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--- |
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title: Climate Vulnerability Analysis |
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emoji: 🌡️ |
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colorFrom: green |
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colorTo: gray |
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sdk: docker |
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app_file: app.py |
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app_port: 8501 |
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pinned: false |
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short_description: Uncover and summarize vulnerable groups findings |
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authors: |
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- user: https://huggingface.co/mtyrrell |
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- user: https://huggingface.co/leavoigt |
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- user: https://huggingface.co/TeresaK |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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### Technical Documentation of the system in accordance with EU AI Act. |
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System Name: Climate Vulnerability App |
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Provider / Supplier: GIZ Data Lab & Data Service Center |
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As of: July 2025 |
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1. General Description of the System |
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The Climate Vulnerability App is an AI-powered tool to quickly retrieve and summarize relevant information on marginalised groups from (climate) policy documents, |
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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 |
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transformer models to classify references related to pre-determined marginalised groups and an LLM of choice to summarize the most important information that has |
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been identified. |
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3. Model's Used |
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Text Classification: |
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- Model Name: [VULNERABILITY-multilabel-mpnet-multilingual-v2](https://huggingface.co/GIZ/VULNERABILITY-multilabel-mpnet-multilingual-v2) |
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- Model Name: [https://huggingface.co/GIZ/TARGET-VULNERABILITY-multiclass-mpnet](https://huggingface.co/GIZ/TARGET-VULNERABILITY-multiclass-mpnet) |
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- Both models mentioned above are fine-tuned versions of the [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) |
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Generative LLM used for summaries: |
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- Model Name: [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
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5. Model Training Data: |
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- 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) |
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- The training data has been collected by human annotators that are expert in their fields |
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- 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. |
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8. System Limitations and Non-Purposes |
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- The system is designed to provide a quick overview of the most relevant information on marginalised groups in climate policy |
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- 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. |
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- The system does not make autonomous decisions but just provides information. |
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- No personal data of users is being processed. |
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- Results are intended for orientation only – not for legal or political advice. |
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10. Transparency Towards Users |
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- The user interface clearly indicates the use of a generative AI model. |
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12. Monitoring, Feedback, and Incident Reporting |
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- Technical development is carried out by the GIZ Data Service Center. |
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- Please reach out through the contact details provided below, if there are any issues or feedback. |
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13. Contact: For any questions, please contact us via [email protected] |
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