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Eli5 All Mpnet Base V2

Developed by addy88
This is a sentence embedding model based on sentence-transformers, capable of mapping text to a 768-dimensional vector space, suitable for semantic search and clustering tasks.
Downloads 14
Release Time : 3/2/2022

Model Overview

This model is fine-tuned on the ELI5 dataset, specifically designed for generating dense vector representations of sentences and paragraphs, supporting tasks such as semantic similarity calculation and information retrieval.

Model Features

High-Dimensional Vector Representation
Converts text into 768-dimensional dense vectors, preserving rich semantic information
Semantic Similarity Calculation
Optimized for sentence and paragraph-level semantic similarity tasks
ELI5 Dataset Fine-Tuning
Fine-tuned on the ELI5 Q&A dataset, suitable for processing explanatory content

Model Capabilities

Sentence Embedding
Semantic Search
Text Clustering
Feature Extraction

Use Cases

Information Retrieval
Q&A Systems
Used to match user questions with answers in a knowledge base
Improves the accuracy of question-answer matching
Content Analysis
Document Clustering
Automatically groups documents with similar content
Enables unsupervised document classification
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