MTEB Text Retrieval

소개

MTEB Text Retrieval provides a structured framework for performing semantic search and document ranking tasks, specifically optimized for MTEB benchmark requirements. It guides users through the entire lifecycle of an embedding-based retrieval task, from meticulously cleaning raw data artifacts and loading transformer models to computing cosine similarities and validating semantic relevance. By emphasizing rigorous data parsing and sanity-checking of results, this skill helps avoid common pitfalls like encoding metadata or off-by-one errors in ranking, ensuring high-quality, semantically accurate retrieval outcomes.

주요 기능

• Standardized procedures for computing cosine similarity scores and ranking results
• Comprehensive verification checklists to prevent off-by-one errors and indexing anomalies
• Data parsing logic to strip line numbers and formatting artifacts from raw text
• Semantic validation techniques to verify the logical relevance of retrieved content
• 16 GitHub stars
• Implementation patterns for encoding documents and queries with Sentence Transformer models

사용 사례

• Automating document ranking and retrieval based on specific query relevance
• Executing MTEB benchmark evaluations and text similarity tasks
• Developing semantic search functionality for large document collections