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---
language:
- en
- vi
tags:
- esg
- classification
- hierarchical
- multi-task-learning
- sustainability
datasets:
- custom
library_name: transformers
pipeline_tag: text-classification
---
# ESG Hierarchical Multi-Task Learning Model
This model performs hierarchical ESG (Environmental, Social, Governance) classification using a multi-task learning approach.
## Model Description
- **Model Type**: Hierarchical Multi-Task Classifier
- **Backbone**: Alibaba-NLP/gte-multilingual-base
- **Language**: English, Vietnamese
- **Task**: ESG Factor and Sub-factor Classification
## Architecture
The model uses a hierarchical approach:
1. **Main ESG Classification**: Predicts E, S, G, or Others_ESG
2. **Sub-factor Classification**: Based on main category, predicts specific sub-factors:
- **E (Environmental)**: Emission, Resource Use, Product Innovation
- **S (Social)**: Community, Diversity, Employment, HS, HR, PR, Training
- **G (Governance)**: BFunction, BStructure, Compensation, Shareholder, Vision
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("chungpt2123/esg-subfactor-classifier", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
# Example usage
text = "The company has implemented renewable energy solutions to reduce carbon emissions."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=4096)
# Get predictions
esg_factor, sub_factor = model.predict(inputs.input_ids, inputs.attention_mask)
print(f"ESG Factor: {esg_factor}, Sub-factor: {sub_factor}")
```
## Training Details
- **Training Data**: Custom ESG dataset
- **Training Approach**: Two-phase training (freeze backbone → fine-tune entire model)
- **Loss Function**: Weighted multi-task loss
- **Optimization**: AdamW with learning rate scheduling
## Model Performance
The model achieves strong performance on ESG classification tasks with hierarchical prediction accuracy.
## Limitations
- Trained primarily on English and Vietnamese text
- Performance may vary on domain-specific or technical ESG content
- Best performance on texts similar to training data distribution
```bibtex
@misc{esg_hierarchical_model,
title={ESG Hierarchical Multi-Task Learning Model},
author={Chung},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/chungpt2123/test1}
}
```
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