Count Dataset Tokens

概要

Streamlines the process of auditing and analyzing dataset sizes by providing a structured workflow for tokenization tasks. It guides users through exploring HuggingFace dataset structures, applying exact categorical filters, and implementing robust tokenization logic with popular models like Qwen or GPT. By emphasizing data validation and error handling for null values, this skill ensures accurate token metrics for machine learning projects and benchmark preparation.

主な機能

• Precise domain and category filtering logic
• Dataset structure exploration and schema validation
• Sanity checks and verification workflows for aggregate statistics
• Implementation patterns for various tokenizers like Qwen and GPT
• 16 GitHub stars
• Robust handling of null, empty, and special character values

ユースケース

• Calculating token counts for HuggingFace datasets before model training
• Estimating compute requirements based on specific tokenizer outputs
• Filtering large datasets by specific domains for subset analysis