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Finetune Embedding All MiniLM L6 V2 Geotechnical Test V4

Developed by GbrlOl
A pre-trained model for sentence similarity calculation, capable of converting sentences into embedded representations in a high-dimensional vector space and computing their semantic similarity.
Downloads 20
Release Time : 1/26/2025

Model Overview

This model is based on the sentence-transformers/all-MiniLM-L6-v2 architecture, specifically designed for sentence similarity calculation and feature extraction. It can convert sentences into vector representations and measure their semantic similarity by computing the similarity between vectors.

Model Features

Efficient Sentence Embedding
Capable of converting sentences into high-dimensional vector representations, capturing the semantic information of sentences.
Multiple Similarity Metrics
Supports various similarity measurement methods, including cosine similarity, Euclidean distance, Manhattan distance, etc.
Compact and Efficient Model
Based on the MiniLM architecture, the model is compact yet highly efficient, suitable for resource-constrained environments.

Model Capabilities

Sentence Similarity Calculation
Sentence Feature Extraction
Semantic Search
Text Matching

Use Cases

Information Retrieval
Question-Answering Systems
Used to match the semantic similarity between user questions and candidate answers.
Achieved a Pearson cosine similarity of 0.569 on the STS development set.
Text Classification
Duplicate Question Detection
Identifying duplicate questions on platforms like Quora.
Achieved an accuracy of 0.794 on the Quora duplicate questions development set.
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