| --- |
| 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://www.sciencedirect.com/science/article/pii/S1544612324000096), 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{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}, |
| } |
| ``` |
|
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|
