nemo-curator

To clean and deduplicate large-scale datasets for LLM training, apply heuristic and classifier-based quality filters to remove low-quality content, then use exact, fuzzy, and semantic deduplication across your text, image, video, and audio data.

GPU-accelerated data curation uses distributed processing to rapidly filter noisy web corpus data, remove duplicate entries, and redact personally identifiable information (PII) at scale, generating high-quality inputs for model training.

Yes, you can apply PII redaction and quality filtering to multimodal datasets, as the curation workflows support scalable processing across large text, image, video, and audio corpora simultaneously.

GPU acceleration is required for dataset deduplication and quality filtering to efficiently process massive, web-scale training corpora and perform distributed computing tasks across large text and multimodal datasets.

The best way to prepare Common Crawl data for LLM training is to automate data cleaning by filtering low-quality content, removing duplicates, and applying PII redaction to generate a curated training dataset.