IndoBERT-Sentiment: Context-Conditioned Sentiment Classifier for Indonesian Text

A context-conditioned sentiment classifier built on IndoBERT Large P2 (335M parameters). Unlike standard sentiment models that classify text in isolation, this model takes a topical context as additional input, enabling it to determine sentiment with respect to a specific topic.

Model Details

• Base model: indobenchmark/indobert-large-p2 (335M params)
• Task: Context-conditioned 3-class sentiment classification
• Labels: Negatif (0), Netral (1), Positif (2)
• Input format: [CLS] context [SEP] text [SEP]
• Training data: 31,360 context-text pairs across 188 topics

Performance

Head-to-head benchmark on same test set (4,704 samples)

Per-class F1

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("apriandito/indobert-sentiment-classifier")
model = AutoModelForSequenceClassification.from_pretrained("apriandito/indobert-sentiment-classifier")
model.eval()

LABELS = {0: "Negatif", 1: "Netral", 2: "Positif"}

context = "Pertumbuhan ekonomi Indonesia"
text = "ekonomi Indonesia tumbuh 5.2%, tertinggi di ASEAN"

encoding = tokenizer(context, text, truncation=True, max_length=256, return_tensors="pt")
with torch.no_grad():
    probs = torch.softmax(model(**encoding).logits, dim=-1)[0]
    pred = torch.argmax(probs).item()

print(f"{LABELS[pred]} ({probs[pred]:.4f})")
# Output: Positif (0.9999)

Why Context Matters

Standard sentiment models classify text in isolation. This fails when sentiment depends on the topic:

Training Details

• Epochs: 5 (early stopping patience 2)
• Batch size: 16
• Learning rate: 2e-5
• Max length: 256 tokens
• Class weights: Negatif 1.009, Netral 0.604, Positif 2.834
• GPU: NVIDIA RTX 3090
• Training time: ~30 minutes

Related Models

• apriandito/indobert-relevancy-classifier - Context-conditioned relevancy classifier using the same architecture

Citation

@article{saputra2026indobert-sentiment,
  title={IndoBERT-Sentiment: Context-Conditioned Sentiment Classification for Indonesian Text},
  author={Saputra, Muhammad Apriandito Arya and Alamsyah, Andry and Ramadhani, Dian Puteri and Siadari, Thomhert Suprapto and Fakhrurroja, Hanif},
  year={2026}
}