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All MiniLM L6 V2 128dim

Developed by freedomfrier
This is a sentence embedding model based on the MiniLM architecture, capable of mapping text to a 384-dimensional vector space, suitable for tasks such as semantic search and sentence similarity calculation.
Downloads 1,377
Release Time : 12/21/2022

Model Overview

This model is a sentence transformer that can convert sentences and paragraphs into 384-dimensional dense vector representations, applicable to natural language processing tasks like clustering and semantic search.

Model Features

Efficient Vector Representation
Converts text into compact 384-dimensional vector representations, balancing computational efficiency and semantic expressiveness.
Large-scale Training
Trained on datasets with over 1 billion sentence pairs, covering multiple domains and tasks.
Contrastive Learning
Fine-tuned using contrastive learning objectives to optimize semantic similarity calculation for sentence pairs.

Model Capabilities

Sentence Embedding
Semantic Similarity Calculation
Information Retrieval
Text Clustering
Question Answering System Support

Use Cases

Information Retrieval
Document Search
Converts queries and documents into vectors for efficient retrieval through similarity calculation.
Improves search relevance and efficiency
Question Answering Systems
Question-Answer Matching
Calculates semantic similarity between questions and candidate answers.
Enhances the accuracy of question answering systems
Text Analysis
Text Clustering
Automatically groups documents with similar content.
Achieves unsupervised document organization
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