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license: apache-2.0 |
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# Model card for climateBUG-LM |
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## Model Description |
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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. |
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## Access and Usage |
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- Models, dataset and tools are available at the [climateBUG project page](https://www.climatebug.se/). |
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- Suitable for researchers and professionals in finance, sustainability, and climate policy. |
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## Applications |
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The model is ideal for: |
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+ Analyzing financial reports for climate change-related content. |
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+ Research in financial sustainability and climate economics. |
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+ Tracking how banks articulate their climate-related activities. |
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## Example Usage |
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Here is an example of how to use the climateBUG-LM model for classifying text as climate-related or not: |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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tokenizer_name = "lumilogic/climateBUG-LM" |
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model_name = "lumilogic/climateBUG-LM" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, max_len=512) |
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pipe = pipeline("text-classification", model=model, |
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tokenizer=tokenizer, device_map='auto') |
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# Climate related text |
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text = 'This issue represents around 10% of the outstanding volume of green sovereign bonds and will used to finance Germany’s climate and environmental strategy.' |
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output = pipe(text) |
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print(output) # [{'label': 'LABEL_1', 'score': 0.9974282383918762}] |
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# Non-climate related text |
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text = 'Our model, based on a customer-centric universal banking relationship, therefore demonstrated its resilience and usefulness for all stakeholders in all our regions.' |
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output = pipe(text) |
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print(output) # [{'label': 'LABEL_0', 'score': 0.9931207299232483}] |
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``` |
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## Limitations |
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+ Optimized for EU bank reports; performance may vary for other regions. |
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+ Primarily focused on climate and finance domains. |
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## Citation |
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Please cite this model as follows: |
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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. |
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```bibtex |
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@article{yu2024climatebug, |
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title = {climateBUG : A data-driven framework for analyzing bank reporting through a climate lens}, |
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journal = {Expert Systems with Applications}, |
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volume = {239}, |
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pages = {122162}, |
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year = {2024}, |
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author = {Yinan Yu and Samuel Scheidegger and Jasmine Elliott and Åsa Löfgren} |
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} |
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``` |
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## Support and Contact |
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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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