You can deploy this model and start earning money today!

charlottegl/
CantoSent-Qwen-7B-OpenRice-v1

Name: CantoSent-Qwen-7B-OpenRice-v1
Availability: InStock

Views

No views yet

🐉 CantoSent-Qwen-7B-OpenRice-v1

📌 Model Summary

CantoSent-Qwen-7B-OpenRice-v1 is a fine-tuned version of stvlynn/Qwen-7B-Chat-Cantonese, designed for Cantonese sentiment classification on OpenRice restaurant reviews.
The task is a binary classification: positive (3–5 stars) vs negative (1–2 stars).
This model is optimized for research and academic use in low-resource language sentiment analysis, especially in the domain of Cantonese NLP.

🧠 Model Details

Base Model: stvlynn/Qwen-7B-Chat-Cantonese
Fine-tuned by: Geng Le
Language: Cantonese (Chinese Traditional)
Task: Binary Sentiment Classification
Dataset: Cantonese OpenRice Reviews
Emoji Handling: Emojis removed (no-emojis version)

📊 Evaluation Results

Metric	Score
Accuracy	89.83%
Precision	98.87%
Recall	89.65%
F1-score	94.03%

Evaluated on a held-out test set (10% of 60,000 examples).

📁 Dataset Information

Source: OpenRice review dataset from a public GitHub repository
Size: 60,000 examples
Labels:
- Positive = 3–5 stars
- Negative = 1–2 stars
Split:
- 90% training, 10% testing
Emoji Handling: Version without emojis used in this training

🎯 Use Cases

Cantonese sentiment analysis for food review systems
Research in low-resource or regional dialect NLP
Benchmarking multilingual/multi-dialect LLMs
Educational or academic projects

🔬 Intended Use & Limitations

This model is research-priority. It is intended for:

Academic research
Model benchmarking
Sentiment classification on food/dining-related Cantonese text

Limitations:

Domain-specific (trained only on restaurant reviews)
Does not support emojis in the current version
Performance may degrade on informal or non-Cantonese text

📄 License

This model is fine-tuned from [stvlynn/Qwen-7B-Chat-Cantonese], which follows the Qwen license.
The dataset is derived from a public GitHub repository for academic purposes.
Please use this model in accordance with the license terms of the base model and dataset.

✨ Citation

If you use this model in your research or applications, please consider citing:

@misc{cantosent2025,
  author = {Geng Le},
  title = {CantoSent-Qwen-7B-OpenRice-v1: A Cantonese Sentiment Classifier},
  year = {2025},
  howpublished = {\url{https://huggingface.co/charlottegl/CantoSent-Qwen-7B-OpenRice-v1}},
  note = {Fine-tuned from Qwen for sentiment analysis on OpenRice reviews}
}

📬 Contact

For questions or collaborations, feel free to reach out via Hugging Face or GitHub.