---
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},
}
```