88 lines
2.1 KiB
Markdown
# Deduplication Guide
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Complete guide to exact, fuzzy, and semantic deduplication.
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## Exact deduplication
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Remove documents with identical content.
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```python
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from nemo_curator.modules import ExactDuplicates
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# Exact deduplication
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exact_dedup = ExactDuplicates(
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id_field="id",
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text_field="text",
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hash_method="md5" # or "sha256"
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)
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deduped = exact_dedup(dataset)
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```
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**Performance**: ~16× faster on GPU vs CPU
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## Fuzzy deduplication
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Remove near-duplicate documents using MinHash + LSH.
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```python
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from nemo_curator.modules import FuzzyDuplicates
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fuzzy_dedup = FuzzyDuplicates(
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id_field="id",
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text_field="text",
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num_hashes=260, # MinHash permutations (more = accurate)
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num_buckets=20, # LSH buckets (more = faster, less recall)
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hash_method="md5",
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jaccard_threshold=0.8 # Similarity threshold
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)
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deduped = fuzzy_dedup(dataset)
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```
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**Parameters**:
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- `num_hashes`: 128-512 (default 260)
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- `num_buckets`: 10-50 (default 20)
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- `jaccard_threshold`: 0.7-0.9 (default 0.8)
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**Performance**: 16× faster on 8TB dataset (120h → 7.5h)
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## Semantic deduplication
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Remove semantically similar documents using embeddings.
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```python
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from nemo_curator.modules import SemanticDuplicates
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semantic_dedup = SemanticDuplicates(
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id_field="id",
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text_field="text",
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embedding_model="sentence-transformers/all-MiniLM-L6-v2",
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embedding_batch_size=256,
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threshold=0.85, # Cosine similarity threshold
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device="cuda"
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)
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deduped = semantic_dedup(dataset)
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```
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**Models**:
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- `all-MiniLM-L6-v2`: Fast, 384 dims
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- `all-mpnet-base-v2`: Better quality, 768 dims
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- Custom models supported
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## Comparison
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| Method | Speed | Recall | Use Case |
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|--------|-------|--------|----------|
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| Exact | Fastest | 100% | Exact matches only |
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| Fuzzy | Fast | ~95% | Near-duplicates (recommended) |
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| Semantic | Slow | ~90% | Paraphrases, rewrites |
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## Best practices
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1. **Start with exact dedup** - Remove obvious duplicates
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2. **Use fuzzy for large datasets** - Best speed/quality trade-off
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3. **Semantic for high-value data** - Expensive but thorough
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4. **GPU acceleration required** - 10-16× speedup
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