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Instructor Large

Developed by hkunlp
INSTRUCTOR is a text embedding model based on the T5 architecture, focusing on sentence similarity calculation and text classification tasks, and supports English language processing.
Downloads 186.12k
Release Time : 12/20/2022

Model Overview

This model is mainly used for tasks such as text embedding, sentence similarity calculation, information retrieval, and text classification, and performs excellently in multiple benchmark tests.

Model Features

Multi-task support
Supports multiple text processing tasks, including sentence similarity, text classification, information retrieval, and text clustering.
High performance
Performs excellently on multiple benchmark test datasets, such as the MTEB and BEIR datasets.
Flexible text embedding
Can generate high-quality text embeddings suitable for various downstream tasks.

Model Capabilities

Text embedding
Sentence similarity calculation
Information retrieval
Text classification
Text clustering
Text re-ranking
Feature extraction

Use Cases

E-commerce
Product review classification
Perform sentiment analysis (positive/negative) classification on Amazon product reviews.
Achieved an accuracy of 91.53% on the MTEB AmazonPolarityClassification dataset.
Counterfactual review detection
Identify counterfactual reviews on Amazon.
Achieved an accuracy of 88.13% on the MTEB AmazonCounterfactualClassification dataset.
Customer service
Bank question classification
Classify bank customer questions.
Achieved an accuracy of 78.51% on the MTEB Banking77Classification dataset.
Academic research
Academic paper clustering
Perform topic clustering on academic papers from arXiv and bioRxiv.
Achieved a V-measure of 43.16% on the arXiv paper clustering task.
Question-answering system
Question-answering retrieval
Retrieve relevant questions in a technical Q&A community.
Achieved an average precision of 64.30% on the AskUbuntuDupQuestions dataset.
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