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--- |
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license: cc-by-4.0 |
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language: |
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- th |
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base_model: |
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- airesearch/wangchanberta-base-att-spm-uncased |
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pipeline_tag: token-classification |
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--- |
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library_name: transformers |
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tags: [ner, thai, food, review, token-classification] |
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--- |
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# Model Card for wttw/modchelin_thainer-base-model |
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This model performs Named Entity Recognition (NER) on Thai-language food reviews. It is designed to extract domain-specific aspects such as dish names, ingredients, restaurant service, and sentiment-related phrases from customer-written content. |
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## Model Details |
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### Model Description |
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This is the model card of a 🤗 Transformers model that has been pushed to the Hugging Face Hub. |
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- **Developed by:** Vitawat Kitipatthavorn |
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- **Finetuned from model:** `airesearch/wangchanberta-base-att-spm-uncased` |
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- **Model type:** Token Classification (NER) |
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- **Language(s) (NLP):** Thai |
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- **License:** cc-by-sa-4.0 |
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- **Shared by:** wttw |
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- **Model ID:** `wttw/modchelin_thainer-base-model` |
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## Uses |
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### Direct Use |
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This model is designed for extracting domain-specific entities from Thai-language food reviews. It identifies and classifies named entities related to: |
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- Food/menu items |
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- Taste |
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- Service |
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- Ambiance |
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- Price and value |
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- Other aspects relevant to customer dining experiences |
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**Example:** |
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- **Input:** `"ต้มยำกุ้งอร่อยมาก แต่บริการช้า"` |
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- **Output:** |
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- `ต้มยำกุ้ง: FOOD` |
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- `บริการ: SERVICE` |
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The model is suitable for NLP pipelines aimed at analyzing restaurant reviews, powering sentiment dashboards, or supporting aspect-based sentiment analysis (ABSA). |
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### Downstream Use |
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The model can be integrated into: |
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- Thai ABSA pipelines |
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- Restaurant feedback summarization systems |
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- Chatbots or moderation tools for food delivery and review platforms |
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### Out-of-Scope Use |
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The model is not designed for: |
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- Non-food-related documents (e.g., legal, clinical, political) |
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- General-purpose Thai NER tasks |
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- Use cases requiring high confidence on ambiguous or out-of-domain text |
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## Bias, Risks, and Limitations |
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The model is trained specifically on food review content and may: |
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- Struggle with informal slang or regional dialects |
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- Over-predict `FOOD` entities in unrelated contexts |
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- Misclassify ambiguous phrases without surrounding context |
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### Recommendations |
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Users should: |
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- Avoid applying this model outside food-related domains |
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- Fine-tune further if working with reviews in specific dialects or contexts |
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- Evaluate on a sample of target data before production use |
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- Consider setting confidence thresholds before using predictions downstream |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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model_name = "wttw/modchelin_thainer-base-model" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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example = "ต้มยำกุ้งอร่อยมาก แต่บริการช้า" |
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entities = ner_pipeline(example) |
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print(entities) |
