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Speed Embedding 7b Instruct
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Speed Embedding 7b Instruct

Developed by Haon-Chen
Speed Embedding 7B Instruct is a large-scale language model based on the Transformer architecture, focusing on text embedding and classification tasks, and has demonstrated outstanding performance in multiple benchmarks.
Large Language Model
Transformers
 EnglishOpen Source License:MIT#Multi-task Text Embedding#High Accuracy Classification#Retrieval Optimization
Downloads 37
Release Time : 10/31/2024

Model Overview

This model is primarily used for text classification, retrieval, and clustering tasks, supporting various natural language processing applications with high accuracy and efficiency.

Model Features

High-performance Text Classification
Excels in multiple text classification tasks, such as Amazon review classification and sentiment analysis.
Powerful Retrieval Capabilities
Performs exceptionally well in various retrieval tasks, supporting high-precision document retrieval and Q&A systems.
Efficient Clustering Capabilities
Capable of effectively handling large-scale text clustering tasks, such as academic papers and biomedical literature clustering.

Model Capabilities

Text Classification
Text Retrieval
Text Clustering
Sentiment Analysis
Q&A Systems

Use Cases

E-commerce
Product Review Classification
Used to classify Amazon product reviews as positive or negative.
Accuracy as high as 96.18%
Information Retrieval
Document Retrieval
Used to retrieve relevant documents from a large-scale document library.
Performs exceptionally well in multiple retrieval tasks
Academic Research
Paper Clustering
Used for topic clustering of academic papers.
V-measure reaches 51.12%
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