Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- indonesian
+- finance
+- sentiment
+- text-classification
+- finbert
+- transformers
+- pytorch
+- huggingface
+language: id
+datasets:
+- custom
+base_model: ProsusAI/finbert
+model-index:
+- name: FinBERT Indonesia
+  results: []
+---
+
+# 🇮🇩 FinBERT Indonesia — Sentiment Classification for Financial News in Bahasa Indonesia
+
+This model is a fine-tuned version of [`ProsusAI/finbert`](https://huggingface.co/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`.
+
+---
+
+## 🏗️ Model Architecture
+
+The base model is [FinBERT](https://huggingface.co/ProsusAI/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 is based on accuracy using a stratified train/test split.
+
+| Metric   | Score   |
+|----------|---------|
+| Accuracy | *TBD*   |
+
+To reproduce the benchmark or compare other models, see the sample inference code below.
+
+---
+
+## 🧪 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'