Esci MiniLM L6 V2
This is a sentence embedding model based on sentence-transformers that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.
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Release Time : 4/3/2023
Model Overview
This model is a fine-tuned version of MiniLM-L6-v2 on the Amazon ESCI dataset, specifically designed to generate dense vector representations of sentences to support semantic similarity calculations and information retrieval tasks.
Model Features
Efficient vector representation
Converts sentences and paragraphs into 384-dimensional dense vectors, facilitating subsequent similarity calculations and retrieval.
ESCI dataset fine-tuning
Specially fine-tuned on the Amazon ESCI dataset, optimizing semantic understanding capabilities in the e-commerce domain.
Lightweight model
Based on the MiniLM-L6-v2 architecture, it reduces computational resource requirements while maintaining performance.
Model Capabilities
Sentence vectorization
Semantic similarity calculation
Text clustering
Information retrieval
Use Cases
E-commerce
Product search relevance ranking
Improves search result ranking by calculating the semantic similarity between queries and product descriptions.
Enhances search result relevance and user experience.
Similar product recommendations
Discovers related products based on the vector similarity of product descriptions.
Increases cross-selling and upselling opportunities.
General text processing
Document clustering
Automatically groups documents with similar content.
Simplifies document management and information organization.
Semantic search
Deep semantic search beyond keyword matching.
Provides more accurate search results.
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