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Developed by spacy
A CPU-optimized multilingual named entity recognition model supporting location, organization, person, and other entity types
Sequence Labeling OtherOpen Source License:MIT#Multilingual NER#Wikipedia Entity Recognition#CPU Optimization
Downloads 245
Release Time : 3/2/2022

Model Overview

This is a multilingual named entity recognition model trained on the WikiNER dataset, specifically optimized for CPU usage, capable of identifying locations (LOC), organizations (ORG), persons (PER), and other (MISC) entities in text

Model Features

Multilingual Support
Capable of handling named entity recognition tasks in multiple languages
CPU Optimization
Specifically optimized for CPU usage, suitable for resource-constrained environments
High Accuracy
Achieves an F1 score of over 83% in named entity recognition tasks

Model Capabilities

Identify named entities in text
Support multilingual processing
Classify entity types (LOC/ORG/PER/MISC)

Use Cases

Text Analysis
News Article Entity Extraction
Automatically identify people, organizations, and locations from news articles
Quickly annotate key entities in articles
Social Media Monitoring
Analyze mentioned entities in social media content
Help track brand or person mentions on social media
Knowledge Graph Construction
Knowledge Base Entity Linking
Identify and link entities in text for knowledge graphs
Assist in building and expanding knowledge graphs
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