Can I run SQL queries on the datasets?

Yes, the skill provides instructions for using 'parquetlens' to run SQL queries directly against the parquet shards hosted on the Hugging Face Hub.

Can I use this to upload my own datasets?

Yes, the skill includes documented workflows for uploading local parquet files to the Hugging Face Hub using the @huggingface/hub CLI tool.

Does this skill require a Hugging Face token?

For public datasets, a token is not required. However, accessing private or gated datasets requires setting an environment variable with your HF_TOKEN.

What is the limit for retrieving rows?

The Dataset Viewer API typically limits individual requests to 100 rows, but this skill supports pagination using offset and length parameters to browse larger sets.

Hugging Face Dataset Explorer

Name: Hugging Face Dataset Explorer
Author: sickn33

bysickn33

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31,721

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데이터 과학 및 ML

Interfaces with the Hugging Face Dataset Viewer API to browse, search, and analyze machine learning datasets directly within your coding environment.

This skill provides a robust interface for interacting with the Hugging Face Dataset Viewer API, allowing developers and data scientists to explore millions of datasets without leaving their AI-powered terminal. It facilitates comprehensive data inspection workflows, including split validation, row previewing, and paginated retrieval. Users can perform granular text searches, apply complex predicate filters, and even execute SQL queries against parquet shards using integrated tools like parquetlens. Whether you are auditing data for model training or managing your own uploads to the Hub, this skill streamlines the entire dataset lifecycle.

주요 기능

01SQL-based querying of parquet shards via parquetlens integration

02Comprehensive Dataset Viewer API integration for splits, rows, and metadata

03Support for private and gated datasets via HF_TOKEN authentication

04Advanced search and filtering capabilities with predicate support

0531,721 GitHub stars

06Detailed column statistics and Croissant metadata retrieval

사용 사례

01Automating the upload and verification of custom datasets to the Hugging Face Hub

02Validating and inspecting dataset schemas and sample data before model training

03Performing complex data filtering and SQL analysis on large-scale ML datasets

주요 기능

01SQL-based querying of parquet shards via parquetlens integration

02Comprehensive Dataset Viewer API integration for splits, rows, and metadata

03Support for private and gated datasets via HF_TOKEN authentication

04Advanced search and filtering capabilities with predicate support

0531,721 GitHub stars

06Detailed column statistics and Croissant metadata retrieval

사용 사례

01Automating the upload and verification of custom datasets to the Hugging Face Hub

02Validating and inspecting dataset schemas and sample data before model training

03Performing complex data filtering and SQL analysis on large-scale ML datasets