Stella Base En V2
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Stella Base En V2
Developed by infgrad
stella-base-en-v2 is an English text embedding model based on sentence-transformers, focusing on sentence similarity and feature extraction tasks.
Downloads 16.89k
Release Time : 10/19/2023
Model Overview
This model is primarily used to generate high-quality sentence embeddings, suitable for various natural language processing tasks such as sentence similarity calculation, text classification, and information retrieval.
Model Features
High-performance Sentence Embedding
Capable of generating high-quality sentence embeddings suitable for various downstream tasks.
Multi-task Support
Supports multiple natural language processing tasks such as sentence similarity, text classification, and information retrieval.
Comprehensive Evaluation
Extensively evaluated on multiple standard datasets with excellent performance.
Model Capabilities
Sentence similarity calculation
Text classification
Information retrieval
Clustering analysis
Reranking tasks
Use Cases
E-commerce
Product Review Classification
Classify Amazon product reviews to identify positive and negative feedback.
Achieved 93.26% accuracy on the AmazonPolarityClassification dataset.
Counterfactual Classification
Identify counterfactual statements in Amazon product reviews.
Achieved 77.19% accuracy on the AmazonCounterfactualClassification dataset.
Question Answering Systems
Duplicate Question Detection
Identify duplicate technical questions in the AskUbuntu community.
Achieved a MAP of 62.72 on the AskUbuntuDupQuestions dataset.
Academic Research
Paper Clustering
Perform clustering analysis on academic papers from arXiv and bioRxiv.
Achieved a V-measure of 47.24 on the ArxivClusteringP2P dataset.
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