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FINE-TUNING MODEL BERT UNTUK NAMED ENTITY RECOGNITION

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Penilaian

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A sentence can contain various named entities with important meanings, such as names of people, locations, organizations, and time expressions. However, extracting this information manually requires significant time and resources. Named Entity Recognition (NER) offers an automated solution that improves the efficiency of this task. One method for developing an NER system is by using BERT, a transformer-based model that has proven effective in a wide range of natural language processing tasks. The BERT-base-cased model was fine-tuned using several hyperparameter configurations to identify the best combination based on the F1-score. The evaluation results showed that the best configuration was achieved with a learning rate of 5e-5, a batch size of 32, and 2 epochs, yielding an F1-score of 0.836765

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003334	T175150	T1751502025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1751502025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 128 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

EM

Other version/related

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