indo-roBERTa-financial-sentiment-v2

IndoRoBERTa Financial Sentiment v2

Fine-tuned Indonesian financial sentiment classifier built on top of ihsan31415/indo-roBERTa-financial-sentiment, which itself derives from w11wo/indonesian-roberta-base-sentiment-classifier.

Model Details

Label Mapping

⚠️ Non-standard label order — inherited from the base model.

Training Data

This model was fine-tuned on a combination of three datasets, augmented with synthetic data:

Data Augmentation

Three augmentation methods were used to balance class distribution:

1. Gemini Synthetic Generation (gemini-2.5-flash) — Generated structured financial news sentences targeting underrepresented classes
2. GPT-2 Prompt Completion (indonesian-nlp/gpt2-medium-indonesian) — Diverse paraphrasing from financial prompt templates
3. RoBERTa Masked Augmentation — Strategic token masking with protected financial keywords (tickers, directional terms)

Final Dataset Distribution

Training Configuration

TrainingArguments(
    per_device_train_batch_size=64,
    gradient_accumulation_steps=4,       # Effective batch size: 256
    num_train_epochs=15,                 # With early stopping (patience=3)
    learning_rate=1e-5,                  # Lower LR for continued fine-tuning
    weight_decay=0.01,
    warmup_ratio=0.1,
    lr_scheduler_type="cosine",
    metric_for_best_model="f1",
    fp16=True,
)

Training Progress

Evaluation Results

Per-Class Performance

Confusion Matrix

Usage

Pipeline API

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    model="will702/indo-roBERTa-financial-sentiment-v2"
)

result = classifier("IHSG ditutup menguat 1.5% didorong aksi beli investor asing")
# {'label': 'LABEL_0', 'score': 0.98}  → Positive

Manual Inference

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained(
    "will702/indo-roBERTa-financial-sentiment-v2"
)
tokenizer = AutoTokenizer.from_pretrained(
    "will702/indo-roBERTa-financial-sentiment-v2"
)

text = "Rupiah melemah tajam terhadap dolar AS akibat sentimen global"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)

label_map = {0: "Positive", 1: "Neutral", 2: "Negative"}
predicted = torch.argmax(outputs.logits, dim=1).item()
print(f"Sentiment: {label_map[predicted]}")

Improvements Over Base Model

Limitations

• Primarily trained on formal financial news — may underperform on very informal social media text or slang
• Label mapping is non-standard (0=Positive) — ensure downstream systems account for this
• Augmented data includes synthetic samples which may not perfectly reflect real-world distribution

Citation

@misc{indo_roberta_fin_v2_2026,
  title        = {IndoRoBERTa Financial Sentiment v2},
  author       = {Gregorius Willson},
  howpublished = {\url{https://huggingface.co/will702/indo-roBERTa-financial-sentiment-v2}},
  year         = {2026},
  note         = {Fine-tuned from ihsan31415/indo-roBERTa-financial-sentiment with multi-source data and augmentation},
}

Acknowledgments

• Original model by Khoirul Ihsan
• Base architecture: w11wo/indonesian-roberta-base-sentiment-classifier
• CNBC Indonesia dataset by triagungj
• IndoNLU benchmark by indonlp