nemo-curator

Deduplicating Common Crawl data for LLM training involves running a GPU-accelerated curation pipeline that applies quality heuristics, fuzzy MinHash/LSH matching, and semantic embedding similarity to output cleaned Parquet shards.

Yes, you can use GPU acceleration for PII redaction on web-scale datasets via distributed multi-GPU execution with RAPIDS components, which processes sensitive content removal efficiently before model training.

Fuzzy and semantic deduplication in multimodal data curation is the process of removing near-duplicate documents using MinHash/LSH algorithms and embedding-based similarity to improve dataset diversity across text, image, video, and audio modalities.

Multimodal data curation supports audio and video filtering through modality-specific filters and classifiers, including ASR and WER filtering for audio, to ensure high-quality training corpora across all media types.

Yes, you need NVIDIA RAPIDS components to run distributed multi-GPU deduplication, as the curation pipeline requires GPU-capable RAPIDS libraries to execute fuzzy and semantic deduplication at web scale.

The metadata does not specify exact limitations of NSFW detection in large corpus cleaning, but it confirms the pipeline removes NSFW content alongside low-quality, duplicate, and PII data during web-scale dataset preparation.