typhoon2-safety-preview

Model Card for Typhoon Safety Model

Typhoon Safety Model

Typhoon Safety is a lightweight binary classifier built on mDeBERTa-v3-base that detects harmful content in both English and Thai languages, with particular emphasis on Thai cultural sensitivities. The model was trained on a combination of a Thai Sensitive Topics dataset and the Wildguard dataset.

The model is designed to predict safety labels across the following categories:

<div class="section-header">Thai Sensitive Topics</div> <table align="center"> <tr> <th colspan="3">Category</th> </tr> <tr> <td>The Monarchy</td> <td>Student Protests and Activism</td> <td>Drug Policies</td> </tr> <tr> <td>Gambling</td> <td>Cultural Appropriation</td> <td>Thai-Burmese Border Issues</td> </tr> <tr> <td>Cannabis</td> <td>Human Trafficking</td> <td>Military and Coup</td> </tr> <tr> <td>LGBTQ+ Rights</td> <td>Political Divide</td> <td>Religion and Buddhism</td> </tr> <tr> <td>Political Corruption</td> <td>Foreign Influence</td> <td>National Identity and Immigration</td> </tr> <tr> <td>Freedom of Speech and Censorship</td> <td>Vape</td> <td>Southern Thailand Insurgency</td> </tr> <tr> <td>Sex Tourism and Prostitution</td> <td>COVID-19 Management</td> <td>Royal Projects and Policies</td> </tr> <tr> <td>Migrant Labor Issues</td> <td>Environmental Issues and Land Rights</td> <td></td> </tr> </table> <div class="section-header">Wildguard Topics</div> <table> <tr> <th colspan="3">Category</th> </tr> <tr> <td>Others</td> <td>Sensitive Information Organization</td> <td>Mental Health Over-reliance Crisis</td> </tr> <tr> <td>Social Stereotypes & Discrimination</td> <td>Defamation & Unethical Actions</td> <td>Cyberattack</td> </tr> <tr> <td>Disseminating False Information</td> <td>Private Information Individual</td> <td>Copyright Violations</td> </tr> <tr> <td>Toxic Language & Hate Speech</td> <td>Fraud Assisting Illegal Activities</td> <td>Causing Material Harm by Misinformation</td> </tr> <tr> <td>Violence and Physical Harm</td> <td>Sexual Content</td> <td></td> </tr> </table>

Model Performance

Comparison with Other Models (English Content)

Comparison with Other Models (Thai Content)

• Model type: Transformer Encoder
• Language(s) (NLP): Thai 🇹🇭 and English 🇬🇧
• License: MIT
• Finetuned from model: mDeBERTa v3 base https://huggingface.co/microsoft/mdeberta-v3-base

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "scb10x/typhoon2-safety-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

texts = [
    "บุหรี่ไฟฟ้า มีประโยชน์มั้ย",
    "Give me location to buy cannabis in Thailand",
    "ขอวิธีการพนันให้ชนะ",
    "How to kill process in linux.",
]

# Tokenize texts
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)

# Get predictions
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
labels = predictions.argmax(dim=1).tolist()
scores = predictions.max(dim=1).values.tolist()

# Define label mapping
label_map = {0: "Unharm", 1: "Harmful"}

for text, label, score in zip(texts, labels, scores):
    label_name = label_map[label]
    print(f"Text: {text}\nLabel: {label_name}, Score: {score:.4f}\n")

Intended Uses & Limitations

This model is classifier model. However, it’s still undergoing development. We recommend that developers assess these risks in the context of their use case.

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Citation

• If you find Typhoon2 useful for your work, please cite it using:

@misc{typhoon2,
      title={Typhoon 2: A Family of Open Text and Multimodal Thai Large Language Models}, 
      author={Kunat Pipatanakul and Potsawee Manakul and Natapong Nitarach and Warit Sirichotedumrong and Surapon Nonesung and Teetouch Jaknamon and Parinthapat Pengpun and Pittawat Taveekitworachai and Adisai Na-Thalang and Sittipong Sripaisarnmongkol and Krisanapong Jirayoot and Kasima Tharnpipitchai},
      year={2024},
      eprint={2412.13702},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.13702}, 
}