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| title: Climate Vulnerability Analysis | |
| emoji: 🌡️ | |
| colorFrom: green | |
| colorTo: gray | |
| sdk: docker | |
| app_file: app.py | |
| app_port: 8501 | |
| pinned: false | |
| short_description: Uncover and summarize vulnerable groups findings | |
| authors: | |
| - user: https://huggingface.co/mtyrrell | |
| - user: https://huggingface.co/leavoigt | |
| - user: https://huggingface.co/TeresaK | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| ### Technical Documentation of the system in accordance with EU AI Act. | |
| System Name: Climate Vulnerability App | |
| Provider / Supplier: GIZ Data Lab & Data Service Center | |
| As of: July 2025 | |
| 1. General Description of the System | |
| The Climate Vulnerability App is an AI-powered tool to quickly retrieve and summarize relevant information on marginalised groups from (climate) policy documents, | |
| in order to enable users to get a broad overview of the extent to which different marginalised groups are represented in policies. This tool leverages fine-tuned | |
| transformer models to classify references related to pre-determined marginalised groups and an LLM of choice to summarize the most important information that has | |
| been identified. | |
| 3. Model's Used | |
| Text Classification: | |
| - Model Name: [VULNERABILITY-multilabel-mpnet-multilingual-v2](https://huggingface.co/GIZ/VULNERABILITY-multilabel-mpnet-multilingual-v2) | |
| - Model Name: [https://huggingface.co/GIZ/TARGET-VULNERABILITY-multiclass-mpnet](https://huggingface.co/GIZ/TARGET-VULNERABILITY-multiclass-mpnet) | |
| - Both models mentioned above are fine-tuned versions of the [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | |
| Generative LLM used for summaries: | |
| - Model Name: [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) | |
| 5. Model Training Data: | |
| - The data used to fine-tune the text classification model can be found here: [vulnerability_training_data_full](https://huggingface.co/datasets/GIZ/vulnerability_training_data_full) | |
| - The training data has been collected by human annotators that are expert in their fields | |
| - The data does not contain any known bias, however some classes perform better than others (see dataset card) and risk of potential bias can never be fully excluded. | |
| 8. System Limitations and Non-Purposes | |
| - The system is designed to provide a quick overview of the most relevant information on marginalised groups in climate policy | |
| - The system is NOT designed to give an in-depth analysis of the document. Output may always be incomplete or falsly classified and should ALWAYS be reviewed by a human. | |
| - The system does not make autonomous decisions but just provides information. | |
| - No personal data of users is being processed. | |
| - Results are intended for orientation only – not for legal or political advice. | |
| 10. Transparency Towards Users | |
| - The user interface clearly indicates the use of a generative AI model. | |
| 12. Monitoring, Feedback, and Incident Reporting | |
| - Technical development is carried out by the GIZ Data Service Center. | |
| - Please reach out through the contact details provided below, if there are any issues or feedback. | |
| 13. Contact: For any questions, please contact us via dataservicecenter@giz.de | |