nemo-curator

Data curation for LLM training streamlines multi-modal web-scraped data by applying quality filtering, deduplication, and PII redaction. It processes text, image, video, and audio to ensure high-quality training datasets.

GPU acceleration improves multi-modal data deduplication by leveraging RAPIDS and cuDF to rapidly compute exact, fuzzy, and semantic duplicate removal. This significantly reduces processing time for large-scale LLM training datasets.

Yes, you can use Dask for distributed data curation tasks. It works in conjunction with GPU acceleration and RAPIDS to scale quality filtering and PII redaction across massive multi-modal datasets efficiently.

The best way to redact PII and filter NSFW content is using an automated data curation workflow. It safely removes personally identifiable information and detects inappropriate content during the multi-modal preparation pipeline.

Yes, nemo-curator supports audio and video modalities alongside text and images. It applies advanced processing like heuristic quality filtering and NSFW detection across all these formats for comprehensive LLM training data preparation.

Yes, GPU-accelerated data curation requires specific hardware to utilize dependencies like cudf and RAPIDS. This setup enables the fast processing of 30+ heuristic filters and semantic deduplication for large-scale datasets.