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---
license: apache-2.0
---
# Model card for climateBUG-LM
## Model Description
climateBUG-LM is a deep learning language model fine-tuned for analyzing bank reports in the context of climate change and sustainability. It leverages a unique annotated corpus, climateBUG-Data, which consists of statements from EU banks' annual and sustainability reports, focusing on climate change and finance. This model aims to classify statements as relevant or irrelevant to climate-related subjects, offering enhanced performance due to its domain-specific training.
## Access and Usage
- Models, dataset and tools are available at the [climateBUG project page](https://www.climatebug.se/).
- Suitable for researchers and professionals in finance, sustainability, and climate policy.
## Applications
The model is ideal for:
+ Analyzing financial reports for climate change-related content.
+ Research in financial sustainability and climate economics.
+ Tracking how banks articulate their climate-related activities.
## Limitations
+ Optimized for EU bank reports; performance may vary for other regions.
+ Primarily focused on climate and finance domains.
## Citation
Please cite this model as follows:
Yu, Y., Scheidegger, S., Elliott, J., & Löfgren, Å. (2024). climateBUG: A data-driven framework for analyzing bank reporting through a climate lens. Expert Systems With Applications, 239, 122162.
```bibtex
@article{yu2024climatebug,
title = {climateBUG : A data-driven framework for analyzing bank reporting through a climate lens},
journal = {Expert Systems with Applications},
volume = {239},
pages = {122162},
year = {2024},
author = {Yinan Yu and Samuel Scheidegger and Jasmine Elliott and Åsa Löfgren}
}
```
## Support and Contact
For support, additional information, or inquiries, please reach out through [email protected] or visit the [climateBUG project page](https://www.climatebug.se/).
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