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tags: [esg, greenwashing, phobert, vietnamese, sustainability]
license: mit
language: vi
---
# ESG Greenwashing Detection Model
Multi-task PhoBERT model for Vietnamese ESG content analysis.
## Model Architecture
4-task learning:
1. **Greenwashing Classification** (Legitimate/Greenwashing/Uncertain)
2. **ESG Pillar Classification** (Environmental/Social/Governance/General)
3. **Content Quality Scoring** (0-100)
4. **ESG Score Prediction** (0-100)
## Training
- **Base Model:** vinai/phobert-base
- **Strategy:** stratified_group_kfold_5
- **Folds:** 5
- **Total Samples:** 2617
## Performance
### Greenwashing Detection
- F1 Score: 0.550
- Precision: 0.558
- Recall: 0.551
### Pillar Classification
- Accuracy: 0.787
- F1 Macro: 0.220
### Quality Scoring
- MAE: 7.794
- R²: 0.100
### ESG Score Prediction
- MAE: 13.822
- R²: 0.104
- Correlation: 0.409
## Usage
```python
from transformers import AutoTokenizer, AutoModel
import torch
tokenizer = AutoTokenizer.from_pretrained("hiennthp/esg-bank-model-v4")
# Load model architecture then weights
# model = MultiTaskPhoBERT(config)
# model.load_state_dict(torch.load("best_model_fold0.pt"))
```
## Files
- `best_model_fold0.pt` - Fold 0 model weights
- `best_model_fold1.pt` - Fold 1 model weights
- `best_model_fold2.pt` - Fold 2 model weights
- `best_model_fold3.pt` - Fold 3 model weights
- `best_model_fold4.pt` - Fold 4 model weights
- `step5_metrics.json` - Detailed metrics with per-fold breakdown
- `tokenizer/` - PhoBERT tokenizer files
## Citation
```bibtex
@software{esg_greenwashing_model,
author = {ESG Research Team},
title = {Vietnamese ESG Greenwashing Detection Model},
year = {2026},
url = {https://huggingface.co/hiennthp/esg-bank-model-v4}
}
```
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