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The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
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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