Model Card for Model ID
The qwen3-malaysia-cs-lora-500-data model is a LoRA adapter based on the Qwen/Qwen3-1.7B base model, fine-tuned on a synthetic Malaysian customer feedback dataset of 500 records. The dataset contains multilingual customer service messages written in English, Malay, Mandarin Chinese, and mixed Malaysian-style language. The model is designed to classify customer feedback into structured JSON outputs, including category, urgency, sentiment, key phrases, and the responsible department, while preserving the base model’s general language understanding capabilities.
Model Details
Model Description
This model is a fine-tuned LoRA adapter based on the Qwen3 language model. It was developed for a Malaysian customer feedback classification system, with the goal of helping organizations automatically understand and classify customer complaints, feedback, and service-related messages.
The model is designed to process customer feedback written in a Malaysian communication style, including English, Malay, Chinese, and mixed-language expressions commonly used in Malaysia. It is especially useful for classifying feedback into structured JSON outputs such as category, urgency level, sentiment, key phrases, and the responsible department.
- Developed by: Chai Jie Sheng
- Model type: LoRA Adapter from Pre-Trained LLM
- Language(s) (NLP): Malaysian languages and dialects in Chinese, English and Malay
- License: MIT License
- Finetuned from model [optional]: Qwen/Qwen3-1.7B
Model Sources [optional]
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Uses
This model is intended for customer service and feedback management systems. It can help automate the first stage of complaint analysis by identifying the main issue, estimating urgency, detecting sentiment, extracting important phrases, and routing the case to the correct department.
Direct Use
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Bias, Risks, and Limitations
This model is developed for educational and project purposes.
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Training Details
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Summary
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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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