Main ESG Classification: Predicts E, S, G, or Others_ESG
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}
}