wttw
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modchelin_thainer-base-model

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