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---
language: en
license: apache-2.0
datasets:
- ESGBERT/environment_data
tags:
- ESG
- environmental
---
# Model Card for EnvRoBERTa-base
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), 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](https://huggingface.co/ESGBERT/EnvironmentalBERT-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 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{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},
}
```
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