Keetawan/xlm-roberta-large-aspect-based-sentiment

XLM-RoBERTa-Large for Aspect-Based Sentiment Analysis

This is a fine-tuned XLM-RoBERTa-Large model for Aspect-Based Sentiment Analysis in Thai. The model is fine-tuned on a dataset specifically for the task of identifying sentiments related to specific aspects within sentences.

This model was the winning model in the Aspect-Based Sentiment Analysis competition of Super AI Engineer Season 4 - Hackathon Online. It achieved the best performance among all participating models, demonstrating its effectiveness in the given task.

Model Description

XLM-RoBERTa is a large multilingual language model that has been fine-tuned for sequence tagging tasks. This model has been further fine-tuned for Aspect-Based Sentiment Analysis, making it suitable for applications that require understanding of sentiments expressed towards specific aspects within a text.

Classes

The model can predict the following classes:

ประเด็น	ป้ายกำกับเชิงบวก (Positive)	ป้ายกำกับเชิงลบ (Negative)
คุณภาพของสินค้า	Quality	NEG-Quality
ระยะเวลาที่ใช้ในการจัดส่ง	DeliveryTime	NEG-DeliveryTime
การบริการของร้านค้า	StoreService	NEG-StoreService
รูปลักษณ์ของสินค้า	Appearance	NEG-Appearance
การแพ็กสินค้า	Packaging	NEG-Packaging
ราคาของสินค้า	Price	NEG-Price
ขนาดของสินค้า	Size	NEG-Size
ไม่เกี่ยวข้องกับประเด็นที่สนใจ	O

Usage

You can use this model for sequence tagging and aspect-based sentiment analysis in the Thai language. Here is a quick example of how to use it:

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("Keetawan/xlm-roberta-large-aspect-based-sentiment")
model = AutoModelForTokenClassification.from_pretrained("Keetawan/xlm-roberta-large-aspect-based-sentiment")

nlp = pipeline("token-classification", model=model, tokenizer=tokenizer)

text = "ใส่ประโยคภาษาไทยที่ต้องการวิเคราะห์ที่นี่"
result = nlp(text)

for item in result:
    print(item)

Citation

If you use this model in your research or applications, please cite it as follows:

@misc{keetawan2024aspectsentiment,
  author = {Keetawan Limaroon},
  title = {XLM-RoBERTa-Large for Aspect-Based Sentiment Analysis in Thai},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/Keetawan/xlm-roberta-large-aspect-based-sentiment},
}