Add Documentation about dagster_hf_datasets

docs/docs/integrations/libraries/datasets/index.md

@@ -0,0 +1,47 @@
+---
+title: Dagster & HF Datasets
+sidebar_label: HF Datasets
+sidebar_position: 1
+description: Use Dagster and Hugging Face Datasets to orchestrate data pipelines, materialize dataset assets, track metadata and lineage, and publish datasets to the Hub.
+tags: [community-supported, datasets, metadata, streaming]
+source: https://github.com/dagster-io/community-integrations/tree/main/libraries/dagster-hf-datasets
+pypi: https://pypi.org/project/dagster_hf_datasets/
+sidebar_custom_props:
+  logo: images/integrations/hf_datasets.svg
+  community: true
+partnerlink: https://github.com/huggingface/datasets
+canonicalUrl: '/integrations/libraries/hf-datasets'
+slug: '/integrations/libraries/hf-datasets'
+---
+
+import CommunityIntegration from '@site/docs/partials/\_CommunityIntegration.md';
+
+<CommunityIntegration />
+
+<p>{frontMatter.description}</p>
+
+The integration with HF Datasets makes it easy within Dagster to:
+
+- Load a Hugging Face dataset as a Dagster asset with metadata.
+- Stream large datasets efficiently in runtime-only mode
+- Capture metadata and lineage for improved observability
+- Build multi-asset pipelines that support dataset splits.
+- Publish processed datasets back to the Hugging Face Hub. 
+
+## Installation
+
+<PackageInstallInstructions packageName="dagster-hf-datasets" />
+
+## Example
+
+These examples illustrate two usage examples: The first example is a basic asset pipeline for materializing an HF dataset with metadata and persistence.
+
+<CodeExample path="docs_snippets/docs_snippets/integrations/basic_asset_example.py" language="python" />
+
+The second example is a fuller dataset pipeline for transformation, split-aware assets, and Hugging Face Hub publishing.
+
+<CodeExample path="docs_snippets/docs_snippets/integrations/dataset_pipeline_example.py" language="python" />
+
+## About HF Datasets
+
+[HF Datasets](https://huggingface.co/docs/datasets/index) is a library for accessing, processing, and sharing AI datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks.