from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("fabhiansan/indo-roberta-nli")
model = AutoModelForSequenceClassification.from_pretrained("fabhiansan/indo-roberta-nli")

# Prepare the input
premise = "Seorang wanita sedang makan di restoran."
hypothesis = "Seorang wanita sedang berada di luar ruangan."

# Tokenize the input
inputs = tokenizer(premise, hypothesis, return_tensors="pt")

# Get the prediction
outputs = model(**inputs)
predictions = outputs.logits.argmax(dim=1)

# Map predictions to labels
id2label = {0: "entailment", 1: "neutral", 2: "contradiction"}
predicted_label = id2label[predictions.item()]
print(f"Predicted label: {predicted_label}")
## Citation

If you use this model, please cite the IndoNLI paper:

```bibtex
@inproceedings{mahendra-etal-2021-indonli,
    title = {IndoNLI: A Natural Language Inference Dataset for Indonesian},
    author = {Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara},
    booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
    year = {2021},
    publisher = {Association for Computational Linguistics},
}