Create notebook tutorials for distributed data classifiers

README.md

@@ -158,7 +158,7 @@ To get started with NeMo Curator, you can follow the tutorials [available here](
 
 - [`tinystories`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/tinystories) which focuses on data curation for training LLMs from scratch.
 - [`peft-curation`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/peft-curation) which focuses on data curation for LLM parameter-efficient fine-tuning (PEFT) use-cases.
-- [`distributed_data_classification`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/distributed_data_classification) which focuses on using the domain and quality classifiers to help with data annotation.
+- [`distributed_data_classification`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/distributed_data_classification) which demonstrates how to use NVIDIA's Hugging Face classifiers to help with data annotation.
 - [`single_node_tutorial`](https://github.com/NVIDIA/NeMo-Curator/tree/main/tutorials/single_node_tutorial) which demonstrates an end-to-end data curation pipeline for curating Wikipedia data in Thai.
 - [`image-curation`](https://github.com/NVIDIA/NeMo-Curator/blob/main/tutorials/image-curation/image-curation.ipynb) which explores the scalable image curation modules.
 

tutorials/README.md

@@ -19,7 +19,7 @@ To get started, we recommend starting with the following tutorials to become fam
 | [pretraining-data-curation](./pretraining-data-curation/) | Demonstrates accelerated pipeline for curating large-scale data for LLM pretraining in a distributed environment | |
 | [pretraining-vietnamese-data-curation](./pretraining-vietnamese-data-curation/) | Demonstrates how to use NeMo Curator to process large-scale and high-quality Vietnamese data in a distributed environment | |
 | [dapt-curation](./dapt-curation) | Data curation sample for domain-adaptive pre-training (DAPT), focusing on [ChipNeMo](https://blogs.nvidia.com/blog/llm-semiconductors-chip-nemo/) data curation as an example | [Blog post](https://developer.nvidia.com/blog/streamlining-data-processing-for-domain-adaptive-pretraining-with-nvidia-nemo-curator/) |
-| [distributed_data_classification](./distributed_data_classification) | Demonstrates data domain and data quality classification at scale in a distributed environment | |
+| [distributed_data_classification](./distributed_data_classification) | Demonstrates machine learning classification with NVIDIA's Hugging Face models at scale in a distributed environment | |
 | [nemotron_340B_synthetic_datagen](./nemotron_340B_synthetic_datagen) | Demonstrates the use of NeMo Curator synthetic data generation modules to leverage [Nemotron-4 340B Instruct](https://build.nvidia.com/nvidia/nemotron-4-340b-instruct) for generating synthetic preference data | |
 | [nemo-retriever-synthetic-data-generation](./nemo_retriever_synthetic_data_generation) | Demonstrates the use of NeMo Curator synthetic data generation modules to leverage [NIM models](https://ai.nvidia.com) for generating synthetic data and perform data quality assesement on generated data using LLM-as-judge and embedding-model-as-judge. The generated data would be used to evaluate retrieval/RAG pipelines |
 | [peft-curation](./peft-curation/) | Data curation sample for parameter efficient fine-tuning (PEFT) use-cases | [Blog post](https://developer.nvidia.com/blog/curating-custom-datasets-for-llm-parameter-efficient-fine-tuning-with-nvidia-nemo-curator/) |

tutorials/distributed_data_classification/README.md

@@ -0,0 +1,27 @@
+# Distributed Data Classification
+The following is a set of Jupyter notebook tutorials which demonstrate how to use various text classification models supported by NeMo Curator.
+The goal of using these classifiers is to help with data annotation, which is useful in data blending for foundation model training.
+
+Each of these classifiers are available on Hugging Face and can be run independently with the [Transformers](https://github.com/huggingface/transformers) library.
+By running them with NeMo Curator, the classifiers are accelerated using [CrossFit](https://github.com/rapidsai/crossfit), a library that leverages intellegent batching and RAPIDS to accelerate the offline inference on large datasets.
+Each of the Jupyter notebooks in this directory demonstrate how to run the classifiers on text data and are easily scalable to large amounts of data.
+
+Before running any of these notebooks, please see this [Getting Started](https://github.com/NVIDIA/NeMo-Curator?tab=readme-ov-file#get-started) page for instructions on how to install NeMo Curator.
+
+## List of Classifiers
+
+<div align="center">
+
+| NeMo Curator Classifier | Hugging Face page |
+| --- | --- |
+| `AegisClassifier` | [nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0) and [nvidia/Aegis-AI-Content-Safety-LlamaGuard-Permissive-1.0](https://huggingface.co/nvidia/Aegis-AI-Content-Safety-LlamaGuard-Permissive-1.0) |
+| `ContentTypeClassifier` | [nvidia/content-type-classifier-deberta](https://huggingface.co/nvidia/content-type-classifier-deberta) |
+| `DomainClassifier` | [nvidia/domain-classifier](https://huggingface.co/nvidia/domain-classifier) |
+| `FineWebEduClassifier` | [HuggingFaceFW/fineweb-edu-classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) |
+| `InstructionDataGuardClassifier` | [nvidia/instruction-data-guard](https://huggingface.co/nvidia/instruction-data-guard) |
+| `MultilingualDomainClassifier` | [nvidia/multilingual-domain-classifier](https://huggingface.co/nvidia/multilingual-domain-classifier) |
+| `PromptTaskComplexityClassifier` | [nvidia/prompt-task-and-complexity-classifier](https://huggingface.co/nvidia/prompt-task-and-complexity-classifier) |
+| `PyTorchClassifier` | Requires local .pth file(s) for any DeBERTa-based text classifier(s) |
+| `QualityClassifier` | [quality-classifier-deberta](https://huggingface.co/nvidia/quality-classifier-deberta) |
+
+</div>