%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
All MiniLM L6 V2 Onnx
A
All MiniLM L6 V2 Onnx
Developed by nixiesearch
This is an ONNX-based sentence transformer model that maps text to a 384-dimensional vector space, suitable for semantic search and clustering tasks.
Downloads 187
Release Time : 8/7/2023
Model Overview
This model is an efficient sentence embedding model capable of converting sentences and paragraphs into 384-dimensional dense vector representations, applicable to natural language processing tasks such as information retrieval and semantic similarity computation.
Model Features
Efficient Vector Representation
Converts text into compact 384-dimensional vectors, balancing computational efficiency and representation capability.
Multi-dataset Training
Trained on multiple high-quality datasets such as s2orc, StackExchange, and MS MARCO.
ONNX Format
Provides ONNX runtime support for easy deployment across different platforms.
Model Capabilities
Text Vectorization
Semantic Similarity Computation
Information Retrieval
Text Clustering
Question Answering System Support
Use Cases
Information Retrieval
Document Search
Converts queries and documents into vectors to enable semantic-based document retrieval.
Delivers more relevant results compared to keyword-based search.
Question Answering Systems
Question Matching
Computes the similarity between user questions and knowledge base questions to find the best answer.
Improves the accuracy of question answering systems.
Content Recommendation
Similar Content Recommendation
Recommends related articles or products based on content vector similarity.
Enhances user experience and engagement.
Transformers
SafetensorsFeatured Recommended AI Models