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

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

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)