--- 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} } ```