This repository contains code and data for a machine learning model that predicts ESG (Environmental, Social, and Governance) scores based on sustainability reports and company data. It's a valuable resource for researchers, investors, and sustainability professionals interested in ESG score prediction using machine learning techniques.
BERT (Bidirectional Encoder Representations from Transformers) and its variants (RoBERTa, DistilBERT, etc.) have shown state-of-the-art performance in various NLP tasks.
Fine-Tuning: You can fine-tune a pre-trained BERT model on your dataset to predict ESG scores.
Advantage: BERT understands context in both directions (left-to-right and right-to-left) and can capture intricate patterns in text. It's particularly powerful for complex textual data like sustainability reports.
BERT (Bidirectional Encoder Representations from Transformers) and its variants (RoBERTa, DistilBERT, etc.) have shown state-of-the-art performance in various NLP tasks. Fine-Tuning: You can fine-tune a pre-trained BERT model on your dataset to predict ESG scores. Advantage: BERT understands context in both directions (left-to-right and right-to-left) and can capture intricate patterns in text. It's particularly powerful for complex textual data like sustainability reports.