README.md

---
license: apache-2.0
base_model: bartpho
tags:
- vietnamese
- aspect-based-sentiment-analysis
- VLSP-ABSA
datasets:
- visolex/VLSP2018-ABSA-Restaurant
metrics:
- accuracy
- macro-f1
model-index:
- name: bartpho-absa-restaurant
  results:
  - task:
      type: text-classification
      name: Aspect-based Sentiment Analysis
    dataset:
      name: VLSP2018-ABSA-Restaurant
      type: VLSP-ABSA
    metrics:
    - type: accuracy
      value: 0.8336
    - type: macro-f1
      value: 0.2228
    - type: macro_precision
      value: 0.2930
    - type: macro_recall
      value: 0.1798
---

# bartpho-absa-restaurant: Aspect-based Sentiment Analysis for Vietnamese Reviews

This model is a fine-tuned version of [bartpho](https://huggingface.co/bartpho) 
on the **VLSP2018-ABSA-Restaurant** dataset for aspect-based sentiment analysis in Vietnamese reviews.

## Model Details

* **Base Model**: bartpho
* **Description**: BartPho for Vietnamese ABSA
* **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**: `0.8336`
* **Macro-F1**: `0.2228`
* **Weighted-F1**: `0.4893`
* **Macro-Precision**: `0.2930`
* **Macro-Recall**: `0.1798`

## Usage Example

```python
import torch
from transformers import AutoTokenizer, AutoModel

# Load model and tokenizer
repo = "visolex/bartpho-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_bartpho_absa_restaurant,
  title={BartPho for Vietnamese ABSA for Vietnamese Aspect-based Sentiment Analysis},
  author={ViSoLex Team},
  year={2025},
  url={https://huggingface.co/visolex/bartpho-absa-restaurant}
}
```

## License

This model is released under the Apache-2.0 license.

## Acknowledgments

* Base model: [bartpho](https://huggingface.co/bartpho)
* Dataset: VLSP2018-ABSA-Restaurant
* ViSoLex Toolkit

---