---
license: mit
base_model: FacebookAI/xlm-roberta-large
language:
- th
tags:
- sequence-tagging
- aspect-based-sentiment
---

# XLM-RoBERTa-Large for Aspect-Based Sentiment Analysis

This is a fine-tuned [XLM-RoBERTa-Large](https://huggingface.co/FacebookAI/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.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/640414c7929304a3c8065f8b/JX-naE_D_6SkGVhOVeut-.png)

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:

```python
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)