This model is a fine-tuned version of ProsusAI/finbert on a custom dataset of ~500 financial news headlines written in Bahasa Indonesia. The task is 3-class sentiment classification: positive, neutral, and negative.

🚀 Live Demo

Try it here: https://huggingface.co/spaces/michaelmanurung/finbert-indonesia-demo

🏗️ Model Architecture

The base model is FinBERT, which itself is a BERT model pre-trained on financial texts. It has been fine-tuned using the Hugging Face transformers library with the following modifications:

Multilingual financial context adaptation via custom labeled data in Bahasa Indonesia
Classification head for 3 sentiment labels

🧾 Dataset

The training dataset consists of 500 manually labeled financial news titles from Indonesian sources. Each entry is categorized as:

positive – bullish or growth-related headlines
neutral – factual or event-based reporting
negative – bearish or risk-indicative headlines

Example:

Title	Label
IHSG diperkirakan rebound minggu ini	positive
BI umumkan suku bunga tetap	neutral
Rupiah melemah terhadap dolar AS	negative

🧪 Evaluation

Evaluation was conducted on the intanm/indonesian-financial-sentiment-analysis benchmark dataset using accuracy, F1 score, precision, and recall metrics.

The benchmark compares:

michaelmanurung/finbert-indonesia (Indonesian financial sentiment model)
ProsusAI/finbert (English financial sentiment model)
ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa (general Indonesian sentiment model)

Model	Accuracy	F1 Score	Precision	Recall
michaelmanurung/finbert-indonesia	0.299	0.276	0.347	0.299
ProsusAI/finbert	0.285	0.134	0.560	0.285
ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa	0.334	0.238	0.669	0.334

Metrics are reported using weighted averaging.

🧪 Usage

python

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="michaelmanurung/finbert-indonesia",
    tokenizer="michaelmanurung/finbert-indonesia"
)

result = classifier("IHSG turun tipis karena aksi ambil untung investor.")
print(result)
# Output: [{'label': 'LABEL_2', 'score': 0.89}] -> e.g. 'positive'