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--- |
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language: en |
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license: apache-2.0 |
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datasets: |
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- ESGBERT/environment_data |
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tags: |
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- ESG |
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- environmental |
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--- |
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# Model Card for EnvRoBERTa-base |
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## Model Description |
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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. |
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*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.* |
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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. |
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## More details can be found in the paper |
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```bibtex |
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@article{Schimanski23ESGBERT, |
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title={{Bridiging the Gap in ESG Measurement: Using NLP to Quantify Environmental, Social, and Governance Communication}}, |
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author={Tobias Schimanski and Andrin Reding and Nico Reding and Julia Bingler and Mathias Kraus and Markus Leippold}, |
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year={2023}, |
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journal={Available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514}, |
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} |
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``` |
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