Instructions to use peter2000/laya-vulnerability-groups with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter2000/laya-vulnerability-groups with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peter2000/laya-vulnerability-groups")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("peter2000/laya-vulnerability-groups", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from peter2000/laya-vulnerability-groups: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
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https://huggingface.co/peter2000/laya-vulnerability-groups/resolve/main/README.md
- Command line
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hf download hf://peter2000/laya-vulnerability-groups/README.md
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curl -L -o README.md https://huggingface.co/peter2000/laya-vulnerability-groups/resolve/main/README.md
1.7 kB
| license: apache-2.0 | |
| library_name: transformers | |
| tags: [laya, text-classification, multi-label, climate, vulnerability, rlcd] | |
| pipeline_tag: text-classification | |
| # Laya fine-tuned for climate-vulnerability group detection (multi-label) | |
| [convaiinnovations/laya](https://huggingface.co/convaiinnovations/laya) (421M, ModernBERT-large backbone) | |
| fine-tuned with the official RLCD recipe on [GIZ/vulnerability_training_data_full](https://huggingface.co/datasets/GIZ/vulnerability_training_data_full) | |
| (380 train rows x 17 binary vulnerability-group questions; 36 all-negative rows included as negatives). | |
| Each vulnerability group is asked as one binary (`noul`) typed question; all 17 are answered in a single forward pass. | |
| Evaluate with `laya.load("peter2000/laya-vulnerability-groups")` and `agent.predict(state, questions)`. | |
| ## Test-set metrics (held-out 95 rows, threshold 0.5) | |
| | metric | value | | |
| |---|---| | |
| | macro-F1 | 0.6544 | | |
| | micro-F1 | 0.6694 | | |
| | ECE | 0.0263 | | |
| | subset accuracy | 0.4737 | | |
| Per-label F1: | |
| | label | F1 | | |
| |---|---| | |
| | Agricultural communities | 0.9565 | | |
| | Coastal communities | 0.5000 | | |
| | Ethnic, racial or other minorities | 0.6000 | | |
| | Fishery communities | 0.4000 | | |
| | Informal sector workers | 1.0000 | | |
| | Members of indigenous and local communities | 0.9333 | | |
| | Migrants and displaced persons | 0.5714 | | |
| | Older persons | 0.8889 | | |
| | Other | 0.0000 | | |
| | Persons living in poverty | 0.3333 | | |
| | Persons with disabilities | 1.0000 | | |
| | Persons with pre-existing health conditions | 0.8000 | | |
| | Residents of drought-prone regions | 0.5714 | | |
| | Rural populations | 0.8000 | | |
| | Sexual minorities (LGBTQI+) | 0.3333 | | |
| | Urban populations | 0.5714 | | |
| | Women and other genders | 0.8649 | | |