50 Pulls Updated 2 months ago


Bahasa-4b Model Report

Model Name


Model Detail

Bahasa-4b is continued training from qwen-4b using 10 billion high quality text of Indonesian. The model outperforms some 4b, and even 7b models for Indonesian tasks.

Model Developers

Bahasa AI

Intended Use

This model is intended for various NLP tasks that require understanding and generating Indonesian language. It is suitable for applications such as question answering, sentiment analysis, document summarization, and more.

Training Data

Bahasa-4b was trained on a 10 billion subset data of Indonesian dataset from a collected pool of 100 billion.


The following table shows the performance of Bahasa-4b compared to the models Sailor_4b and Mistral-7B-v0.1 across several benchmarks:

Dataset Version Metric Mode Sailor_4b Bahasa-4b-hf Mistral-7B-v0.1
tydiqa-id 0e9309 EM gen 53.98 55.04 63.54
tydiqa-id 0e9309 F1 gen 73.48 75.39 78.73
xcopa-id 36c11c EM ppl 69.2 73.2 62.40
xcopa-id 36c11c F1 ppl 69.2 73.2 -
m3exam-id-ppl ede415 EM ppl 31.27 44.47 26.68
belebele-id-ppl 7fe030 EM ppl 41.33 42.33 41.33

This data demonstrates that Bahasa-4b consistently outperforms the Sailor_4b model in various Indonesian language tasks, showing improvements in both EM (Exact Match) and F1 scores across different datasets, and is competitive with the Mistral-7B-v0.1 model.

Bahasalab/Bahasa-4b-chat · Hugging Face