chungpt2123/esg-subfactor-classifier

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

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

@misc{esg_hierarchical_model,
  title={ESG Hierarchical Multi-Task Learning Model},
  author={Chung},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/chungpt2123/test1}
}