ThaiSafetyClassifier

ThaiSafetyClassifier

A binary classifier that predicts whether an LLM response to a given prompt is safe or harmful for Thai language and culture. Built by fine-tuning DeBERTaV3-base with LoRA for parameter-efficient training.

Model Details

• Model type: Text classification (binary)
• Base model: microsoft/deberta-v3-base
• Fine-tuning method: LoRA (Low-Rank Adaptation)
• Language: Thai
• Labels: 0 → safe, 1 → harmful

Input Format

The model takes a prompt–response pair concatenated as:

input: <prompt> output: <llm_response>

Tokenized with the DeBERTa tokenizer at a maximum sequence length of 256.

Training Details

LoRA Configuration

Hyperparameters

Loss Function

Class-balanced loss with β = 0.9999 to address class imbalance.

Dataset

Class distribution: 79.5% safe, 20.5% harmful

Evaluation Results

Evaluated on the held-out test set (4,690 samples):

How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

base_model_name = "microsoft/deberta-v3-base"
model_name = "trapoom555/ThaiSafetyClassifier"

tokenizer = AutoTokenizer.from_pretrained(model_name)
base_model = AutoModelForSequenceClassification.from_pretrained(base_model_name, num_labels=2)
model = PeftModel.from_pretrained(base_model, model_name)
model.eval()

prompt = "your prompt here"
response = "llm response here"
text = f"input: {prompt} output: {response}"

inputs = tokenizer(text, return_tensors="pt", max_length=256, truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
    pred = logits.argmax(-1).item()

label = "harmful" if pred == 1 else "safe"
print(label)

Citation

If you use this model, please cite the relevant works:

@misc{ukarapol2026thaisafetybenchassessinglanguagemodel,
      title={ThaiSafetyBench: Assessing Language Model Safety in Thai Cultural Contexts}, 
      author={Trapoom Ukarapol and Nut Chukamphaeng and Kunat Pipatanakul and Pakhapoom Sarapat},
      year={2026},
      eprint={2603.04992},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.04992}, 
}