Based on this paper, this is the EnvRoBERTa-base language model. A language model that is trained to better understand environmental texts in the ESG domain.

Note: We generally recommend choosing the EnvironmentalBERT-base model since it is quicker, less resource-intensive and only marginally worse in performance.

Using the RoBERTa model as a starting point, the EnvRoBERTa-base Language Model is additionally pre-trained on a text corpus comprising environmental-related annual reports, sustainability reports, and corporate and general news.

More details can be found in the paper

bibtex

@article{schimanski_ESGBERT_2024,
title = {Bridging the gap in ESG measurement: Using NLP to quantify environmental, social, and governance communication},
journal = {Finance Research Letters},
volume = {61},
pages = {104979},
year = {2024},
issn = {1544-6123},
doi = {https://doi.org/10.1016/j.frl.2024.104979},
url = {https://www.sciencedirect.com/science/article/pii/S1544612324000096},
author = {Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold},
}

ESGBERT/EnvRoBERTa-base

Model Card for EnvRoBERTa-base

Model Description

More details can be found in the paper

ESGBERT/
EnvRoBERTa-base