How to Install and Uninstall sentencepiece Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 19,2024

1. Install "sentencepiece" package

Here is a brief guide to show you how to install sentencepiece on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                sentencepiece
            
                    
    
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2. Uninstall "sentencepiece" package

Please follow the guidance below to uninstall sentencepiece on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                sentencepiece
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the sentencepiece package on Ubuntu 21.10 (Impish Indri)

Package: sentencepiece
Architecture: amd64
Version: 0.1.95-1
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 233
Depends: libc6 (>= 2.14), libgcc-s1 (>= 3.0), libsentencepiece0 (>= 0.1.95), libstdc++6 (>= 5.2)
Filename: pool/universe/s/sentencepiece/sentencepiece_0.1.95-1_amd64.deb
Size: 53452
MD5sum: a9e9320efd18d1374bf408d6ce670b5b
SHA1: 6a28a73be65add44abb8fdd17bc3cb50827259e6
SHA256: 1c6fe56e9a5400e2c3955d2a9038d8e68aa0f92ba11c3ce13a093d2516bf434a
SHA512: 3b2aeead4903edea1359723fd3df4aaea9798be74a967bfb828b34580650197a0cd58c7ce7c0eecc379475d20de2f8c397ea753ebf562f5d36eeb4d0a4c716e4
Homepage: https://github.com/google/sentencepiece
Description-en: Unsupervised text tokenizer and detokenizer
SentencePiece is an unsupervised text tokenizer/detokenizer mainly
designed for Neural Network-based text generation systems where the
vocabulary size is predetermined prior to the neural model training.
Description-md5: 0fa3d641e1931e574bfffda46a21e57f