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The first-ever vast natural language processing benchmark for Indonesian Language. We provide multiple downstream tasks, pre-trained IndoBERT models, and a starter code! (AACL-IJCNLP 2020)
IndoLEM is a comprehensive Indonesian NLU benchmark, comprising three pillars NLP task: morpho-syntax, semantic, and discourse. Presented in COLING 2020.
Model analisis sentimen berbasis IndoBERT yang dapat memprediksi 6 jenis emosi dalam suatu kalimat, yaitu marah, sedih, senang, cinta, takut, dan jijik.
🥈🏆 SEPAKAT - Modul Integrasi is a winning project in Regsosek Hackathon 2022 organized by The Ministry of National Development Planning/Bappenas Indonesia. This module provides a single individual identification model by integrating Regsosek data as basic information which is then linked with related data using the idea of entity resolution.
This repository contains the final project (skripsi) for sentiment classification on Indonesian Twitter data using the hashtag #KaburAjaDulu. It explores the performance comparison between a fine-tuned IndoBERT model and traditional machine learning models (such as SVM with IndoBERT embeddings). Built with 🤗 Hugging Face Transformers.
IndoBERT is used for sentiment analysis of product reviews, helping businesses understand customer opinions. With fine-tuning, the model improves sentiment classification accuracy, enabling more effective marketing strategies such as ad personalisation, quick response, and service improvement based on customer feedback.
A simple and interactive Streamlit web app to classify Indonesian text sentiment (positive, negative, or neutral) using IndoBERT, a pre-trained BERT model fine-tuned for sentiment analysis.
Proyek ini berfokus pada analisis demografis pengguna Twitter selama Pemilihan Presiden Indonesia 2019 dengan menggunakan teknik BERT. Studi ini bertujuan untuk memahami perbedaan opini politik berdasarkan demografi dan mengeksplorasi pengaruh media sosial, khususnya Twitter, terhadap lanskap politik Indonesia.
End-to-end Sentiment analysis project using natural language processing (NLP) to analyze reviews of cellular operator applications in the Google App Store.
This repository contains the final project (skripsi) for sentiment classification on Indonesian Twitter data using the hashtag #KaburAjaDulu. It explores the performance comparison between a fine-tuned IndoBERT model and traditional machine learning models (such as SVM with IndoBERT embeddings). Built with 🤗 Hugging Face Transformers.