Tagging FAQs

Question 1

How does Tagging ensure high performance for large datasets?

Accepted Answer

Tagging achieves high performance through its parallel processing capabilities, allowing it to tag hundreds or thousands of CSV rows concurrently. This makes it ideal for batch classification tasks and large-scale data analysis.

Question 2

Which Large Language Model (LLM) providers does Tagging support?

Accepted Answer

Tagging offers broad compatibility, supporting a wide array of LLM providers including Claude (Anthropic), OpenAI, Gemini, Groq, and AWS Bedrock. Users can select their preferred provider and model for tagging tasks.

Question 3

What is Tagging?

Accepted Answer

Tagging is a powerful software tool designed for fast, parallel classification and tagging of CSV data rows. It leverages advanced Large Language Models (LLMs) from multiple providers to categorize data efficiently.

Question 4

Can I define custom or complex taxonomies with Tagging?

Accepted Answer

Yes, Tagging provides highly flexible taxonomy definition. You can create simple lists of tags for basic classification or define complex multi-field, multi-dimensional taxonomies using Pydantic models for advanced use cases like sentiment and urgency analysis.

Question 5

Does Tagging provide transparency into its classification decisions?

Accepted Answer

Absolutely. Tagging is designed for transparency, providing detailed reasoning, reflection, and confidence scores (0.0-1.0) for each tag it assigns. This helps users understand the model's decision-making process and evaluate the reliability of the classifications.

Tagging

Tagging

주요 기능

사용 사례

주요 기능

사용 사례