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wttw/
modchelin_thainer-base-model

Name: modchelin_thainer-base-model
Rating: 2.5 (7 reviews)

safetensors

camembert

token-classification

airesearch/wangchanberta-base-att-spm-uncased

finetune

cc-by-4.0

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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

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)