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language:
- en
- vi
tags:
- esg
- scoring
- classification
- sustainability
datasets:
- custom
library_name: transformers
pipeline_tag: text-classification
---
# ESG Scoring Model
This model performs ESG (Environmental, Social, Governance) scoring for text classification.
## Model Description
- **Model Type**: Sequence Classification for ESG Scoring
- **Language**: English, Vietnamese
- **Task**: ESG Factor Scoring (E, S, G)
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("chungpt2123/esg-scoring", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("chungpt2123/esg-scoring")
# Example usage
text = "The company has implemented renewable energy solutions to reduce carbon emissions."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)
# Get probabilities for each ESG factor
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
# Get scores for each factor
e_score = probabilities[0, 0].item() # Environmental score
s_score = probabilities[0, 1].item() # Social score
g_score = probabilities[0, 2].item() # Governance score
print(f"Environmental: {e_score:.4f}")
print(f"Social: {s_score:.4f}")
print(f"Governance: {g_score:.4f}")
```
## Training Details
- **Training Data**: Custom ESG dataset
- **Training Approach**: Fine-tuned for ESG factor scoring
- **Labels**: E (Environmental), S (Social), G (Governance)
## Model Performance
The model achieves strong performance on ESG scoring tasks across multiple languages.
## Limitations
- Trained primarily on English and Vietnamese text
- Performance may vary on domain-specific or technical content
- Best performance on texts similar to training data distribution
```bibtex
@misc{esg_scoring_model,
title={ESG Scoring Model},
author={Chung},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/chungpt2123/esg-scoring}
}
```
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