This model is a fine-tuned version of xlm-roberta-base on the VLSP2018-ABSA-Restaurant dataset for aspect-based sentiment analysis in Vietnamese reviews.

Model Details

Base Model: xlm-roberta-base
Description: XLM-RoBERTa base fine-tuned
Dataset: VLSP2018-ABSA-Restaurant
Fine-tuning Framework: HuggingFace Transformers
Task: Aspect-based Sentiment Classification (3 classes)

Hyperparameters

Batch size: 32
Learning rate: 3e-5
Epochs: 100
Max sequence length: 256
Weight decay: 0.01
Warmup steps: 500
Optimizer: AdamW

Dataset

Model was trained on VLSP2018 ABSA Restaurant dataset for aspect-based sentiment analysis.

Sentiment Labels:

0 - Negative (Tiêu cực): Negative opinions
1 - Neutral (Trung lập): Neutral, objective opinions
2 - Positive (Tích cực): Positive opinions

Aspect Categories:

Model được train để phân tích sentiment cho các aspects sau:

AMBIENCE#GENERAL
DRINKS#PRICES
DRINKS#QUALITY
DRINKS#STYLE&OPTIONS
FOOD#PRICES
FOOD#QUALITY
FOOD#STYLE&OPTIONS
LOCATION#GENERAL
RESTAURANT#GENERAL
RESTAURANT#MISCELLANEOUS
RESTAURANT#PRICES
SERVICE#GENERAL

Evaluation Results

The model was evaluated on test set with the following metrics:

Accuracy: 0.8897
Macro-F1: 0.6717
Weighted-F1: 0.8107
Macro-Precision: 0.7911
Macro-Recall: 0.5992

Usage Example

python

import torch
from transformers import AutoTokenizer, AutoModel

# Load model and tokenizer
repo = "visolex/xlm-roberta-absa-restaurant"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModel.from_pretrained(repo, trust_remote_code=True)
model.eval()

# Aspect labels for VLSP2018-ABSA-Restaurant
aspect_labels = [
    "AMBIENCE#GENERAL",
    "DRINKS#PRICES",
    "DRINKS#QUALITY",
    "DRINKS#STYLE&OPTIONS",
    "FOOD#PRICES",
    "FOOD#QUALITY",
    "FOOD#STYLE&OPTIONS",
    "LOCATION#GENERAL",
    "RESTAURANT#GENERAL",
    "RESTAURANT#MISCELLANEOUS",
    "RESTAURANT#PRICES",
    "SERVICE#GENERAL"
]

# Sentiment labels
sentiment_labels = ["POSITIVE", "NEGATIVE", "NEUTRAL"]

# Example review text
text = "Nhà hàng có không gian đẹp, đồ ăn ngon nhưng giá hơi đắt."

# Tokenize
inputs = tokenizer(
    text,
    return_tensors="pt",
    padding=True,
    truncation=True,
    max_length=256
)
inputs.pop("token_type_ids", None)

# Predict
with torch.no_grad():
    outputs = model(**inputs)

# Get logits: shape [1, num_aspects, num_sentiments + 1]
logits = outputs.logits.squeeze(0)  # [num_aspects, num_sentiments + 1]
probs = torch.softmax(logits, dim=-1)

# Predict for each aspect
none_id = probs.size(-1) - 1  # Index of "none" class
results = []

for i, aspect in enumerate(aspect_labels):
    prob_i = probs[i]
    pred_id = int(prob_i.argmax().item())
    
    if pred_id != none_id and pred_id < len(sentiment_labels):
        score = prob_i[pred_id].item()
        if score >= 0.5:  # threshold
            results.append((aspect, sentiment_labels[pred_id].lower()))

print(f"Text: {text}")
print(f"Predicted aspects: {results}")
# Output example: [('aspects', 'positive'), ('aspects', 'positive'), ('aspects', 'negative')]

Citation

If you use this model, please cite:

bibtex

@misc{visolex_absa_xlm_roberta_absa_restaurant,
  title={XLM-RoBERTa base fine-tuned for Vietnamese Aspect-based Sentiment Analysis},
  author={ViSoLex Team},
  year={2025},
  url={https://huggingface.co/visolex/xlm-roberta-absa-restaurant}
}

License

This model is released under the Apache-2.0 license.

Acknowledgments

Base model: xlm-roberta-base
Dataset: VLSP2018-ABSA-Restaurant
ViSoLex Toolkit