nemo-curator

GPU-accelerated fuzzy deduplication processes large text datasets 16x faster using NVIDIA RAPIDS. It scales across multiple GPUs to efficiently identify and remove near-duplicate content in web-scraped corpora.

GPU-accelerated data curation prepares high-quality datasets for LLM training by using NVIDIA RAPIDS to speed up tasks like quality filtering and deduplication. It supports multimodal data including text, image, video, and audio formats.

You can redact PII and detect NSFW content during LLM data preparation using built-in advanced filtering features. The process applies these safety filters alongside 30+ quality heuristics to clean web-scraped text.

Data curation with RAPIDS supports multimodal data formats including text, image, video, and audio. It processes these diverse data types on GPU clusters to prepare comprehensive training corpora for large language models.

The best way to filter low-quality content from scraped web data is applying 30+ quality heuristics and semantic deduplication. This GPU-accelerated approach removes near-duplicates and inferior content efficiently.

You need multiple GPUs to scale data curation for LLM training across large corpora. The workflow leverages Dask and RAPIDS to distribute fuzzy deduplication and filtering tasks efficiently across a GPU cluster.