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Bert Base Uncased Xnli Sts Finetuned Education

Developed by inokufu
BERT-based English sentence similarity model, optimized for educational course descriptions
Downloads 53
Release Time : 6/7/2022

Model Overview

This model maps sentences to a 768-dimensional vector space, suitable for semantic search, clustering, and similarity calculation tasks in the educational domain. Fine-tuned in multiple stages on course description datasets, XNLI, and STS data.

Model Features

Education Domain Optimization
Fine-tuned on 500,000 course description entries, specifically tailored for semantic understanding in educational scenarios
Multi-stage Fine-tuning
Progressively fine-tuned through MLM, natural language inference, and semantic similarity tasks
High-quality Embeddings
Generates 768-dimensional dense vectors that preserve rich semantic information

Model Capabilities

Sentence Vectorization
Semantic Similarity Calculation
Text Clustering
Semantic Search

Use Cases

EdTech
Course Recommendation System
Achieves precise recommendations by calculating similarity between course descriptions
Learning Resource Clustering
Automatically categorizes similar educational content
General NLP
Semantic Search
Enhances relevance in educational content retrieval
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