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Title:Klasifikacija besedila s prenosnim učenjem : magistrsko delo
Authors:ID Žerak, Jure (Author)
ID Karakatič, Sašo (Mentor) More about this mentor... New window
Files:.pdf MAG_Zerak_Jure_2020.pdf (1,99 MB)
MD5: 7C9C0FF29C1B8078C99A4AF3BC808FF8
PID: 20.500.12556/dkum/f31311e4-f917-41e0-8bdd-03fcc3752859
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo ima namen preizkusiti metodo prenosnega učenja na obdelavi naravnega jezika in jo primerjati s klasičnimi metodami učenja nevronskih mrež, metodo LSTM. V delu sta uporabljena opisna metoda za teoretični in eksperiment za praktični del dela. V slednjem smo ugotovili, da je metoda prenosnega učenja na majhni količini podatkov bolj točna od klasičnih metod, vendar za to potrebuje več časa. Delo primerja prednaučeni model Bert in klasično metodo LSTM, zato je priporočljivo primerjati rezultate tudi z drugimi prednaučenimi modeli in klasičnimi metodami.
Keywords:nevronske mreže, prenosno učenje, NLP, PyTorch, LSTM
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Žerak]
Year of publishing:2020
Number of pages:IX, 64 f.
PID:20.500.12556/DKUM-78108 New window
UDC:004.85(043.2)
COBISS.SI-ID:44103683 New window
NUK URN:URN:SI:UM:DK:VRY2V0LQ
Publication date in DKUM:01.12.2020
Views:932
Downloads:116
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
:
ŽERAK, Jure, 2020, Klasifikacija besedila s prenosnim učenjem : magistrsko delo [online]. Master’s thesis. Maribor : J. Žerak. [Accessed 31 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=78108
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:27.10.2020

Secondary language

Language:English
Title:Classification of text using transfer learning
Abstract:The aim of this Master's thesis is to test the method of transfer learning with natural language processing and to compare it to a standard neural network model, namely LSTM. The thesis is using the descriptive method for the theoretical part and experimental method for the practical part. In the experiment we have discovered that, while transfer learning is more accurate than the standard model, it is also slower in the learning process. The thesis compares only the pretrained model Bert and standard model LSTM and that is why it is recommended to also check other pretrained models and standard models for comparison.
Keywords:neural networks, transfer learning, NLP, PyTorch, LSTM


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