nemo-curator

SKILL.md preview

---
name: nemo-curator
description: "Curate LLM training data: dedupe, filter, PII redaction."
version: 1.0.1
author: Orchestra Research
license: MIT
dependencies: [nemo-curator, cudf, dask, rapids]
platforms: [linux, macos]
metadata:
  hermes:
    tags: [Data Processing, NeMo Curator, Data Curation, GPU Acceleration, Deduplication, Quality Filtering, NVIDIA, RAPIDS, PII Redaction, Multimodal, LLM Training Data]

---

# NeMo Curator - GPU-Accelerated Data Curation

NVIDIA's toolkit for preparing high-quality training data for LLMs.

## When to use NeMo Curator

**Use NeMo Curator when:**
- Preparing LLM training data from web scrapes (Common Crawl)
- Need fast deduplication (16× faster than CPU)
- Curating multi-modal datasets (text, images, video, audio)
- Filtering low-quality or toxic content
- Scaling data processing across GPU cluster

**Performance**:
- **16× faster** fuzzy deduplication (8TB RedPajama v2)
- **40% lower TCO** vs CPU alternatives
- **Near-linear scaling** across GPU nodes

**Use alternatives instead**:
- **datatrove**: CPU-based, open-source data processing
- **dolma**: Allen AI's data toolkit
- **Ray Data**: General ML data processing (no curation focus)

## Quick start

### Installation

```bash
# NeMo Curator 1.x installs with uv. Extras use hyphens (PyPI-normalized):
#   text-cuda12 / text-cpu (and image/video/audio/math variants), or `all`.

# Text curation (CUDA 12)
uv pip install "nemo-curator[text-cuda12]"

# All modalities
uv pip install "nemo-curator[all]"

# CPU-only text (slower)
uv pip install "nemo-curator[text-cpu]"
```

### Basic text curation pipeline

> **Major version rewrite (1.x):** NeMo Curator was rewritten around a **Ray-based
> pipeline/stage architecture**. The old `DocumentDataset` + `nemo_curator.modules.*` /
> `ScoreFilter` / `Modify` call-the-object-on-a-dataset API from 0.x is gone. In 1.x you
> compose `ProcessingStage`s into a `Pipeline` and run it with an executor. The exact
> stage/import surface differs per modality — treat the examples in this skill below as
> **conceptual** (0.x-style) and follow the current
> [quickstart](https://github.com/NVIDIA-NeMo/Curator/blob/main/tutorials/quickstart.py)
> and [text guide](https://docs.nvidia.com/nemo/curator/latest/get-started/text) for the
> exact 1.x APIs rather than copying imports verbatim.

Shape of a 1.x pipeline (from the upstream quickstart):

```python
from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.base import ProcessingStage
from nemo_curator.stages.resources import Resources
from nemo_curator.backends.xenna import XennaExecutor
from nemo_curator.core.client import RayClient

# 1. Define/compose stages (load -> filter -> dedupe -> classify -> write).
#    Each stage declares its own Resources (CPU cores, GPU memory, replicas).
pipeline = Pipeline(name="curation", stages=[...])

# 2. Run it with an executor (Ray-backed).
client = RayClient()
client.start()
pipeline.run(XennaExecutor())
client.stop()
```

The 0.x-style snippets in the sections that follow illustrate the *concepts* (quality
filtering, exact/fuzzy/semantic dedup, PII redaction, classifier filtering). For runnable
1.x code, map each concept onto the corresponding stage from the modality guide.

## Data curation pipeline

### Stage 1: Quality filtering

```python
from nemo_curator.filters import (
    WordCountFilter,
    RepeatedLinesFilter,
    UrlRatioFilter,
    NonAlphaNumericFilter
)

# Apply 30+ heuristic filters
from nemo_curator import ScoreFilter

# Word count filter
dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

# Remove repetitive content
dataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))

# URL ratio filter
dataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))
```

### Stage 2: Deduplication

**Exact deduplication**:
```python
from nemo_curator.modules import ExactDuplicates

# Remove exact duplicates
deduped = ExactDuplicates(id_field="id", text_field="text")(d

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