|
--- |
|
language: en |
|
license: apache-2.0 |
|
datasets: |
|
- ESGBERT/governance_data |
|
tags: |
|
- ESG |
|
- governance |
|
--- |
|
|
|
# Model Card for GovRoBERTa-base |
|
|
|
## Model Description |
|
|
|
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the GovRoBERTa-base language model. A language model that is trained to better understand governance texts in the ESG domain. |
|
|
|
*Note: We generally recommend choosing the [GovernanceBERT-base](https://huggingface.co/ESGBERT/GovernanceBERT-base) model since it is quicker, less resource-intensive and only marginally worse in performance.* |
|
|
|
Using the [RoBERTa](https://huggingface.co/roberta-base) model as a starting point, the GovRoBERTa-base Language Model is additionally pre-trained on a text corpus comprising governance-related annual reports, sustainability reports, and corporate and general news. |
|
|
|
## More details can be found in the paper |
|
```bibtex |
|
@article{Schimanski23ESGBERT, |
|
title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}}, |
|
author={Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold}, |
|
year={2023}, |
|
journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514}, |
|
} |
|
``` |
|
|
