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.

The first large-scale summarization corpus for the Indonesian language. AACL 2020.

indoBERT Base-Uncased fine-tuned on Translated Squad v2.0

DAC Unpad 2021 Final, predicting government sentiment analytics topic of PPKM COVID-19 Policy

A fine tuned IndoBERT model for University Sentiment On Social Media

Model analisis sentimen berbasis IndoBERT yang dapat memprediksi 6 jenis emosi dalam suatu kalimat, yaitu marah, sedih, senang, cinta, takut, dan jijik.

Just an example of how to use indobenchmark transformer (IndoBERT, IndoGPT, IndoBertTweet) in hugging face.

Web-based hoax detection using IndoBert Fine-tuned model

A simple implementation of IndoBERT model REST API for University Sentiment Analysis using FAST API

NusaBERT: Teaching IndoBERT to be multilingual and multicultural!

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.

Model analisis sentimen berbasis IndoBERT yang dapat memprediksi sentimen dalam teks berbahasa Jawa Ngoko Lugu

dataset came from scraping in play store livin by mandiri app

🥈🏆 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.

Gojek reviews sentiment analysis using IndoELECTRA and IndoBERT with full NLP pipeline (preprocessing, labeling, training, evaluation).

Analyzing last 5 years of Tokopedia’s Google Play reviews to uncover product issues, sentiment trends, and recurring user feedback, supported by a semantic search/RAG system for insight retrieval.

The system is designed to compare and utilize multiple state-of-the-art multilingual language models to produce more accurate and context-aware translations between Indonesian and Manado language.

End-to-end pipeline for collecting, preprocessing, and analyzing app reviews to determine user sentiment using Python.