Fix Typos and Update Documentation Links in Dask and Tabby Guides

docs/hub/datasets-dask.md

@@ -93,7 +93,7 @@ the `meta` argument to know the type of the new column in the meantime.
 
 When reading Parquet data from Hugging Face, Dask automatically leverages the metadata in Parquet files to skip entire files or row groups if they are not needed. For example if you apply a filter (predicate) on a Hugging Face Dataset in Parquet format or if you select a subset of the columns (projection), Dask will read the metadata of the Parquet files to discard the parts that are not needed without downloading them.
 
-This is possible thanks to a [reimplmentation of the Dask DataFrame API](https://docs.coiled.io/blog/dask-dataframe-is-fast.html?utm_source=hf-docs) to support query optimization, which makes Dask faster and more robust.
+This is possible thanks to a [reimplementation of the Dask DataFrame API](https://docs.coiled.io/blog/dask-dataframe-is-fast.html?utm_source=hf-docs) to support query optimization, which makes Dask faster and more robust.
 
 For example this subset of FineWeb-Edu contains many Parquet files. If you can filter the dataset to keep the text from recent CC dumps, Dask will skip most of the files and only download the data that match the filter:
 

docs/hub/spaces-sdks-docker-tabby.md

@@ -6,7 +6,7 @@ In this guide, you will learn how to deploy your own Tabby instance and use it f
 
 ## Your first Tabby Space
 
-In this section, you will learn how to deploy a Tabby Space and use it for yourself or your orgnization.
+In this section, you will learn how to deploy a Tabby Space and use it for yourself or your organization.
 
 ### Deploy Tabby on Spaces