hilaler's Stars
google-research/bert
TensorFlow code and pre-trained models for BERT
RioChndr/jaksel-language
Jaksel Script, Programming language very modern and Indonesian style
sharmaroshan/Twitter-Sentiment-Analysis
It is a Natural Language Processing Problem where Sentiment Analysis is done by Classifying the Positive tweets from negative tweets by machine learning models for classification, text mining, text analysis, data analysis and data visualization
kirralabs/indonesian-NLP-resources
data resource untuk NLP bahasa indonesia
okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection
The Dataset for Multi Label Hate Speech and Abusive Language Detection in Indonesian Twitter
yasirutomo/text-normalization
Normalisasi teks atau preprocessing untuk data media sosial
ilhamfp/indonesian-text-classification-multilingual
Improving Indonesian text classification using multilingual language model
ShinyQ/Kampus-Merdeka-Reports-Crawler
A reports crawler for kampus merdeka website. I don't know if it's legal or not but whatever... you don't want to copas 1 by 1 for documentation too, don't you?
milhamm/aang.dev
My personal website (hopefully)
emarkou/multilingual-bert-text-classification
text classification using mbert
wasiahmad/Syntax-MBERT
Official code of our work, Syntax-augmented Multilingual BERT for Cross-lingual Transfer [ACL 2021].
ShinyQ/Thesis_University-Feedback-Sentiment-Model_IndoBERT
A fine tuned IndoBERT model for University Sentiment On Social Media
ShinyQ/Thesis_University-Sentiment_REST-FastAPI
A simple implementation of IndoBERT model REST API for University Sentiment Analysis using FAST API
hilaler/Sentiment-Analysis-on-Indonesia-English-Code-Mixed-Data
Social media like facebook and twitter were really famous over the past decade. The users of social media has exponentially risen in some countries like Indonesia has given rise to large volumes of code-mixed data, in which users use more than one language in a single text. Data with code-mixed is often noisy because the same word is written multiple times, the words in the sentence are not clearly ordered, random abbreviations are used, and most importantly the monolingual model usually does not work well on it. In this work, the author will explore sentiment analysis on English-Indonesian code-mixed data. The approach that will be used is by utilizing a multilingual pre-trained model, mBERT. The evaluation will be performed based on the classification performance metrics: precision, recall, and F-1 score.