Important
NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to NeMo 2.0 overview for information on getting started.
Data Curation
- Downloading and Extracting Text
Downloading a massive public dataset is usually the first step in data curation, and it can be cumbersome due to the dataset’s massive size and hosting method. This section describes how to download and extract large corpora efficiently.
- Working with DocumentDataset
DocumentDataset is the standard format for datasets in NeMo Curator. This section describes how to get datasets in and out of this format, as well as how DocumentDataset interacts with the modules.
- CPU and GPU Modules with Dask
NeMo Curator provides both CPU based modules and GPU based modules and supports methods for creating compatible Dask clusters and managing the dataset transfer between CPU and GPU.
- Document Filtering
This section describes how to use the 30+ heuristic and classifier filters available within the NeMo Curator and implement custom filters to apply to the documents within the corpora.
- Language Identification and Unicode Fixing
Large, unlabeled text corpora often contain a variety of languages. The NeMo Curator provides utilities to identify languages and fix improperly decoded Unicode characters.
- GPU Accelerated Exact and Fuzzy Deduplication
Both exact and fuzzy deduplication functionalities are supported in NeMo Curator and accelerated using RAPIDS cuDF.
- GPU Accelerated Semantic Deduplication
NeMo-Curator provides scalable and GPU accelerated semantic deduplication functionality using RAPIDS cuML, cuDF, crossfit and Pytorch.
- Synthetic Data Generation
Synthetic data generation tools and example piplines are available within NeMo Curator.
- Downstream Task Decontamination
After training, large language models are usually evaluated by their performance on downstream tasks consisting of unseen test data. When dealing with large datasets, there is a potential for leakage of this test data into the model’s training dataset. NeMo Curator allows you to remove sections of documents in your dataset that are present in downstream tasks.
- Personally Identifiable Information Identification and Removal
The purpose of the personally identifiable information (PII) redaction tool is to help scrub sensitive data out of training datasets
- NeMo Curator on Kubernetes
Demonstration of how to run the NeMo Curator on a Dask Cluster deployed on top of Kubernetes
- Best Practices
A collection of suggestions on how to best use NeMo Curator to curate your dataset
- Next Steps
Now that you’ve curated your data, let’s discuss where to go next in the NeMo Framework to put it to good use.
- Tutorials
To get started, you can explore the NeMo Curator GitHub repository and follow the available tutorials and notebooks. These resources cover various aspects of data curation, including training from scratch and Parameter-Efficient Fine-Tuning (PEFT).
- API Docs
API Documentation for all the modules in NeMo Curator
- Download and Extract Text
- Working with DocumentDataset
- CPU and GPU Modules with Dask
- Classifier and Heuristic Quality Filtering
- Language Identification and Unicode Fixing
- GPU Accelerated Exact and Fuzzy Deduplication
- Semantic Deduplication
- Synthetic Data Generation
- Downstream Task Decontamination/Deduplication
- PII Identification and Removal
- Distributed Data Classification
- Running NeMo Curator on Kubernetes
- Best Practices
- Next Steps
- API Reference