IndoBERT Tweet Spam Classifier

Fine-tuned indolem/indobert-base-uncased for binary twitter (X) post spam / not-spam classification of Indonesian social media posts.

Model Description

• Base model: indolem/indobert-base-uncased
• Task: Text classification (binary)
• Language: Indonesian (id)
• Labels: spam (1), not_spam (0)

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="fahmisyaifudin/indobert-tweet-spam-classifier")
clf("GIVEAWAY ALERT! Dapatkan undian berhadiah 100 juta, klik link bio")
# [{'label': 'spam', 'score': 0.98}]

Training Data

Fine-tuned on a labeled dataset of 4000+ indonesian twitter social media posts, cleaned to remove links and emojis prior to training. Labels: 0 = not_spam, 1 = spam.

Training Procedure

• Epochs: 4
• Batch size: 16
• Learning rate: 2e-5
• Max sequence length: 256
• Optimizer: AdamW

Evaluation Results