embeddings
CommunityMeasure text meaning, effortlessly.
Authoraviferdman
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Manually comparing the semantic similarity of texts is subjective and time-consuming. This Skill automates the objective measurement of how similar or different two pieces of text are in meaning, saving you time and ensuring consistent analysis.
Core Features & Use Cases
- Semantic Similarity Analysis: Convert text into numerical vectors (embeddings) that capture semantic meaning, allowing for quantitative comparison.
- Distance Calculation: Objectively measure the cosine or Euclidean distance between text embeddings to quantify their semantic relationship.
- Use Case: Quickly compare a translated document to its original to ensure meaning preservation, analyze customer feedback for thematic clusters, or detect plagiarism by comparing document similarity.
Quick Start
Use the embeddings skill to calculate the cosine distance between "The quick brown fox" and "A swift russet canine."
Dependency Matrix
Required Modules
sentence-transformersnumpy
Components
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: embeddings Download link: https://github.com/aviferdman/LLMs-and-Multi-Agent-Orchestration---Assignment3/archive/main.zip#embeddings Please download this .zip file, extract it, and install it in the .claude/skills/ directory.